text1,text2,same
"This article presents a model of general-purpose computing on a semantic
network substrate. The concepts presented are applicable to any semantic
network representation. However, due to the standards and technological
infrastructure devoted to the NORP Web effort, this article is presented
from this point of view. In the proposed model of computing, the application
programming interface, the run-time program, and the state of the computing
virtual machine are all represented in ORG
(ORG). The implementation of the concepts presented provides a practical
computing paradigm that leverages the highly-distributed and standardized
representational-layer of the Semantic Web.","We review ORG's paradox (or EVENT"" problem), not only arguably
the oldest and crucial problem for ORG
(ORG), but also a conundrum of profound scientific, philosophical and cultural
importance. By a simple analysis of observation selection effects, the correct
resolution of ORG's paradox is certain to tell us something about the future
of humanity. Already a DATE puzzle - and a
DATE since the last major review paper in the field by PERSON paradox has generated many ingenious discussions and hypotheses.
We analyze the often tacit methodological assumptions built into various
answers to this puzzle and attempt a new classification of the numerous
solutions proposed in an already huge literature on the subject. Finally, we
consider the ramifications of various classes of hypotheses for the practical
ORG projects. Somewhat paradoxically, it seems that the class of
(neo)catastrophic hypotheses gives, on balance, the strongest justification for
guarded optimism regarding our current and near-future ORG efforts.",0
"We try to perform geometrization of psychology by representing mental states,
<<ideas>>, by points of a metric space, <<mental space>>. Evolution of ideas is
described by dynamical systems in metric mental space. We apply the mental
space approach for modeling of flows of unconscious and conscious information
in the human brain. In a series of models, Models 1-4, we consider cognitive
systems with increasing complexity of psychological behavior determined by
structure of flows of ideas. Since our models are in fact models of the
AI-type, one immediately recognizes that they can be used for creation of
AI-systems, which we call psycho-robots, exhibiting important elements of human
psyche. Creation of such psycho-robots may be useful improvement of domestic
robots. At the moment domestic robots are merely simple working devices (e.g.
vacuum cleaners or lawn mowers) . However, in future one can expect demand in
systems which be able not only perform simple work tasks, but would have
elements of human self-developing psyche. Such AI-psyche could play an
important role both in relations between psycho-robots and their owners as well
as between psycho-robots. Since the presence of a huge numbers of
psycho-complexes is an essential characteristic of human psychology, it would
be interesting to model them in the AI-framework.","We compute the anomalous dimension of the ORDINAL and ORDINAL moments of the
flavour non-singlet twist-2 ORG and transversity operators at CARDINAL loops in
both the ORG and ORG' schemes. To assist with the extraction of estimates of
matrix elements computed using lattice regularization, the finite parts of the
PERSON's function where the operator is inserted in a quark CARDINAL-point function are
also provided at CARDINAL loops in both schemes.",0
"Any real interaction process produces many incompatible system versions, or
realisations, giving rise to omnipresent dynamic randomness and universally
defined complexity (arXiv:physics/9806002). Since ORG behaviour dynamically
emerges as the lowest complexity level (arXiv:quant-ph/9902016), ORG
interaction randomness can only be relatively strong, which reveals the causal
origin of quantum indeterminacy (arXiv:quant-ph/9511037) and true ORG chaos
(arXiv:quant-ph/9511035), but rigorously excludes the possibility of unitary
quantum computation, even in an ""ideal"", noiseless system. Any real computation
is an internally chaotic (multivalued) process of system complexity development
occurring in different regimes. Unitary ORG machines, including their
postulated ""magic"", cannot be realised as such because their dynamically
single-valued scheme is incompatible with the irreducibly high dynamic
randomness at ORG levels and should be replaced by explicitly
chaotic, intrinsically creative machines already realised in living organisms
and providing their quite different, realistic kind of magic. The related
concepts of reality-based, complex-dynamical nanotechnology, biotechnology and
intelligence are outlined, together with the ensuing change in research
strategy. The unreduced, dynamically multivalued solution to the many-body
problem reveals the true, complex-dynamical basis of solid-state dynamics,
including the origin and internal dynamics of macroscopic quantum states. The
critical, ""end-of-science"" state of unitary knowledge and the way to positive
change are causally specified within the same, universal concept of complexity.","A quite general interaction process of a multi-component system is analysed
by the extended effective potential method liberated from usual limitations of
perturbation theory or integrable model. The obtained causally complete
solution of the many-body problem reveals the phenomenon of dynamic
multivaluedness, or redundance, of emerging, incompatible system realisations
and dynamic entanglement of system components within each realisation. The
ensuing concept of dynamic complexity (and related intrinsic chaoticity) is
absolutely universal and can be applied to the problem of (natural and
artificial) intelligence and consciousness that dynamically emerge now as a
high enough, properly specified levels of unreduced complexity of a suitable
interaction process. Emergent consciousness can be identified with the
appearance of bound, permanently localised states in the multivalued brain
dynamics from strongly chaotic states of unconscious intelligence, by analogy
with classical behaviour emergence from quantum states at the lowest levels of
complex world dynamics. We show that the main properties of this dynamically
emerging consciousness (and intelligence, at the preceding complexity level)
correspond to empirically derived properties of natural consciousness and
obtain causally substantiated conclusions about their artificial realisation,
including the fundamentally justified paradigm of genuine machine
consciousness. This rigorously defined machine consciousness is different from
both natural consciousness and any mechanistic, dynamically single-valued
imitation of the latter. We use then the same, truly universal concept of
complexity to derive equally rigorous conclusions about mental and social
implications of this complex-dynamic consciousness concept, demonstrating its
critical importance for further progress of science and civilisation.",1
"The purpose of this paper is to obtain exact solutions of the GPE field
equations describing traversable wormholes supported by phantom energy. Their
relationship to exact solutions in the literature is also discussed, as well as
the conditions required to determine such solutions.","We hereby consider the problem of detectability of macro-engineering projects
over interstellar distances, in the context of ORG (SETI). PERSON and his imaginative precursors, like
PERSON, PERSON or PERSON, suggested
macro-engineering projects as focal points in the context of extrapolations
about the future of humanity and, by analogy, other intelligent species in the
LOC. We emphasize that the search for signposts of extraterrestrial
macro-engineering projects is not an optional pursuit within the family of
ongoing and planned ORG projects; LOC, the failure of the orthodox ORG
thus far clearly indicates this. Instead, this approach (for which we suggest a
name of ""Dysonian"") should be the front-line and mainstay of any cogent ORG
strategy in future, being significantly more promising than searches for
directed, intentional radio or microwave emissions. This is in accord with our
improved astrophysical understanding of the structure and evolution of the
LOC, as well as with the recent wake-up call of PERSON to investigate consequences of postbiological evolution for astrobiology
in general and ORG programs in particular. The benefits this multidisciplinary
approach may bear for astrobiologists, evolutionary theorists and
macro-engineers are also briefly highlighted.",0
"We study the use of ""sign $\alpha$-stable random projections"" (where
$MONEY 2$) for building basic data processing tools in the context of
large-scale machine learning applications (e.g., classification, regression,
clustering, and near-neighbor search). After the processing by sign stable
random projections, the inner products of the processed data approximate
various types of nonlinear kernels depending on the value of MONEY,
this approach provides an effective strategy for approximating nonlinear
learning algorithms essentially at the cost of ORG learning. When $\alpha
=MONEY, it is known that the corresponding nonlinear kernel is the arc-cosine
kernel. When MONEY, the procedure approximates the arc-cos-$\chi^2$ kernel
(under certain condition). When $MONEY, it corresponds to the
resemblance kernel.
  From practitioners' perspective, the method of sign $\alpha$-stable random
projections is ready to be tested for large-scale learning applications, where
$PERSON can be simply viewed as a tuning parameter. What is missing in the
literature is an extensive empirical study to show the effectiveness of sign
stable random projections, especially for MONEY 2$ or CARDINAL. The paper
supplies such a study on a wide variety of classification datasets. In
particular, we compare shoulder-by-shoulder sign stable random projections with
the recently proposed ""0-bit consistent weighted sampling (ORG)"" (PERSON DATE).","Based on $\alpha$-stable random projections with small $PERSON, we develop a
simple algorithm for compressed sensing (sparse signal recovery) by utilizing
only the signs (i.e., CARDINAL-bit) of the measurements. Using only 1-bit information
of the measurements results in substantial cost reduction in collection,
storage, communication, and decoding for compressed sensing. The proposed
algorithm is efficient in that the decoding procedure requires CARDINAL scan of
the coordinates. Our analysis can precisely show that, for a CARDINALPERSON signal
of length $MONEY, MONEY measurements (where $\delta$ is the
confidence) would be sufficient for recovering the support and the signs of the
signal. While the method is very robust against typical measurement noises, we
also provide the analysis of the scheme under random flipping of the signs of
the measurements.
  \noindent Compared to the well-known work on 1-bit marginal regression (which
can also be viewed as a CARDINAL-scan method), the proposed algorithm requires
orders of magnitude fewer measurements. Compared to QUANTITY FAC (ORG) (which is not a CARDINAL-scan algorithm), our method is still
significantly more accurate. Furthermore, the proposed method is reasonably
robust against random sign flipping while ORG is known to be very sensitive to
this type of noise.",1
"Exploring further the properties of ITRM-recognizable reals, we provide a
detailed analysis of recognizable reals and their distribution in PERSON
constructible universe L. In particular, we show that, for unresetting infinite
time register machines, the recognizable reals coincide with the computable
reals and that, for ITRMs, unrecognizables are generated at every index bigger
than the ORDINAL limit of admissibles. We show that a real r is recognizable iff
it is $\Sigma_{1}$-definable over $PERSON,r}}$, that $r\in
ORG,r}}$ for every recognizable real $r$ and that either all
or no real generated over an index stage $ORG are recognizable.","We define an ordinalized version of PERSON's realizability interpretation of
intuitionistic logic by replacing Turing machines with PERSON's ordinal Turing
machines (OTMs), thus obtaining a notion of realizability applying to arbitrary
statements in the language of set theory. We observe that every instance of the
axioms of intuitionistic ORDINAL-order logic are ORG-realizable and consider the
question which axioms of ORG (ORG) and ORG's
ORG (CZF) are ORG-realizable.
  This is an introductory note, and proofs are mostly only sketched or omitted
altogether. It will soon be replaced by a more elaborate version.",1
"In this paper CARDINAL presents new similarity, cardinality and entropy measures
for bipolar fuzzy set and for its particular forms like intuitionistic,
paraconsistent and fuzzy set. All these are constructed in the framework of
multi-valued representations and are based on a penta-valued logic that uses
the following logical values: true, false, unknown, contradictory and
ambiguous. Also a new distance for bounded real interval was defined.","The Cauchy problem for the ORG equations in GPE gauge in
$n$ space dimensions (MONEY) is locally well-posed for low regularity data,
in CARDINAL and CARDINAL space dimensions even for data without finite energy. The
result relies on the null structure for the main bilinear terms which was shown
to be not only present in GPE gauge but also in GPE gauge by PERSON and
LOC, who proved global well-posedness for finite energy data in CARDINAL
space dimensions. This null structure is combined with product estimates for
wave-Sobolev spaces given systematically by GPE, GPE and GPE.",0
"In this paper, we prove that some NORP structural equation models with
dependent errors having equal variances are identifiable from their
corresponding NORP distributions. Specifically, we prove identifiability
for the NORP structural equation models that can be represented as
ORG chain graphs (Andersson et al., DATE). These chain
graphs were originally developed to represent independence models. However,
they are also suitable for representing causal models with additive noise
(Pe\~na, DATE. Our result implies then that these causal models can be
identified from observational data alone. Our result generalizes the result by
PERSON and B\""uhlmann (DATE), who considered independent errors having equal
variances. The suitability of the equal error variances assumption should be
assessed on a per domain basis.","An interesting consequence of the modern cosmological paradigm is the spatial
infinity of the universe. When coupled with naturalistic understanding of the
origin of life and intelligence, which follows the basic tenets of
astrobiology, and with some fairly incontroversial assumptions in the theory of
observation selection effects, this infinity leads, as PERSON has recently
shown, to a paradoxical conclusion. Olum's paradox is related, to the famous
GPE's paradox in astrobiology and ORG studies. We, hereby, present an
evolutionary argument countering the apparent inconsistency, and show how, in
the framework of a simplified model, deeper picture of the coupling between
histories of intelligent/technological civilizations and astrophysical
evolution of the PRODUCT, can be achieved. This strategy has consequences of
importance for both astrobiological studies and philosophy.",0
"We present a multidimensional optimization problem that is formulated and
solved in the tropical mathematics setting. The problem consists of minimizing
a nonlinear objective function defined on vectors over an idempotent semifield
by means of a conjugate transposition operator, subject to constraints in the
form of ORG vector inequalities. A complete direct solution to the problem
under fairly general assumptions is given in a compact vector form suitable for
both further analysis and practical implementation. We apply the result to
solve a multidimensional minimax single facility location problem with
ORG distance and with inequality constraints imposed on the feasible
location area.","A knowledge base is redundant if it contains parts that can be inferred from
the rest of it. We study the problem of checking whether a ORG formula (a set
of clauses) is redundant, that is, it contains clauses that can be derived from
the other ones. Any CNF formula can be made irredundant by deleting some of its
clauses: what results is an irredundant equivalent subset (I.E.S.) We study the
complexity of some related problems: verification, checking existence of a
I.E.S. with a given size, checking necessary and possible presence of clauses
in ORG's, and uniqueness. We also consider the problem of redundancy with
different definitions of equivalence.",0
"While statistics focusses on hypothesis testing and on estimating (properties
of) the true sampling distribution, in machine learning the performance of
learning algorithms on future data is the primary issue. In this paper we
bridge the gap with a general principle (FAC) that identifies hypotheses with
best predictive performance. This includes predictive point and interval
estimation, simple and composite hypothesis testing, (mixture) model selection,
and others as special cases. For concrete instantiations we will recover
well-known methods, variations thereof, and new ones. PHI nicely justifies,
reconciles, and blends (a reparametrization invariant variation of) ORG, ORG,
ORG, and moment estimation. CARDINAL particular feature of ORG is that it can
genuinely deal with nested hypotheses.","We introduce a new principle for model selection in regression and
classification. Many regression models are controlled by some smoothness or
flexibility or complexity parameter c, e.g. the number of neighbors to be
averaged over in k nearest neighbor (kNN) regression or the polynomial degree
in regression with polynomials. Let ORG be the (best) regressor of complexity
c on data NORP A more flexible regressor can fit more data D' well than a more
rigid one. If something (here small loss) is easy to achieve it's typically
worth less. We define the loss rank of ORG as the number of other
(fictitious) data D' that are fitted better by f_D'^c than D is fitted by
ORG. We suggest selecting the model complexity c that has minimal loss rank
(LoRP). Unlike most penalized maximum likelihood variants (ORG,ORG,ORG), PRODUCT
only depends on the regression function and loss function. It works without a
stochastic noise model, and is directly applicable to any non-parametric
regressor, like kNN. In this paper we formalize, discuss, and motivate PERSON,
study it for specific regression problems, in particular linear ones, and
compare it to other model selection schemes.",1
"We consider a distributed source coding problem of $MONEY correlated NORP
observations $Y_i, i=1,2,...,L$. We assume that the random vector
$PERSON t} (Y_1,Y_2,$ $...,PERSON is an observation of the NORP
random vector $PERSON...,X_K)$, having the form $Y^L=AX^K+N^L
,$ where $MONEY is a $L\times K$ matrix and $PERSON t}(N_1,N_2,...,N_L)$ is a
vector of $MONEY independent PERSON random variables also independent of $PERSON
The estimation error on $PERSON is measured by the distortion covariance matrix.
The rate distortion region is defined by a set of all rate vectors for which
the estimation error is upper bounded by an arbitrary prescribed covariance
matrix in the meaning of positive semi definite. In this paper we derive
explicit outer and inner bounds of the rate distortion region. This result
provides a useful tool to study the direct and indirect source coding problems
on this NORP distributed source coding system, which remain open in
general.","Traditional image processing is a field of science and technology developed
to facilitate human-centered image management. But DATE, when huge volumes of
visual data inundate our surroundings (due to the explosive growth of
image-capturing devices, proliferation of Internet communication means and
video sharing services over WORK_OF_ART), human-centered handling of
Big-data flows is impossible anymore. Therefore, it has to be replaced with a
machine (computer) supported counterpart. Of course, such an artificial
counterpart must be equipped with some cognitive abilities, usually
characteristic for a human being. Indeed, in DATE, a new computer
design trend - ORG development - is become visible. Cognitive
image processing definitely will be one of its main duties. It must be
specially mentioned that this trend is a particular case of a much more general
movement - the transition from a ""computational data-processing paradigm"" to a
""cognitive information-processing paradigm"", which affects DATE many fields of
science, technology, and engineering. This transition is a blessed novelty, but
its success is hampered by the lack of a clear delimitation between the notion
of data and the notion of information. Elaborating the case of cognitive image
processing, the paper intends to clarify these important research issues.",0
"We define the notion of a well-clusterable data set combining the point of
view of the objective of $k$-means clustering algorithm (minimising the centric
spread of data elements) and common sense (clusters shall be separated by
gaps). We identify conditions under which the optimum of $k$-means objective
coincides with a clustering under which the data is separated by predefined
gaps.
  We investigate CARDINAL cases: when the whole clusters are separated by some gap
and when only the cores of the clusters meet some separation condition.
  We overcome a major obstacle in using clusterability criteria due to the fact
that known approaches to clusterability checking had the disadvantage that they
are related to the optimal clustering which is ORG hard to identify.
  Compared to other approaches to clusterability, the novelty consists in the
possibility of an a posteriori (after running $k$-means) check if the data set
is well-clusterable or not. As the $k$-means algorithm applied for this purpose
has polynomial complexity so does therefore the appropriate check.
Additionally, if $k$-means++ fails to identify a clustering that meets
clusterability criteria, with high probability the data is not
well-clusterable.","We prove in this paper that the expected value of the objective function of
the $k$-means++ algorithm for samples converges to population expected value.
As $k$-means++, for samples, provides with constant factor approximation for
$k$-means objectives, such an approximation can be achieved for the population
with increase of the sample size.
  This result is of potential practical relevance when one is considering using
subsampling when clustering large data sets (large data bases).",1
"Process modeling (PM) in software engineering involves a specific way of
understanding the world. In this context, philosophical work is not merely
intrinsically important; it can also stand up to some of the more established
software engineering research metrics. The object-oriented methodology takes an
object as the central concept of modeling. This paper follows from a series of
papers that focus on the notion of thinging in the context of the analysis
phase of software system modeling. We use an abstract machine named ORGORG) as the mechanism by which things reveal themselves. We
introduce a more in-depth investigation of a grand ORG that Signifies the
totality of entities in the modeled system. We also present new notions, such
as maximum grip, which refers to the level of granularity of the significance
where optimum visibility of the model s meaning is given. The outcomes of this
research indicate a positive improvement in the field of PM that may lead to
enhance understanding of the object-oriented approach. ORG also presents the
possibility of developing a new method in GPE.","The notion of events has occupied a central role in modeling and has an
influence in computer science and philosophy. Recent developments in
diagrammatic modeling have made it possible to examine conceptual
representation of events. This paper explores some aspects of the notion of
events that are produced by applying a new diagrammatic methodology with a
focus on the interaction of events with such concepts as time and space,
objects. The proposed description applies to abstract machines where events
form the dynamic phases of a system. The results of this nontechnical research
can be utilized in many fields where the notion of an event is typically used
in interdisciplinary application.",1
"We propose a long-term memory design for artificial general intelligence
based on PERSON's incremental machine learning methods. We use R5RS Scheme
and its standard library with a few omissions as the reference machine. We
introduce a PERSON variant based on ORG
together with CARDINAL synergistic update algorithms that use the same grammar as a
guiding probability distribution of programs. The update algorithms include
adjusting production probabilities, re-using previous solutions, learning
programming idioms and discovery of frequent subprograms. Experiments with CARDINAL
training sequences demonstrate that our approach to incremental learning is
effective.","We propose that PERSON induction is complete in the physical sense via
several strong physical arguments. We also argue that PERSON induction is
fully applicable to quantum mechanics. We show how to choose an objective
reference machine for universal induction by defining a physical message
complexity and physical message probability, and argue that this choice
dissolves some well-known objections to universal induction. We also introduce
many more variants of physical message complexity based on energy and action,
and discuss the ramifications of our proposals.",1
"We consider a system model of a general finite-state machine (ratchet) that
simultaneously interacts with CARDINAL kinds of reservoirs: a heat reservoir, a
work reservoir, and an information reservoir, the latter being taken to be a
running digital tape whose symbols interact sequentially with the machine. As
has been shown in earlier work, this finite-state machine can act as a demon
(with memory), which creates a net flow of energy from the heat reservoir into
the work reservoir (thus extracting useful work) at the price of increasing the
entropy of the information reservoir. Under very few assumptions, we propose a
simple derivation of a family of inequalities that relate the work extraction
with the entropy production. These inequalities can be seen as either upper
bounds on the extractable work or as lower bounds on the entropy production,
depending on the point of view. Many of these bounds are relatively easy to
calculate and they are tight in the sense that equality can be approached
arbitrarily closely. In their basic forms, these inequalities are applicable to
any finite number of cycles (and not only asymptotically), and for a general
input information sequence (possibly correlated), which is not necessarily
assumed even stationary. Several known results are obtained as special cases.","We design games for truly concurrent bisimilarities, including strongly truly
concurrent bisimilarities and branching truly concurrent bisimilarities, such
as pomset bisimilarities, step bisimilarities, history-preserving
bisimilarities and hereditary history-preserving bisimilarities.",0
"The paper briefly describes a basic set of special combinatorial engineering
frameworks for solving complex problems in the field of hierarchical modular
systems. The frameworks consist of combinatorial problems (and corresponding
models), which are interconnected/linked (e.g., by preference relation).
Mainly, hierarchical morphological system model is used. The list of basic
standard combinatorial engineering (technological) frameworks is the following:
(CARDINAL) design of system hierarchical model, (CARDINAL) combinatorial synthesis
('bottom-up' process for system design), (CARDINAL) system evaluation, (CARDINAL) detection
of system bottlenecks, (CARDINAL) system improvement (re-design, upgrade), (CARDINAL)
multi-stage design (design of system trajectory), (CARDINAL) combinatorial modeling of
system evolution/development and system forecasting. The combinatorial
engineering frameworks are targeted to maintenance of some system life cycle
stages. The list of main underlaying combinatorial optimization problems
involves the following: knapsack problem, multiple-choice problem, assignment
problem, spanning trees, morphological clique problem.","The paper described a generalized integrated glance to PERSON packing problems
including a brief literature survey and some new problem formulations for the
cases of multiset estimates of items. A new systemic viewpoint to PERSON packing
problems is suggested: (a) basic element sets (item set, PERSON set, item subset
assigned to bin), (b) binary relation over the sets: relation over item set as
compatibility, precedence, dominance; relation over items and bins (i.e.,
correspondence of items to bins). A special attention is targeted to the
following versions of PERSON packing problems: (a) problem with multiset estimates
of items, (b) problem with colored items (and some close problems). Applied
examples of bin packing problems are considered: (i) planning in paper industry
(framework of combinatorial problems), (ii) selection of information messages,
(iii) packing of messages/information packages in WiMAX communication system
(brief description).",1
"In this paper, we propose an extremely simple deep model for the unsupervised
nonlinear dimensionality reduction -- deep distributed random samplings, which
performs like a stack of unsupervised bootstrap aggregating. ORDINAL, its network
structure is novel: each layer of the network is a group of mutually
independent $k$-centers clusterings. ORDINAL, its learning method is extremely
simple: the $PERSON centers of each clustering are MONEYPERSON randomly selected
examples from the training data; for small-scale data sets, the $PERSON centers are
further randomly reconstructed by a simple cyclic-shift operation. Experimental
results on nonlinear dimensionality reduction show that the proposed method can
learn abstract representations on both large-scale and small-scale problems,
and meanwhile is much faster than deep neural networks on large-scale problems.","Voting is a simple mechanism to combine together the preferences of multiple
agents. Agents may try to manipulate the result of voting by mis-reporting
their preferences. CARDINAL barrier that might exist to such manipulation is
computational complexity. In particular, it has been shown that it is ORG-hard
to compute how to manipulate a number of different voting rules. However,
ORG-hardness only bounds the worst-case complexity. Recent theoretical results
suggest that manipulation may often be easy in practice. In this paper, we
study empirically the manipulability of single transferable voting (ORG) to
determine if computational complexity is really a barrier to manipulation. NORP
was CARDINAL of the ORDINAL voting rules shown to be ORG-hard. It also appears CARDINAL of
the harder voting rules to manipulate. We sample a number of distributions of
votes including uniform and real world elections. In almost every election in
our experiments, it was easy to compute how a single agent could manipulate the
election or to prove that manipulation by a single agent was impossible.",0
"Decision theory formally solves the problem of rational agents in uncertain
worlds if the true environmental probability distribution is known.
PERSON's theory of universal induction formally solves the problem of
sequence prediction for unknown distribution. We unify both theories and give
strong arguments that the resulting universal AIXI model behaves optimal in any
computable environment. The major drawback of the AIXI model is that it is
uncomputable. To overcome this problem, we construct a modified algorithm
ORG, which is still superior to any other time t and space l bounded agent.
The computation time of NORP is of the order t x CARDINAL.","PERSON sequence prediction is a scheme to predict digits of binary
strings without knowing the underlying probability distribution. We call a
prediction scheme informed when it knows the true probability distribution of
the sequence. Several new relations between universal PERSON sequence
prediction and informed prediction and general probabilistic prediction schemes
will be proved. Among others, they show that the number of errors in PERSON
prediction is finite for computable distributions, if finite in the informed
case. Deterministic variants will also be studied. The most interesting result
is that the deterministic variant of PERSON prediction is optimal compared
to any other probabilistic or deterministic prediction scheme apart from
additive square root corrections only. This makes it well suited even for
difficult prediction problems, where it does not suffice when the number of
errors is minimal to within some factor greater than one. PERSON's original
bound and the ones presented here complement each other in a useful way.",1
"In this article, we study the vector meson transitions among the charmonium
and bottomonium states with the heavy quark effective theory in an systematic
way, and make predictions for the ratios among the vector PERSON widths of
a special multiplet to another multiplet. The predictions can be confronted
with the experimental data in the future.","In this article, we introduce a P-wave between the diquark and antidiquark
explicitly to construct the vector tetraquark currents, and study the vector
tetraquark states with the ORG sum rules systematically, and obtain the lowest
vector tetraquark masses up to now. The present predictions support assigning
the $MONEY, $MONEY, $Y(4390)$ and $Z(4250)$ to be the vector
tetraquark states with a relative P-wave between the diquark and antidiquark
pair.",1
"CARDINAL common type of symmetry is when values are symmetric. For example, if we
are assigning colours (values) to nodes (variables) in a graph colouring
problem then we can uniformly interchange the colours throughout a colouring.
For a problem with value symmetries, all symmetric solutions can be eliminated
in polynomial time. However, as we show here, both static and dynamic methods
to deal with symmetry have computational limitations. With static methods,
pruning all symmetric values is ORG-hard in general. With dynamic methods, we
can take exponential time on problems which static methods solve without
search.","Some contemporary views of the universe assume information and computation to
be key in understanding and explaining the basic structure underpinning
physical reality. We introduce the PERSON exploring some of the
basic arguments giving foundation to these visions. We will focus on the
algorithmic and ORG aspects, and how these may fit and support the
computable universe hypothesis.",0
"The increasing popularity of web-based applications has led to several
critical services being provided over the Internet. This has made it imperative
to monitor the network traffic so as to prevent malicious attackers from
depleting the resources of the network and denying services to legitimate
users. This paper has presented a mechanism for protecting a web-server against
a distributed denial of service (DDoS) attack. Incoming traffic to the server
is continuously monitored and any abnormal rise in the inbound traffic is
immediately detected. The detection algorithm is based on a statistical
analysis of the inbound traffic on the server and a robust hypothesis testing
framework. While the detection process is on, the sessions from the legitimate
sources are not disrupted and the load on the server is restored to the normal
level by blocking the traffic from the attacking sources. To cater to different
scenarios, the detection algorithm has various modules with varying level of
computational and memory overheads for their execution. While the approximate
modules are fast in detection and involve less overhead, they have lower
detection accuracy. The accurate modules involve complex detection logic and
hence involve more overhead for their execution, but they have very high
detection accuracy. Simulations carried out on the proposed mechanism have
produced results that demonstrate effectiveness of the scheme.","A universal inequality that bounds the angular momentum of a body by the
square of its size is presented and heuristic physical arguments are given to
support it. We prove a version of this inequality, as consequence of GPE
equations, for the case of rotating axially symmetric, constant density,
bodies. Finally, the physical relevance of this result is discussed.",0
"Currently, organizations are transforming their business processes into
e-services and service-oriented architectures to improve coordination across
sales, marketing, and partner channels, to build flexible and scalable systems,
and to reduce integration-related maintenance and development costs. However,
this new paradigm is still fragile and lacks many features crucial for building
sustainable and progressive computing infrastructures able to rapidly respond
and adapt to the always-changing market and environmental business. This paper
proposes a novel framework for building sustainable Ecosystem- Oriented
Architectures (ORG) using e-service models. The backbone of this framework is
an ecosystem layer comprising several computing units whose aim is to deliver
universal interoperability, transparent communication, automated management,
self-integration, self-adaptation, and security to all the interconnected
services, components, and devices in the ecosystem. Overall, the proposed model
seeks to deliver a comprehensive and a generic sustainable business IT model
for developing agile e-enterprises that are constantly up to new business
constraints, trends, and requirements. Future research can improve upon the
proposed model so much so that it supports computational intelligence to help
in decision making and problem solving.","Currently, cryptography is in wide use as it is being exploited in various
domains from data confidentiality to data integrity and message authentication.
Basically, cryptography shuffles data so that they become unreadable by
unauthorized parties. However, clearly visible encrypted messages, no matter
how unbreakable, will arouse suspicions. A better approach would be to hide the
very existence of the message using steganography. Fundamentally, steganography
conceals secret data into innocent-looking mediums called carriers which can
then travel from the sender to the receiver safe and unnoticed. This paper
proposes a novel steganography scheme for hiding digital data into uncompressed
image files using a randomized algorithm and a context-free grammar. Besides,
the proposed scheme uses CARDINAL mediums to deliver the secret data: a carrier
image into which the secret data are hidden into random pixels, and a
well-structured LANGUAGE text that encodes the location of the random carrier
pixels. The LANGUAGE text is generated at runtime using a context-free grammar
coupled with a lexicon of LANGUAGE words. The proposed scheme is stealthy, and
hard to be noticed, detected, and recovered. Experiments conducted showed how
the covering and the uncovering processes of the proposed scheme work. As
future work, a semantic analyzer is to be developed so as to make the LANGUAGE
text medium semantically correct, and consequently safer to be transmitted
without drawing any attention.",1
"Following a review of metric, ultrametric and generalized ultrametric, we
review their application in data analysis. We show how they allow us to explore
both geometry and topology of information, starting with measured data. Some
themes are then developed based on the use of metric, ultrametric and
generalized ultrametric in logic. In particular we study approximation chains
in an ultrametric or generalized ultrametric context. Our aim in this work is
to extend the scope of data analysis by facilitating reasoning based on the
data analysis; and to show how quantitative and qualitative data analysis can
be incorporated into logic programming.","Innovation is slowing greatly in the pharmaceutical sector. It is considered
here how part of the problem is due to overly limiting intellectual property
relations in the sector. On the other hand, computing and software in
particular are characterized by great richness of intellectual property
frameworks. Could the intellectual property ecosystem of computing come to the
aid of the biosciences and life sciences? We look at how the answer might well
be yes, by looking at (i) the extent to which a drug mirrors a software
program, and (ii) what is to be gleaned from trends in research publishing in
the life and biosciences.",1
"In this paper we investigate the opportunities offered by the new LOC
gravity models from the dedicated ORG and, especially, ORG missions to the
project of measuring the general relativistic PERSON effect with a new
LOC's artificial satellite. It turns out that it would be possible to abandon
the stringent, and expensive, requirements on the orbital geometry of the
originally prosed PERSON mission (same semimajor axis a=12270 km of the existing
LAGEOS and inclination i=70 deg) by inserting the new spacecraft in a
relatively low, and cheaper, orbit (a=7500-8000 km, i\sim 70 deg) and suitably
combining its node PERSON with those of ORG and LAW in order to cancel
out the ORDINAL even zonal harmonic coefficients of the multipolar expansion of
the terrestrial gravitational potential J_2, J_4 along with their temporal
variations. The total systematic error due to the mismodelling in the remaining
even zonal harmonics would amount to \sim PERCENT and would be insensitive to
departures of the inclination from the originally proposed value of many
degrees. No semisecular long-period perturbations would be introduced because
the period of the node, which is also the period of the solar PRODUCT tidal
perturbation, would amount to \sim DATE. Since the coefficient of the node
of the new satellite would be smaller than CARDINAL for such low altitudes, the
impact of the non-gravitational perturbations of it on the proposed combination
would be negligible. Then, a particular financial and technological effort for
suitably building the satellite in order to minimize the non-conservative
accelerations would be unnecessary.","We study a general relativistic gravitomagnetic CARDINAL-body effect induced by the
spin angular momentum ${\boldsymbol S}_\textrm{X}$ of a rotating mass
$PERSON orbited at distance $r_\textrm{X}$ by a local gravitationally
bound restricted CARDINAL-body system $\mathcal{S}$ of size CARDINALr\ll r_\textrm{X}$
consisting of a test particle revolving around a massive body $M$. At the
lowest post-Newtonian order, we analytically work out the doubly averaged rates
of change of the NORP orbital elements of the test particle by finding
non-vanishing long-term effects for the inclination $I$, the node CARDINALPERSON and
the pericenter $\omega$. Such theoretical results are confirmed by a numerical
integration of the equations of motion for a fictitious CARDINAL-body system. We
numerically calculate the magnitudes of the NORP gravitomagnetic
CARDINAL-body precessions for some astronomical scenarios in our solar system. For
putative man-made orbiters of the natural moons NORP and LOC in the
external fields of PRODUCT and LOC, the relativistic precessions due to the
angular momenta of the gaseous giant planets can be MONEY\simeq
CARDINAL~per~NORP~yr}^{-1}\right)$. A
preliminary numerical simulation shows that, for certain orbital configurations
of a hypothetical LOC orbiter, its range-rate signal $MONEY can
become larger than the current PERSON accuracy of the existing spacecraft PRODUCT
at LOC, i.e. $MONEY~s}^{-1}$, after QUANTITY The
effects induced by the ORG's angular momentum on artificial probes of ORG
and the LOC are at the level of $PERSONTIME~per~year}~\left(\mu\textrm{as~yr}^{-1}\right)$.",1
"The relationship between Popper spaces (conditional probability spaces that
satisfy some regularity conditions), lexicographic probability systems (ORG's),
and nonstandard probability spaces (ORG's) is considered. If countable
additivity is assumed, Popper spaces and a subclass of ORG's are equivalent;
without the assumption of countable additivity, the equivalence no longer
holds. If the state space is finite, ORG's are equivalent to ORG's. However, if
the state space is infinite, ORG's are shown to be more general than ORG's.","Despite the several successes of deep learning systems, there are concerns
about their limitations, discussed most recently by PERSON. This paper
discusses PERSON's concerns and some others, together with solutions to several
of these problems provided by the ""P theory of intelligence"" and its
realisation in the ""SP computer model"". The main advantages of the NORP system
are: relatively small requirements for data and the ability to learn from a
single experience; the ability to model both hierarchical and non-hierarchical
structures; strengths in several kinds of reasoning, including `commonsense'
reasoning; transparency in the representation of knowledge, and the provision
of an audit trail for all processing; the likelihood that the NORP system could
not be fooled into bizarre or eccentric recognition of stimuli, as deep
learning systems can be; the NORP system provides a robust solution to the
problem of `catastrophic forgetting' in deep learning systems; the NORP system
provides a theoretically-coherent solution to the problems of correcting over-
and under-generalisations in learning, and learning correct structures despite
errors in data; unlike most research on deep learning, the NORP programme of
research draws extensively on research on human learning, perception, and
cognition; and the NORP programme of research has an overarching theory,
supported by evidence, something that is largely missing from research on deep
learning. In general, the NORP system provides a much firmer foundation than deep
learning for the development of artificial general intelligence.",0
"We present a quantum-like (PERSON) model in that contexts (complexes of e.g.
mental, social, biological, economic or even political conditions) are
represented by complex probability amplitudes. This approach gives the
possibility to apply the mathematical quantum formalism to probabilities
induced in any domain of science. In our model ORG randomness appears not
as irreducible randomness (as it is commonly accepted in conventional quantum
mechanics, e.g., by PERSON and Dirac), but as a consequence of obtaining
incomplete information about a system. We pay main attention to the PERSON
description of processing of incomplete information. Our PERSON model can be useful
in cognitive, social and political sciences as well as economics and artificial
intelligence. In this paper we consider in a more detail CARDINAL special
application -- PERSON modeling of brain's functioning. The brain is modeled as a
PERSON-computer.","The paper describes a general glance to the use of element exchange
techniques for optimization over permutations. A multi-level description of
problems is proposed which is a fundamental to understand nature and complexity
of optimization problems over permutations (e.g., ordering, scheduling,
traveling salesman problem). The description is based on permutation
neighborhoods of several kinds (e.g., by improvement of an objective function).
Our proposed operational digraph and its kinds can be considered as a way to
understand convexity and polynomial solvability for combinatorial optimization
problems over permutations. Issues of an analysis of problems and a design of
hierarchical heuristics are discussed. The discussion leads to a multi-level
adaptive algorithm system which analyzes an individual problem and
selects/designs a solving strategy (trajectory).",0
"This article presents an overview of computability logic -- the
game-semantically constructed logic of interactive computational tasks and
resources. There is CARDINAL non-overview, technical section in it, devoted to
a proof of the soundness of affine logic with respect to the semantics of
computability logic. A comprehensive online source on the subject can be found
at ORG","Computability logic (CL) is a systematic formal theory of computational tasks
and resources, which, in a sense, can be seen as a semantics-based alternative
to (the syntactically introduced) linear logic. With its expressive and
flexible language, where formulas represent computational problems and ""truth""
is understood as algorithmic solvability, ORG potentially offers a comprehensive
logical basis for constructive applied theories and computing systems
inherently requiring constructive and computationally meaningful underlying
logics.
  Among the best known constructivistic logics is ORG's intuitionistic
calculus ORG, whose language can be seen as a special fragment of that of ORG.
The constructivistic philosophy of ORG, however, has never really found an
intuitively convincing and mathematically strict semantical justification. CL
has good claims to provide such a justification and hence a materialization of
ORG's known thesis ""INT = logic of problems"". The present paper contains
a soundness proof for ORG with respect to the ORG semantics. A comprehensive
online source on ORG is available at ORG",1
"General purpose intelligent learning agents cycle through (complex,ORG)
sequences of observations, actions, and rewards. On the other hand,
reinforcement learning is well-developed for small finite state PERSON
Processes (MDPs). So far it is an art performed by human designers to extract
the right state representation out of the bare observations, i.e. to reduce the
agent setup to the ORG framework. Before we can think of mechanizing this
search for suitable MDPs, we need a formal objective criterion. The main
contribution of this article is to develop such a criterion. I also integrate
the various parts into CARDINAL learning algorithm. Extensions to more realistic
dynamic NORP networks are developed in a companion article.","The impact of the latest combined ORG/GRACE/terrestrial measurements LOC
gravity model ORG-CG03C on the measurement of the Lense-Thirring effect with
some ORG combinations of the nodes of some of the existing LOC's
artificial satellites is presented. The CARDINAL-sigma upper bound of the systematic
error in the node-node LAGEOS-LAGEOS II combination is PERCENT (PERCENT with
ORG-GRACE02S, \sim PERCENT with ORG-CG01C and \sim PERCENT with ORG), while it is
DATE for the node-only LAGEOS-LAGEOS II-Ajisai-Jason-1 combination (PERCENT with
ORG-GRACE02S, PERCENT with ORG-CG01C and PERCENT with ORG).",0
"We explore a simple mathematical model of network computation, based on
PERSON chains. Similar models apply to a broad range of computational
phenomena, arising in networks of computers, as well as in genetic, and neural
nets, in social networks, and so on. The main problem of interaction with such
spontaneously evolving computational systems is that the data are not uniformly
structured. An interesting approach is to try to extract the semantical content
of the data from their distribution among the nodes. A concept is then
identified by finding the community of nodes that share it. The task of data
structuring is thus reduced to the task of finding the network communities, as
groups of nodes that together perform some non-local data processing. Towards
this goal, we extend the ranking methods from nodes to paths. This allows us to
extract some information about the likely flow biases from the available static
information about the network.","Dialectical logic is the logic of dialectical processes. The goal of
dialectical logic is to reveal the dynamical notions inherent in logical
computational systems. The fundamental notions of proposition and truth-value
in standard logic are subsumed by the notions of process and flow in
dialectical logic. Standard logic motivates the core sequential aspect of
dialectical logic. Horn-clause logic requires types and nonsymmetry and also
motivates the parallel aspect of dialectical logic. The process logics of
PERSON and ORG reveal the internal/external aspects of dialectical logic. The
sequential internal aspect of dialectical logic should be viewed as a typed or
distributed version of GPE's linear logic with ORG tensor. The
simplest version of dialectical logic is inherently intuitionistic. However, by
following GPE's approach in standard logic using double negation closure,
we can define a classical version of dialectical logic.",0
"Complementary strands in DNA double helix show temporary fluctuational
openings which are essential to biological functions such as transcription and
replication of the genetic information. Such large amplitude fluctuations,
known as the breathing of DNA, are generally localized and, microscopically,
are due to the breaking of the hydrogen bonds linking the base pairs
(\emph{bps}). I apply imaginary time path integral techniques to a mesoscopic
NORP which accounts for the helicoidal geometry of a short circular DNA
molecule. The \emph{bps} displacements with respect to the ground state are
interpreted as time dependent paths whose amplitudes are consistent with the
model potential for the hydrogen bonds. The portion of the paths configuration
space contributing to the partition function is determined by selecting the
ensemble of paths which fulfill the ORDINAL law of thermodynamics. Computations
of the thermodynamics in the denaturation range show the energetic advantage
for the equilibrium helicoidal geometry peculiar of B-DNA. I discuss the
interplay between twisting of the double helix and anharmonic stacking along
the molecule backbone suggesting an interesting relation between intrinsic
nonlinear character of the microscopic interactions and molecular topology.","We develop ORG-logitboost, based on the prior work on ORG-boost and robust
logitboost. Our extensive experiments on a variety of datasets demonstrate the
considerable improvement of ORG-logitboost over logitboost and ORG.",0
"More than a speculative technology, ORG computing seems to challenge our
most basic intuitions about how the physical world should behave. In this
thesis I show that, while some intuitions from classical computer science must
be jettisoned in the light of modern physics, many others emerge nearly
unscathed; and I use powerful tools from computational complexity theory to
help determine which are which.","A celebrated DATE theorem of PERSON asserts that honest, rational NORP
agents with common priors will never ""agree to disagree"": if their opinions
about any topic are common knowledge, then those opinions must be equal.
Economists have written numerous papers examining the assumptions behind this
theorem. But CARDINAL key questions went unaddressed: ORDINAL, can the agents reach
agreement after a conversation of reasonable length? ORDINAL, can the
computations needed for that conversation be performed efficiently? This paper
answers both questions in the affirmative, thereby strengthening PERSON's
original conclusion.
  We ORDINAL show that, for CARDINAL agents with a common prior to agree within
epsilon about the expectation of a [CARDINAL] variable with high probability over
their prior, it suffices for them to exchange order CARDINAL/epsilon^2 bits. This
bound is completely independent of the number of bits n of relevant knowledge
that the agents have. We then extend the bound to CARDINAL or more agents; and we
give an example where the economists' ""standard protocol"" (which consists of
repeatedly announcing one's current expectation) nearly saturates the bound,
while a new ""attenuated protocol"" does better. Finally, we give a protocol that
would cause CARDINAL NORP to agree within epsilon after exchanging order
CARDINAL/epsilon^2 messages, and that can be simulated by agents with limited
computational resources. By this we mean that, after examining the agents'
knowledge and a transcript of their conversation, no one would be able to
distinguish the agents from perfect NORP. The time used by the simulation
procedure is exponential in CARDINAL/epsilon^6 but not in n.",1
"This paper studies sequence prediction based on the monotone NORP
complexity NORP m, i.e. based on universal deterministic/CARDINAL-part ORG. m is
extremely close to PERSON's prior M, the latter being an excellent
predictor in deterministic as well as probabilistic environments, where
performance is measured in terms of convergence of posteriors or losses.
Despite this closeness to M, it is difficult to assess the prediction quality
of m, since little is known about the closeness of their posteriors, which are
the important quantities for prediction. We show that for deterministic
computable environments, the ""posterior"" and losses of m converge, but rapid
convergence could only be shown on-sequence; the off-sequence behavior is
unclear. In probabilistic environments, neither the posterior nor the losses
converge, in general.","I present a NORP model and a computational method suitable to evaluate
structural and thermodynamic properties of helical molecules embedded in
crowded environments which may confine the space available to the base pair
fluctuations. It is shown that, for the specific case of a short DNA fragment
in a nanochannel, the molecule is markedly over-twisted and stretched by
narrowing the width of the channel.",0
"In this article, we study the axial-vector tetraquark state and
QUANTITY mixed state consist of light quarks using the ORG sum
rules. The present predictions disfavor assigning the $MONEY as the
axial-vector tetraquark state with $PERSON, while support assigning the
$MONEY as the axial-vector MONEY mixed state.","In this article, we study the radiative transitions among the vector and
scalar heavy quarkonium states with the covariant light-front quark model. In
calculations, we observe that the radiative decay widths are sensitive to the
constituent quark masses and the shape parameters of the wave-functions, and
reproduce the experimental data with suitable parameters.",1
"This is a chapter in a book \emph{Quantum Error Correction} edited by D. A.
FAC and PERSON, and published by ORG (CARDINAL
(http://www.cambridge.org/us/academic/subjects/physics/quantum-physics-quantum-information-and-quantum-computation/quantum-error-correction)\\
presenting the author's view on feasibility of fault-tolerant quantum
information processing.","Recent papers by DATE and NORP have emphasized that wormholes supported
by arbitrarily small amounts of exotic matter will have to be incredibly
fine-tuned if they are to be traversable. This paper discusses a wormhole model
that strikes a balance between CARDINAL conflicting requirements, reducing the
amount of exotic matter and fine-tuning the metric coefficients, ultimately
resulting in an engineering challenge: CARDINAL requirement can only be met at the
expense of the other. The wormhole model is macroscopic and satisfies various
traversability criteria.",0
"This letter introduces a new, substantially simplified version of the
branching recurrence operation of computability logic (see
ORG), and proves its equivalence to the
old, ""canonical"" version.","The earlier paper ""Introduction to clarithmetic I"" constructed an axiomatic
system of arithmetic based on computability logic (see
ORG), and proved its soundness and
extensional completeness with respect to polynomial time computability. The
present paper elaborates CARDINAL additional sound and complete systems in the
same style and sense: CARDINAL for polynomial space computability, CARDINAL for
elementary recursive time (and/or space) computability, and one for primitive
recursive time (and/or space) computability.",1
"PERSON, who does not have any sophisticated quantum technology, delegates her
ORG computing to PERSON, who has a fully-fledged ORG computer. Can she
check whether the computation PERSON performs for her is correct? She cannot
recalculate the result by herself, since she does not have any quantum
computer. A recent experiment with photonic qubits suggests she can. Here, I
explain the basic idea of the result, and recent developments about secure
cloud ORG computing.","PERSON is usually defined as a subfield of ORG, which is busy with
information extraction from raw data sets. Despite of its common acceptance and
widespread recognition, this definition is wrong and groundless. Meaningful
information does not belong to the data that bear it. It belongs to the
observers of the data and it is a shared agreement and a convention among them.
Therefore, this private information cannot be extracted from the data by any
means. Therefore, all further attempts of ORG apologists to
justify their funny business are inappropriate.",0
"Purpose: To compare CARDINAL major Web search engines (ORG, ORG, ORG,
ORG, and ORG) for their retrieval effectiveness, taking into account
not only the results but also the results descriptions.
  Design/Methodology/Approach: The study uses real-life queries. Results are
made anonymous and are randomised. Results are judged by the persons posing the
original queries.
  Findings: The CARDINAL major search engines, ORG and ORG, perform best, and
there are no significant differences between them. ORG delivers
significantly more relevant result descriptions than any other search engine.
This could be CARDINAL reason for users perceiving this engine as superior.
  Research Limitations: The study is based on a user model where the user takes
into account a certain amount of results rather systematically. This may not be
the case in real life.
  Practical Implications: Implies that search engines should focus on relevant
descriptions. Searchers are advised to use other search engines in addition to
ORG.
  Originality/Value: This is the ORDINAL major study comparing results and
descriptions systematically and proposes new retrieval measures to take into
account results descriptions","The path to greater diversity, as we have seen, cannot be achieved by merely
hoping for a new search engine nor will government support for a single
alternative achieve this goal. What is instead required is to create the
conditions that will make establishing such a search engine possible in the
ORDINAL place. I describe how building and maintaining a proprietary index is the
greatest deterrent to such an undertaking. We must ORDINAL overcome this
obstacle. Doing so will still not solve the problem of the lack of diversity in
the search engine marketplace. But it may establish the conditions necessary to
achieve that desired end.",1
"Nowadays folksonomy is used as a system derived from user-generated
electronic tags or keywords that annotate and describe online content. But it
is not a classification system as an ontology. To consider it as a
classification system it would be necessary to share a representation of
contexts by all the users. This paper is proposing the use of folksonomies and
network theory to devise a new concept: a ""WORK_OF_ART"" to
represent folksonomies. This paper proposed and analyzed the network structure
of PERSON tags thought as folsksonomy tags suggestions for the user on a
dataset built on chosen websites. It is observed that the PRODUCT
has relative low path lengths checking it with classic networking measures
(clustering coefficient). Experiment result shows it can facilitate
serendipitous discovery of content among users. Neat examples and clear
formulas can show how a ""WORK_OF_ART"" can be used to tackle
ontology mapping challenges.","Information retrieval is not only the most frequent application executed on
the Web but it is also the base of different types of applications. Considering
collective intelligence of groups of individuals as a framework for evaluating
and incorporating new experiences and information we often cannot retrieve such
knowledge being tacit. ORG knowledge underlies many competitive capabilities
and it is hard to articulate on discrete ontology structure. It is unstructured
or unorganized, and therefore remains hidden. Developing generic solutions that
can find the hidden knowledge is extremely complex. Moreover this will be a
great challenge for the developers of semantic technologies. This work aims to
explore ways to make explicit and available the tacit knowledge hidden in the
collective intelligence of a collaborative environment within organizations.
The environment was defined by folksonomies supported by a faceted semantic
search. Vector space model which incorporates an analogy with the mathematical
apparatus of quantum theory is adopted for the representation and manipulation
of the meaning of folksonomy. Vector space retrieval has been proven efficiency
when there isn't a data behavioural because it bears ranking algorithms
involving a small number of types of elements and few operations. A solution to
find what the user has in mind when posing a query could be based on ""joint
meaning"" understood as a joint construal of the creator of the contents and the
reader of the contents. The joint meaning was proposed to deal with vagueness
on ontology of folksonomy indeterminacy, incompleteness and inconsistencies on
collective intelligence. A proof-of concept prototype was built for
collaborative environment as evolution of the actual social networks (like
GPE, GPE,..) using the information visualization on a ORG application
with ORG techniques and technologies.",1
"A chiral field theory of $MONEY glueball is presented. The coupling between
the quark operator and the $MONEY glueball field is revealed from the ORG)
anomaly. The NORP of this theory is constructed by adding a $MONEY
glueball field to a successful NORP of chiral field theory of
pseudoscalar, vector, and axial-vector mesons. Quantitative study of the
physical processes of the $MONEY glueball of $m=1.405\textrm{GeV}$ is
presented. The theoretical predictions can be used to identify the $MONEY
glueball.","Based on an effective chiral theory of pseudoscalar, vector, and axial-vector
mesons, the coefficients of the chiral perturbation theory are predicted. There
is no new parameter in these predictions.",1
"The scope of this teaching package is to make a brief introduction to some
notions and properties of chaotic systems. We ORDINAL make a brief introduction
to chaos in general and then we show some important properties of chaotic
systems using the logistic map and its bifurcation diagram. We also show the
universality found in ""the route to chaos"". The user is only required to have
notions of algebra, so it is quite accessible. The formal basis of chaos theory
are not covered in this introduction, but are pointed out for the reader
interested in them. Therefore, this package is also useful for people who are
interested in going deep into the mathematical theories, because it is a simple
introduction of the terminology, and because it points out which are the
original sources of information (so there is no danger in falling in the trap
of ""WORK_OF_ART in TIME"" or ""Bifurcation Diagrams for Dummies""). The
included exercises are suggested for consolidating the covered topics. The
on-line resources are highly recommended for extending this brief induction.","This paper discusses the benefits of describing the world as information,
especially in the study of the evolution of life and cognition. Traditional
studies encounter problems because it is difficult to describe life and
cognition in terms of matter and energy, since their laws are valid only at the
physical scale. However, if matter and energy, as well as life and cognition,
are described in terms of information, evolution can be described consistently
as information becoming more complex.
  The paper presents CARDINAL tentative laws of information, valid at multiple
scales, which are generalizations of NORP, cybernetic, thermodynamic,
psychological, philosophical, and complexity principles. These are further used
to discuss the notions of life, cognition and their evolution.",1
"Cirquent calculus is a novel proof theory permitting component-sharing
between logical expressions. Using it, the predecessor article ""Elementary-base
cirquent calculus I: Parallel and choice connectives"" built the sound and
complete axiomatization CL16 of a propositional fragment of computability logic
(see http://www.csc.villanova.edu/~japaridz/CL/ ). The atoms of the language of
CL16 represent elementary, i.e., moveless, games, and the logical vocabulary
consists of negation, parallel connectives and choice connectives. The present
paper constructs the ORDINAL-order version CL17 of ORG, also enjoying soundness
and completeness. The language of CL17 augments that of CL18 by including
choice quantifiers. Unlike classical predicate calculus, CL17 turns out to be
decidable.","Clarithmetics are number theories based on computability logic (see
http://www.csc.villanova.edu/~japaridz/CL/ ). Formulas of these theories
represent interactive computational problems, and their ""truth"" is understood
as existence of an algorithmic solution. Various complexity constraints on such
solutions induce various versions of clarithmetic. The present paper introduces
a parameterized/schematic version PRODUCT). By tuning the CARDINAL
parameters P1,P2,P3 in an essentially mechanical manner, CARDINAL automatically
obtains sound and complete theories with respect to a wide range of target
tricomplexity classes, i.e. combinations of time (set by ORG), space (set by PERSON)
and so called amplitude (set by CARDINAL) complexities. Sound in the sense that every
theorem T of the system represents an interactive number-theoretic
computational problem with a solution from the given tricomplexity class and,
furthermore, such a solution can be automatically extracted from a proof of NORP
And complete in the sense that every interactive number-theoretic problem with
a solution from the given tricomplexity class is represented by some theorem of
the system. Furthermore, through tuning the ORDINAL parameter CARDINAL, at the cost of
sacrificing recursive axiomatizability but not simplicity or elegance, the
above extensional completeness can be strengthened to intensional completeness,
according to which every formula representing a problem with a solution from
the given tricomplexity class is a theorem of the system. This article is
published in CARDINAL parts. The previous Part I has introduced the system and
proved its completeness, while the present Part II is devoted to proving
soundness.",1
"In the same sense as classical logic is a formal theory of truth, the
recently initiated approach called computability logic is a formal theory of
computability. It understands (interactive) computational problems as games
played by a machine against the environment, their computability as existence
of a machine that always wins the game, logical operators as operations on
computational problems, and validity of a logical formula as being a scheme of
""always computable"" problems. The present contribution gives a detailed
exposition of a soundness and completeness proof for an axiomatization of CARDINAL
of the most basic fragments of computability logic. The logical vocabulary of
this fragment contains operators for the so called parallel and choice
operations, and its atoms represent elementary problems, i.e. predicates in the
standard sense. This article is self-contained as it explains all relevant
concepts. While not technically necessary, however, familiarity with the
foundational paper ""Introduction to computability logic"" [WORK_OF_ART and
ORG (DATE), CARDINAL] would greatly help the reader in
understanding the philosophy, underlying motivations, potential and utility of
computability logic, -- the context that determines the value of the present
results. Online introduction to the subject is available at
ORG and
http://www.csc.villanova.edu/~japaridz/CL/gsoll.html .","We consider the state dependent channels with full state information with at
the sender and partial state information at the receiver. For this state
dependent channel, the channel capacity under rate constraint on the state
information at the decoder was determined by PERSON. In this paper, we study
the correct probability of decoding at rates above the capacity. We prove that
when the transmission rate is above the capacity this probability goes to CARDINAL
exponentially and derive an explicit lower bound of this exponent function.",0
"We extend the algebra of reversible computation to support ORG computing.
Since the algebra is based on true concurrency, it is reversible for quantum
computing and it has a sound and complete theory.","We have unified quantum and classical computing in open ORG systems
called NORP which is a quantum generalization of process algebra ORG. But, an
axiomatization for quantum and classical processes with an assumption of closed
ORG systems is still missing. For closed ORG, unitary operator,
ORG measurement and ORG are CARDINAL basic components for
ORG computing. This leads to probability unavoidable. Along the solution of
NORP to unify quantum and classical computing in open ORG, we unify
quantum and classical computing with an assumption of closed systems under the
framework of ORG-like probabilistic process algebra. This unification make it
can be used widely in verification for quantum and classical computing mixed
systems, such as most quantum communication protocols.",1
"A major challenge of interdisciplinary description of complex system
behaviour is whether real systems of higher complexity levels can be understood
with at least the same degree of objective, ""scientific"" rigour and
universality as ""simple"" systems of classical, NORP science paradigm. The
problem is reduced to that of arbitrary, many-body interaction (unsolved in
standard theory). Here we review its causally complete solution, the ensuing
concept of complexity and applications. The discovered key properties of
dynamic multivaluedness and entanglement give rise to a qualitatively new kind
of mathematical structure providing the exact version of real system behaviour.
The extended mathematics of complexity contains the truly universal definition
of dynamic complexity, randomness (chaoticity), classification of all possible
dynamic regimes, and the unifying principle of any system dynamics and
evolution, the universal symmetry of complexity. Every real system has a
non-zero (and actually high) value of unreduced dynamic complexity determining,
in particular, ""mysterious"" behaviour of ORG systems and relativistic
effects causally explained now as unified manifestations of complex interaction
dynamics. The observed differences between various systems are due to different
regimes and levels of their unreduced dynamic complexity. We outline
applications of universal concept of dynamic complexity emphasising cases of
""truly complex"" systems from higher complexity levels (ecological and living
systems, brain operation, intelligence and consciousness, autonomic information
and communication systems) and show that the urgently needed progress in social
and intellectual structure of civilisation inevitably involves qualitative
transition to unreduced complexity understanding (we call it ""revolution of
complexity"").","This paper examines whether unitary evolution alone is sufficient to explain
emergence of the classical world from the perspective of computability theory.
Specifically, it looks at the problem of how the choice related to the
measurement is made by the observer viewed as a quantum system. In
interpretations where the system together with the observers is completely
described by unitary transformations, the observer cannot make any choices and
so measurement is impossible. From the perspective of computability theory, a
ORG machine cannot halt and so it cannot observe the computed state,
indicating that unitarity alone does not explain all matter processes. Further
it is argued that the consideration of information and observation requires an
overarching system of knowledge and expectations about outcomes.",0
"We calculate the limiting behavior of relative NORP entropy when the ORDINAL
probability distribution is close to the ORDINAL one in a non-regular
location-shift family which is generated by a probability distribution whose
support is an interval or a CARDINAL-line. This limit can be regarded as a
generalization of ORG information, and plays an important role in large
deviation theory.","We derive a new upper bound for PERSON's information in secret key generation
from a common random number without communication. This bound improves on
PERSON et al(1995)'s bound based on the R\'enyi entropy of order CARDINAL because the
bound obtained here uses the R\'enyi entropy of order $MONEY for $s \in [0,1]$.
This bound is applied to a wire-tap channel. Then, we derive an exponential
upper bound for PERSON's information. Our exponent is compared with
Hayashi(2006)'s exponent. For the additive case, the bound obtained here is
better. The result is applied to secret key agreement by public discussion.",1
"The theory of rational choice assumes that when people make decisions they do
so in order to maximize their utility. In order to achieve this goal they ought
to use all the information available and consider all the choices available to
choose an optimal choice. This paper investigates what happens when decisions
are made by artificially intelligent machines in the market rather than human
beings. ORDINAL, the expectations of the future are more consistent if they are
made by an artificially intelligent machine and the decisions are more rational
and thus marketplace becomes more rational.","This paper proposes the response surface method for finite element model
updating. The response surface method is implemented by approximating the
finite element model surface response equation by a multi-layer perceptron. The
updated parameters of the finite element model were calculated using genetic
algorithm by optimizing the surface response equation. The proposed method was
compared to the existing methods that use simulated annealing or genetic
algorithm together with a full finite element model for finite element model
updating. The proposed method was tested on an unsymmetri-cal H-shaped
structure. It was observed that the proposed method gave the updated natural
frequen-cies and mode shapes that were of the same order of accuracy as those
given by simulated annealing and genetic algorithm. Furthermore, it was
observed that the response surface method achieved these results at a
computational speed that was CARDINAL times as fast as the genetic
algorithm and a full finite element model and CARDINAL times faster than the
simulated annealing.",1
"The launching of NORP and ORG, and methodological developments
in ORG have made many more indicators for evaluating
journals available than the traditional ORG, Cited Half-life, and
Immediacy Index of the ORG. In this study, these new indicators are compared
with one another and with the older ones. Do the various indicators measure new
dimensions of the citation networks, or are they highly correlated among them?
Are they robust and relatively stable over time? CARDINAL main dimensions are
distinguished -- size and impact -- which together shape influence. The H-index
combines the CARDINAL dimensions and can also be considered as an indicator of reach
(like NORP). ORG is mainly an indicator of size, but has important
interactions with centrality measures. ORG (ORG)
indicator provides an alternative to ORG, but the
computation is less easy.","One can study communications by using FAC's (DATE) mathematical theory of
communication. In social communications, however, the channels are not ""fixed"",
but themselves subject to change. Communication systems change by communicating
information to related communication systems; co-variation among systems if
repeated over time, can lead to co-evolution. Conditions for stabilization of
higher-order systems are specifiable: segmentation, stratification,
differentiation, reflection, and self-organization can be distinguished in
terms of developmental stages of increasingly complex networks. In addition to
natural and cultural evolution, a condition for the artificial evolution of
communication systems can be specified.",1
"We explore multi-terminal quantum transport through a benzene molecule
threaded by an LOC flux $\phi$. A simple tight-binding model is used
to describe the system and all the calculations are done based on the PERSON's
function formalism. With a brief description of CARDINAL-terminal quantum transport,
we present a detailed study of CARDINAL-terminal transport properties through the
benzene molecule to reveal the actual mechanism of electron transport. Here we
numerically compute the multi-terminal conductances, reflection probabilities
and current-voltage characteristics in the aspects of molecular coupling
strength and magnetic flux $MONEY Most significantly we observe that, the
molecular system where the benzene molecule is attached to CARDINAL terminals can
be operated as a transistor, and we call it a molecular transistor. This aspect
can be utilized in designing nano-electronic circuits and our investigation may
provide a basic framework to study electron transport in any complicated
multi-terminal quantum system.","Computability logic is a formal theory of computational tasks and resources.
Its formulas represent interactive computational problems, logical operators
stand for operations on computational problems, and validity of a formula is
understood as being a scheme of problems that always have algorithmic
solutions. A comprehensive online source on the subject is available at
ORG . The earlier article ""Propositional
computability logic I"" proved soundness and completeness for the (in a sense)
minimal nontrivial fragment CL1 of computability logic. The present paper
extends that result to the significantly more expressive propositional system
CL2. What makes CL2 more expressive than CL1 is the presence of CARDINAL sorts of
atoms in its language: elementary atoms, representing elementary computational
problems (i.e. predicates), and general atoms, representing arbitrary
computational problems. CL2 conservatively extends CL1, with the latter being
nothing but the general-atom-free fragment of the former.",0
"We analyze electroproduction of light vector meson at small GPE $x$
within the generalized parton distribution (ORG) approach. Calculation is based
on the modified perturbative approach, where the quark transverse degrees of
freedom in the hard subprocess are considered. Our results on the cross section
are in fair agreement with experiment from GPE to ORG energies.","The term ""PRODUCT kernel"" stands for correlation-resemblance kernel. In many
applications (e.g., vision), the data are often high-dimensional, sparse, and
non-binary. We propose CARDINAL types of (nonlinear) PRODUCT kernels for non-binary
sparse data and demonstrate the effectiveness of the new kernels through a
classification experiment. PRODUCT kernels are simple with no tuning parameters.
However, training nonlinear kernel ORG can be (very) costly in time and memory
and may not be suitable for truly large-scale industrial applications (e.g.
search). In order to make the proposed PRODUCT kernels more practical, we develop
basic probabilistic hashing algorithms which transform nonlinear kernels into
ORG kernels.",0
"The complementary roles played by parallel quantum computation and quantum
measurement in originating the quantum speed-up are illustrated through an
analogy with a famous metaphor by ORG.","The topical quantum computation paradigm is a transposition of the ORG
machine into the quantum framework. Implementations based on this paradigm have
limitations as to the number of: qubits, computation steps, efficient quantum
algorithms (found so far). A new exclusively ORG paradigm (with no
classical counterpart) is propounded, based on the speculative notion of
continuous uncomplete von ORG measurement. Under such a notion, ORG-complete
is equal to P. This can provide a mathematical framework for the search of
implementable paradigms, possibly exploiting particle statistics.",1
"Data processing lower bounds on the expected distortion are derived in the
finite-alphabet semi-deterministic setting, where the source produces a
deterministic, individual sequence, but the channel model is probabilistic, and
the decoder is subjected to various kinds of limitations, e.g., decoders
implementable by finite-state machines, with or without counters, and with or
without a restriction of common reconstruction with high probability. Some of
our bounds are given in terms of the Lempel-Ziv complexity of the source
sequence or the reproduction sequence. We also demonstrate how some analogous
results can be obtained for classes of ORG encoders and linear decoders in
the continuous alphabet case.","This document consists of lecture notes for a graduate course, which focuses
on the relations between Information Theory and Statistical Physics. The course
is aimed at EE graduate students in the area of ORG, as well as to graduate students in ORG who have basic background in
ORG. Strong emphasis is given to the analogy and parallelism
between ORG, as well as to the insights,
the analysis tools and techniques that can be borrowed from ORG
and `imported' to certain problem areas in ORG. This is a
research trend that has been very active in DATE, and the hope
is that by exposing the student to the meeting points between these CARDINAL
disciplines, we will enhance his/her background and perspective to carry out
research in the field.
  A short outline of the course is as follows: Introduction; PERSONORG and its ORG; PERSON in
ORG; Systems of Interacting Particles and ORG;
ORG (ORG) and ORG; Additional Topics
(optional).",1
"The problem of calculating multicanonical parameters recursively is
discussed. I describe in detail a computational implementation which has worked
reasonably well in practice.","According to contemporary views, equilibrium constant is relevant only to
true thermodynamic equilibria in isolated systems with CARDINAL chemical reaction.
The paper presents a novel formula that ties-up equilibrium constant and
chemical system composition at any state, isolated or open as well. Extending
the logarithmic logistic map of ORG, this formula maps the system population at isolated equilibrium
into the population at any open equilibrium at p,T=const, using equilibrium
constant as a measure. Real chemical systems comprise multiple subsystems;
given the resources are limited, joint solution to the set of such expressions,
each relevant to a specific subsystem, gives equilibrium composition for each
of them. This result means a fundamental break through in the open systems
thermodynamics and leads to formerly unknown opportunities in the analysis of
real chemical objects.",0
"A file repository for calculations of cross sections and kinematic
distributions using PERSON generators for high-energy collisions is
discussed. The repository is used to facilitate effective preservation and
archiving of data from theoretical calculations, as well as for comparisons
with experimental data. The ORG data library is publicly accessible and
includes a number of PERSON event samples with PERSON predictions
for current and future experiments. The ORG project includes a software
package to automate the process of downloading and viewing online PERSON
event samples. A data streaming over a network for end-user analysis is
discussed.","Multiplicity correlations between the current and target regions of the GPE
frame in deep-inelastic scattering processes are studied. It is shown that the
correlations are sensitive to the ORDINAL-order perturbative ORG effects and can
be used to extract the behavior of the boson-gluon fusion rates as a function
of the GPE variable. The behavior of the correlations is derived
analytically and analyzed using a PERSON simulation.",1
"We analytically work out the long-term orbital perturbations induced by a
homogeneous circular ring of radius PERSON and mass mr on the motion of a test
particle in the cases (I): r > R_r and (II): r < R_r. In order to extend the
validity of our analysis to the orbital configurations of, e.g., some proposed
spacecraftbased mission for fundamental physics like ORG and ORG, of
possible GPE around the supermassive black hole in ORG* coming from tidal
disruptions of incoming gas clouds, and to the effect of artificial space
debris belts around the LOC, we do not restrict ourselves to the case in
which the ring and the orbit of the perturbed particle lie just in the same
plane. From the corrections to the standard secular perihelion precessions,
recently determined by a team of astronomers for some planets of the PRODUCT, we infer upper bounds on mr for various putative and known annular
matter distributions of natural origin (close circumsolar ring with R_r =
CARDINAL-0.13 au, dust ring with R_r = CARDINAL au, minor asteroids, NORP
Objects). We find m_r <= CARDINAL CARDINAL^-4 m_E (circumsolar ring with R_r = CARDINAL au),
m_r <= DATE^-6 m_E (circumsolar ring with R_r = CARDINAL au), m_r <= DATE^-7
m_E (ring with R_r = CARDINAL au), m_r <= CARDINAL 10^-12 M_S (asteroidal ring with R_r =
CARDINAL au), m_r <= CARDINAL <= CARDINAL^PRODUCT (asteroidal ring with R_r = CARDINAL au), m_r <=
CARDINAL^-8 M_S (TNOs ring with R_r = CARDINAL au). In principle, our analysis is valid
both for baryonic and non-baryonic PERSON distributions.","There is significant concern that technological advances, especially in
LOC and ORG (AI), could lead to high levels of
unemployment in DATE. Studies have estimated that CARDINAL of
all current jobs are at risk of automation. To look into this issue in more
depth, we surveyed experts in ORG and ORG about the risk, and compared
their views with those of non-experts. Whilst the experts predicted a
significant number of occupations were at risk of automation in DATE, they were more cautious than people outside the field in predicting
occupations at risk. Their predictions were consistent with their estimates for
when computers might be expected to reach human level performance across a wide
range of skills. These estimates were typically DATE than those of the
non-experts. Technological barriers may therefore provide society with more
time to prepare for an automated future than the public fear. In addition,
public expectations may need to be dampened about the speed of progress to be
expected in GPE and ORG.",0
"In a complete metric space that is equipped with a doubling measure and
supports a Poincar\'e inequality, we show that functions of bounded variation
(BV functions) can be approximated in the strict sense and pointwise uniformly
by special functions of bounded variation, without adding significant jumps. As
a main tool, we study the variational CARDINAL-capacity and its ORG analog.","We study a stochastic control system, described by Ito controllable equation,
and evaluate the solutions by an entropy functional (EF), defined by the
equation functions of controllable drift and diffusion. Considering a control
problem for this functional, we solve the ORG control variation problem (VP),
which leads to both a dynamic approximation of the process entropy functional
by an information path functional (ORG) and information dynamic model (IDM) of
the stochastic process. The ORG variation equations allow finding the optimal
control functions, applied to both stochastic system and the ORG for joint
solution of the identification and optimal control problems, combined with
state consolidation. In this optimal dual strategy, the ORG optimum predicts
each current control action not only in terms of total functional path goal,
but also by setting for each following control action the renovated values of
this functional controllable drift and diffusion, identified during the optimal
movement, which concurrently correct this goal. The VP information invariants
allow optimal encoding of the identified dynamic model operator and control.
The introduced method of cutting off the process by applying an impulse control
estimates the cutoff information, accumulated by the process inner connections
between its states. It has shown that such a functional information measure
contains more information than the sum of FAC entropies counted for all
process separated states, and provides information measure of ORG kernel.
Examples illustrate the procedure of solving these problems, which has been
implemented in practice. Key words: Entropy and information path functionals,
variation equations, information invariants, controllable dynamics, impulse
controls, cutting off the diffusion process, identification, cooperation,
encoding.",0
"This paper provides an overview of the NORP theory of intelligence and its
central idea that artificial intelligence, mainstream computing, and much of
human perception and cognition, may be understood as information compression.
  The background and origins of the NORP theory are described, and the main
elements of the theory, including the key concept of multiple alignment,
borrowed from bioinformatics but with important differences. Associated with
the NORP theory is the idea that redundancy in information may be understood as
repetition of patterns, that compression of information may be achieved via the
matching and unification (merging) of patterns, and that computing and
information compression are both fundamentally probabilistic. It appears that
the NORP system is Turing-equivalent in the sense that anything that may be
computed with a Turing machine may, in principle, also be computed with an NORP
machine.
  CARDINAL of the main strengths of the NORP theory and the multiple alignment concept
is in modelling concepts and phenomena in artificial intelligence. Within that
area, the NORP theory provides a simple but versatile means of representing
different kinds of knowledge, it can model both the parsing and production of
natural language, with potential for the understanding and translation of
natural languages, it has strengths in pattern recognition, with potential in
computer vision, it can model several kinds of reasoning, and it has
capabilities in planning, problem solving, and unsupervised learning.
  The paper includes CARDINAL examples showing how alternative parsings of an
ambiguous sentence may be modelled as multiple alignments, and another example
showing how the concept of multiple alignment may be applied in medical
diagnosis.","This article introduces the idea that probabilistic reasoning (PR) may be
understood as ""information compression by multiple alignment, unification and
search"" (ICMAUS). In this context, multiple alignment has a meaning which is
similar to but distinct from its meaning in bio-informatics, while unification
means a simple merging of matching patterns, a meaning which is related to but
simpler than the meaning of that term in logic.
  A software model, SP61, has been developed for the discovery and formation of
'good' multiple alignments, evaluated in terms of information compression. The
model is described in outline.
  Using examples from the SP61 model, this article describes in outline how the
ICMAUS framework can model various kinds of PR including: PR in best-match
pattern recognition and information retrieval; CARDINAL-step 'deductive' and
'abductive' PR; inheritance of attributes in a class hierarchy; chains of
reasoning (probabilistic decision networks and decision trees, and PR with
'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with
default values; modelling the function of a NORP network.",1
"Information is the basic concept of information theory. However, there is no
definition of this concept that can encompass all uses of the term information
in information theories and beyond. Many question a possibility of such a
definition. However, foundations of information theory developed in the context
of the general theory of information made it possible to build such a relevant
and at the same time, encompassing definition. Foundations of information
theory are built in a form of ontological principles, which reflect basic
features of information and information processes.","In this thesis I present a virtual laboratory which implements CARDINAL different
models for controlling animats: a rule-based system, a behaviour-based system,
a concept-based system, a neural network, and a GPE architecture.
Through different experiments, I compare the performance of the models and
conclude that there is no ""best"" model, since different models are better for
different things in different contexts.
  The models I chose, although quite simple, represent different approaches for
studying cognition. Using the results as an empirical philosophical aid,
  I note that there is no ""best"" approach for studying cognition, since
different approaches have all advantages and disadvantages, because they study
different aspects of cognition from different contexts. This has implications
for current debates on ""proper"" approaches for cognition: all approaches are a
bit proper, but none will be ""proper enough"". I draw remarks on the notion of
cognition abstracting from all the approaches used to study it, and propose a
simple classification for different types of cognition.",0
"We give an elementary review of black holes in string theory. We discuss BPS
holes, the microscopic computation of entropy and the `fuzzball' picture of the
black hole interior suggested by microstates of the CARDINAL-charge system.","We study the model of massless MONEY electrodynamics with nonconstant
coupling, introduced by ORG as the `charge hole'. But
we take the boundary of the strong coupling region to be ORDINAL timelike, then
spacelike for a distance $MONEY, and then timelike again (to mimic the structure
of a black hole). For an incident charge pulse entering this `charge trap' the
charge and information get separated. The charge comes out near the endpoint of
the singularity. The `information' travels a well localised path through the
strong coupling region and comes out later.",1
"There are very significant changes taking place in the university sector and
in related higher education institutes in many parts of the world. In this work
we look at financial data from DATE and DATE from the GPE higher education
sector. Situating ourselves to begin with in the context of teaching versus
research in universities, we look at the data in order to explore the new
divergence between the broad agendas of teaching and research in universities.
The innovation agenda has become at least equal to the research and teaching
objectives of universities. From the financial data, published in the ORG
Higher Education DATE newspaper, we explore the interesting contrast, and
very opposite orientations, in specialization of universities in the GPE. We
find a polarity in specialism that goes considerably beyond the usual one of
research-led elite versus more teaching-oriented new universities. Instead we
point to the role of medical/bioscience research income in the former, and
economic and business sectoral niche player roles in the latter.","Discussion of ""Treelets--An adaptive multi-Scale basis for sparse unordered
data"" [arXiv:0707.0481]",1
"A resonance search has been made in FAC, K^{0}s-pbar and ORG
invariant-mass spectra measured with the ORG detector at ORG using an
integrated luminosity of CARDINAL pb^{-1}. The search was performed in the central
rapidity region of inclusive deep inelastic scattering at an ep centre-of-mass
energy of CARDINAL--318 GeV for exchanged photon virtuality, CARDINAL, above CARDINAL GeV^{2}.
The results support the existence of a narrow state in ORG and K^{0}s-pbar
decay channels, consistent with the pentaquark prediction. No signal was found
in the PERSON decay channel.","Starting from the primary representation of neutrosophic information, namely
the degree of truth, degree of indeterminacy and degree of falsity, we define a
nuanced representation in a penta valued fuzzy space, described by the index of
truth, index of falsity, index of ignorance, index of contradiction and index
of hesitation. Also, it was constructed an associated penta valued logic and
then using this logic, it was defined for the proposed penta valued structure
the following operators: union, intersection, negation, complement and dual.
Then, the penta valued representation is extended to a hexa valued one, adding
the ORDINAL component, namely the index of ambiguity.",0
"The paper considers a linear regression model with multiple change-points
occurring at unknown times. The ORG technique is very interesting since it
allows the parametric estimation, including the change-points, and automatic
variable selection simultaneously. The asymptotic properties of the ORG-type
(which has as particular case the ORG estimator) and of the adaptive ORG
estimators are studied. For this last estimator the oracle properties are
proved. In both cases, a model selection criterion is proposed. Numerical
examples are provided showing the performances of the adaptive ORG estimator
compared to the ORG estimator.","In this paper we are interested in parameters estimation of ORG model when
number of parameters increases with sample size. Without any assumption about
moments of the model error, we propose and study the seamless MONEY quantile
estimator. For this estimator we ORDINAL give the convergence rate. Afterwards,
we prove that it correctly distinguishes CARDINAL and nonzero parameters
and that the estimators of the nonzero parameters are asymptotically normal. A
consistent ORG criterion to select the tuning parameters is given.",1
"This empirical study is mainly devoted to comparing CARDINAL tree-based boosting
algorithms: mart, ORG, robust logitboost, and ORG-logitboost, for
multi-class classification on a variety of publicly available datasets. Some of
those datasets have been thoroughly tested in prior studies using a broad range
of classification algorithms including ORG, neural nets, and deep learning.
  In terms of the empirical classification errors, our experiment results
demonstrate:
  CARDINAL. Abc-mart considerably improves mart. CARDINAL Abc-logitboost considerably
improves (robust) logitboost. CARDINAL. Robust) logitboost} considerably improves mart
on most datasets. CARDINAL Abc-logitboost considerably improves ORG on most
datasets. CARDINAL These CARDINAL boosting algorithms (especially ORG-logitboost)
outperform ORG on many datasets. CARDINAL Compared to the best deep learning methods,
these CARDINAL boosting algorithms (especially ORG-logitboost) are competitive.","Counting is among the most fundamental operations in computing. For example,
counting the pth frequency moment has been a very active area of research, in
theoretical computer science, databases, and data mining. When p=1, the task
(i.e., counting the sum) can be accomplished using a simple counter.
  PERSON (ORG) is proposed for efficiently computing the pth
frequency moment of a data stream signal A_t, where 0<p<=2. ORG is applicable if
the streaming data follow the PERSON model, with the restriction that at the
time t for the evaluation, A_t[i]>= 0, which includes the strict PERSON
model as a special case. For natural data streams encountered in practice, this
restriction is minor.
  The underly technique for ORG is what we call skewed stable random
projections, which captures the intuition that, when p=1 a simple counter
suffices, and when p = DATE with small ORG, the sample complexity of a
counter system should be low (continuously as a function of \Delta). We show at
small \Delta the sample complexity (number of projections) k = O(1/\epsilon)
instead of O(1/\epsilon^2).
  PERSON can serve a basic building block for other tasks in
statistics and computing, for example, estimation entropies of data streams,
parameter estimations using the method of moments and maximum likelihood.
  Finally, another contribution is an algorithm for approximating the
logarithmic norm, \sum_{i=1}^D\log A_t[i], and logarithmic distance. The
logarithmic distance is useful in machine learning practice with heavy-tailed
data.",1
"Computability logic (CL) (see ORG) is a
semantical platform and research program for redeveloping logic as a formal
theory of computability, as opposed to the formal theory of truth which it has
more traditionally been. Formulas in ORG stand for (interactive) computational
problems, understood as games between a machine and its environment; logical
operators represent operations on such entities; and ""truth"" is understood as
existence of an effective solution, i.e., of an algorithmic winning strategy.
  The formalism of ORG is open-ended, and may undergo series of extensions as
the study of the subject advances. The main groups of operators on which ORG has
been focused so far are the parallel, choice, branching, and blind operators.
The present paper introduces a new important group of operators, called
sequential. The latter come in the form of sequential conjunction and
disjunction, sequential quantifiers, and sequential recurrences. As the name
may suggest, the algorithmic intuitions associated with this group are those of
sequential computations, as opposed to the intuitions of parallel computations
associated with the parallel group of operations: playing a sequential
combination of games means playing its components in a sequential fashion, CARDINAL
after one.
  The main technical result of the present paper is a sound and complete
axiomatization of the propositional fragment of computability logic whose
vocabulary, together with negation, includes all CARDINAL -- parallel, choice and
sequential -- sorts of conjunction and disjunction. An extension of this result
to the ORDINAL-order level is also outlined.","There are many examples in the literature that suggest that
indistinguishability is intransitive, despite the fact that the
indistinguishability relation is typically taken to be an equivalence relation
(and thus transitive). It is shown that if the uncertainty perception and the
question of when an agent reports that CARDINAL things are indistinguishable are
both carefully modeled, the problems disappear, and indistinguishability can
indeed be taken to be an equivalence relation. Moreover, this model also
suggests a logic of vagueness that seems to solve many of the problems related
to vagueness discussed in the philosophical literature. In particular, it is
shown here how the logic can handle the sorites paradox.",0
"The paper explores a possible application of the discrete thermodynamics to a
CARDINAL-level laser. The model accounts for the laser openness to incoming pumping
power and coming out energy with the emitted light. As an open system, a laser
should be in open equilibrium with thermodynamic forces, related to both energy
flows. Conditions of equilibria are expressed by a logistic map with specially
developed dynamic inverse pitchfork bifurcation diagrams for graphical
presentation of the solutions. The graphs explicitly confirm the triggering
nature of a laser where bistability is manifested by pitchfork ground and laser
branches, with the relative population equilibrium values close to CARDINAL and CARDINAL
correspondingly. Simulation was run for a CARDINAL-level laser emitting light from far
infrared to short wave UV. A newly discovered feature of such a laser is the
line spectrum of up and down transitions of the laser excitable dwellers,
occurring between the laser and the ground pitchfork branches beyond
bifurcation point. The density of the spectra lines tangibly increases as the
branches approach their limits. Transitions of both types are overlapping in
opposite phases. This effect is a new confirmation of the PERSON's
prohibition on practical realization of a CARDINAL-level laser. Wide enough gaps
between the lines of the spectra were also discovered in this research. The
gaps are shielding the light irradiation and may be considered as potential
areas of control over the CARDINAL-level laser emissions.","PERSON defined an evolutionary unit as hereditary information for
which the selection bias between competing units dominates the informational
decay caused by imperfect transmission. In this article, I extend PERSON'
approach to show that the ratio of selection bias to transmission bias provides
a unifying framework for diverse biological problems. Specific examples include
GPE and ORG's mutation-selection balance, ORG's error threshold and
quasispecies, PERSON clade selection, ORG's multilevel formulation of
group selection, Szathmary and PERSON's evolutionary origin of primitive
cells, PERSON and PERSON's short-sighted evolution of HIV virulence, PERSON's
timescale analysis of microbial metabolism, and PERSON and GPE's
major transitions in evolution. The insights from these diverse applications
lead to a deeper understanding of kin selection, group selection, multilevel
evolutionary analysis, and the philosophical problems of evolutionary units and
individuality.",0
"We consider the inverse mean curvature flow in ORG spacetimes
that satisfy the PERSON equations and have a big crunch singularity and prove
that under natural conditions the rescaled inverse mean curvature flow provides
a smooth transition from big crunch to big bang. We also construct an example
showing that in general the transition flow is only of class $MONEY","We consider optimization problems that are formulated and solved in the
framework of tropical mathematics. The problems consist in minimizing or
maximizing functionals defined on vectors of finite-dimensional semimodules
over idempotent semifields, and may involve constraints in the form of ORG
equations and inequalities. The objective function can be either a linear
function or nonlinear function calculated by means of multiplicative conjugate
transposition of vectors. We start with an overview of known tropical
optimization problems and solution methods. Then, we formulate certain new
problems and present direct solutions to the problems in a closed compact
vector form suitable for further analysis and applications. For many problems,
the results obtained are complete solutions.",0
"The availability of interaction devices has raised interest in techniques to
support the user interface (UI). A ORG specification describes the functions
that a system provides to its users by capturing the interface details and
includes possible actions through interaction elements. UI developers of
interactive systems have to address multiple sources of heterogeneity,
including end users heterogeneity and variability of the context of use. This
paper contributes to the notion of interactivity and interfacing by proposing a
methodology for producing engineering-type diagrams of (abstract) machine
processes that can specify uniform structure and behavior of systems through a
synchronic order of states (stages): creation, release, transfer, receive, and
process. As an example, the diagrammatic methodology is applied to
conceptualizing space as a machine. The resulting depiction seems suitable for
use in designing UIs in certain environments.","The aim of this paper is to promote the terms thing and thinging (which
refers to the act of defining a boundary around some portion of reality and
labeling it with a name) as valued notions that play an important role in
software engineering modeling. Additionally, we attempt to furnish operational
definitions for terms thing, object, process, and thinging. The substantive
discussion is based on the conception of an (abstract) machine, named ORGORG), used in several research works. The ORG creates,
processes, receives, releases, and transfers things. Accordingly, a
diagrammatic representation of the ORG is used to model reality. In the
discussion section, this paper clarifies interesting issues related to
conceptual modeling in software engineering. The substance of this paper and
its conclusion suggest that thinging should be more meaningfully emphasized as
a valuable research and teaching topic, at least in the requirement analysis
phase of the software development cycle.",1
"We propose the possibilities of designing nano-scale rectifiers using
mesoscopic rings. A single mesoscopic ring is used for CARDINAL-wave rectification,
while full-wave rectification is achieved using CARDINAL such rings and in both
cases each ring is threaded by a time varying magnetic flux CARDINAL\phi$ which plays
a central role in the rectification action. Within a tight-binding framework,
all the calculations are done based on the ORG's function formalism. We
present numerical results for the CARDINAL-terminal conductance and current which
support the general features of CARDINAL-wave and full-wave rectifications. The
analysis may be helpful in fabricating mesoscopic or nano-scale rectifiers.","In the measurement-based ORG computing, there is a natural ""causal cone""
among qubits of the resource state, since the measurement angle on a qubit has
to depend on previous measurement results in order to correct the effect of
byproduct operators. If we respect the no-signaling principle, byproduct
operators cannot be avoided. In this paper, we study the possibility of acausal
measurement-based ORG computing by using the process matrix framework [PERSON, PERSON, and PERSON, WORK_OF_ART {\bf3}, DATE (DATE)].
We construct a resource process matrix for acausal measurement-based ORG
computing. The resource process matrix is an analog of the resource state of
the causal measurement-based ORG computing. We find that the resource
process matrix is (up to a normalization factor and trivial ancilla qubits)
equivalent to the decorated graph state created from the graph state of the
corresponding causal measurement-based ORG computing.",0
"Maybe active discussions about entanglement in ORG information science
demonstrate some immaturity of this rather young area. So recent tries to look
for more accurate ways of classification devote rather encouragement than
criticism.","In this presentation are discussed some problems, relevant with application
of information technologies in nano-scale systems and devices. Some methods
already developed in ORG may be very useful here.
Here are considered CARDINAL illustrative models: representation of data by ORG
bits and transfer of signals in ORG wires.",1
"The information-theoretic point of view proposed by ORG in DATE and
developed by algorithmic information theory (ORG) suggests that mathematics and
physics are not that different. This will be a ORDINAL-person account of some
doubts and speculations about the nature of mathematics that I have entertained
for DATE, and which have now been incorporated in a digital
philosophy paradigm shift that is sweeping across the sciences.","The approach defines information process from probabilistic observation,
emerging microprocess,qubit, encoding bits, evolving macroprocess, and extends
to Observer information self-organization, cognition, intelligence and
understanding communicating information. Studying information originating in
quantum process focuses not on particle physics but on natural interactive
impulse modeling Bit composing information observer. Information emerges from
NORP probabilities field when sequences of CARDINAL-0 probabilities link PERSON
probabilities modeling arising observer. These objective yes-no probabilities
virtually cuts observing entropy hidden in cutting correlation decreasing
PERSON process entropy and increasing entropy of cutting impulse running
minimax principle. Merging impulse curves and rotates yes-no conjugated
entropies in microprocess. The entropies entangle within impulse time interval
ending with beginning space. The opposite curvature lowers potential energy
converting entropy to memorized bit. The memorized information binds reversible
microprocess with irreversible information macroprocess. Multiple interacting
Bits self-organize information process encoding causality, logic and
complexity. Trajectory of observation process carries probabilistic and certain
wave function self-building structural macrounits. Macrounits logically
self-organize information networks encoding in triplet code. Multiple IN
enclose observer information cognition and intelligence. Observer cognition
assembles attracting common units in resonances forming IN hierarchy accepting
only units recognizing IN node. Maximal number of accepted triplets measures
the observer information intelligence. Intelligent observer recognizes and
encodes digital images in message transmission enables understanding the
message meaning. Cognitive logic self-controls encoding the intelligence in
double helix code.",0
"A large body of research in machine learning is concerned with supervised
learning from examples. The examples are typically represented as vectors in a
multi-dimensional feature space (also known as attribute-value descriptions). A
teacher partitions a set of training examples into a finite number of classes.
The task of the learning algorithm is to induce a concept from the training
examples. In this paper, we formally distinguish CARDINAL types of features:
primary, contextual, and irrelevant features. We also formally define what it
means for one feature to be context-sensitive to another feature.
Context-sensitive features complicate the task of the learner and potentially
impair the learner's performance. Our formal definitions make it possible for a
learner to automatically identify context-sensitive features. After
context-sensitive features have been identified, there are several strategies
that the learner can employ for managing the features; however, a discussion of
these strategies is outside of the scope of this paper. The formal definitions
presented here correct a flaw in previously proposed definitions. We discuss
the relationship between our work and a formal definition of relevance.","We show that combining CARDINAL different hypothetical enhancements to quantum
computation---namely, quantum advice and non-collapsing measurements---would
let a ORG computer solve any decision problem whatsoever in polynomial
time, even though neither enhancement yields extravagant power by itself. This
complements a related result due to Raz. The proof uses locally decodable
codes.",0
"The ORG problem for the PERSON system is shown to be locally
well-posed for low regularity Schr\""odinger data u_0 ORG,p}} and wave
data (ORG,p}} \times \hat{H^{l-1,p}} under certain
assumptions on the parameters k,l and 1<p\le CARDINAL, where ORG,p}}}
:= \| < \xi >^k \hat{u_0}\|_{L^{p'}}, generalizing the results for p=2 by
PERSON, PERSON, and PERSON. Especially we are able to improve the results from
the scaling point of view, and also allow suitable k<0, l<-1/2, i.e. data u_0
\not\in L^2 and (n_0,n_1)\not\in H^{-1/2}\times H^{-3/2}, which was excluded in
the case p=2.","We consider the ORG system in GPE gauge and use a
null condition to show local well-psoedness for low regularity data. This
improves a recent result of ORG.",1
"ORG) seem to have displaced traditional 'smooth'
nonlinearities as activation-function-du-jour in many - but not all - deep
neural network (DNN) applications. However, nobody seems to know why. In this
article, we argue that PRODUCT are useful because they are ideal demodulators -
this helps them perform fast abstract learning. However, this fast learning
comes at the expense of serious nonlinear distortion products - decoy features.
We show that ORG acts to suppress the decoy features, preventing
overfitting and leaving the true features cleanly demodulated for rapid,
reliable learning.","Convolutional deep neural networks (DNN) are state of the art in many
engineering problems but have not yet addressed the issue of how to deal with
complex spectrograms. Here, we use circular statistics to provide a convenient
probabilistic estimate of spectrogram phase in a complex convolutional DNN. In
a typical cocktail party source separation scenario, we trained a convolutional
DNN to re-synthesize the complex spectrograms of CARDINAL source speech signals
given a complex spectrogram of the monaural mixture - a discriminative deep
transform (ORG). We then used this complex convolutional ORG to obtain
probabilistic estimates of the magnitude and phase components of the source
spectrograms. Our separation results are on a par with equivalent binary-mask
based non-complex separation approaches.",1
"We derive, for a bistochastic strictly contractive ORG channel on a
matrix algebra, a relation between the contraction rate and the rate of entropy
production. We also sketch some applications of our result to the statistical
physics of irreversible processes and to quantum information processing.","This paper presents a new version of a branching batch classifier that has
added fixed value ranges through bands, for each column or feature of the input
dataset. Each layer branches like a tree, but has a different architecture to
the current classifiers. Each branch is not for a feature, but for a change in
output category. Therefore, each classifier classifies its own subset of data
rows and categories, using averaged values only and with decreasing numbers of
data row in each new level. When considering features however, it is shown that
some of the data can be correctly classified through using fixed value ranges,
while the rest can be classified by using the classifier technique. Tests show
that the method can successfully classify benchmark datasets to better than the
state-of-the-art. Fixed value ranges are like links and so the paper discusses
the biological analogy with neurons and neuron links.",0
"The black hole information paradox tells us something important about the way
quantum mechanics and gravity fit together. In these lectures I try to give a
pedagogical review of the essential physics leading to the paradox, using
mostly pictures. Hawking's argument is recast as a `theorem': if quantum
gravity effects are confined to within a given length scale and the vacuum is
assumed to be unique, then there will be information loss. We conclude with a
brief summary of how quantum effects in string theory violate the ORDINAL
condition and make the interior of the hole a `fuzzball'.","We reminisce and discuss applications of algorithmic probability to a wide
range of problems in artificial intelligence, philosophy and technological
society. We propose that PERSON has effectively axiomatized the field of
artificial intelligence, therefore establishing it as a rigorous scientific
discipline. We also relate to our own work in incremental machine learning and
philosophy of complexity.",0
"Combinatorial evolution and forecasting of system requirements is examined.
The morphological model is used for a hierarchical requirements system (i.e.,
system parts, design alternatives for the system parts, ordinal estimates for
the alternatives). A set of system changes involves changes of the system
structure, component alternatives and their estimates. The composition process
of the forecast is based on combinatorial synthesis (knapsack problem, multiple
choice problem, hierarchical morphological design). An illustrative numerical
example for CARDINAL-phase evolution and forecasting of requirements to
communications is described.","In this note is touched upon an application of quantum information science
(QIS) in nanotechnology area. The laws of quantum mechanics may be very
important for nano-scale objects. A problem with simulating of ORG systems
is well known and ORG computer was initially suggested by PERSON just
as the way to overcome such difficulties. Mathematical methods developed in QIS
also may be applied for description of nano-devices. Few illustrative examples
are mentioned and they may be related with so-called ORDINAL generation of
nanotechnology products.",0
"The article proposes a heuristic approximation approach to the bin packing
problem under multiple objectives. In addition to the traditional objective of
minimizing the number of bins, the heterogeneousness of the elements in each
PERSON is minimized, leading to a biobjective formulation of the problem with a
tradeoff between the number of bins and their heterogeneousness. An extension
of the Best-Fit approximation algorithm is presented to solve the problem.
Experimental investigations have been carried out on benchmark instances of
different size, ranging from CARDINAL items. Encouraging results have been
obtained, showing the applicability of the heuristic approach to the described
problem.","The article presents a local search approach for the solution of timetabling
problems in general, with a particular implementation for competition track CARDINAL
of ORG DATE (ORG 2007). The heuristic
search procedure is based on PERSON to overcome local optima. A
stochastic neighborhood is proposed and implemented, randomly removing and
reassigning events from the current solution.
  The overall concept has been incrementally obtained from a series of
experiments, which we describe in each (sub)section of the paper. In result, we
successfully derived a potential candidate solution approach for the finals of
track CARDINAL of the ORG DATE.",1
"This paper describes a new entropy-style of equation that may be useful in a
general sense, but can be applied to a cognitive model with related processes.
The model is based on the human brain, with automatic and distributed pattern
activity. Methods for carrying out the different processes are suggested. The
main purpose of this paper is to reaffirm earlier research on different
knowledge-based and experience-based clustering techniques. The overall
architecture has stayed essentially the same and so it is the localised
processes or smaller details that have been updated. For example, a counting
mechanism is used slightly differently, to measure a level of 'cohesion'
instead of a 'correct' classification, over pattern instances. The introduction
of features has further enhanced the architecture and the new entropy-style
equation is proposed. While an earlier paper defined CARDINAL levels of functional
requirement, this paper re-defines the levels in a more human vernacular, with
higher-level goals described in terms of action-result pairs.","This paper continues the research that considers a new cognitive model based
strongly on the human brain. In particular, it considers the neural binding
structure of an earlier paper. It also describes some new methods in the areas
of image processing and behaviour simulation. The work is all based on earlier
research by the author and the new additions are intended to fit in with the
overall design. For image processing, a grid-like structure is used with 'full
linking'. Each cell in the classifier grid stores a list of all other cells it
gets associated with and this is used as the learned image that new input is
compared to. For the behaviour metric, a new prediction equation is suggested,
as part of a simulation, that uses feedback and history to dynamically
determine its course of action. While the new methods are from widely different
topics, both can be compared with the binary-analog type of interface that is
the main focus of the paper. It is suggested that the simplest of linking
between a tree and ensemble can explain neural binding and variable signal
strengths.",1
"The aim of this note is to attract once again attention of the quantum
community to statistical analysis of data which was reported as violating
ORG's inequality. This analysis suffers of a number of problems. And the main
problem is that rough data is practically unavailable. However, experiments
which are not followed by the open access to the rough data have to be
considered as with no result. The absence of rough data generates a variety of
problems in statistical interpretation of the results of ORG's type
experiment. CARDINAL may hope that this note would stimulate experimenters to create
the open access data-base for, e.g., ORG tests. Unfortunately, recently
announced experimental loophole-free violation of a ORG inequality using
entangled ORG spins separated by QUANTITY was not supported by open-access
data. Therefore in accordance with our approach ""it has no result."" The
promising data after publication is, of course, a step towards fair analysis
quantum experiments. May be this is a consequence of appearance of this
preprint, v1. But there are a few questions which would be interesting to
clarify before publication (and which we shall discuss in this note).","We discuss foundation of ORG (interpretations, superposition,
principle of complementarity, locality, hidden variables) and quantum
information theory.",1
"Transcript of PERSON DATE ORG
of Computer Science Distinguished Lecture. The notion of randomness is taken
from physics and applied to pure mathematics in order to shed light on the
incompleteness phenomenon discovered by PERSON.","This article discusses what can be proved about the foundations of
mathematics using the notions of algorithm and information. The ORDINAL part is
retrospective, and presents a beautiful antique, PERSON's proof, the ORDINAL
modern incompleteness theorem, PERSON's halting problem, and a piece of
postmodern metamathematics, the halting probability PERSON. The ORDINAL part
looks forward to DATE and discusses the convergence of theoretical
physics and theoretical computer science and hopes for a theoretical biology,
in which the notions of algorithm and information are again crucial.",1
"Multi-agent approach has become popular in computer science and technology.
However, the conventional models of multi-agent and multicomponent systems
implicitly or explicitly assume existence of absolute time or even do not
include time in the set of defining parameters. At the same time, it is proved
theoretically and validated experimentally that there are different times and
time scales in a variety of real systems - physical, chemical, biological,
social, informational, etc. Thus, the goal of this work is construction of a
multi-agent multicomponent system models with concurrency of processes and
diversity of actions. To achieve this goal, a mathematical system actor model
is elaborated and its properties are studied.","ORG means statistical analysis of population or sample
that has indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data.
For example, the population or sample size might not be exactly determinate
because of some individuals that partially belong to the population or sample,
and partially they do not belong, or individuals whose appurtenance is
completely unknown. Also, there are population or sample individuals whose data
could be indeterminate. In this book, we develop the DATE notion of
neutrosophic statistics. We present various practical examples. It is possible
to define the neutrosophic statistics in many ways, because there are various
types of indeterminacies, depending on the problem to solve.",0
"In this article, we perform a systematic study of the mass spectrum of the
axial-vector hidden charmed and hidden bottom tetraquark states using the ORG
sum rules, and identify the $Z^+(4430)$ as an axial-vector tetraquark state
tentatively.","In this paper, we consider supervised learning problems such as logistic
regression and study the stochastic gradient method with averaging, in the
usual stochastic approximation setting where observations are used only once.
We show that after $MONEY iterations, with a constant step-size proportional to
MONEY \sqrt{N}$ where $MONEY is the number of observations and $MONEY is the maximum
norm of the observations, the convergence rate is always of order
$PERSON, and improves to $O(R^2 / \mu N)$ where $\mu$ is the lowest
eigenvalue of the Hessian at the global optimum (when this eigenvalue is
MONEYR^2/\sqrt{N}$). Since $\mu$ does not need to be known in advance,
this shows that averaged stochastic gradient is adaptive to \emph{unknown
local} strong convexity of the objective function. Our proof relies on the
generalized self-concordance properties of the logistic loss and thus extends
to all generalized ORG models with uniformly bounded features.",0
"The article sets forth comprehensive basics of thermodynamics of chemical
equilibrium as balance of the thermodynamic forces. Based on the linear
equations of irreversible thermodynamics, ORG definition of the
thermodynamic force, and FAC principle, new thermodynamics of
chemical equilibrium offers an explicit account for multiple chemical
interactions within the system. Basic relations between energetic
characteristics of chemical transformations and reaction extents are based on
the idea of chemical equilibrium as balance between internal and external
thermodynamic forces, which is presented in the form of a logistic equation,
containing CARDINAL new parameter. Solutions to the basic equation define the
domain of states of the chemical system, from true equilibrium to true chaos.
The new theory is derived exclusively from the currently recognized ideas and
covers equilibrium thermodynamics as well as non-equilibrium thermodynamics in
a unique concept.","The paper presents new thermodynamic paradigm of chemical equilibrium,
setting forth comprehensive basics of ORG (DTd). Along with previous results by the author during DATE, this work contains also some new developments of DTd. Based on the
ORG's constitutive equations, reformulated by the author thermodynamic
affinity and reaction extent, and FAC principle, DTd brings forward
a notion of chemical equilibrium as a balance of internal and external
thermodynamic forces (TdF), acting against a chemical system. Basic expression
of DTd is the chemical system logistic map of thermodynamic states that ties
together energetic characteristics of chemical reaction, occurring in the
system, the system shift from ""true"" thermodynamic equilibrium (ORG), and
causing that shift external thermodynamic forces. Solutions to the basic map
are pitchfork bifurcation diagrams in coordinates ""shift from ORG - growth
factor (or TdF)""; points, corresponding to the system thermodynamic states, are
dwelling on its branches. The diagrams feature CARDINAL typical areas: true
thermodynamic equilibrium and open equilibrium along the thermodynamic branch
before the threshold of its stability, i.e. bifurcation point, and bifurcation
area with bistability and chaotic oscillations after the point. The set of
solutions makes up the chemical system domain of states. The new paradigm
complies with the correspondence principle: in isolated chemical system
external TdF vanish, and the basic map turns into traditional expression of
chemical equilibrium via thermodynamic affinity. The theory binds together
classical and contemporary thermodynamics of chemical equilibria on a unique
conceptual basis. The paper is essentially reworked and refocused version of
the earlier preprint on the DTd basics, supplemented with new results.",1
"We consider the problem of high-dimensional non-linear variable selection for
supervised learning. Our approach is based on performing linear selection among
exponentially many appropriately defined positive definite kernels that
characterize non-linear interactions between the original variables. To select
efficiently from these many kernels, we use the natural hierarchical structure
of the problem to extend the multiple kernel learning framework to kernels that
can be embedded in a directed acyclic graph; we show that it is then possible
to perform kernel selection through a graph-adapted sparsity-inducing norm, in
polynomial time in the number of selected kernels. Moreover, we study the
consistency of variable selection in high-dimensional settings, showing that
under certain assumptions, our regularization framework allows a number of
irrelevant variables which is exponential in the number of observations. Our
simulations on synthetic datasets and datasets from the ORG repository show
state-of-the-art predictive performance for non-linear regression problems.","Hawking's black hole information puzzle highlights the incompatibility
between our present understanding of gravity and quantum physics. However,
Hawking's prediction of black-hole evaporation is at a semiclassical level. CARDINAL
therefore suspects some modifications of the character of the radiation when
quantum properties of the {\it black hole itself} are properly taken into
account. In fact, during DATE evidence has been mounting
that, in a quantum theory of gravity black holes may have a discrete mass
spectrum, with concomitant {ORG discrete} line emission. A direct consequence
of this intriguing prediction is that, compared with blackbody radiation,
black-hole radiance is {\it less} entropic, and may therefore carry a
significant amount of {ORG information}. Using standard ideas from quantum
information theory, we calculate the rate at which information can be recovered
from the black-hole spectral lines. We conclude that the information that was
suspected to be lost may gradually leak back, encoded into the black-hole
spectral lines.",0
"Game theoretic equilibria are mathematical expressions of rationality.
Rational agents are used to model not only humans and their software
representatives, but also organisms, populations, species and genes,
interacting with each other and with the environment. Rational behaviors are
achieved not only through conscious reasoning, but also through spontaneous
stabilization at equilibrium points.
  Formal theories of rationality are usually guided by informal intuitions,
which are acquired by observing some concrete economic, biological, or network
processes. Treating such processes as instances of computation, we reconstruct
and refine some basic notions of equilibrium and rationality from the some
basic structures of computation.
  It is, of course, well known that equilibria arise as fixed points; the point
is that semantics of computation of fixed points seems to be providing novel
methods, algebraic and GPE, for reasoning about them.","The diverse views of science of security have opened up several alleys
towards applying the methods of science to security. We pursue a different kind
of connection between science and security. This paper explores the idea that
security is not just a suitable subject for science, but that the process of
security is also similar to the process of science. This similarity arises from
the fact that both science and security depend on the methods of inductive
inference. Because of this dependency, a scientific theory can never be
definitely proved, but can only be disproved by new evidence, and improved into
a better theory. Because of the same dependency, every security claim and
method has a lifetime, and always eventually needs to be improved.
  In this general framework of security-as-science, we explore the ways to
apply the methods of scientific induction in the process of trust. The process
of trust building and updating is viewed as hypothesis testing. We propose to
formulate the trust hypotheses by the methods of algorithmic learning, and to
build more robust trust testing and vetting methodologies on the solid
foundations of statistical inference.",1
"We study light vector meson electroproduction at small $x$ within the
generalized parton distributions (GPDs) model. The modified perturbative
approach is used, where the quark transverse degrees of freedom in the vector
meson wave function and hard subprocess are considered. Our results on ORG section and spin observables are in good agreement with experiment","On the basis of the handbag approach we study cross sections and spin
asymmetries for leptoproduction of various vector and pseudoscalar mesons. Our
results are in good agrement with high energy experiments. We analyse what
information about ORG (GPDs) can be obtained from
these reactions.",1
"The commonly used circuit model of ORG computing leaves out the problems
of imprecision in the initial state preparation, particle statistics
(indistinguishability of particles belonging to the same quantum state), and
error correction (current techniques cannot correct all small errors). The
initial state in the circuit model computation is obtained by applying
potentially imprecise ORG gate operations whereas useful quantum
computation requires a state with no uncertainty. We review some limitations of
the circuit model and speculate on the question if a hierarchy of quantum-type
computing models exists.","GPE computing is the use of multiple autonomic and parallel modules
together with integrative processors at a higher level of abstraction to embody
""intelligent"" processing. The biological basis of this computing is sketched
and the matter of learning is examined.",1
"We show in this article that if a holomorphic vector bundle has a nonnegative
NORP metric in the sense of PERSON and ORG, which always exists on
globally generated holomorphic vector bundles, then some special linear
combinations of ORG forms are strongly nonnegative. This particularly implies
that all the ORG numbers of such a holomorphic vector bundle are nonnegative
and can be bounded below and above respectively by CARDINAL special ORG numbers.
As applications, we obtain a family of new results on compact connected complex
manifolds which are homogeneous or can be holomorphically immersed into complex
tori, some of which improve several classical results.","Separation of competing speech is a key challenge in signal processing and a
feat routinely performed by the human auditory brain. A long standing benchmark
of the spectrogram approach to source separation is known as the ideal binary
mask. Here, we train a convolutional deep neural network, on a CARDINAL-speaker
cocktail party problem, to make probabilistic predictions about binary masks.
Our results approach ideal binary mask performance, illustrating that
relatively simple deep neural networks are capable of robust binary mask
prediction. We also illustrate the trade-off between prediction statistics and
separation quality.",0
"We analyse the diffractive $Q \bar Q$ production and final jet kinematics in
polarized deep-inelastic lp scattering at $\sqrt{s}=20 GeV$. We show that this
reaction can be used in the new spectrometer of the COMPASS Collaboration at
GPE to study the quark-pomeron coupling structure.","Connections between the sequentiality/concurrency distinction and the
semantics of proofs are investigated, with particular reference to games and
ORG.",0
"We present new findings in regard to data analysis in very high dimensional
spaces. We use dimensionalities up to CARDINAL. A particular benefit
of ORG is its suitability for carrying out an orthonormal
mapping, or scaling, of power law distributed data. Power law distributed data
are found in many domains. Correspondence factor analysis provides a latent
semantic or principal axes mapping. Our experiments use data from digital
chemistry and finance, and other statistically generated data.","Errors in data are usually unwelcome and so some means to correct them is
useful. However, it is difficult to define, detect or correct errors in an
unsupervised way. Here, we train a deep neural network to re-synthesize its
inputs at its output layer for a given class of data. We then exploit the fact
that this abstract transformation, which we call a deep transform (ORG),
inherently rejects information (errors) existing outside of the abstract
feature space. Using the ORG to perform probabilistic re-synthesis, we
demonstrate the recovery of data that has been subject to extreme degradation.",0
"We derive an exact and efficient NORP regression algorithm for piecewise
constant functions of unknown segment number, boundary location, and levels. It
works for any noise and segment level prior, ORG which can handle
outliers. We derive simple but good estimates for the in-segment variance. We
also propose a NORP regression curve as a better way of smoothing data
without blurring boundaries. The NORP approach also allows straightforward
determination of the evidence, break probabilities and error estimates, useful
for model selection and significance and robustness studies. We discuss the
performance on synthetic and real-world examples. Many possible extensions will
be discussed.","PERSON's uncomputable universal prediction scheme $\xi$ allows to predict
the next symbol $x_k$ of a sequence $x_1...x_{k-1}$ for any Turing computable,
but otherwise unknown, probabilistic environment $\mu$. This scheme will be
generalized to arbitrary environmental classes, which, among others, allows the
construction of computable universal prediction schemes $\xi$. Convergence of
$\xi$ to $\mu$ in a conditional mean squared sense and with $\mu$ probability CARDINAL
is proven. It is shown that the average number of prediction errors made by the
universal $\xi$ scheme rapidly converges to those made by the best possible
informed $\mu$ scheme. The schemes, theorems and proofs are given for general
finite alphabet, which results in additional complications as compared to the
binary case. Several extensions of the presented theory and results are
outlined. They include general loss functions and bounds, games of chance,
infinite alphabet, partial and delayed prediction, classification, and more
active systems.",1
"Computability logic is a formal theory of computational tasks and resources.
PERSON in it represent interactive computational problems, and ""truth"" is
understood as algorithmic solvability. Interactive computational problems, in
turn, are defined as a certain sort games between a machine and its
environment, with logical operators standing for operations on such games.
Within the ambitious program of finding axiomatizations for incrementally rich
fragments of this semantically introduced logic, the earlier article ""From
truth to computability I"" proved soundness and completeness for system PERSON,
whose language has the so called parallel connectives (including negation),
choice connectives, choice quantifiers, and blind quantifiers. The present
paper extends that result to the significantly more expressive system CL4 with
the same collection of logical operators. What makes CL4 expressive is the
presence of CARDINAL sorts of atoms in its language: elementary atoms, representing
elementary computational problems (i.e. predicates, i.e. problems of CARDINAL
degree of interactivity), and general atoms, representing arbitrary
computational problems. CL4 conservatively extends PERSON, with the latter being
nothing but the general-atom-free fragment of the former. Removing the blind
(classical) group of quantifiers from the language of CL4 is shown to yield a
decidable logic despite the fact that the latter is still ORDINAL-order. A
comprehensive online source on computability logic can be found at
ORG","We propose a new class of ORG computing algorithms which generalize many
standard ones. The goal of our algorithms is to estimate probability
distributions. Such estimates are useful in, for example, applications of
WORK_OF_ART, where inferences are made based on
uncertain knowledge. The class of algorithms that we propose is based on a
construction method that generalizes a Fredkin-Toffoli (F-T) construction
method used in the field of classical reversible computing. F-T showed how,
given any binary deterministic circuit, one can construct another binary
deterministic circuit which does the same calculations in a reversible manner.
We show how, given any classical stochastic network (classical NORP net),
one can construct a quantum network (quantum NORP net). By running this
quantum NORP net on a ORG computer, one can calculate any conditional
probability that one would be interested in calculating for the original
classical NORP net. Thus, we generalize PRODUCT construction method so that
it can be applied to any classical stochastic circuit, not just binary
deterministic ones. We also show that, in certain situations, our class of
algorithms can be combined with PERSON's algorithm to great advantage.",0
"This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
PERSON belief functions","Several approaches of structuring (factorization, decomposition) of
PERSON joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
  It is demonstrated that PERSON and PERSON's {Zhu:93} logical networks and NORP'
{Smets:93} directed acyclic graphs are unable to capture statistical
dependence/independence of NORP networks {Pearl:88}. On the other hand,
though Shenoy and GPE's hypergraphs can explicitly represent bayesian
network factorization of NORP belief functions, they disclaim any need for
representation of independence of variables in belief functions.
  Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
GPE just on grounds of missing representation of variable independence, but
in their frameworks some belief functions factorizable in ORG
framework cannot be factored.
  The approach in {Klopotek:93f} on the other hand combines the merits of both
Cano et al. and of ORG approach in that for Shenoy/Shafer approach no
simpler factorization than that in {GPE} approach exists and on the
other hand all independences among variables captured in GPE et al. framework
and many more are captured in {Klopotek:93f} approach.%",1
"The speed and transformative power of human cultural evolution is evident
from the change it has wrought on our planet. This chapter proposes a human
computation program aimed at (CARDINAL) distinguishing algorithmic from
non-algorithmic components of cultural evolution, (CARDINAL) computationally modeling
the algorithmic components, and amassing human solutions to the non-algorithmic
(generally, creative) components, and (CARDINAL) combining them to develop
human-machine hybrids with previously unforeseen computational power that can
be used to solve real problems. Drawing on recent insights into the origins of
evolutionary processes from biology and complexity theory, human minds are
modeled as self-organizing, interacting, autopoietic networks that evolve
through a GPE (NORP) process of communal exchange. Existing
computational models as well as directions for future research are discussed.","General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
NORP. On the other hand, reinforcement learning is well-developed for
small finite state PERSON decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the ORG framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into CARDINAL learning algorithm.
Extensions to more realistic dynamic NORP networks are developed in Part
II. The role of POMDPs is also considered there.",0
"In a previous paper, we showed how entanglement of formation can be defined
as a minimum of the quantum conditional mutual information (a.k.a. ORG). In classical information theory, the
NORP-Blahut method is one of the preferred methods for calculating extrema
of mutual information. In this paper, we present a new method, akin to the
NORP-Blahut method, for calculating entanglement of formation. We also
present several examples computed with a computer program called PERSON
that implements the ideas of this paper.","ORG (QMR) is a compendium of statistical knowledge
connecting diseases to findings (symptoms). The information in ORG can be
represented as a NORP network. The inference problem (or, in more medical
language, giving a diagnosis) for the ORG is to, given some findings, find the
probability of each disease. Rejection sampling and likelihood weighted
sampling (a.k.a. likelihood weighting) are CARDINAL simple algorithms for making
approximate inferences from an arbitrary NORP net (and from the QMR
NORP net in particular). Heretofore, the samples for these CARDINAL algorithms
have been obtained with a conventional ""classical computer"". In this paper, we
will show that CARDINAL analogous algorithms exist for the QMR NORP net, where
the samples are obtained with a ORG computer. We expect that these CARDINAL
algorithms, implemented on a quantum computer, can also be used to make
inferences (and predictions) with other NORP nets.",1
"ORG computers use continuous properties of physical system for modeling.
In the paper is described possibility of modeling by analogue ORG computers
for some model of data analysis. It is analogue associative memory and a formal
neural network. A particularity of the models is combination of continuous
internal processes with discrete set of output states. The modeling of the
system by classical analogue computers was offered long times ago, but now it
is not very effectively in comparison with modern digital computers. The
application of ORG analogue modelling looks quite possible for modern level
of technology and it may be more effective than digital one, because number of
element may be about PERSON number (N=6.0E23).","This paper presents a CARDINAL-valued representation of bifuzzy sets. This
representation is related to a CARDINAL-valued logic that uses the following
values: true, false, inconsistent, incomplete and ambiguous. In the framework
of CARDINAL-valued representation, formulae for similarity, entropy and syntropy of
bifuzzy sets are constructed.",0
"The place of an anthropic argument in the discrimination between various
cosmological models is to be reconsidered following the classic criticisms of
PERSON and PERSON. Different versions of the anthropic
argument against cosmologies involving an infinite series of past events are
analyzed and applied to several instructive instances. This is not only of
historical significance but presents an important topic for the future of
cosmological research if some of the contemporary inflationary models,
particularly ORG's chaotic inflation, turn out to be correct. Cognitive
importance of the anthropic principle(s) to the issue of extraterrestrial
intelligent observers is reconsidered in this light and several related
problems facing cosmologies with past temporal infinities are also clearly
defined. This issue is not only a clear example of the epistemological
significance of the anthropic principle, but also has consequences for such
diverse topics as ORG studies, epistemological status of cosmological
concepts, theory of observation selection effects, and history of astronomy.","The intriguing suggestion of ORG (DATE) that the universe--contrary to
all our experiences and expectations--contains only a small amount of
information due to an extremely high degree of internal symmetry is critically
examined. It is shown that there are several physical processes, notably
Hawking evaporation of black holes and NORP decoherence time effects
described by PERSON, as well as thought experiments of GPE and GPE
himself, which can be construed as arguments against the low-information
universe hypothesis. In addition, an extreme form of physical reductionism is
entailed by this hypothesis, and therefore any possible argumentation against
such reductionism would count against it either. Some ramifications for both
quantum mechanics and cosmology are briefly discussed.",1
"An overview of recent ORG results on inclusive production of D* mesons in
deep inelastic scattering is given.","There exists a large number of experimental and theoretical results
supporting the picture of ""macroscopic qubits"" implemented, for instance, by
ORG atoms, PERSON junctions or ORG condensates - the systems
which should rather emerge in localized semiclassical states. In this note it
is shown how, under realistic conditions, the false qubit interpretation can be
consistent with the restricted set of experimental data collected for
semiclassical systems. The recent experiments displaying semiclassical
character of ORG condensates and possible quantumness tests for a
single system are briefly invoked also.",0
"Hidden variables are well known sources of disturbance when recovering belief
networks from data based only on measurable variables. Hence models assuming
existence of hidden variables are under development.
  This paper presents a new algorithm ""accelerating"" the known ORG algorithm of
Spirtes, Glymour and ORG {Spirtes:93}. We prove that this algorithm does
not produces (conditional) independencies not present in the data if
statistical independence test is reliable.
  This result is to be considered as non-trivial since e.g. the same claim
fails to be true for ORG algorithm, another ""accelerator"" of ORG, developed in
{Spirtes:93}.","It is proven, by example, that the version of $k$-means with random
initialization does not have the property \emph{probabilistic $k$-richness}.",1
"This paper is a survey discussing ORG concepts, methods,
and applications. It goes deep into the document and query modelling involved
in ORG systems, in addition to pre-processing operations such as removing stop
words and searching by synonym techniques. The paper also tackles text
categorization along with its application in neural networks and machine
learning. Finally, the architecture of web crawlers is to be discussed shedding
the light on how internet spiders index web documents and how they allow users
to search for items on the web.","CARDINAL of the main purposes of a computer is automation. In fact, automation is
the technology by which a manual task is performed with minimum or CARDINAL human
assistance. Over DATE, automation has proved to reduce operation cost and
maintenance time in addition to increase system productivity, reliability, and
performance. DATE, most computerized automation are done by a computer program
which is a set of instructions executed from within the computer memory by the
computer central processing unit to control the computers various operations.
This paper proposes a compiler program that automates the validation and
translation of input documents written in the LANGUAGE language into ORG output
files that can be read by a computer. The input document is by nature
unstructured and in plain-text as it is written by people manually; while, the
generated output is a structured machine-readable XML file. The proposed
compiler program is actually a part of a bigger project related to digital
government and is meant to automate the processing and archiving of juridical
data and documents. In essence, the proposed compiler program is composed of a
scanner, a parser, and a code generator. Experiments showed that such
automation practices could prove to be a starting point for a future digital
government platform for the NORP government. As further research, other
types of juridical documents are to be investigated, mainly those that require
error detection and correction.",1
"This paper looks at Turing's postulations about ORG in
his paper 'Computing Machinery and ORG', published in DATE. It notes
how accurate they were and how relevant they still are DATE. This paper notes
the arguments and mechanisms that he suggested and tries to expand on them
further. The paper however is mostly about describing the essential ingredients
for building an intelligent model and the problems related with that. The
discussion includes recent work by the author himself, who adds his own
thoughts on the matter that come from a purely technical investigation into the
problem. These are personal and quite speculative, but provide an interesting
insight into the mechanisms that might be used for building an intelligent
system.","This paper describes some biologically-inspired processes that could be used
to build the sort of networks that we associate with the human brain. New to
this paper, a 'refined' neuron will be proposed. This is a group of neurons
that by joining together can produce a more analogue system, but with the same
level of control and reliability that a binary neuron would have. With this new
structure, it will be possible to think of an essentially binary system in
terms of a more variable set of values. The paper also shows how recent
research associated with the new model, can be combined with established
theories, to produce a more complete picture. The propositions are largely in
line with conventional thinking, but possibly with CARDINAL or CARDINAL more radical
suggestions. An earlier cognitive model can be filled in with more specific
details, based on the new research results, where the components appear to fit
together almost seamlessly. The intention of the research has been to describe
plausible 'mechanical' processes that can produce the appropriate brain
structures and mechanisms, but that could be used without the magical
'intelligence' part that is still not fully understood. There are also some
important updates from an earlier version of this paper.",1
"The purpose of a wireless sensor network (WSN) is to provide the users with
access to the information of interest from data gathered by spatially
distributed sensors. Generally the users require only certain aggregate
functions of this distributed data. Computation of this aggregate data under
the end-to-end information flow paradigm by communicating all the relevant data
to a central collector PERSON is a highly inefficient solution for this purpose.
An alternative proposition is to perform in-network computation. This, however,
raises questions such as: what is the optimal way to compute an aggregate
function from a set of statistically correlated values stored in different
nodes; what is the security of such aggregation as the results sent by a
compromised or faulty node in the network can adversely affect the accuracy of
the computed result. In this paper, we have presented an energy-efficient
aggregation algorithm for WSNs that is secure and robust against malicious
insider attack by any compromised or faulty node in the network. In contrast to
the traditional snapshot aggregation approach in WSNs, a node in the proposed
algorithm instead of unicasting its sensed information to its parent node,
broadcasts its estimate to all its neighbors. This makes the system more
fault-tolerant and increase the information availability in the network. The
simulations conducted on the proposed algorithm have produced results that
demonstrate its effectiveness.","Intrusion detection in wireless ad hoc networks is a challenging task because
these networks change their topologies dynamically, lack concentration points
where aggregated traffic can be analyzed, utilize infrastructure protocols that
are susceptible to manipulation, and rely on noisy, intermittent wireless
communications. Security remains a major challenge for these networks due their
features of open medium, dynamically changing topologies, reliance on
co-operative algorithms, absence of centralized monitoring points, and lack of
clear lines of defense. In this paper, we present a cooperative, distributed
intrusion detection architecture based on clustering of the nodes that
addresses the security vulnerabilities of the network and facilitates accurate
detection of attacks. The architecture is organized as a dynamic hierarchy in
which the intrusion data is acquired by the nodes and is incrementally
aggregated, reduced in volume and analyzed as it flows upwards to the
cluster-head. The cluster-heads of adjacent clusters communicate with each
other in case of cooperative intrusion detection. For intrusion related message
communication, mobile agents are used for their efficiency in lightweight
computation and suitability in cooperative intrusion detection. Simulation
results show effectiveness and efficiency of the proposed architecture.",1
"Cryptography and PERSON are CARDINAL techniques commonly used to secure and
safely transmit digital data. Nevertheless, they do differ in important ways.
In fact, cryptography scrambles data so that they become unreadable by
eavesdroppers; while, steganography hides the very existence of data so that
they can be transferred unnoticed. Basically, steganography is a technique for
hiding data such as messages into another form of data such as images.
Currently, many types of steganography are in use; however, there is yet no
known steganography application for query languages such as ORG. This paper
proposes a new steganography method for textual data. It encodes input text
messages into ORG carriers made up of ORG queries. In effect, the output ORG
carrier is dynamically generated out of the input message using a dictionary of
words implemented as a hash table and organized into CARDINAL categories, each of
which represents a particular character in the language. Generally speaking,
every character in the message to hide is mapped to a random word from a
corresponding category in the dictionary. Eventually, all input characters are
transformed into output words which are then put together to form an ORG query.
Experiments conducted, showed how the proposed method can operate on real
examples proving the theory behind it. As future work, other types of ORG
queries are to be researched including ORG, ORG, and ORG queries,
making the ORG carrier quite puzzling for malicious ORDINAL parties to recuperate
the secret message that it encodes.","The classical methods used by recursion theory and formal logic to block
paradoxes do not work in ORG information theory. Since ORG information
can exist as a coherent superposition of the classical ``yes'' and ``no''
states, certain tasks which are not conceivable in the classical setting can be
performed in the quantum setting. Classical logical inconsistencies do not
arise, since there exist fixed point states of the diagonalization operator. In
particular, closed timelike curves need not be eliminated in the quantum
setting, since they would not lead to any paradoxical outcome controllability.
ORG information theory can also be subjected to the treatment of
inconsistent information in databases and expert systems. It is suggested that
any CARDINAL pieces of contradicting information are stored and processed as
coherent superposition. In order to be tractable, this strategy requires
quantum computation.",0
"The purpose of this paper is to examine the possible existence or
construction of traversable wormholes supported by generalized ORG gas
(ORG) by starting with a general line element and the PERSON tensor, together
with the equation of state, thereby continuing an earlier study by the author
of wormholes supported by phantom energy. Numerical techniques are used to
demonstrate the existence of wormhole spacetimes that (CARDINAL) meet the flare-out
conditions at the throat, (CARDINAL) are traversable by humanoid travelers, thanks to
low tidal forces and short proper distances near the throat, and (CARDINAL) are
asymptotically flat. There appears to be an abundance of solutions that avoid
an event horizon, suggesting the possibility of naturally occurring wormholes.","A recent study by PERSON et GPE has shown that the galactic halo possesses
the necessary properties for supporting traversable wormholes, based on CARDINAL
observational results, the density profile due to NORP et al. and the
observed flat rotation curves of galaxies. Using a method for calculating the
deflection angle pioneered by PERSON, it is shown that the deflection angle
diverges at the throat of the wormhole. The resulting photon sphere has a
radius of CARDINAL ly. Given the dark-matter background, detection may be possible
from past data using ordinary light.",1
"We prove that the mean curvature $\tau$ of the slices given by a constant
mean curvature foliation can be used as a time function, i.e. $PERSON is smooth
with non-vanishing gradient.","The existence of closed hypersurfaces of prescribed scalar curvature in
globally hyperbolic NORP manifolds is proved provided there are barriers.",1
"This paper describes a novel approach to grammar induction that has been
developed within a framework designed to integrate learning with other aspects
of computing, ORG, mathematics and logic. This framework, called ""information
compression by multiple alignment, unification and search"" (ICMAUS), is founded
on principles of PERSON pioneered by PERSON and others.
Most of the paper describes SP70, a computer model of the ORG framework that
incorporates processes for unsupervised learning of grammars. An example is
presented to show how the model can infer a plausible grammar from appropriate
input. Limitations of the current model and how they may be overcome are
briefly discussed.","We establish an axiomatization for ORG processes, which is a quantum
generalization of process algebra ORG (Algebra of Communicating Processes). We
use the framework of a quantum process configuration $MONEY p,
\varrho\rangle$, but we treat it as CARDINAL relative independent part: the
structural part $p$ and the quantum part $PERSON, because the establishment
of a sound and complete theory is dependent on the structural properties of the
structural part $PERSON We let the quantum part $PERSON be the outcomes of
execution of $p$ to examine and observe the function of the basic theory of
quantum mechanics. We establish not only a strong bisimularity for quantum
processes, but also a weak bisimularity to model the silent step and abstract
internal computations in ORG processes. The relationship between ORG
bisimularity and classical bisimularity is established, which makes an
axiomatization of ORG processes possible. An axiomatization for quantum
processes called NORP is designed, which involves not only quantum information,
but also classical information and unifies ORG computing and classical
computing. ORG can be used easily and widely for verification of most quantum
communication protocols.",0
"ORG algorithms require less operations than classical algorithms. The
exact reason of this has not been pinpointed until now. Our explanation is that
ORG algorithms know in advance PERCENT of the solution of the problem they will
find in the future. In fact they can be represented as the sum of all the
possible histories of a respective ""advanced information classical algorithm"".
This algorithm, given the advanced information (PERCENT of the bits encoding the
problem solution), performs the operations (oracle's queries) still required to
identify the solution. Each history corresponds to a possible way of getting
the advanced information and a possible result of computing the missing
information. This explanation of the quantum speed up has an immediate
practical consequence: the speed up comes from comparing CARDINAL classical
algorithms, with and without advanced information, with no physics involved.
This simplification could open the way to a systematic exploration of the
possibilities of speed up.","Parametric density estimation, for example as NORP distribution, is the
base of the field of statistics. Machine learning requires inexpensive
estimation of much more complex densities, and the basic approach is relatively
costly maximum likelihood estimation (ORG). There will be discussed inexpensive
density estimation, for example literally fitting a polynomial (or PERSON
series) to the sample, which coefficients are calculated by just averaging
monomials (or sine/cosine) over the sample. Another discussed basic application
is fitting distortion to some standard distribution like NORP - analogously
to ORG, but additionally allowing to reconstruct the disturbed density.
Finally, by using weighted average, it can be also applied for estimation of
non-probabilistic densities, like modelling mass distribution, or for various
clustering problems by using negative (or complex) weights: fitting a function
which sign (or argument) determines clusters. The estimated parameters are
approaching the optimal values with error dropping like $MONEY, where $n$
is the sample size.",0
"Experimentally observed violations of ORG inequalities rule out local
realistic theories. Consequently, the ORG vector becomes a strong
candidate for providing an objective picture of reality. However, such an
ontological view of quantum theory faces difficulties when spacelike
measurements on entangled states have to be described, because time ordering of
spacelike events can change under PERSON-Poincar\'e transformations. In the
present paper it is shown that a necessary condition for consistency is to
require state vector reduction on the backward light-cone. A fresh approach to
the quantum measurement problem appears feasible within such a framework.","The agenda of quantum algorithmic information theory, ordered `top-down,' is
the ORG halting amplitude, followed by the quantum algorithmic information
content, which in turn requires the theory of quantum computation. The
fundamental atoms processed by ORG computation are the quantum bits which
are dealt with in ORG information theory. The theory of quantum computation
will be based upon a model of universal ORG computer whose elementary unit
is a CARDINAL-port interferometer capable of MONEYU(2)$ transformations. Basic
to all these considerations is quantum theory, in particular PERSON space
quantum mechanics.",0
"The detection of some tiny gravitomagnetic effects in the field of the LOC
by means of artificial satellites is a very demanding task because of the
various other perturbing forces of gravitational and non-gravitational origin
acting upon them. Among the gravitational perturbations a relevant role is
played by the LOC solid and ocean tides. In this communication I outline
their effects on the detection of the Lense-Thirring drag of the orbits of
ORG and LAW, currently analyzed, and the proposed ORG experiment
devoted to the measurement of the clock effect.","The discovery that the ORG is undergoing an accelerated expansion has
suggested the existence of an evolving equation of state. This paper discusses
various wormhole solutions in a spherically symmetric spacetime with an
equation of state that is both space and time dependent. The solutions obtained
are exact and generalize earlier results on static wormholes supported by
phantom energy.",0
"These informal notes deal with some topics related to analysis on metric
spaces.","These informal notes are concerned with sums and averages in various
situations in analysis.",1
"We present a concrete design for PERSON's incremental machine learning
system suitable for desktop computers. We use R5RS Scheme and its standard
library with a few omissions as the reference machine. We introduce a PERSON variant based on a stochastic PERSON together with new
update algorithms that use the same grammar as a guiding probability
distribution for incremental machine learning. The updates include adjusting
production probabilities, re-using previous solutions, learning programming
idioms and discovery of frequent subprograms. The issues of extending the a
priori probability distribution and bootstrapping are discussed. We have
implemented a good portion of the proposed algorithms. Experiments with toy
problems show that the update algorithms work as expected.","The theory introduced, presented and developed in this paper, is concerned
with an enriched extension of the theory of ORG pioneered by ORG. The enrichment discussed here is in the sense of valuated categories as
developed by ORG. This paper relates ORG to an abstraction of
the theory of ORG pioneered by PERSON, and provides a natural
foundation for ""soft computation"". To paraphrase PERSON, the impetus for
the transition from a hard theory to a soft theory derives from the fact that
both the generality of a theory and its applicability to real-world problems
are substantially enhanced by replacing various hard concepts with their soft
counterparts. Here we discuss the corresponding enriched notions for
indiscernibility, subsets, upper/lower approximations, and rough sets.
Throughout, we indicate linkages with the theory of ORG
pioneered by PERSON. We pay particular attention to the all-important
notion of a ""linguistic variable"" - developing its enriched extension,
comparing it with the notion of conceptual scale from ORG,
and discussing the pragmatic issues of its creation and use in the
interpretation of data. These pragmatic issues are exemplified by the
discovery, conceptual analysis, interpretation, and categorization of networked
information resources in ORG, ORG
currently being developed for the management and interpretation of the universe
of resource information distributed over ORG.",0
"Let $PERSON be real-valued compactly supported sufficiently smooth function.
It is proved that the scattering data MONEY MONEY
S^2$, $\forall k>0,$ determine $q$ uniquely. Here $ORG S^2$ is a fixed
direction of the incident plane wave.","This paper investigates the randomness properties of a function of the
divisor pairs of a natural number. This function, the antecedents of which go
to very ancient times, has randomness properties that can find applications in
cryptography, key distribution, and other problems of computer science. It is
shown that the function is aperiodic and it has excellent autocorrelation
properties.",0
"A universal ORG computer can be constructed using NORP anyons. CARDINAL
qubit quantum logic gates such as controlled-NOT operations are performed using
topological effects. Single-anyon operations such as hopping from site to site
on a lattice suffice to perform all quantum logic operations. ORG
computation using NORP anyons shares some but not all of the robustness of
quantum computation using non-abelian anyons.","Before PERSON made his crucial contributions to the theory of
computation, he studied the question of whether ORG mechanics could throw
light on the nature of free will. This article investigates the roles of
quantum mechanics and computation in free will. Although quantum mechanics
implies that events are intrinsically unpredictable, the `pure stochasticity'
of ORG mechanics adds only randomness to decision making processes, not
freedom. By contrast, the theory of computation implies that even when our
decisions arise from a completely deterministic decision-making process, the
outcomes of that process can be intrinsically unpredictable, even to --
especially to -- ourselves. I argue that this intrinsic computational
unpredictability of the decision making process is what give rise to our
impression that we possess free will. Finally, I propose a `Turing test' for
free will: a decision maker who passes this test will tend to believe that he,
she, or it possesses free will, whether the world is deterministic or not.",1
"A path information is defined in connection with different possible paths of
irregular dynamic systems moving in its phase space between CARDINAL points. On the
basis of the assumption that the paths are physically differentiated by their
actions, we show that the maximum path information leads to a path probability
distribution in exponentials of action. This means that the most probable paths
are just the paths of least action. This distribution naturally leads to
important laws of normal diffusion. A conclusion of this work is that, for
probabilistic mechanics or irregular dynamics, the principle of maximization of
path information is equivalent to the least action principle for regular
dynamics.
  We also show that an average path information between the initial phase
volume and the final phase volume can be related to the entropy change defined
with natural invariant measure of dynamic system. Hence the principles of least
action and maximum path information suggest the maximum entropy change. This
result is used for some chaotic systems evolving in fractal phase space in
order to derive their invariant measures.","I study the class of problems efficiently solvable by a ORG computer,
given the ability to ""postselect"" on the outcomes of measurements. I prove that
this class coincides with a classical complexity class called ORG, or
ORG. Using this result, I show that several simple
changes to the axioms of quantum mechanics would let us solve ORDINAL-complete
problems efficiently. The result also implies, as an easy corollary, a
celebrated theorem of PERSON, PERSON, and NORP that ORG is closed under
intersection, as well as a generalization of that theorem due to Fortnow and
PERSON. This illustrates that ORG computing can yield new and simpler
proofs of major results about classical computation.",0
"In this paper, we give a frequency interpretation of negative probability, as
well as of extended probability, demonstrating that to a great extent, these
new types of probabilities, behave as conventional probabilities. Extended
probability comprises both conventional probability and negative probability.
The frequency interpretation of negative probabilities gives supportive
evidence to the axiomatic system built in (PERSON, DATE; GPE) for
extended probability as it is demonstrated in this paper that frequency
probabilities satisfy all axioms of extended probability.","Supervised artificial neural networks with the rapidity-mass matrix (ORG)
inputs were studied using several PERSON event samples for various pp
collision processes. The study shows the usability of this approach for general
event classification problems. The proposed standardization of the ORG feature
space can simplify searches for signatures of new physics at the LHC when using
machine learning techniques. In particular, we illustrate how to improve
signal-over-background ratios in searches for new physics, how to filter out
PERSON events for model-agnostic searches, and how to separate gluon
and quark jets for PERSON measurements.",0
"We treat secret key extraction when the eavesdropper has correlated quantum
states. We propose quantum privacy amplification theorems different from
ORG's, which are based on quantum conditional R\'{e}nyi entropy of order
1+s. Using those theorems, we derive an exponential decreasing rate for leaked
information and the asymptotic equivocation rate, which have not been derived
hitherto in the quantum setting.","We consider branes $MONEY in a NORP bulk, where
the stress energy tensor is dominated by the energy density of a scalar fields
map $WORK_OF_ARTPERSON with potential $MONEY, where $\mc S$ is a semi-NORP
moduli space. By transforming the field equation appropriately, we get an
equivalent field equation that is smooth across the singularity $r=0$, and
which has smooth and uniquely determined solutions which exist across the
singularity in MONEY-\e,\e)$. Restricting a solution to $(-\e,0)$
\resp $(0,\e)$, and assuming $n$ odd, we obtain branes $MONEY \resp $\hat N$ which
together form a smooth hypersurface. Thus a smooth transition from big crunch
to big bang is possible both geometrically as well as physically.",0
"A partial wave analysis of FAC data for ORG Lambda-bar NORP is
presented. A CARDINAL cusp is identified in the inverse process NORP-bar NORP to
pbar-p at threshold using detailed balance. Partial wave amplitudes for pbar-p
CARDINAL, DATE, DATE and ORDINAL exhibit a behaviour very similar to resonances observed
in LOC data. With this identification, the pbar-p to NORP-bar
NORP data then provide evidence for a new I = DATE, PERSON} = CARDINAL} resonance
with mass M = DATE +- 20 MeV, PERSON = CARDINAL +- 35 ORG, coupling to both CARDINAL and
CARDINAL.","We discuss how to generate singled peaked votes uniformly from the Impartial
Culture model.",0
"The education system for students in physics suffers (worldwide) from the
absence of a deep course in probability and randomness. This is the real
problem for students interested in ORG theory, ORG,
and quantum foundations. Here the primitive treatment of probability and
randomness may lead to deep misunderstandings of theory and wrong
interpretations of experimental results. Since during my visits (in DATE and
DATE) to ORG a number of students (experimenters!) asked me permanently about
foundational problems of probability and randomness, especially inter-relation
between classical and quantum structures, DATE I gave CARDINAL lectures on
these problems. Surprisingly the interest of experiment-oriented students to
mathematical peculiarities was very high. This (as well as permanent reminding
of prof. PERSON) motivated me to write a text based on these lectures which
were originally presented in the traditional black-board form. I hope that this
might be useful for students from ORG as well as other young physicists.","The information that mobiles can access becomes very wide nowadays, and the
user is faced with a dilemma: there is an unlimited pool of information
available to him but he is unable to find the exact information he is looking
for. This is why the current research aims to design ORG (ORG)
able to continually send information that matches the user's interests in order
to reduce his navigation time. In this paper, we treat the different approaches
to recommend.",0
"Rational decision making in its linguistic description means making logical
decisions. In essence, a rational agent optimally processes all relevant
information to achieve its goal. Rationality has CARDINAL elements and these are the
use of relevant information and the efficient processing of such information.
In reality, relevant information is incomplete, imperfect and the processing
engine, which is a brain for humans, is suboptimal. Humans are risk averse
rather than utility maximizers. In the real world, problems are predominantly
non-convex and this makes the idea of rational decision-making fundamentally
unachievable and PERSON called this bounded rationality. There is a
trade-off between the amount of information used for decision-making and the
complexity of the decision model used. This explores whether machine
rationality is subjective and concludes that indeed it is.","This paper proposes the use of particle swarm optimization method (PSO) for
finite element (FE) model updating. The PSO method is compared to the existing
methods that use simulated annealing (ORG) or genetic algorithms (GA) for ORG
model for model updating. The proposed method is tested on an unsymmetrical
H-shaped structure. It is observed that the proposed method gives updated
natural frequencies the most accurate and followed by those given by an updated
model that was obtained using the ORG and a full ORG model. It is also observed
that the proposed method gives updated mode shapes that are best correlated to
the measured ones, followed by those given by an updated model that was
obtained using the ORG and a full ORG model. Furthermore, it is observed that the
PSO achieves this accuracy at a computational speed that is faster than that by
the ORG and a full ORG model which is faster than the ORG and a full ORG model.",1
"The oracle chooses a function out of a known set of functions and gives to
the player a black box that, given an argument, evaluates the function. The
player should find out a certain character of the function through function
evaluation. This is the typical problem addressed by the ORG algorithms. In
former theoretical work, we showed that a quantum algorithm requires the number
of function evaluations of a classical algorithm that knows in advance PERCENT of
the information that specifies the solution of the problem. Here we check that
this PERCENT rule holds for the main quantum algorithms. In the structured
problems, a classical algorithm with the advanced information, to identify the
missing information should perform CARDINAL function evaluation. The speed up is
exponential since a classical algorithm without advanced information should
perform an exponential number of function evaluations. In unstructured database
search, a classical algorithm that knows in advance PERCENT of the n bits of the
database location, to identify the ORG missing bits should perform Order(2
power ORG) function evaluations. The speed up is quadratic since a classical
algorithm without advanced information should perform ORG) function
evaluations. The PERCENT rule identifies the problems solvable with a quantum sped
up in an entirely classical way, in fact by comparing CARDINAL classical algorithms,
with and without the advanced information.","We show that CARDINAL of heat dissipation per qubit occurs in
measurement-based ORG computation according to ORG's principle. This
result is derived by using only the fundamental fact that ORG physics
respects the no-signaling principle.",0
"The debate about which similarity measure one should use for the
normalization in the case of ORG (ORG) is further
complicated when one distinguishes between the symmetrical co-citation--or,
more generally, co-occurrence--matrix and the underlying asymmetrical
citation--occurrence--matrix. In the Web environment, the approach of
retrieving original citation data is often not feasible. In that case, CARDINAL
should use the ORG index, but preferentially after adding the number of
total citations (occurrences) on the main diagonal. Unlike PERSON's cosine and
the PRODUCT correlation, the ORG index abstracts from the shape of the
distributions and focuses only on the intersection and the sum of the CARDINAL sets.
Since the correlations in the co-occurrence matrix may partially be spurious,
this property of the ORG index can be considered as an advantage in this
case.","In this paper the theory of flexibly-bounded rationality which is an
extension to the theory of bounded rationality is revisited. Rational decision
making involves using information which is almost always imperfect and
incomplete together with some intelligent machine which if it is a human being
is inconsistent to make decisions. In bounded rationality, this decision is
made irrespective of the fact that the information to be used is incomplete and
imperfect and that the human brain is inconsistent and thus this decision that
is to be made is taken within the bounds of these limitations. In the theory of
flexibly-bounded rationality, advanced information analysis is used, the
correlation machine is applied to complete missing information and artificial
intelligence is used to make more consistent decisions. Therefore
flexibly-bounded rationality expands the bounds within which rationality is
exercised. Because human decision making is essentially irrational, this paper
proposes the theory of marginalization of irrationality in decision making to
deal with the problem of satisficing in the presence of irrationality.",0
"This paper examines how black holes might compute in light of recent models
of the black-hole final state. These models suggest that ORG information
can escape from the black hole by a process akin to teleportation. They require
a specific final state and restrictions on the interaction between the
collapsing matter and the incoming Hawking radiation for ORG information to
escape. This paper shows that for an arbitrary final state and for generic
interactions between matter and Hawking radiation, the ORG information
about how the hole was formed and the results of any computation performed by
the matter inside the hole escapes with ORG exponentially close to CARDINAL.","This article explores the ideas that went into PERSON development of
an algebra for logical inference in his book WORK_OF_ART. We explore in
particular his wife PERSON's claim that he was deeply influenced by NORP
logic and argue that his work was more than a framework for processing
propositions. By exploring parallels between his work and NORP logic, we are
able to explain several peculiarities of this work.",0
"CARDINAL aspects of the physical side of ORG thesis are discussed.
The ORDINAL issue is a variant of the LOC argument against motion, dealing
with PERSON squeezed time cycles of computers. The ORDINAL argument reviews the
issue of CARDINAL-to-CARDINAL computation, that is, the bijective (unique and reversible)
evolution of computations and its relation to the measurement process.","In this highly speculative Letter it is argued that, under certain physical
conditions, ORG's demon might be capable of breaking the ORDINAL law of
thermodynamics, thereby allowing a perpetual motion machine of the ORDINAL kind,
by accessing single particle capabilities.",1
"Recurrent neural networks (ORG) are capable of learning to encode and exploit
activation history over an arbitrary timescale. However, in practice, state of
the art gradient descent based training methods are known to suffer from
difficulties in learning long term dependencies. Here, we describe a novel
training method that involves concurrent parallel cloned networks, each sharing
the same weights, each trained at different stimulus phase and each maintaining
independent activation histories. Training proceeds by recursively performing
batch-updates over the parallel clones as activation history is progressively
increased. This allows conflicts to propagate hierarchically from short-term
contexts towards longer-term contexts until they are resolved. We illustrate
the parallel clones method and hierarchical conflict propagation with a
character-level deep ORG tasked with memorizing a paragraph of PERSON (by
PERSON).","I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of PERSON and FAC but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the ORDINAL derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since DATE) artificial systems.",0
"PERSON's inequality plays an important role in linear elasticity theory. This
inequality bounds the norm of the derivatives of the displacement vector by the
norm of the linearized strain tensor. The kernel of the linearized strain
tensor are the infinitesimal rigid-body translations and rotations (Killing
vectors). We generalize this inequality by replacing the linearized strain
tensor by its trace free part. That is, we obtain a stronger inequality in
which the kernel of the relevant operator are the conformal ORG vectors.
The new inequality has applications in General Relativity.","In this paper we try to suggest a possible novel method to determine some
selected even zonal harmonics J_l of the LOC's geopotential. Time series many
DATE long of suitably linearly combined residuals of some NORP orbital
elements of certain existing geodetic SLR satellites would be examined. A
ORG/GRACE-only background reference model should be used for the part of the
geopotential which we are not interested in. The retrieved values for the even
zonal harmonics of interest would be, by construction, independent of each
other and of any NORP features. The so obtained mini-model could,
subsequently, be used in order to enhance the accuracy and the reliability of
some tests of NORP gravity, with particular emphasis to the
measurement of the Lense-Thirring effect by means of ORG and LAW.",0
"We introduce a new class of graphical models that generalizes
ORG chain graphs by relaxing the semi-directed
acyclity constraint so that only directed cycles are forbidden. Moreover, up to
CARDINAL edges are allowed between any pair of nodes. Specifically, we present
local, pairwise and global PERSON properties for the new graphical models and
prove their equivalence. We also present an equivalent factorization property.
Finally, we present a causal interpretation of the new models.","Recently, ORG and PERSON suggested representing uncertainty by a weighted
set of probability measures, and suggested a way of making decisions based on
this representation of uncertainty: maximizing weighted regret. Their paper
does not answer an apparently simpler question: what it means, according to
this representation of uncertainty, for an event E to be more likely than an
event E'. In this paper, a notion of comparative likelihood when uncertainty is
represented by a weighted set of probability measures is defined. It
generalizes the ordering defined by probability (and by lower probability) in a
natural way; a generalization of upper probability can also be defined. A
complete axiomatic characterization of this notion of regret-based likelihood
is given.",0
"We consider sets of ORG observables corresponding to eutactic stars.
Eutactic stars are systems of vectors which are the lower dimensional
``shadow'' image, the orthogonal view, of higher dimensional orthonormal bases.
Although these vector systems are not comeasurable, they represent redundant
coordinate bases with remarkable properties. CARDINAL application is ORG secret
sharing.","In view of the sobering findings of science, theology and to a lesser degree
metaphysics is confronted with a humiliating loss, and a need for
reinterpretation, of allegories and narratives which have served as guidance to
the perplexed for millennia. Future revolutions of world perception might
include the emergence of consciousness and superhuman artificial intelligence
from universal computation, extensive virtual reality simulations, the
persistence of claims of irreducible chance in the ORG, as well as
contacts with alien species and the abundance of inhabited planets. As tragic
and as discomforting as this might be perceived for the religious orthodoxy and
by individual believers, a theology guided by science may lead us to a better
and more adequate understanding of our existence. The post factum theological
options are plentiful. These include dualistic scenarios, as well as (to quote
PERSON), a curling or bowling deity, that is, creatio continua, or
ex nihilo. These might be grounded in, or corroborated by the metaphysical
enigma of existence, which appears to be immune and robust with respect to the
aforementioned challenges of science.",1
"Error correction, in the standard meaning of the term, implies the ability to
correct all small analog errors and some large errors. Examining assumptions at
the basis of the recently proposed quantum error-correcting codes, it is
pointed out that these codes can correct only a subset of errors, and are
unable to correct small phase errors which can have disastrous consequences for
a quantum computation. This shortcoming will restrict their usefulness in real
applications.","In this article, we study the mass spectrum of the scalar and axial-vector
heavy diquark states with the ORG sum rules in a systematic way. Once the
reasonable values are obtained, we can take them as basic parameters and study
the new charmonium-like states as the tetraquark states.",0
"Optimization problems are considered in the framework of tropical algebra to
minimize and maximize a nonlinear objective function defined on vectors over an
idempotent semifield, and calculated using multiplicative conjugate
transposition. To find the minimum of the function, we ORDINAL obtain a partial
solution, which explicitly represents a subset of solution vectors. We
characterize all solutions by a system of simultaneous equation and inequality,
and show that the solution set is closed under vector addition and scalar
multiplication. A matrix sparsification technique is proposed to extend the
partial solution, and then to obtain a complete solution described as a family
of subsets. We offer a backtracking procedure that generates all members of the
family, and derive an explicit representation for the complete solution. As
another result, we deduce a complete solution of the maximization problem,
given in a compact vector form by the use of sparsified matrices. The results
obtained are illustrated with illuminating examples and graphical
representations. We apply the results to solve real-world problems drawn from
project (machine) scheduling, and give numerical examples.","ORG decision systems are being increasingly considered for use in
artificial intelligence applications. Classical and quantum nodes can be
distinguished based on certain correlations in their states. This paper
investigates some properties of the states obtained in a decision tree
structure. How these correlations may be mapped to the decision tree is
considered. Classical tree representations and approximations to quantum states
are provided.",0
"Although deep neural networks (DNN) are able to scale with direct advances in
computational power (e.g., memory and processing speed), they are not well
suited to exploit the recent trends for parallel architectures. In particular,
gradient descent is a sequential process and the resulting serial dependencies
mean that DNN training cannot be parallelized effectively. Here, we show that a
DNN may be replicated over a massive parallel architecture and used to provide
a cumulative sampling of local solution space which results in rapid and robust
learning. We introduce a complimentary convolutional bootstrapping approach
that enhances performance of the parallel architecture further. Our
parallelized convolutional bootstrapping DNN out-performs an identical
fully-trained traditional DNN after only a single iteration of training.","Model-based coding, described by PERSON in DATE, has great potential to
reduce the volume of information that needs to be transmitted in moving big
data, without loss of information, from CARDINAL place to another, or in lossless
communications via the internet. Compared with ordinary compression methods,
this potential advantage of model-based coding in the transmission of data
arises from the fact that both the transmitter (""Alice"") and the receiver
(""PERSON"") are equipped with a grammar for the kind of data that is to be
transmitted, which means that, to achieve lossless transmission of a body of
data from PERSON and PERSON, a relatively small amount of information needs to be
sent. Preliminary trials indicate that, with model-based coding, the volume of
information to be sent from PERSON to PERSON to achieve lossless transmission of a
given body of data may be MONEY of the volume of information that
needs to be sent when ordinary compression methods are used.
  Until recently, it has not been feasible to convert PERSON vision into
something that may be applied in practice. Now, with the development of the ""SP
theory of intelligence"" and its realisation in the ""SP computer model"", there
is clear potential to realise the CARDINAL main functions that will be needed:
unsupervised learning of a grammar for the kind of data that is to be
transmitted using a relatively powerful computer that is independent of PRODUCT
and PERSON; the encoding by PERSON of any CARDINAL example of such data in terms of the
grammar; and, with the grammar, decoding of the encoding by PERSON to retrieve the
given example. It appears now to be feasible, within reasonable timescales, to
bring these capabilities to a level where they may be applied to the
transmission of realistically large bodies of data.",0
"We present a unified logical framework for representing and reasoning about
both probability quantitative and qualitative preferences in probability answer
set programming, called probability answer set optimization programs. The
proposed framework is vital to allow defining probability quantitative
preferences over the possible outcomes of qualitative preferences. We show the
application of probability answer set optimization programs to a variant of the
well-known nurse restoring problem, called the nurse restoring with probability
preferences problem. To the best of our knowledge, this development is the
ORDINAL to consider a logical framework for reasoning about probability
quantitative preferences, in general, and reasoning about both probability
quantitative and qualitative preferences in particular.","We present a unified logical framework for representing and reasoning about
both quantitative and qualitative preferences in fuzzy answer set programming,
called fuzzy answer set optimization programs. The proposed framework is vital
to allow defining quantitative preferences over the possible outcomes of
qualitative preferences. We show the application of fuzzy answer set
optimization programs to the course scheduling with fuzzy preferences problem.
To the best of our knowledge, this development is the ORDINAL to consider a
logical framework for reasoning about quantitative preferences, in general, and
reasoning about both quantitative and qualitative preferences in particular.",1
"Dynamics of arbitrary communication system is analysed as unreduced
interaction process. The applied generalised, universally nonperturbative
method of effective potential reveals the phenomenon of dynamic multivaluedness
of competing system configurations forced to permanently replace each other in
a causally random order, which leads to universally defined dynamical chaos,
complexity, fractality, self-organisation, and adaptability (physics/9806002,
physics/0211071, physics/0405063). We demonstrate the origin of huge,
exponentially high efficiency of the unreduced, complex network dynamics and
specify the universal symmetry of complexity (physics/0404006) as the
fundamental guiding principle for creation and control of such qualitatively
new kind of networks and devices. The emerging intelligent communication
paradigm and its practical realisation in the form of knowledge-based networks
involve the features of true, unreduced intelligence and consciousness
(physics/0409140) appearing in complex (multivalued) network dynamics and
results.","In this article we study a problem within ORG theory where CARDINAL -
CARDINAL pieces of evidence are clustered by a neural structure into n clusters. The
clustering is done by minimizing a metaconflict function. Previously we
developed a method based on iterative optimization. However, for large scale
problems we need a method with lower computational complexity. The neural
structure was found to be effective and much faster than iterative optimization
for larger problems. While the growth in metaconflict was faster for the neural
structure compared with iterative optimization in medium sized problems, the
metaconflict per cluster and evidence was moderate. The neural structure was
able to find a global minimum over CARDINAL runs for problem sizes up to CARDINAL
clusters.",0
"This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the ORG algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original PERSON
by additional steps transforming so called partial including path graph into a
belief network. Its correctness is demonstrated.","There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). CARDINAL way to extend beyond words is to compare CARDINAL tuples using a
function that combines pairwise similarities between the component words in the
tuples. A strength of this approach is that it works with both relational
similarity (analogy) and compositional similarity (paraphrase). However, past
work required hand-coding the combination function for different tasks. The
main contribution of this paper is that combination functions are generated by
supervised learning. We achieve state-of-the-art results in measuring
relational similarity between word pairs (ORG analogies and SemEval~2012 PRODUCT) and measuring compositional similarity between GPE-modifier phrases and
unigrams (multiple-choice paraphrase questions).",0
"We address ORG gate response in a mesoscopic ring threaded by a magnetic flux
$MONEY The ring, composed of identical quantum dots, is symmetrically attached
to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes and CARDINAL gate voltages,
viz, $V_a$ and $PERSON, are applied, respectively, in each arm of the ring which
are treated as the CARDINAL inputs of the ORG gate. The calculations are based on
the tight-binding model and the ORG's function method, which numerically
compute the conductance-energy and current-voltage characteristics as functions
of the ring-electrodes coupling strengths, magnetic flux and gate voltages.
Quite interestingly it is observed that, for MONEY ($\phi_0=ch/e$,
the elementary flux-quantum) a high output current (CARDINAL) (in the logical sense)
appears if one, and CARDINAL, of the inputs to the gate is high (1), while if
both inputs are low (0) or both are high (1), a low output current (0) appears.
It clearly demonstrates the ORG behavior and this aspect may be utilized in
designing the electronic logic gate.","In this paper we present a short history of logics: from particular cases of
CARDINAL-symbol or numerical valued logic to the general case of n-symbol or numerical
valued logic. We show generalizations of CARDINAL-valued NORP logic to fuzzy logic,
also from the PERSON and Lukasiewicz CARDINAL-symbol valued logics or FAC
valued logic to the most general n-symbol or numerical valued refined
neutrosophic logic. CARDINAL classes of neutrosophic norm (n-norm) and neutrosophic
conorm (n-conorm) are defined. Examples of applications of neutrosophic logic
to physics are listed in the last section. Similar generalizations can be done
for ORG, and respectively n- ORG
LOC.",0
"I'll outline the latest version of my limits of math course. The purpose of
this course is to illustrate the proofs of the key information-theoretic
incompleteness theorems of algorithmic information theory by means of
algorithms written in a specially designed version of ORG. The course is now
written in HTML with PERSON applets, and is available at
http://www.research.ibm.com/people/c/chaitin/lm . The LISP now used is much
friendlier than before, and because its interpreter is a PERSON applet it will
run in the PRODUCT browser as you browse my limits of math Web site.","We describe a new wavelet transform, for use on hierarchies or binary rooted
trees. The theoretical framework of this approach to data analysis is
described. Case studies are used to further exemplify this approach. A ORDINAL
set of application studies deals with data array smoothing, or filtering. A
ORDINAL set of application studies relates to hierarchical tree condensation.
Finally, a ORDINAL study explores the wavelet decomposition, and the
reproducibility of data sets such as text, including a new perspective on the
generation or computability of such data objects.",0
"Machine Consciousness and Machine Intelligence are not simply new buzzwords
that occupy our imagination. Over DATE, we witness an unprecedented
rise in attempts to create machines with human-like features and capabilities.
However, despite widespread sympathy and abundant funding, progress in these
enterprises is far from being satisfactory. The reasons for this are twofold:
ORDINAL, the notions of cognition and intelligence (usually borrowed from human
behavior studies) are notoriously blurred and ill-defined, and ORDINAL, the
basic concepts underpinning the whole discourse are by themselves either
undefined or defined very vaguely. That leads to improper and inadequate
research goals determination, which I will illustrate with some examples drawn
from recent documents issued by ORG and ORG. On the other
hand, I would like to propose some remedies that, I hope, would improve the
current state-of-the-art disgrace.","Computational Intelligence is a dead-end attempt to recreate human-like
intelligence in a computing machine. The goal is unattainable because the means
chosen for its accomplishment are mutually inconsistent and contradictory:
""Computational"" implies data processing ability while ""Intelligence"" implies
the ability to process information. In the research community, there is a lack
of interest in data versus information divergence. The cause of this
indifference is the FAC's Information theory, which has dominated the
scientific community since DATE. However, DATE it is clear that
FAC's theory is applicable only to a specific case of data communication
and is inapplicable to the majority of other occasions, where information about
semantic properties of a message must be taken into account. The paper will try
to explain the devastating results of overlooking some of these very important
issues - what is intelligence, what is semantic information, how they are
interrelated and what happens when the relationship is disregarded.",1
"CARDINAL of the crown jewels of complexity theory is PERSON's DATE theorem that
computing the permanent of an n*n matrix is #P-hard. Here we show that, by
using the model of linear-optical ORG computing---and in particular, a
universality theorem due to PERSON, PERSON, and GPE---one can give a
different and arguably more intuitive proof of this theorem.","This paper describes a tentative model for how discrete memories transform
into an interconnected conceptual network, or worldview, wherein relationships
between memories are forged by way of abstractions. The model draws on
PERSON's theory of how an information-evolving system could emerge through
the formation and closure of an autocatalytic network. Here, the information
units are not catalytic molecules, but memories and abstractions, and the
process that connects them is not catalysis but reminding events (i.e. CARDINAL
memory evokes another). The result is a worldview that both structures, and is
structured by, self-triggered streams of thought.",0
"Symmetry can be used to help solve many problems. For instance, PERSON's
famous DATE paper (""WORK_OF_ART"") uses symmetry to
help derive the laws of special relativity. In artificial intelligence,
symmetry has played an important role in both problem representation and
reasoning. I describe recent work on using symmetry to help solve constraint
satisfaction problems. Symmetries occur within individual solutions of problems
as well as between different solutions of the same problem. Symmetry can also
be applied to the constraints in a problem to give new symmetric constraints.
Reasoning about symmetry can speed up problem solving, and has led to the
discovery of new results in both graph and number theory.","Sometime in the future we will have to deal with the impact of ORG's being
mistaken for humans. For this reason, I propose that any autonomous system
should be designed so that it is unlikely to be mistaken for anything besides
an autonomous sysem, and should identify itself at the start of any interaction
with another agent.",1
"The evolution equation of ORG cosmic density perturbations in the realm of
FAC theory of gravity is obtained.The de Sitter metric fluctuation
is computed in terms of the spin-torsion background density.","Fluctuations on de Sitter solution of FAC field equations are
obtained in terms of the matter density primordial density fluctuations and
spin-torsion density and matter density fluctuations obtained from ORG data.
Einstein-de Sitter solution is shown to be unstable even in the absence of
torsion.The spin-torsion density fluctuation is simply computed from the
ORG equations and from ORG data.",1
"ORDINAL we describe briefly an information-action method for the study of
stochastic dynamics of hamiltonian systems perturbed by thermal noise and
chaotic instability. It is shown that, for the ensemble of possible paths
between CARDINAL configuration points, the action principle acquires a statistical
form $<\delta A>=0$. The main objective of this paper is to prove that, via
this information-action description, some quantum like uncertainty relations
such as $<\Delta PERSON for action, MONEY\Delta x><\Delta
P>\geq\frac{1}{\eta}$ for position and momentum, and $<\Delta H><\Delta
ORG for hamiltonian and time, can arise for
stochastic dynamics of classical hamiltonian systems. A corresponding
commutation relation can also be found. These relations describe, through
action or its conjugate variables, the fluctuation of stochastic dynamics due
to random perturbation characterized by the parameter $MONEY","We discuss the power and limitation of various ""advice,"" when it is given
particularly to weak computational models of CARDINAL-tape linear-time Turing
machines and CARDINAL-way finite (state) automata. Of various advice types, we
consider deterministically-chosen advice (not necessarily algorithmically
determined) and randomly-chosen advice (according to certain probability
distributions). In particular, we show that certain weak machines can be
significantly enhanced in computational power when randomized advice is
provided in place of deterministic advice.",0
"This article reviews the history of digital computation, and investigates
just how far the concept of computation can be taken. In particular, I address
the question of whether the universe itself is in fact a giant computer, and if
so, just what kind of computer it is. I will show that the universe can be
regarded as a giant ORG computer. The quantum computational model of the
universe explains a variety of observed phenomena not encompassed by the
ordinary laws of physics. In particular, the model shows that the the quantum
computational universe automatically gives rise to a mix of randomness and
order, and to both simple and complex systems.","ORG can be naturally modelled as an
exploration/exploitation trade-off (exr/exp) problem, where the system has to
choose between maximizing its expected rewards dealing with its current
knowledge (exploitation) and learning more about the unknown user's preferences
to improve its knowledge (exploration). This problem has been addressed by the
reinforcement learning community but they do not consider the risk level of the
current user's situation, where it may be dangerous to recommend items the user
may not desire in her current situation if the risk level is high. We introduce
in this paper an algorithm named R-UCB that considers the risk level of the
user's situation to adaptively balance between exr and exp. The detailed
analysis of the experimental results reveals several important discoveries in
the exr/exp behaviour.",0
"It is possible to rely on current corporate law to grant legal personhood to
ORG (AI) agents. In this paper, after introducing pathways
to ORG personhood, we analyze consequences of such AI empowerment on human
dignity, human safety and ORG rights. We emphasize possibility of creating
selfish memes and legal system hacking in the context of artificial entities.
Finally, we consider some potential solutions for addressing described
problems.","The young field of ORG is still in the process of identifying its
challenges and limitations. In this paper, we formally describe CARDINAL such
impossibility result, namely ORG. We prove that it is
impossible to precisely and consistently predict what specific actions a
smarter-than-human intelligent system will take to achieve its objectives, even
if we know terminal goals of the system. In conclusion, impact of
WORK_OF_ART is discussed.",1
"The wide development of mobile applications provides a considerable amount of
data of all types (images, texts, sounds, videos, etc.). Thus, CARDINAL main issues
have to be considered: assist users in finding information and reduce search
and navigation time. In this sense, context-based recommender systems (ORG)
propose the user the adequate information depending on her/his situation. Our
work consists in applying machine learning techniques and reasoning process in
order to bring a solution to some of the problems concerning the acceptance of
recommender systems by users, namely avoiding the intervention of experts,
reducing cold start problem, speeding learning process and adapting to the
user's interest. To achieve this goal, we propose a fundamental modification in
terms of how we model the learning of the ORG. Inspired by models of human
reasoning developed in robotic, we combine reinforcement learning and
case-based reasoning to define a contextual recommendation process based on
different context dimensions (cognitive, social, temporal, geographic). This
paper describes an ongoing work on the implementation of a ORG based on a
hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative
filtering and case-based reasoning techniques. It also presents preliminary
results by comparing PRODUCT and the standard ORG. solving the cold
start problem.","Motivated by earlier results on universal randomized guessing, we consider an
individual-sequence approach to the guessing problem: in this setting, the goal
is to guess a secret, individual (deterministic) vector $PERSON,PERSON,
by using a finite-state machine that sequentially generates randomized guesses
from a stream of purely random bits. We define the finite-state guessing
exponent as the asymptotic normalized logarithm of the minimum achievable
moment of the number of randomized guesses, generated by any finite-state
machine, until $PERSON is guessed successfully. We show that the finite-state
guessing exponent of any sequence is intimately related to its finite-state
compressibility (due to PERSON and PERSON), and it is asymptotically achieved by
the decoder of (a certain modified version of) the DATE ORG data
compression algorithm (a.k.a. the LZ78 algorithm), fed by purely random bits.
The results are also extended to the case where the guessing machine has access
to a side information sequence, $PERSON,PERSON, which is also an
individual sequence.",0
"We extend ORG chain graphs by (i) relaxing the
semidirected acyclity constraint so that only directed cycles are forbidden,
and (ii) allowing up to CARDINAL edges between any pair of nodes. We introduce
global, and ordered local and pairwise PERSON properties for the new models. We
show the equivalence of these properties for strictly positive probability
distributions. We also show that when the random variables are continuous, the
new models can be interpreted as systems of structural equations with
correlated errors. This enables us to adapt GPE's do-calculus to them.
Finally, we describe an exact algorithm for learning the new models from
observational and interventional data via answer set programming.","We present a new family of models that is based on graphs that may have
undirected, directed and bidirected edges. We name these new models marginal
ORG (MAMP) chain graphs because each of them is PERSON equivalent to some ORG
chain graph under marginalization of some of its nodes. However, MAMP chain
graphs do not only subsume ORG chain graphs but also multivariate regression
chain graphs. We describe global and pairwise PERSON properties for ORG chain
graphs and prove their equivalence for compositional graphoids. We also
characterize when CARDINAL MAMP chain graphs are PERSON equivalent.
  For NORP probability distributions, we also show that every MAMP chain
graph is PERSON equivalent to some directed and acyclic graph with
deterministic nodes under marginalization and conditioning on some of its
nodes. This is important because it implies that the independence model
represented by a ORG chain graph can be accounted for by some data generating
process that is partially observed and has selection bias. Finally, we modify
MAMP chain graphs so that they are closed under marginalization for NORP
probability distributions. This is a desirable feature because it guarantees
parsimonious models under marginalization.",1
"The inequality $\sqrt{J}\leq m$ is proved for vacuum, asymptotically flat,
maximal and axisymmetric data close to extreme ORG data. The physical
significance of this inequality and its relation to the standard picture of the
gravitational collapse are discussed.","This paper considers M-estimation of a nonlinear regression model with
multiple change-points occuring at unknown times. The multi-phase random design
regression model, discontinuous in each change-point, have an arbitrary error
$\epsilon$. In the case when the number of jumps is known, the M-estimator of
locations of breaks and of regression parameters are studied. These estimators
are consistent and the distribution of the regression parameter estimators is
PERSON. The estimator of each change-point converges, with the rate $WORK_OF_ART,
to the smallest minimizer of the independent compound PERSON processes. The
results are valid for a large class of error distributions.",0
"This essay explores the limits of Turing machines concerning the modeling of
minds and suggests alternatives to go beyond those limits.","An inverse problem for the wave equation outside an obstacle with a {ORG
dissipative boundary condition} is considered. The observed data are given by a
single solution of the wave equation generated by an initial data supported on
an open ball. An explicit analytical formula for the computation of the
coefficient at a point on the surface of the obstacle which is nearest to the
center of the support of the initial data is given.",0
"For supervised and unsupervised learning, positive definite kernels allow to
use large and potentially infinite dimensional feature spaces with a
computational cost that only depends on the number of observations. This is
usually done through the penalization of predictor functions by PERSON or
NORP norms. In this paper, we explore penalizing by sparsity-inducing
norms such as the l1-norm or the block l1-norm. We assume that the kernel
decomposes into a large sum of individual basis kernels which can be embedded
in a directed acyclic graph; we show that it is then possible to perform kernel
selection through a hierarchical multiple kernel learning framework, in
polynomial time in the number of selected kernels. This framework is naturally
applied to non linear variable selection; our extensive simulations on
synthetic datasets and datasets from the ORG repository show that efficiently
exploring the large feature space through sparsity-inducing norms leads to
state-of-the-art predictive performance.","While tree methods have been popular in practice, researchers and
practitioners are also looking for simple algorithms which can reach similar
accuracy of trees. In DATE, (PERSON) developed the method of
""ORG-robust-logitboost"" and compared it with other supervised learning methods
on datasets used by the deep learning literature. In this study, we propose a
series of ""tunable ORG kernels"" which are simple and perform largely comparably
to tree methods on the same datasets. Note that ""abc-robust-logitboost""
substantially improved the original ""ORG"" in that (a) it developed a
tree-split formula based on ORDINAL-order information of the derivatives of the
loss function; (b) it developed a new set of derivatives for multi-class
classification formulation.
  In the prior study in DATE, the ""generalized PERSON"" (ORG) kernel was shown
to have good performance compared to the ""radial-basis function"" (ORG) kernel.
However, as demonstrated in this paper, the original ORG kernel is often not as
competitive as tree methods on the datasets used in the deep learning
literature. Since the original ORG kernel has no parameters, we propose tunable
ORG kernels by adding tuning parameters in various ways. CARDINAL basic (i.e.,
with CARDINAL parameter) ORG kernels are the ""$e$GMM kernel"", ""$p$GMM kernel"",
and ""$PERSON kernel"", respectively. Extensive experiments show that they
are able to produce good results for a large number of classification tasks.
Furthermore, the basic kernels can be combined to boost the performance.",0
"In this article, we study the axialvector-diquark-axialvector-antidiquark
type scalar, axialvector, tensor and vector $ss\bar{s}\bar{s}$ tetraquark
states with the ORG sum rules. The predicted mass $m_{X}=2.08\pm0.12\,\rm{GeV}$
for the axialvector tetraquark state is in excellent agreement with the
experimental value $(CARDINAL \pm 13.1 \pm 4.2) \,\rm{MeV}$ from the BESIII
collaboration and supports assigning the new $MONEY state to be a
$ss\bar{s}\bar{s}$ tetraquark state with $PERSON predicted mass
$m_{X}=3.08\pm0.11\,\rm{GeV}$ disfavors assigning the MONEY or $Y(2175)$
to be the vector partner of the new $MONEY state. As a byproduct, we obtain the
masses of the corresponding $qq\bar{q}\bar{q}$ tetraquark states. The light
tetraquark states lie in the region MONEYMONEY rather than
$MONEY","This paper shows that, if we could examine the entire history of a hidden
variable, then we could efficiently solve problems that are believed to be
intractable even for ORG computers. In particular, under any
hidden-variable theory satisfying a reasonable axiom called ""indifference to
the identity,"" we could solve the Graph Isomorphism and PERSON
DATE problems in polynomial time, as well as an oracle problem that is known
to require ORG exponential time. We could also search an N-item database
using O(N^{1/3}) queries, as opposed to O(N^{1/2}) queries with PERSON's search
algorithm. On the other hand, the N^{1/3} bound is optimal, meaning that we
could probably not solve ORG-complete problems in polynomial time. We thus
obtain the ORDINAL good example of a model of computation that appears slightly
more powerful than the ORG computing model.",0
"The folksonomy is the result of free personal information or assignment of
tags to an object (determined by the URI) in order to find them. The practice
of tagging is done in a collective environment. Folksonomies are self
constructed, based on co-occurrence of definitions, rather than a hierarchical
structure of the data. The downside of this was that a few sites and
applications are able to successfully exploit the sharing of bookmarks. The
need for tools that are able to resolve the ambiguity of the definitions is
becoming urgent as the need of simple instruments for their visualization,
editing and exploitation in web applications still hinders their diffusion and
wide adoption. An intelligent interactive interface design for folksonomies
should consider the contextual design and inquiry based on a concurrent
interaction for a perceptual user interfaces. To represent folksonomies a new
concept structure called ""WORK_OF_ART"" is used in this paper. While it is
presented FAC (ORG) to resolve the ambiguity of
definitions of folksonomy tags suggestions for the user. On this base a
ORG (HCI) systems is developed for the visualization,
navigation, updating and maintenance of folksonomies Knowledge Bases - the ORG
- through the web. System functionalities as well as its internal architecture
will be introduced.","In this paper we present FAC (ORG) built on GPE and on
NORP technologies. Cloud computing has emerged in DATE as the
new paradigm for the provision of on-demand distributed computing resources.
ORG can be used for relationship between different data and
descriptions of services to annotate provenance of repositories on ontologies.
The ORG service is composed of a back-end which submits and monitors the
documents, and a user front-end which allows users to schedule on-demand
operations and to watch the progress of running processes. The impact of the
proposed method is illustrated on a user since its inception.",1
"Slime mould P. polycephalum is a single cells visible by unaided eye. The
cells shows a wide spectrum of intelligent behaviour. By interpreting the
behaviour in terms of computation one can make a slime mould based computing
device. The ORG computers are capable to solve a range of tasks of
computational geometry, optimisation and logic. Physarum computers designed so
far lack of localised inputs. Commonly used inputs --- illumination and
chemo-attractants and -repellents --- usually act on extended domains of the
slime mould's body. Aiming to design massive-parallel tactile inputs for slime
mould computers we analyse a temporal dynamic of P. polycephalum's electrical
response to tactile stimulation. In experimental laboratory studies we discover
how the ORG responds to application and removal of a local mechanical
pressure with electrical potential impulses and changes in its electrical
potential oscillation patterns.","We introduce CARDINAL notions of effective reducibility for set-theoretical
statements, based on computability with ORG (OTMs), CARDINAL of
which resembles Turing reducibility while the other is modelled after Weihrauch
reducibility. We give sample applications by showing that certain (algebraic)
constructions are not effective in the ORG-sense and considerung the effective
equivalence of various versions of the axiom of choice.",0
"Computability logic (ORG) (see ORG) is a
recently introduced semantical platform and ambitious program for redeveloping
logic as a formal theory of computability, as opposed to the formal theory of
truth that logic has more traditionally been. Its expressions represent
interactive computational tasks seen as games played by a machine against the
environment, and ""truth"" is understood as existence of an algorithmic winning
strategy. With logical operators standing for operations on games, the
formalism of ORG is open-ended, and has already undergone series of extensions.
This article extends the expressive power of ORG in a qualitatively new way,
generalizing formulas (to which the earlier languages of ORG were limited) to
circuit-style structures termed cirquents. The latter, unlike formulas, are
able to account for subgame/subtask sharing between different parts of the
overall game/task. Among the many advantages offered by this ability is that it
allows us to capture, refine and generalize the well known
independence-friendly logic which, after the present leap forward, naturally
becomes a conservative fragment of ORG, just as classical logic had been known
to be a conservative fragment of the formula-based version of CoL. PERSON,
this paper is self-contained, and can be read without any prior familiarity
with CoL.","Computability logic (see http://www.csc.villanova.edu/~japaridz/CL/) is a
long-term project for redeveloping logic on the basis of a constructive game
semantics, with games seen as abstract models of interactive computational
problems. Among the fragments of this logic successfully axiomatized so far is
CL12 --- a conservative extension of classical ORDINAL-order logic, whose
language augments that of classical logic with the so called choice sorts of
quantifiers and connectives. This system has already found fruitful
applications as a logical basis for constructive and complexity-oriented
versions of ORG arithmetic, such as arithmetics for polynomial time
computability, polynomial space computability, and beyond. The present paper
introduces a ORDINAL, indispensable complexity measure for interactive
computations termed amplitude complexity, and establishes the adequacy of CL12
with respect to A-amplitude, S-space and T-time computability under certain
minimal conditions on the triples (A,S,T) of function classes. This result very
substantially broadens the potential application areas of CL12. The paper is
self-contained, and targets readers with no prior familiarity with the subject.",1
"We study the ensemble performance of biometric authentication systems, based
on secret key generation, which work as follows. In the enrollment stage, an
individual provides a biometric signal that is mapped into a secret key and a
helper message, the former being prepared to become available to the system at
a later time (for authentication), and the latter is stored in a public
database. When an authorized user requests authentication, claiming his/her
identity as one of the subscribers, s/he has to provide a biometric signal
again, and then the system, which retrieves also the helper message of the
claimed subscriber, produces an estimate of the secret key, that is finally
compared to the secret key of the claimed user. In case of a match, the
authentication request is approved, otherwise, it is rejected.Referring to an
ensemble of systems based on NORP binning, we provide a detailed
analysis of the false-reject and false-accept probabilities, for a wide class
of stochastic decoders. We also comment on the security for the typical code in
the ensemble.","BES II data for J/Psi->K*(890)Kpi reveal a strong kappa peak in FAC-wave near threshold. Both magnitude and phase are determined in slices of PERSON
mass by interferences with strong PRODUCT), K1(1270) and K1(1400) signals. The
phase variation with mass agrees within errors with LASS data for PERSON elastic
scattering. A combined fit is presented to both ORG and LASS data. The fit uses
a ORG amplitude with an s-dependent width containing an PERSON CARDINAL.
The kappa pole is at CARDINAL-20(stat)+-40(syst) - i(420+-45+-60syst) MeV. The
S-wave I=0 scattering length ORG = CARDINALPERSON (in units of ORG)) is close to
the prediction 0.19+-0.02 of ORG.",0
"In a complete metric space that is equipped with a doubling measure and
supports a Poincar\'e inequality, we prove a new GPE-type property for the
fine topology in the case $p=1$. Then we use this property to prove the
existence of $MONEY open \emph{strict subsets} and \emph{strict
quasicoverings} of $MONEY open sets. As an application, we study fine
ORG spaces in the case MONEY, that is, ORG spaces defined
on $MONEY open sets.","In the setting of a metric space $MONEY equipped with a doubling measure that
supports a Poincar\'e inequality, we show that if $PERSON u$ strictly in
$MONEY, i.e. if $MONEY u$ in $PERSON and $PERSON
ORG, then for a subsequence (not relabeled) we have
MONEY for $\mathcal H$-almost every $PERSON S_u$.",1
"Both self-organization and organization are important for the further
development of the sciences: the CARDINAL dynamics condition and enable each other.
Commercial and public considerations can interact and ""interpenetrate"" in
historical organization; different codes of communication are then
""recombined."" However, self-organization in the symbolically generalized codes
of communication can be expected to operate at the global level. The Triple
NORP model allows for both a neo-institutional appreciation in terms of
historical networks of university-industry-government relations and a
neo-evolutionary interpretation in terms of CARDINAL functions: (i) novelty
production, (i) wealth generation, and (iii) political control. Using this
model, one can appreciate both subdynamics. The mutual information in CARDINAL
dimensions enables us to measure the trade-off between organization and
self-organization as a possible synergy. The question of optimization between
commercial and public interests in the different sciences can thus be made
empirical.","This note deals with a class of variables that, if conditioned on, tends to
amplify confounding bias in the analysis of causal effects. This class,
independently discovered by Bhattacharya and Vogt (DATE) and ORG (DATE),
includes instrumental variables and variables that have greater influence on
treatment selection than on the outcome. We offer a simple derivation and an
intuitive explanation of this phenomenon and then extend the analysis to non
linear models. We show that: CARDINAL. the bias-amplifying potential of instrumental
variables extends over to non-linear models, though not as sweepingly as in
ORG models; CARDINAL. in LOC models, conditioning on instrumental variables
may introduce new bias where none existed before; CARDINAL. in both linear and
non-linear models, instrumental variables have no effect on selection-induced
bias.",0
"It is proved that spherically symmetric compact reflecting objects cannot
support static bound-state configurations made of scalar fields whose
self-interaction potential $PERSON is a monotonically increasing function
of its argument. Our theorem rules out, in particular, the existence of massive
scalar hair outside the surface of a spherically symmetric compact reflecting
star.","Can change in citation patterns among journals be used as an indicator of
structural change in the organization of the sciences? Aggregated
journal-journal citations for DATE are compared with similar data in the
ORG Citation Reports DATE of the Science Citation Index. In addition to
indicating local change, probabilistic entropy measures enable us to analyze
changes in distributions at different levels of aggregation. The results of
various statistics are discussed and compared by elaborating ORG mappings. The relevance of this indicator for science and
technology policies is further specified.",0
"In this paper we critically analyze the so far performed and proposed tests
for measuring the general relativistic PERSON effect in the
gravitational field of the LOC with some of the existing accurately tracked
artificial satellites. The impact of the ORDINAL generation GRACE-only
ORG-GRACE02S LOC gravity model and of DATE CHAMP+GRACE+terrestrial
gravity combined ORG-CG01C LOC gravity model is discussed. The role of the
proposed PERSON is discussed as well.","This paper reviews the DATE proof that the spectral gap of NORP
quantum systems capable of universal computation is uncomputable.",0
"We discuss quark-antiquark leptoproduction within a ORG CARDINAL-gluon exchange
model at small $x$. The double spin asymmetries for longitudinally polarized
leptons and transversely polarized protons in diffractive $Q \bar Q$ production
are analysed at eRHIC energies. The predicted $A_{lT}$ asymmetry is large and
can be used to obtain information on the polarized generalized gluon
distributions in the proton.","We analyze light meson electroproduction within the handbag model, where the
amplitude factorizes into ORG (GPDs) and a hard
scattering part. The cross sections and spin asymmetries for various vector and
pseudoscalar mesons are analyzed. We discuss what information on hadron
structure can be obtained from GPDs.",1
"Cosmological limits on PERSON invariance breaking in ORG
$(CARDINAL+1)-dimensional$ electrodynamics are used to place limits on torsion.
PERSON phenomena is discussed by using extending the propagation equation
to ORG spacetimes instead of treating it in purely NORP
spaces. The parameter of PERSON violation is shown to be proportional to the
axial torsion vector which allows us to place a limit on cosmological
background torsion from the PERSON violation constraint which is given by PERCENTDATE} eV <|S^{\mu}| < 10^{-32} eV$ where $|S^{\mu}|$ is the axial torsion
vector.","PERSON models used in physics and other areas of mathematics applications
become discrete when they are computerized, e.g., utilized for computations.
Besides, computers are controlling processes in discrete spaces, such as films
and television programs. At the same time, continuous models that are in the
background of discrete representations use mathematical technology developed
for continuous media. The most important example of such a technology is
calculus, which is so useful in physics and other sciences. The main goal of
this paper is to synthesize continuous features and powerful technology of the
classical calculus with the discrete approach of numerical mathematics and
computational physics. To do this, we further develop the theory of fuzzy
continuous functions and apply this theory to functions defined on discrete
sets. The main interest is the classical PERSON theorem. Although
the result of this theorem is completely based on continuity, utilization of a
relaxed version of continuity called fuzzy continuity, allows us to prove
discrete versions of ORG theorem. This result provides
foundations for a new approach to discrete dynamics.",0
"Fuzzy answer set programming is a declarative framework for representing and
reasoning about knowledge in fuzzy environments. However, the unavailability of
fuzzy aggregates in disjunctive fuzzy logic programs, ORG, with fuzzy answer
set semantics prohibits the natural and concise representation of many
interesting problems. In this paper, we extend ORG to allow arbitrary fuzzy
aggregates. We define fuzzy answer set semantics for ORG with arbitrary fuzzy
aggregates including monotone, antimonotone, and nonmonotone fuzzy aggregates.
We show that the proposed fuzzy answer set semantics subsumes both the original
fuzzy answer set semantics of ORG and the classical answer set semantics of
classical disjunctive logic programs with classical aggregates, and
consequently subsumes the classical answer set semantics of classical
disjunctive logic programs. We show that the proposed fuzzy answer sets of ORG
with fuzzy aggregates are minimal fuzzy models and hence incomparable, which is
an important property for nonmonotonic fuzzy reasoning.","This paper shows that, even at the most basic level, the parallel, countable
branching and uncountable branching recurrences of ORG (see
ORG) validate different principles.",0
"ORG (ORG) is a descriptive category metatheory
currently under development, which is being offered as the structural aspect of
ORG (SUO). The architecture of the ORG is composed of
metalevels, namespaces and meta-ontologies. The main application of the ORG is
institutional: the notion of institutions and their morphisms are being
axiomatized in the upper metalevels of the ORG, and the lower metalevel of the
ORG has axiomatized various institutions in which semantic integration has a
natural expression as the colimit of theories.","The theory introduced, presented and developed in this paper, is concerned
with ORG. This theory is a synthesis of the theory of ORG pioneered by PERSON with the theory of ORG
pioneered by PERSON. The central notion in this paper of a rough formal
concept combines in a natural fashion the notion of a rough set with the notion
of a formal concept: ""rough set + formal concept = rough formal concept"". A
follow-up paper will provide a synthesis of the CARDINAL important data modeling
techniques: conceptual scaling of ORG and
Entity-Relationship database modeling.",1
"Psychological and social systems provide us with a natural domain for the
study of anticipations because these systems are based on and operate in terms
of intentionality. Psychological systems can be expected to contain a model of
themselves and their environments social systems can be strongly anticipatory
and therefore co-construct their environments, for example, in techno-economic
(co-)evolutions. Using ORG's hyper-incursive and incursive formulations of
the logistic equation, these two types of systems and their couplings can be
simulated. In addition to their structural coupling, psychological and social
systems are also coupled by providing meaning reflexively to each other's
meaning-processing. PERSON's distinctions among (CARDINAL) interactions between
intentions at the micro-level, (CARDINAL) organization at the meso-level, and (CARDINAL)
self-organization of the fluxes of meaningful communication at the global level
can be modeled and simulated using CARDINAL hyper-incursive equations. The global
level of self-organizing interactions among fluxes of communication is retained
at the meso-level of organization. In a knowledge-based economy, these CARDINAL
levels of anticipatory structuration can be expected to propel each other at
the supra-individual level.","Positional and relational perspectives on network data have led to CARDINAL
different research traditions in textual analysis and social network analysis,
respectively. ORG (ORG) focuses on the latent dimensions
in textual data; social network analysis (ORG) on the observable networks. The
CARDINAL coupled topographies of information-processing in the network space and
meaning-processing in the vector space operate with different (nonlinear)
dynamics. The historical dynamics of information processing in observable
networks organizes the system into instantiations; the systems dynamics,
however, can be considered as self-organizing in terms of fluxes of
communication along the various dimensions that operate with different codes.
The development over time adds evolutionary differentiation to the historical
integration; a richer structure can process more complexity.",1
"In this paper, a mathematical schema theory is developed. This theory has
CARDINAL roots: brain theory schemas, grid automata, and block-shemas. In Section
CARDINAL of this paper, elements of the theory of grid automata necessary for the
mathematical schema theory are presented. In LAW, elements of brain
theory necessary for the mathematical schema theory are presented. In Section
CARDINAL, other types of schemas are considered. In LAW, the mathematical schema
theory is developed. The achieved level of schema representation allows one to
model by mathematical tools virtually any type of schemas considered before,
including schemas in neurophisiology, psychology, computer science, Internet
technology, databases, logic, and mathematics.","People solve different problems and know that some of them are simple, some
are complex and some insoluble. The main goal of this work is to develop a
mathematical theory of algorithmic complexity for problems. This theory is
aimed at determination of computer abilities in solving different problems and
estimation of resources that computers need to do this. Here we build the part
of this theory related to static measures of algorithms. At ORDINAL, we consider
problems for finite words and study algorithmic complexity of such problems,
building optimal complexity measures. Then we consider problems for such
infinite objects as functions and study algorithmic complexity of these
problems, also building optimal complexity measures. In the ORDINAL part of the
work, complexity of algorithmic problems, such as the halting problem for
Turing machines, is measured by the classes of automata that are necessary to
solve this problem. To classify different problems with respect to their
complexity, inductive Turing machines, which extend possibilities of Turing
machines, are used. A hierarchy of inductive Turing machines generates an
inductive hierarchy of algorithmic problems. Here we specifically consider
algorithmic problems related to Turing machines and inductive Turing machines,
and find a place for these problems in the inductive hierarchy of algorithmic
problems.",1
"We draw a certain analogy between the classical information-theoretic problem
of lossy data compression (source coding) of memoryless information sources and
the statistical mechanical behavior of a certain model of a chain of connected
particles (e.g., a polymer) that is subjected to a contracting force. The free
energy difference pertaining to such a contraction turns out to be proportional
to the rate-distortion function in the analogous data compression model, and
the contracting force is proportional to the derivative this function. Beyond
the fact that this analogy may be interesting on its own right, it may provide
a physical perspective on the behavior of optimum schemes for lossy data
compression (and perhaps also, an information-theoretic perspective on certain
physical system models). Moreover, it triggers the derivation of lossy
compression performance for systems with memory, using analysis tools and
insights from statistical mechanics.","Biometric authentication systems, based on secret key generation, work as
follows. In the enrollment stage, an individual provides a biometric signal
that is mapped into a secret key and a helper message, the former being
prepared to become available to the system at a later time (for
authentication), and the latter is stored in a public database. When an
authorized user requests authentication, claiming his/her identity as one of
the subscribers, he/she has to provide a biometric signal again, and then the
system, which retrieves also the helper message of the claimed subscriber,
produces an estimate of the secret key, that is finally compared to the secret
key of the claimed user. In case of a match, the authentication request is
approved, otherwise, it is rejected.
  Evidently, there is an inherent tension between CARDINAL desired, but conflicting,
properties of the helper message encoder: on the one hand, the encoding should
be informative enough concerning the identity of the real subscriber, in order
to approve him/her in the authentication stage, but on the other hand, it
should not be too informative, as otherwise, unauthorized imposters could
easily fool the system and gain access. A good encoder should then trade off
the CARDINAL kinds of errors: the false reject (FR) error and the false accept (FA)
error.
  In this work, we investigate trade-offs between the random coding FR error
exponent and the best achievable FA error exponent. We compare CARDINAL types of
ensembles of codes: fixed-rate codes and variable-rate codes, and we show that
the latter class of codes offers considerable improvement compared to the
former. In doing this, we characterize the optimal rate functions for both
types of codes. We also examine privacy leakage constraints for both fixed-rate
codes and variable-rate codes.",1
"Here is discussed application of the Weyl pair to construction of universal
set of ORG gates for high-dimensional quantum system. An application of Lie
algebras (NORP) for construction of universal gates is revisited ORDINAL.
It is shown next, how for quantum computation with qubits can be used
CARDINAL-dimensional analog of this GPE-Weyl matrix algebras, i.e. PERSON
algebras, and discussed well known applications to product operator formalism
in ORG, ORG construction in fermionic quantum computations. It is
introduced universal set of ORG gates for higher dimensional system
(``qudit''), as some generalization of these models. Finally it is briefly
mentioned possible application of such algebraic methods to design of quantum
processors (programmable gates arrays) and discussed generalization to quantum
computation with continuous variables.","This note reviews prospects for ORG computing. It argues that gates need
to be tested for a wide range of probability amplitudes.",0
"We prove the existence of a family of initial data for the Einstein vacuum
equation which can be interpreted as the data for CARDINAL ORG-like black holes in
arbitrary location and with spin in arbitrary direction. This family of initial
data has the following properties: (i) When the mass parameter of CARDINAL of them
is CARDINAL or when the distance between them goes to infinity, it reduces exactly
to the ORG initial data. (ii) When the distance between them is CARDINAL, we
obtain exactly a ORG initial data with mass and angular momentum equal to the
sum of the mass and angular momentum parameters of each of them. The initial
data depends smoothly on the distance, the mass and the angular momentum
parameters.","The assumptions needed to prove Cox's Theorem are discussed and examined.
Various sets of assumptions under which a Cox-style theorem can be proved are
provided, although all are rather strong and, arguably, not natural.",0
"Unsupervised deep learning is one of the most powerful representation
learning techniques. ORG Boltzman machine, sparse coding, regularized
auto-encoders, and convolutional neural networks are pioneering building blocks
of deep learning. In this paper, we propose a new building block -- distributed
random models. The proposed method is a special full implementation of the
product of experts: (i) each expert owns multiple hidden units and different
experts have different numbers of hidden units; (ii) the model of each expert
is a k-center clustering, whose k-centers are only uniformly sampled examples,
and whose output (i.e. the hidden units) is a sparse code that only the
similarity values from a few nearest neighbors are reserved. The relationship
between the pioneering building blocks, several notable research branches and
the proposed method is analyzed. Experimental results show that the proposed
deep model can learn better representations than deep belief networks and
meanwhile can train a much larger network with much less time than deep belief
networks.","Recently, multilayer bootstrap network (ORG) has demonstrated promising
performance in unsupervised dimensionality reduction. It can learn compact
representations in standard data sets, i.e. MNIST and RCV1. However, as a
bootstrap method, the prediction complexity of ORG is high. In this paper, we
propose an unsupervised model compression framework for this general problem of
unsupervised bootstrap methods. The framework compresses a large unsupervised
bootstrap model into a small model by taking the bootstrap model and its
application together as a black box and learning a mapping function from the
input of the bootstrap model to the output of the application by a supervised
learner. To specialize the framework, we propose a new technique, named
compressive ORG. It takes ORG as the unsupervised bootstrap model and deep
neural network (DNN) as the supervised learner. Our initial result on MNIST
showed that compressive ORG not only maintains the high prediction accuracy of
ORG but also is CARDINAL of times faster than ORG at the prediction
stage. Our result suggests that the new technique integrates the effectiveness
of ORG on unsupervised learning and the effectiveness and efficiency of DNN on
supervised learning together for the effectiveness and efficiency of
compressive ORG on unsupervised learning.",1
"PERSON (lightweight internet-based communication for autonomic services) is a
distributed framework for building service-based systems. The framework
provides a p2p server and more intelligent processing of information through
its ORG algorithms. Distributed communication includes ORG-RPC, ORG, ORG and
Web Services. It can now provide a robust platform for building different types
of system, where Microservices or ORG would be possible. However, the system
may be equally suited for the IoT, as it provides classes to connect with
external sources and has an optional NORP Manager with a MAPE control loop
integrated into the communication process. The system is also mobile-compatible
with ORG. This paper focuses in particular on the autonomic setup and how
that might be used. A novel linking mechanism has been described previously and
is considered again, as part of the autonomous framework.","We propose a ORG measure for quantum channels in a straightforward
analogy to the corresponding mixed-state fidelity of PERSON. We describe
properties of this ORG measure and discuss some applications of it to
quantum information science.",0
"Classical simulation is important because it sets a benchmark for quantum
computer performance. Classical simulation is currently the only way to
exercise larger numbers of qubits. To achieve larger simulations, sparse matrix
processing is emphasized below while trading memory for processing. It
performed well within ORG supercomputers, giving a state vector in convenient
continuous portions ready for post processing.","ORG computer versus ORG algorithm processor in ORG are compared to
find (in parallel) all NORP cycles in a graph with m edges and n
vertices, each represented by k bits. A ORG computer uses quantum states
analogous to CMOS registers. With efficient initialization, number of ORG
registers is proportional to (n-1)! Number of qubits in a ORG computer is
approximately proportional to ORG in the approach below. Using ORG, the
bits per register is about proportional to kn, which is less since bits can be
irreversibly reset. In either concept, number of gates, or operations to
identify NORP cycles is proportional to kmn. However, a ORG computer
needs an additional exponentially large number of operations to accomplish a
probabilistic readout. In contrast, ORG is deterministic and readout is
comparable to ordinary memory.",1
"ORG of university-industry-government relations is elaborated
into a systemic model that accounts for interactions among CARDINAL dimensions. By
distinguishing between the respective micro-operations, this model enables us
to recombine the ""Mode CARDINAL"" thesis of a new production of scientific knowledge
and the study of systems of innovation with the neo-classical perspective on
the dynamics of the market. The mutual information in CARDINAL dimensions provides
us with an indicator for the self-organization of the resulting network
systems. The probabilistic entropy in this mutual information can be negative
in knowledge-based configurations. The knowledge base of an economy can be
considered as a ORDINAL-order interaction effect among interactions at
interfaces between institutions and functions in different spheres. Proximity
enhances the chances for couplings and, therefore, the formation of
technological trajectories. The next-order regime of the knowledge base,
however, can be expected to remain pending as selection pressure.","Via the Internet, information scientists can obtain cost-free access to large
databases in the hidden or deep web. These databases are often structured far
more than the Internet domains themselves. The patent database of the GPE
ORG is used in this study to examine the science base of
patents in terms of the literature references in these patents.
ORG-based patents at the global level are compared with results when
using the national economy of the GPE as a system of reference. Methods
for accessing the on-line databases and for the visualization of the results
are specified. The conclusion is that 'biotechnology' has historically
generated a model for theorizing about university-industry relations that
cannot easily be generalized to other sectors and disciplines.",1
"The min-max kernel is a generalization of the popular resemblance kernel
(which is designed for binary data). In this paper, we demonstrate, through an
extensive classification study using kernel machines, that the min-max kernel
often provides an effective measure of similarity for nonnegative data. As the
min-max kernel is nonlinear and might be difficult to be used for industrial
applications with massive data, we show that the min-max kernel can be
linearized via hashing techniques. This allows practitioners to apply min-max
kernel to large-scale applications using well matured ORG algorithms such as
linear ORG or logistic regression.
  The previous remarkable work on consistent weighted sampling (ORG) produces
samples in the form of ($i^*, t^*$) where the $i^*$ records the location (and
in fact also the weights) information analogous to the samples produced by
classical minwise hashing on binary data. Because the $t^*$ is theoretically
unbounded, it was not immediately clear how to effectively implement ORG for
building large-scale ORG classifiers. In this paper, we provide a simple
solution by discarding $t^*$ (which we refer to as the ""0-bit"" scheme). Via an
extensive empirical study, we show that this 0-bit scheme does not lose
essential information. We then apply the ""0-bit"" WORK_OF_ART
classifiers to approximate PERSON classifiers, as extensively validated
on a wide range of publicly available classification datasets. We expect this
work will generate interests among data mining practitioners who would like to
efficiently utilize the nonlinear information of non-binary and nonnegative
data.","This article addresses the question of when physical laws and their
consequences can be computed. If a physical system is capable of universal
computation, then its energy gap can't be computed. At an even more fundamental
level, the most concise, simply applicable formulation of the underlying laws
of physics is uncomputable. That is, physicists are in the same boat as
mathematicians: many quantities of interest can be computed, but not all.",0
"On the basis of an analysis of previous research, we present a generalized
approach for measuring the difference of plans with an exemplary application to
machine scheduling. Our work is motivated by the need for such measures, which
are used in dynamic scheduling and planning situations. In this context,
quantitative approaches are needed for the assessment of the robustness and
stability of schedules. Obviously, any `robustness' or `stability' of plans has
to be defined PERSON the particular situation and the requirements of the
human decision maker. Besides the proposition of an instability measure, we
therefore discuss possibilities of obtaining meaningful information from the
decision maker for the implementation of the introduced approach.","Previously a model of only vector fields with a local U(2) symmetry was
introduced for which one finds a massless U(1) photon and a massive SU(2)
PERSON in the lattice regularization. Here it is shown that quantization
of its classical continuum action leads to perturbative renormalization
difficulties. But, non-perturbative PERSON calculations favor the
existence of a quantum continuum limit.",0
"An analysis of light vector PERSON at small GPE $x \leq
MONEY is done on the basis of the generalized parton distributions (GPDs). Our
results on the cross section and spin density matrix elements (SDME) are in
good agreement with experiments.","The purpose of a wireless sensor network (WSN) is to provide the users with
access to the information of interest from data gathered by spatially
distributed sensors. Generally the users require only certain aggregate
functions of this distributed data. Computation of this aggregate data under
the end-to-end information flow paradigm by communicating all the relevant data
to a central collector PERSON is a highly inefficient solution for this purpose.
An alternative proposition is to perform in-network computation. This, however,
raises questions such as: what is the optimal way to compute an aggregate
function from a set of statistically correlated values stored in different
nodes; what is the security of such aggregation as the results sent by a
compromised or faulty node in the network can adversely affect the accuracy of
the computed result. In this paper, we have presented an energy-efficient
aggregation algorithm for WSNs that is secure and robust against malicious
insider attack by any compromised or faulty node in the network. In contrast to
the traditional snapshot aggregation approach in WSNs, a node in the proposed
algorithm instead of unicasting its sensed information to its parent node,
broadcasts its estimate to all its neighbors. This makes the system more
fault-tolerant and increase the information availability in the network. The
simulations conducted on the proposed algorithm have produced results that
demonstrate its effectiveness.",0
"Data analysis and data mining are concerned with unsupervised pattern finding
and structure determination in data sets. The data sets themselves are
explicitly linked as a form of representation to an observational or otherwise
empirical domain of interest. ""Structure"" has long been understood as symmetry
which can take many forms with respect to any transformation, including point,
translational, rotational, and many others. Beginning with the role of number
theory in expressing data, we show how we can naturally proceed to hierarchical
structures. We show how this both encapsulates traditional paradigms in data
analysis, and also opens up new perspectives towards issues that are on the
order of DATE, including data mining of massive, high dimensional,
heterogeneous data sets. Linkages with other fields are also discussed
including computational logic and symbolic dynamics. The structures in data
surveyed here are based on hierarchy, represented as p-adic numbers or an
ultrametric topology.","We consider a large number of text data sets. These are cooking recipes. Term
distribution and other distributional properties of the data are investigated.
Our aim is to look at various analytical approaches which allow for mining of
information on both high and low detail scales. Metric space embedding is
fundamental to our interest in the semantic properties of this data. We
consider the projection of all data into analyses of aggregated versions of the
data. We contrast that with projection of aggregated versions of the data into
analyses of all the data. Analogously for the term set, we look at analysis of
selected terms. We also look at inherent term associations such as between
singular and plural. In addition to our use of ORG in R,
for latent semantic space mapping, we also use PRODUCT. Setting up the PERSON
server and carrying out querying is described. A further novelty is that
querying is supported in PERSON based on the principal factor plane mapping of
all the data. This uses a bounding box query, based on factor projections.",1
"The aim of this paper is twofold: ORDINAL, to extend the area of applications
of tropical optimization by solving new constrained location problems, and
ORDINAL, to offer new closed-form solutions to general problems that are of
interest to location analysis. We consider a constrained minimax
single-facility location problem with addends on the plane with rectilinear
distance. The solution commences with the representation of the problem in a
standard form, and then in terms of tropical mathematics, as a constrained
optimization problem. We use a transformation technique, which can act as a
template to handle optimization problems in other application areas, and hence
is of independent interest. To solve the constrained optimization problem, we
apply methods and results of tropical optimization, which provide direct,
explicit solutions. The results obtained serve to derive new solutions of the
location problem, and of its special cases with reduced sets of constraints, in
a closed form, ready for practical implementation and immediate computation. As
illustrations, numerical solutions of example problems and their graphical
representation are given. We conclude with an application of the results to
optimal location of the central monitoring facility in an indoor video
surveillance system in a multi-floor building environment.","Configurational information is generated when CARDINAL or more sources of
variance interact. The variations not only disturb each other relationally, but
by selecting upon each other, they are also positioned in a configuration. A
configuration can be stabilized and/or globalized. Different stabilizations can
be considered as ORDINAL-order variation, and globalization as a ORDINAL-order
selection. The positive manifestations and the negative selections operate upon
one another by adding and reducing uncertainty, respectively. Reduction of
uncertainty in a configuration can be measured in bits of information. The
variables can also be considered as dimensions of the probabilistic entropy in
the system(s) under study. The configurational information then provides us
with a measure of synergy within a complex system. For example, the knowledge
base of an economy can be considered as such a synergy in the otherwise virtual
(that is, ORDINAL) dimension of a regime.",0
"Similarly to the modelling of entanglement in the algebra of ORG
computing, we also model entanglement as a synchronization among an event and
its shadows in reversible ORG computing. We give the semantics and axioms
of shadow constant for reversible ORG computing.","We provide here a proof theoretic account of constraint programming that
attempts to capture the essential ingredients of this programming style. We
exemplify it by presenting proof rules for ORG constraints over interval
domains, and illustrate their use by analyzing the constraint propagation
process for the {ORG SEND + MORE = MONEY} puzzle. We also show how this
approach allows one to build new constraint solvers.",0
"In former work, we showed that a quantum algorithm requires the number of
operations (oracle's queries) of a classical algorithm that knows in advance
PERCENT of the information that specifies the solution of the problem. We gave a
preliminary theoretical justification of this ""PERCENT rule"" and checked that the
rule holds for a variety of ORG algorithms. Now, we make explicit the
information about the solution available to the algorithm throughout the
computation. The final projection on the solution becomes acquisition of the
knowledge of the solution on the part of the algorithm. Backdating to before
running the algorithm a time-symmetric part of this projection, feeds back to
the input of the computation PERCENT of the information acquired by reading the
solution.","Military is CARDINAL of many industries that is more computer-dependent than ever
before, from soldiers with computerized weapons, and tactical wireless devices,
to commanders with advanced battle management, command and control systems.
PERSON, command and control is the process of planning, monitoring, and
commanding military personnel, weaponry equipment, and combating vehicles to
execute military missions. In fact, command and control systems are
revolutionizing as war fighting is changing into cyber, technology,
information, and unmanned warfare. As a result, a new design model that
supports scalability, reusability, maintainability, survivability, and
interoperability is needed to allow commanders, QUANTITY away from the
battlefield, to plan, monitor, evaluate, and control the war events in a
dynamic, robust, agile, and reliable manner. This paper proposes a
service-oriented architecture for weaponry and battle command and control
systems, made out of loosely-coupled and distributed web services. The proposed
architecture consists of CARDINAL elementary tiers: the client tier that
corresponds to any computing military equipment; the server tier that
corresponds to the web services that deliver the basic functionalities for the
client tier; and the middleware tier that corresponds to an enterprise service
bus that promotes interoperability between all the interconnected entities. A
command and control system was simulated and experimented and it successfully
exhibited the desired features of ORG. Future research can improve upon the
proposed architecture so much so that it supports encryption for securing the
exchange of data between the various communicating entities of the system.",0
"The direct effect of CARDINAL eventon another can be defined and measured
byholding constant all intermediate variables between the CARDINAL.Indirect effects
present conceptual andpractical difficulties (in nonlinear models), because
they cannot be isolated by holding certain variablesconstant. This paper shows
a way of defining any path-specific effectthat does not invoke blocking the
remainingpaths.This permits the assessment of a more naturaltype of direct and
indirect effects, CARDINAL thatis applicable in both linear and nonlinear models.
The paper establishesconditions under which such assessments can be estimated
consistentlyfrom experimental and nonexperimental data,and thus extends
path-analytic techniques tononlinear and nonparametric models.","This paper extends the applications of belief-networks to include the
revision of belief commitments, i.e., the categorical acceptance of a subset of
hypotheses which, together, constitute the most satisfactory explanation of the
evidence at hand. A coherent model of non-monotonic reasoning is established
and distributed algorithms for belief revision are presented. We show that, in
singly connected networks, the most satisfactory explanation can be found in
linear time by a message-passing algorithm similar to the one used in belief
updating. In multiply-connected networks, the problem may be exponentially hard
but, if the network is sparse, topological considerations can be used to render
the interpretation task tractable. In general, finding the most probable
combination of hypotheses is no more complex than computing the degree of
belief for any individual hypothesis. Applications to medical diagnosis are
illustrated.",1
"In this paper we present an unconventional image segmentation approach which
is devised to meet the requirements of image understanding and pattern
recognition tasks. Generally image understanding assumes interplay of CARDINAL
sub-processes: image information content discovery and image information
content interpretation. Despite of its widespread use, the notion of ""image
information content"" is still ill defined, intuitive, and ambiguous. Most
often, it is used in the FAC's sense, which means information content
assessment averaged over the whole signal ensemble. Humans, however,rarely
resort to such estimates. They are very effective in decomposing images into
their meaningful constituents and focusing attention to the perceptually
relevant image parts. We posit that following the latest findings in human
attention vision studies and the concepts of ORG's complexity theory an
unorthodox segmentation approach can be proposed that provides effective image
decomposition to information preserving image fragments well suited for
subsequent image interpretation. We provide some illustrative examples,
demonstrating effectiveness of this approach.","Traditionally, semantics has been seen as a feature of human language. The
advent of the information era has led to its widespread redefinition as an
information feature. Contrary to this praxis, I define semantics as a special
kind of information. Revitalizing the ideas of LOC and Carnap I have
recreated and re-established the notion of semantics as the notion of ORG. I have proposed a new definition of information (as a description,
a linguistic text, a piece of a story or a tale) and a clear segregation
CARDINAL different types of information - physical and semantic information.
I hope, I have clearly explained the (usually obscured and mysterious)
interrelations between data and physical information as well as the relation
between physical information and semantic information. Consequently, usually
indefinable notions of ""information"", ""knowledge"", ""memory"", ""learning"" and
""semantics"" have also received their suitable illumination and explanation.",1
"We consider the problem $PRODUCT=f(\nu)$ for strictly convex, closed hypersurfaces
in hyperbolic space and solve it for curvature functions $MONEY the inverses of
which are of class $(K^*)$.","We consider branes $N=I\times\so$, where $\so$ is an $MONEY dimensional
space form, not necessarily compact, in a ORG)} bulk $MONEY
CARDINAL The branes have a big crunch singularity. If a brane is an ORG space, then,
under certain conditions, there exists a smooth natural transition flow through
the singularity to a reflected brane $\hat N$, which has a big bang singularity
and which can be viewed as a brane in a reflected ORG)}
bulk $PERSON The joint branes CARDINALN\uu \hat N$ can thus be naturally
embedded in $R^2\times \so$, hence there exists a ORDINAL possibility of
defining a smooth transition from big crunch to big bang by requiring that
$N\uu\hat N$ forms a $C^\infty$-hypersurface in MONEY This last
notion of a smooth transition also applies to branes that are not ORG spaces,
allowing a wide range of possible equations of state.",1
"An interactive stochastics, evaluated by an entropy functional (EF) of a
random field and informational process' path functional (ORG), allows us
modeling the evolutionary information processes and revealing regularities of
evolution dynamics. Conventional ORG's information measure evaluates a
sequence of the process' static events for each information state and do not
reveal hidden dynamic connections between these events. The paper formulates
the mathematical forms of the information regularities, based on a minimax
variation principle (VP) for ORG, applied to the evolution's both random
microprocesses and dynamic macroprocesses. The paper shows that the ORG single
form of the mathematical law leads to the following evolutionary regularities:
-creation of the order from stochastics through the evolutionary macrodynamics,
described by a gradient of dynamic potential, evolutionary speed and the
evolutionary conditions of a fitness and diversity; -the evolutionary hierarchy
with growing information values and potential adaptation; -the adaptive
self-controls and a self-organization with a mechanism of copying to a genetic
code. This law and the regularities determine unified functional informational
mechanisms of evolution dynamics. By introducing both objective and subjective
information observers, we consider the observers' information acquisition,
interactive cognitive evolution dynamics, and neurodynamics, based on the
EF-IPF approach. An evolution improvement consists of the subjective observer s
ability to attract and encode information whose value progressively increases.
The specific properties of a common information structure of evolution
processes are identifiable for each particular object-organism by collecting a
behavioral data from these organisms.","What is information originating in observation? Until now it has no
scientifically conclusive definition. Information is memorized entropy cutting
in random observations which processing interactions. Randomness of various
interactive observations is source of entropy as uncertainty. Observation under
random CARDINAL-0 impulses probabilities reveals hidden correlation which connects
NORP probabilities increasing each posterior correlation. That sequentially
reduces relational entropy conveying probabilistic casualty with temporal
memory of correlations which interactive impulse innately cuts. Within hidden
correlation emerges reversible time space microprocess with conjugated
entangled entropy which probing impulse intentionally cuts and memorizes
information as certainty. Sequential interactive cuts integrates cutting
information in information macroprocess with irreversible time course.
NORP information binds reversible microprocess within impulse with
irreversible information macroprocess. Observer probes collect cutting
information data bits of observing frequencies impulses. Each impulse cuts
maximum of impulse minimal information performing dual PERSON principle of
converting process entropy to information through uncertain gap. Multiple
naturally encoding bits moving in macroprocess join triplet macrounits which
logically organize information networks encoding macrounits in structures
enclosing triplet code. Network time space distributed structure self renews
and cooperates information decreasing its complexity. Integrating process
entropy functional and bits information in information path integral embraces
variation minimax law which determines processes regularities. Solving problem
mathematically describes micro macro processes, network, and invariant
conditions of observer network self replication.",1
"There are versions of ""calculus"" in many settings, with various mixtures of
algebra and analysis. In these informal notes we consider a few examples that
suggest a lot of interesting questions.","DATE ORG discovered CARDINAL mathematical methods for the
purpose of extracting information about the location and shape of unknown
discontinuity embedded in a known background medium from observation data. The
methods are called the probe and enclosure methods. This paper presents their
past and recent applications to inverse obstacle scattering problems of
NORP wave.",0
"The ongoing discussion whether modern vision systems have to be viewed as
visually-enabled cognitive systems or cognitively-enabled vision systems is
groundless, because perceptual and cognitive faculties of vision are separate
components of human (and consequently, artificial) information processing
system modeling.","Pattern recognition is generally assumed as an interaction of CARDINAL inversely
directed image-processing streams: the bottom-up information details gathering
and localization (segmentation) stream, and the top-down information features
aggregation, association and interpretation (recognition) stream. Inspired by
recent evidence from biological vision research and by the insights of
ORG theory, we propose a new, just top-down evolving,
procedure of initial image segmentation. We claim that traditional top-down
cognitive reasoning, which is supposed to guide the segmentation process to its
final result, is not at all a part of the image information content evaluation.
And that initial image segmentation is certainly an unsupervised process. We
present some illustrative examples, which support our claims.",1
"Not only did Turing help found CARDINAL of the most exciting areas of modern
science (computer science), but it may be that his contribution to our
understanding of our physical reality is greater than we had hitherto supposed.
Here I explore the path that PERSON would have certainly liked to follow,
that of complexity science, which was launched in the wake of his seminal work
on computability and structure formation. In particular, I will explain how the
theory of algorithmic probability based on PERSON's universal machine can also
explain how structure emerges at the most basic level, hence reconnecting CARDINAL
of PERSON's most cherished topics: computation and pattern formation.","Models of computation operating over the real numbers and computing a larger
class of functions compared to the class of general recursive functions
invariably introduce a non-finite element of infinite information encoded in an
arbitrary non-computable number or non-recursive function. In this paper we
show that Turing universality is only possible at every Turing degree but not
over all, in that sense universality at the ORDINAL level is elegantly well
defined while universality at higher degrees is at least ambiguous. We propose
a concept of universal relativity and universal jump between levels in the
arithmetical and analytical hierarchy.",1
"The name of PERSON is common both in ORG and computer
science. Are they really CARDINAL absolutely unconnected areas? Many works devoted
to quantum computations and communications are serious argument to suggest
about existence of such a relation, but it is impossible to touch the new and
active theme in a short review. In the paper are described the structures and
models of ORG algebra and just due to their generality it is possible to use
universal description of very different areas as quantum mechanics and theory
of NORP image analysis, associative memory, neural networks, fuzzy logic.","Clifford algebras are used for definition of spinors. Because of using
spin-1/2 systems as an adequate model of quantum bit, a relation of the
algebras with quantum information science has physical reasons. But there are
simple mathematical properties of the algebras those also justifies such
applications.
  ORDINAL, any complex PERSON algebra with CARDINAL generators, Cl(2n,C), has
representation as algebra of all CARDINAL x 2^n complex matrices and so includes
unitary matrix of any quantum n-gate. An arbitrary element of whole algebra
corresponds to general form of linear complex transformation. The last property
is also useful because linear operators are not necessary should be unitary if
they used for description of restriction of some unitary operator to ORG.
  The ORDINAL advantage is simple algebraic structure of Cl(2n) that can be
expressed via tenzor product of standard ""building units"" and similar with
behavior of composite quantum systems. The compact notation with CARDINAL generators
also can be used in software for modeling of simple quantum circuits by modern
conventional computers.",1
"We study here the well-known propagation rules for NORP constraints. ORDINAL
we propose a simple notion of completeness for sets of such rules and establish
a completeness result. Then we show an equivalence in an appropriate sense
between NORP constraint propagation and unit propagation, a form of
resolution for propositional logic.
  Subsequently we characterize one set of such rules by means of the notion of
hyper-arc consistency introduced in (PERSON and PERSON DATE). Also, we clarify
the status of a similar, though different, set of rules introduced in (NORP
1989a) and more fully in (Codognet and PERSON DATE).","This is a tutorial on logic programming and PERSON appropriate for a course
on programming languages for students familiar with imperative programming.",1
"For many voting rules, it is ORG-hard to compute a successful manipulation.
However, ORG-hardness only bounds the worst-case complexity. Recent theoretical
results suggest that manipulation may often be easy in practice. We study
empirically the cost of manipulating the single transferable vote (NORP) rule.
This was one of the ORDINAL rules shown to be ORG-hard to manipulate. It also
appears to be one of the harder rules to manipulate since it involves multiple
rounds and since, unlike many other rules, it is ORG-hard for a single agent to
manipulate without weights on the votes or uncertainty about how the other
agents have voted. In almost every election in our experiments, it was easy to
compute how a single agent could manipulate the election or to prove that
manipulation by a single agent was impossible. It remains an interesting open
question if manipulation by a coalition of agents is hard to compute in
practice.","To model combinatorial decision problems involving uncertainty and
probability, we introduce stochastic constraint programming. Stochastic
constraint programs contain both decision variables (which we can set) and
stochastic variables (which follow a probability distribution). They combine
together the best features of traditional constraint satisfaction, stochastic
integer programming, and stochastic satisfiability. We give a semantics for
stochastic constraint programs, and propose a number of complete algorithms and
approximation procedures. Finally, we discuss a number of extensions of
stochastic constraint programming to relax various assumptions like the
independence between stochastic variables, and compare with other approaches
for decision making under uncertainty.",1
"We study the problem of estimating time-varying coefficients in ordinary
differential equations. Current theory only applies to the case when the
associated state variables are observed without measurement errors as presented
in \cite{chenwu08b,CARDINAL}. The difficulty arises from the quadratic
functional of observations that one needs to deal with instead of the linear
functional that appears when state variables contain no measurement errors. We
derive the asymptotic bias and variance for the previously proposed CARDINAL-step
estimators using quadratic regression functional theory.","Functional linear regression is a useful extension of simple linear
regression and has been investigated by many researchers. However, functional
variable selection problems when multiple functional observations exist, which
is the counterpart in the functional context of multiple linear regression, is
seldom studied. Here we propose a method using group smoothly clipped absolute
deviation penalty (gSCAD) which can perform regression estimation and variable
selection simultaneously. We show the method can identify the true model
consistently and discuss construction of pointwise confidence interval for the
estimated functional coefficients. Our methodology and theory is verified by
simulation studies as well as an application to spectrometrics data.",1
"PERSON (DATE) defined society as a communication system which is
structurally coupled to, but not an aggregate of, human action systems. The
communication system is then considered as self-organizing (""autopoietic""), as
are human actors. Communication systems can be studied by using FAC's
(DATE) mathematical theory of communication. The update of a network by action
at CARDINAL of the local nodes is then a well-known problem in artificial
intelligence (Pearl DATE). By combining these various theories, a general
algorithm for probabilistic structure/action contingency can be derived. The
consequences of this contingency for each system, its consequences for their
further histories, and the stabilization on each side by counterbalancing
mechanisms are discussed, in both mathematical and theoretical terms. An
empirical example is elaborated.","A concept of randomness for infinite time register machines (ITRMs) is
defined and studied. In particular, we show that for this notion of randomness,
computability from mutually random reals implies computability and that an
analogue of PERSON theorem holds. This is then applied to obtain
results on the structure of ITRM-degrees. Finally, we consider autoreducibility
for ITRMs and show that randomness implies non-autoreducibility.",0
"The standard approach to logic in the literature in philosophy and
mathematics, which has also been adopted in computer science, is to define a
language (the syntax), an appropriate class of models together with an
interpretation of formulas in the language (the semantics), a collection of
axioms and rules of inference characterizing reasoning (the proof theory), and
then relate the proof theory to the semantics via soundness and completeness
results. Here we consider an approach that is more common in the economics
literature, which works purely at the semantic, set-theoretic level. We provide
set-theoretic completeness results for a number of epistemic and conditional
logics, and contrast the expressive power of the syntactic and set-theoretic
approaches","I consider issues in distributed computation that should be of relevance to
game theory. In particular, I focus on (a) representing knowledge and
uncertainty, (b) dealing with failures, and (c) specification of mechanisms.",1
"We discuss quantum non-locality and contextuality, emphasising logical and
structural aspects. We also show how the same mathematical structures arise in
various areas of classical computation.","This paper describes a new method for classifying a dataset that partitions
elements into their categories. It has relations with neural networks but a
slightly different structure, requiring only a single pass through the
classifier to generate the weight sets. A grid-like structure is required as
part of a novel idea of converting a DATE of real values into a CARDINAL-D
structure of value bands. Each cell in any band then stores a distinct set of
weights, to represent its own importance and its relation to each output
category. During classification, all of the output weight lists can be
retrieved and summed to produce a probability for what the correct output
category is. The bands possibly work like hidden layers of neurons, but they
are variable specific, making the process orthogonal. The construction process
can be a single update process without iterations, making it potentially much
faster. It can also be compared with ORG and may be practical for partial or
competitive updating.",0
"Molecular variants of vitamin ORG, siderophores and glycans occur. To take up
variant forms, bacteria may express an array of receptors. The gut microbe
Bacteroides thetaiotaomicron has CARDINAL different receptors to take up variants
of vitamin ORG and CARDINAL receptors to take up various glycans. The design of
receptor arrays reflects key processes that shape cellular evolution.
Competition may focus each species on a subset of the available nutrient
diversity. Some gut bacteria can take up only a narrow range of carbohydrates,
whereas species such as ORG can digest many different complex
glycans. Comparison of different nutrients, habitats, and genomes provide
opportunity to test hypotheses about the breadth of receptor arrays. Another
important process concerns fluctuations in nutrient availability. Such
fluctuations enhance the value of cellular sensors, which gain information
about environmental availability and adjust receptor deployment. Bacteria often
adjust receptor expression in response to fluctuations of particular
carbohydrate food sources. Some species may adjust expression of uptake
receptors for specific siderophores. How do cells use sensor information to
control the response to fluctuations? That question about regulatory wiring
relates to problems that arise in control theory and artificial intelligence.
Control theory clarifies how to analyze environmental fluctuations in relation
to the design of sensors and response systems. Recent advances in deep learning
studies of artificial intelligence focus on the architecture of regulatory
wiring and the ways in which complex control networks represent and classify
environmental states. I emphasize the similar design problems that arise in
cellular evolution, control theory, and artificial intelligence. I connect
those broad concepts to testable hypotheses for bacterial uptake of ORG,
siderophores and glycans.","Computability logic is a formal theory of (interactive) computability in the
same sense as classical logic is a formal theory of truth. This approach was
initiated very recently in ""Introduction to computability logic"" (Annals of
PRODUCT and ORG (DATE), ORG). The present paper reintroduces
computability logic in a more compact and less technical way. It is written in
a semitutorial style with a general computer science, logic or mathematics
audience in mind. An Internet source on the subject is available at
ORG, and additional material at
http://www.csc.villanova.edu/~japaridz/CL/gsoll.html .",0
"In this article, we perform a systematic study of the mass spectrum of the
vector hidden charmed and bottomed tetraquark states using the ORG sum rules.","In this article, we construct the $ORG \gamma_\mu C$ and $MONEY
ORG type currents to interpolate the vector
tetraquark states, then carry out the operator product expansion up to the
vacuum condensates of dimension-10 in a consistent way, and CARDINAL ORG sum
rules. In calculations, we use the formula
$\mu=\sqrt{M^2_{Y}-(2{\mathbb{M}}_c)^2}$ to determine the optimal energy scales
of the ORG spectral densities, moreover, we take the experimental values of the
masses of the $GPE, $MONEY, $Y(4390)$ and $PERSON as
input parameters and fit the pole residues to reproduce the correlation
functions at the ORG side. The numerical results support assigning the
$PERSON to be the $C \otimes \gamma_\mu CARDINAL type vector tetraquark ORG
$c\bar{c}s\bar{s}$, assigning the $Y(4360/4320)$ to be $MONEY \otimes
\gamma_5\gamma_\mu C$ type vector tetraquark state $PERSON, and
disfavor assigning the $GPE and $PERSON to be the pure vector
tetraquark states.",1
"This paper considers an inverse problem for the classical wave equation in an
exterior domain. It is a mathematical interpretation of an inverse obstacle
problem which employs the dynamical scattering data of NORP wave over a
finite time interval. It is assumed that the wave satisfies a PERSON type
boundary condition with an unknown variable coefficient.
  The wave is generated by the initial data localized outside the obstacle and
observed over a finite time interval at the same place as the support of the
initial data. It is already known that, using the enclosure method, one can
extract the maximum sphere whose exterior encloses the obstacle, from the data.
  In this paper, it is shown that the enclosure method enables us to extract
also:
  (i) a quantity which indicates the deviation of the geometry between the
maximum sphere and the boundary of the obstacle at the ORDINAL reflection points
of the wave;
  (ii) the value of the coefficient of the boundary condition at an arbitrary
ORDINAL reflection point of the wave provided, for example, the surface of the
obstacle is known in a neighbourhood of the point.
  Another new obtained knowledge is that: the enclosure method can cover the
case when the data are taken over a sphere whose centre coincides with that of
the support of an initial data and yields corresponding results to (i) and
(ii).","A mathematical method for through-wall imaging via wave phenomena in the time
domain is introduced. The method makes use of a single reflected wave over a
finite time interval and gives us a criterion whether a penetrable obstacle
exists or not in a general rough background medium. Moreover, if the obstacle
exists, the lower and upper estimates of the distance between the obstacle and
the center point of the support of the initial data are given. As an evidence
of the potential of the method CARDINAL applications are also given.",1
"We propose that operator induction serves as an adequate model of perception.
We explain how to reduce universal agent models to operator induction. We
propose a universal measure of operator induction fitness, and show how it can
be used in a reinforcement learning model and a homeostasis (self-preserving)
agent based on the free energy principle. We show that the action of the
homeostasis agent can be explained by the operator induction model.","The advantages of mixed approach with using different kinds of programming
techniques for symbolic manipulation are discussed. The main purpose of
approach offered is merge the methods of object oriented programming that
convenient for presentation data and algorithms for user with advantages of
functional languages for data manipulation, internal presentation, and
portability of software.",0
"We show that several constraint propagation algorithms (also called (local)
consistency, consistency enforcing, PERSON, filtering or narrowing algorithms)
are instances of algorithms that deal with chaotic iteration. To this end we
propose a simple abstract framework that allows us to classify and compare
these algorithms and to establish in a uniform way their basic properties.","The covariance graph (PERSON graph) of a probability distribution
$p$ is the undirected graph $MONEY where CARDINAL nodes are adjacent iff their
corresponding random variables are marginally dependent in $p$. In this paper,
we present a graphical criterion for reading dependencies from $MONEY, under the
assumption that $p$ satisfies the graphoid properties as well as weak
transitivity and composition. We prove that the graphical criterion is sound
and complete in certain sense. We argue that our assumptions are not too
restrictive. For instance, all the regular NORP probability distributions
satisfy them.",0
"Urban mobility systems are composed multiple elements with strong
interactions, i.e. their future is co-determined by the state of other
elements. Thus, studying components in isolation, i.e. using a reductionist
approach, is inappropriate. I propose CARDINAL recommendations to improve urban
mobility based on insights from the scientific study of complex systems: use
adaptation over prediction, regulate interactions to avoid friction, use
sensors to recover real time information, develop adaptive algorithms to
exploit that information, and deploy agents to act on the urban environment.","It is found that in the SM the Ward-Takahashi(WT) identities of the
axial-vector currents and the charged vector currents of fermions are invalid
after spontaneous symmetry breaking. The spin-0 components of ORG and PERSON fields
are revealed from the invalidity of these GPE identities. The masses of these
spin-0 components are at $10^{14}$GeV. They are ghosts. Therefore, unitarity of
the ORG after spontaneous symmetry breaking is broken at $MONEY",0
"This chapter presents a theoretical framework for evaluating next generation
search engines. We focus on search engines whose results presentation is
enriched with additional information and does not merely present the usual list
of CARDINAL blue links, that is, of CARDINAL links to results, accompanied by a short
description. While Web search is used as an example here, the framework can
easily be applied to search engines in any other area. The framework not only
addresses the results presentation, but also takes into account an extension of
the general design of retrieval effectiveness tests. The chapter examines the
ways in which this design might influence the results of such studies and how a
reliable test is best designed.","Given a compatible vector field on a compact connected almost-complex
manifold, we show in this article that the multiplicities of eigenvalues among
the CARDINAL point set of this vector field have intimate relations. We highlight a
special case of our result and reinterpret it as a vanishing-type result in the
framework of the celebrated ORG localization formula. This new
point of view, via the Chern-Weil theory and a strengthened version of PERSON's
residue formula observed by ORG, can lead to an obstruction to
Killing real holomorphic vector fields on compact NORP manifolds in terms
of a curvature integral.",0
"We give formulae that yield an information about the location of an unknown
polygonal inclusion having unknown constant conductivity inside a known
conductive material having known constant conductivity from a partial knowledge
of the Neumann -to-Dirichlet operator.","This encyclopedic article gives a mini-introduction into the theory of
universal learning, founded by PERSON in DATE and significantly
developed and extended in DATE. It explains the spirit of universal
learning, but necessarily glosses over technical subtleties.",0
"This article is a brief personal account of the past, present, and future of
algorithmic randomness, emphasizing its role in inductive inference and
artificial intelligence. It is written for a general audience interested in
science and philosophy. Intuitively, randomness is a lack of order or
predictability. If randomness is the opposite of determinism, then algorithmic
randomness is the opposite of computability. Besides many other things, these
concepts have been used to quantify ORG's razor, solve the induction
problem, and define intelligence.","In this paper, for an even dimensional compact manifold with boundary which
has the non-product metric near the boundary, we use the noncommutative residue
to define a conformal invariant pair. For a CARDINAL-dimensional manifold, we compute
this conformal invariant pair under some conditions and point out the way of
computations in the general.",0
"Machine learning often needs to model density from a multidimensional data
sample, including correlations between coordinates. Additionally, we often have
missing data case: that data points can miss values for some of coordinates.
This article adapts rapid parametric density estimation approach for this
purpose: modelling density as a linear combination of orthonormal functions,
for which MONEY optimization says that (independently) estimated coefficient
for a given function is just average over the sample of value of this function.
Hierarchical correlation reconstruction ORDINAL models probability density for
each separate coordinate using all its appearances in data sample, then adds
corrections from independently modelled pairwise correlations using all samples
having both coordinates, and so on independently adding correlations for
growing numbers of variables using often decreasing evidence in data sample. A
basic application of such modelled multidimensional density can be imputation
of missing coordinates: by inserting known coordinates to the density, and
taking expected values for the missing coordinates, or even their entire joint
probability distribution. Presented method can be compared with cascade
correlations approach, offering several advantages in flexibility and accuracy.
It can be also used as artificial neuron: maximizing prediction capabilities
for only local behavior - modelling and predicting local connections.","We provide a simple physical interpretation, in the context of the ORDINAL law
of thermodynamics, to the information inequality (a.k.a. the GPE' inequality,
which is also equivalent to the log-sum inequality), asserting that the
relative entropy between CARDINAL probability distributions cannot be negative.
Since this inequality stands at the basis of the data processing theorem (ORG),
and the ORG in turn is at the heart of most, if not all, proofs of converse
theorems in FAC theory, it is observed that conceptually, the roots of
fundamental limits of ORG can actually be attributed to the laws
of physics, in particular, to the ORDINAL law of thermodynamics, and at least
indirectly, also to the law of energy conservation. By the same token, in the
other direction: one can view the ORDINAL law as stemming from
information-theoretic principles.",0
"A nonlinear model with response variable missing at random is studied. In
order to improve the coverage accuracy, the empirical likelihood ratio (ORG)
method is considered. The asymptotic distribution of EL statistic and also of
its approximation is MONEY if the parameters are estimated using least
squares(LS) or least absolute deviation(LAD) method on complete data. When the
response are reconstituted using a semiparametric method, the empirical
log-likelihood associated on imputed data is also asymptotically $MONEY The
PERSON's theorem for ORG for parameter on response variable is also satisfied. It
is shown via PERSON simulations that the ORG methods outperform the normal
approximation based method in terms of coverage probability up to and including
on the reconstituted data. The advantages of the proposed method are
exemplified on the real data.","ORG black holes in NORP effective spacetime of moving vortical
plasmas described by moving magnetohydrodynamic (ORG) flows. This example is an
extension of acoustic torsion recently introduced in the literature (PERSON,PRD(2004),7,64004), where now the presence of artificial black holes in
moving plasmas is obtained by the presence of an horizon in the NORP
spacetime. Hawking radiation is computed in terms of the background magnetic
field and the magnetic permeability. The metric is singular although GPE
analogue torsion is not necessarily singular. The effective PERSON invariance
is shown to be broken due to the presence of effective torsion in strong
analogy with the ORG-GPE gravitational case presented recently by
PERSON (PRD 69,2004,105009).",0
"Data for phi -> gamma (eta-pizero) are analysed using the ORG loop model and
compared with parameters of GPE) derived from ORG data. The
eta-pi mass spectrum agrees closely and the absolute normalisation lies just
within errors. However, ORG parameters for fo(980) predict a normalisation for
phi -> gamma (pizero-pizero) at least a factor CARDINAL lower than is observed. This
discrepancy may be eliminated by including constructive interference between
fo(980) and sigma. The magnitude required for sigma -> ORG is consistent with
data on pi-pi -> ORG. A dispersion relation analysis by ORG
and PERSON ORG leads to a similar conclusion. Data on pi-pi ->
eta-eta also require decays of sigma to eta-eta. CARDINAL sets of pi-pi -> ORG data
all require a small but definite fo(1370) signal.","Both sigma and kappa are well established from PRODUCT data on DATE and
Ds->Kpipi and ORG data on J/Psi -> omega pi pi and PERSON. These fits are
accurately consistent with pipi and PERSON elastic scattering when CARDINAL allows for
the PERSON CARDINAL which arises from ORG. The phase variation
with mass is consistent between elastic scattering and production data.
Possible interpretations of sigma, kappa, fo(980) and ao(980) are explored. The
experimental ratio g^2(fo(980)->KK)/g^2(ao(980)->KK) = CARDINAL+-0.5 suggests
strongly that fo(980) has a large ORG component in its wave function. This is a
natural consequence of its pole lying very close to the ORG threshold.",1
"Sequential decision theory formally solves the problem of rational agents in
uncertain worlds if the true environmental prior probability distribution is
known. PERSON's theory of universal induction formally solves the problem
of sequence prediction for unknown prior distribution. We combine both ideas
and get a parameter-free theory of universal ORG. We give
strong arguments that the resulting AIXI model is the most intelligent unbiased
agent possible. We outline how the AIXI model can formally solve a number of
problem classes, including sequence prediction, strategic games, function
minimization, reinforcement and supervised learning. The major drawback of the
AIXI model is that it is uncomputable. To overcome this problem, we construct a
modified algorithm AIXItl that is still effectively more intelligent than any
other time t and length l bounded agent. The computation time of AIXItl is of
the order t x 2^l. The discussion includes formal definitions of intelligence
order relations, the horizon problem and relations of the AIXI theory to other
ORG approaches.","We provide a remarkably compact proof that spherically symmetric neutral
black holes cannot support static nonminimally coupled massless scalar fields.
The theorem is based on causality restrictions imposed on the energy-momentum
tensor of the fields near the regular black-hole horizon.",0
"Is the universe computable? If so, it may be much cheaper in terms of
information requirements to compute all computable universes instead of just
ours. I apply basic concepts of NORP complexity theory to the set of
possible universes, and chat about perceived and true randomness, life,
generalization, and learning in a given universe.","We analyse notion of independence in the ORG framework by using
comparative analysis of independence in conventional and frequency probability
theories. Such an analysis is important to demonstrate that ORG's inequality
was obtained by using totally unjustified assumptions (e.g. the
ORG factorability condition). Our frequency analysis also
demonstrated that ORG arguments based on ""the
experimenter's freedom to choose settings"" to support the standard ORG
approach are neither justified by the structure of the ORG experiment.
Finally, our analysis supports the original PERSON's viewpoint that ORG
mechanics is simply not complete.",0
"When PERSON layed the foundations of theoretical computer science in
DATE, he also introduced essential concepts of the theory of ORG (AI). Although much of subsequent ORG research has focused on
heuristics, which still play a major role in many practical AI applications, in
the new millennium AI theory has finally become a full-fledged formal science,
with important optimality results for embodied agents living in unknown
environments, obtained through a combination of theory a la PERSON and
probability theory. Here we look back at important milestones of ORG history,
mention essential recent results, and speculate about what we may expect from
DATE, emphasizing the significance of the ongoing dramatic
hardware speedups, and discussing ORG-inspired, self-referential,
self-improving universal problem solvers.","I review unsupervised or self-supervised neural networks playing minimax
games in game-theoretic settings. (i) Adversarial Curiosity (ORG, DATE) is based
on CARDINAL such networks. CARDINAL network learns to probabilistically generate outputs,
the other learns to predict effects of the outputs. Each network minimizes the
objective function maximized by the other. (ii) ORG
(GANs, DATE) are an application of ORG where the effect of an output is CARDINAL
if the output is in a given set, and CARDINAL otherwise. (iii) Predictability
Minimization (PM, 1990s) models data distributions through a neural encoder
that maximizes the objective function minimized by a neural predictor of the
code components. We correct a previously published claim that PM is not based
on a minimax game.",1
"Variation of the CARDINAL-D string cosmology action with dynamical torsion and
massless dilatons lead to an expression of torsion in terms of massless
dilatons in the case of de Sitter inflation.The solution is approximated
according to the ORG data.","ORG electrodynamics in CARDINAL+1-spacetimes with torsion is investigated.
We start from the usual ORG (ORG) electrodynamics NORP and GPE
torsion is introduced in the covariant derivative and by a direct coupling of
torsion vector to the ORG field. Variation of the NORP with respect to
torsion shows that ORG field is proportional to the product of the
square of the scalar field and torsion. The electric field is proportional to
torsion vector and the magnetic flux is computed in terms of the time-component
of the CARDINAL dimensional torsion. Contrary to early massive electrodynamics in
the present model the photon mass does not depend on torsion.",1
"The paper describes multistage design of composite (modular) systems (i.e.,
design of a system trajectory). This design process consists of the following:
(i) definition of a set of time/logical points; (ii) modular design of the
system for each time/logical point (e.g., on the basis of combinatorial
synthesis as hierarchical morphological design or multiple choice problem) to
obtain several system solutions; (iii) selection of the system solution for
each time/logical point while taking into account their quality and the quality
of compatibility between neighbor selected system solutions (here,
combinatorial synthesis is used as well). Mainly, the examined time/logical
points are based on a time chain. In addition, CARDINAL complicated cases are
considered: (a) the examined logical points are based on a tree-like structure,
(b) the examined logical points are based on a digraph. Numerical examples
illustrate the approach.","Horizonless spacetimes describing highly compact exotic objects with
reflecting (instead of absorbing) surfaces have recently attracted much
attention from physicists and mathematicians as possible quantum-gravity
alternatives to canonical classical black-hole spacetimes. Interestingly, it
has recently been proved that spinning compact objects with angular momenta in
the sub-critical regime ${\bar a}\equiv ORG are characterized by an
infinite countable set of surface radii, $\{r_{\text{c}}({\bar
a};n)\}^{n=\infty}_{n=1}$, that can support asymptotically flat static
configurations made of massless scalar fields. In the present paper we study
analytically the physical properties of ultra-spinning exotic compact objects
with dimensionless angular momenta in the complementary regime ${\bar a}>1$. It
is proved that ultra-spinning reflecting compact objects with dimensionless
angular momenta in the super-critical regime
MONEY a}|^{-1}<1$ are characterized by a finite
discrete family of surface radii, $MONEY
ORG=ORG, distributed symmetrically around $r=M$, that
can support spatially regular static configurations of massless scalar fields
(here the integers $\{l,PERSON are the harmonic indices of the supported static
scalar field modes). Interestingly, the largest supporting surface radius
$MONEY a})\equiv \text{max}_n\{r_{\text{c}}({\bar
a};n)\}$ marks the onset of superradiant instabilities in the composed
ultra-spinning-exotic-compact-object-massless-scalar-field system.",0
"This paper explores the problem of ORG measurement complexity. In
computability theory, the complexity of a problem is determined by how long it
takes an effective algorithm to solve it. This complexity may be compared to
the difficulty for a hypothetical oracle machine, the output of which may be
verified by a computable function but cannot be simulated on a physical
machine. We define a ORG oracle machine for measurements as one that can
determine the state by examining a single copy. The complexity of measurement
for a realizable machine will then be respect to the number of copies of the
state that needs to be examined. A ORG oracle cannot perform simultaneous
exact measurement of conjugate variables, although approximate measurement may
be performed as circumscribed by the NORP uncertainty relations. When
considering the measurement of a variable, there might be residual uncertainty
if the number of copies of the variable is limited. Specifically, we examine
the quantum measurement complexity of linear polarization of photons that is
used in several quantum cryptography schemes and we present a relation using
information theoretic arguments. The idea of quantum measurement complexity is
likely to find uses in measurements in biological systems.","We discuss philosophical issues concerning the notion of cognition basing
ourselves in experimental results in cognitive sciences, especially in computer
simulations of cognitive systems. There have been debates on the ""proper""
approach for studying cognition, but we have realized that all approaches can
be in theory equivalent. Different approaches model different properties of
cognitive systems from different perspectives, so we can only learn from all of
them. We also integrate ideas from several perspectives for enhancing the
notion of cognition, such that it can contain other definitions of cognition as
special cases. This allows us to propose a simple classification of different
types of cognition.",0
"Statistical inference of genetic regulatory networks is essential for
understanding temporal interactions of regulatory elements inside the cells.
For inferences of large networks, identification of network structure is
typical achieved under the assumption of sparsity of the networks.
  When the number of time points in the expression experiment is not too small,
we propose to infer the parameters in the ordinary differential equations using
the techniques from functional data analysis (ORG) by regarding the observed
time course expression data as continuous-time curves. For networks with a
large number of genes, we take advantage of the sparsity of the networks by
penalizing the linear coefficients with a ORG norm. The ability of the
algorithm to infer network structure is demonstrated using the cell-cycle time
course data for PRODUCT cerevisiae.","An extension of reproducing kernel PERSON space (ORG) theory provides a new
framework for modeling functional regression models with functional responses.
The approach only presumes a general nonlinear regression structure as opposed
to previously studied ORG regression models. Generalized cross-validation
(GCV) is proposed for automatic smoothing parameter estimation. The new ORG
estimate is applied to both simulated and real data as illustrations.",1
"The aggregated citation relations among journals included in the Science
PRODUCT Index provide us with a huge matrix which can be analyzed in various
ways. Using principal component analysis or factor analysis, the factor scores
can be used as indicators of the position of the cited journals in the citing
dimensions of the database. Unrotated factor scores are exact, and the
extraction of principal components can be made stepwise since the principal
components are independent. Rotation may be needed for the designation, but in
the rotated solution a model is assumed. This assumption can be legitimated on
pragmatic or theoretical grounds. Since the resulting outcomes remain sensitive
to the assumptions in the model, an unambiguous classification is no longer
possible in this case. However, the factor-analytic solutions allow us to test
classifications against the structures contained in the database. This will be
demonstrated for the delineation of a set of biochemistry journals.","This is yet another version of the course notes in PERSON. Here we
change the universal Turing machine that is used to measure program-size
complexity so that the constants in our information-theoretic incompleteness
theorems are further reduced. This is done by inventing a more complicated
version of lisp in which the parentheses associating defined functions with
their arguments can be omitted. This is the ORDINAL and last version of my course
notes. It is not clear to me which is to be preferred, so all CARDINAL have been
made available for comment.",0
"In this paper CARDINAL presents a new fuzzy clustering algorithm based on a
dissimilarity function determined by CARDINAL parameters. This algorithm can be
considered a generalization of the ORG algorithm for fuzzy
clustering.","Shannon entropy was defined for probability distributions and then its using
was expanded to measure the uncertainty of knowledge for systems with complete
information. In this article, it is proposed to extend the using of FAC
entropy to under-defined or over-defined information systems. To be able to use
FAC entropy, the information is normalized by an affine transformation. The
construction of affine transformation is done in CARDINAL stages: CARDINAL for homothety
and another for translation. Moreover, the case of information with a certain
degree of imprecision was included in this approach. Besides, the article shows
the using of FAC entropy for some particular cases such as: neutrosophic
information both in the trivalent and bivalent case, bifuzzy information,
intuitionistic fuzzy information, imprecise fuzzy information, and fuzzy
partitions.",1
"ORG is recently modelled as an exploration/
exploitation trade-off (exr/exp) problem, where the system has to choose
between maximizing its expected rewards dealing with its current knowledge
(exploitation) and learning more about the unknown user's preferences to
improve its knowledge (exploration). This problem has been addressed by the
reinforcement learning community but they do not consider the risk level of the
current user's situation, where it may be dangerous to explore the
non-top-ranked documents the user may not desire in his/her current situation
if the risk level is high. We introduce in this paper an algorithm named
CBIR-R-greedy that considers the risk level of the user's situation to
adaptively balance between exr and exp.","Stationary, axisymmetric, vacuum, solutions of PERSON's equations are
obtained as critical points of the total mass among all axisymmetric and
$(t,\phi)$ symmetric initial data with fixed angular momentum. In this
variational principle the mass is written as a positive definite integral over
a spacelike hypersurface. It is also proved that if absolute minimum exists
then it is equal to the absolute minimum of the mass among all maximal,
axisymmetric, vacuum, initial data with fixed angular momentum. Arguments are
given to support the conjecture that this minimum exists and is the extreme
ORG initial data.",0
"Most of the non-asymptotic theoretical work in regression is carried out for
the square loss, where estimators can be obtained through closed-form
expressions. In this paper, we use and extend tools from the convex
optimization literature, namely self-concordant functions, to provide simple
extensions of theoretical results for the square loss to the logistic loss. We
apply the extension techniques to logistic regression with regularization by
the $\ell_2$-norm and regularization by the $\ell_1$-norm, showing that new
results for binary classification through logistic regression can be easily
derived from corresponding results for least-squares regression.","Set-functions appear in many areas of computer science and applied
mathematics, such as machine learning, computer vision, operations research or
electrical networks. Among these set-functions, submodular functions play an
important role, similar to convex functions on vector spaces. In this tutorial,
the theory of submodular functions is presented, in a self-contained way, with
all results shown from ORDINAL principles. A good knowledge of convex analysis is
assumed.",1
"We investigate cortical learning from the perspective of mechanism design.
ORDINAL, we show that discretizing standard models of neurons and synaptic
plasticity leads to rational agents maximizing simple scoring rules. ORDINAL,
our main result is that the scoring rules are proper, implying that neurons
faithfully encode expected utilities in their synaptic weights and encode
high-scoring outcomes in their spikes. ORDINAL, with this foundation in hand, we
propose a biologically plausible mechanism whereby neurons backpropagate
incentives which allows them to optimize their usefulness to the rest of
cortex. Finally, experiments show that networks that backpropagate incentives
can learn simple tasks.","We examine the issue of stability of probability in reasoning about complex
systems with uncertainty in structure. Normally, propositions are viewed as
probability functions on an abstract random graph where it is implicitly
assumed that the nodes of the graph have stable properties. But what if some of
the nodes change their characteristics? This is a situation that cannot be
covered by abstractions of either static or dynamic sets when these changes
take place at regular intervals. We propose the use of sets with elements that
change, and modular forms are proposed to account for CARDINAL type of such change.
An expression for the dependence of the mean on the probability of the
switching elements has been determined. The system is also analyzed from the
perspective of decision between different hypotheses. Such sets are likely to
be of use in complex system queries and in analysis of surveys.",0
"Constraint propagation algorithms form an important part of most of the
constraint programming systems. We provide here a simple, yet very general
framework that allows us to explain several constraint propagation algorithms
in a systematic way. In this framework we proceed in CARDINAL steps. ORDINAL, we
introduce a generic iteration algorithm on partial orderings and prove its
correctness in an abstract setting. Then we instantiate this algorithm with
specific partial orderings and functions to obtain specific constraint
propagation algorithms.
  In particular, using the notions commutativity and semi-commutativity, we
show that the {\tt AC-3}, {ORG PRODUCT}, {ORG DAC} and {\tt DPC} algorithms for
achieving (directional) arc consistency and (directional) path consistency are
instances of a single generic algorithm. The work reported here extends and
simplifies that of NORP \citeyear{Apt99b}.","We discuss here constraint programming (CP) by using a proof-theoretic
perspective. To this end we identify CARDINAL levels of abstraction. Each level
sheds light on the essence of CP.
  In particular, the highest level allows us to bring CP closer to the
computation as deduction paradigm. At the middle level we can explain various
constraint propagation algorithms. Finally, at the lowest level we can address
the issue of automatic generation and optimization of the constraint
propagation algorithms.",1
"The canonical anticommutation relations (ORG) for fermion systems can be
represented by finite-dimensional matrix algebra, but it is impossible for
canonical commutation relations (ORG) for bosons. After description of more
simple case with representation of ORG and (bounded) quantum computational
networks via ORG algebras in the paper are discussed ORG. For
representation of the algebra it is not enough to use ORG networks with
fixed number of qubits and it is more convenient to consider Turing machine
with essential operation of appending new cells for description of infinite
tape in finite terms --- it has straightforward generalization for quantum
case, but for ORG it is necessary to work with symmetrized version of the
quantum PRODUCT machine. The system is called here quantum abacus due to
understanding analogy with the ancient counting devices (abacus).","Current machine learning systems operate, almost exclusively, in a
statistical, or model-free mode, which entails severe theoretical limits on
their power and performance. Such systems cannot reason about interventions and
retrospection and, therefore, cannot serve as the basis for strong ORG. To
achieve human level intelligence, learning machines need the guidance of a
model of reality, similar to the ones used in causal inference tasks. To
demonstrate the essential role of such models, I will present a summary of
CARDINAL tasks which are beyond reach of current machine learning systems and
which have been accomplished using the tools of causal modeling.",0
"Suppose we allow a system to fall freely from infinity to a point near (but
not beyond) the horizon of a black hole. We note that in a sense the
information in the system is already lost to an observer at infinity. Once the
system is too close to the horizon it does not have enough energy to send its
information back because the information carrying quanta would get redshifted
to a point where they get confused with Hawking radiation. If CARDINAL attempts to
turn the infalling system around and bring it back to infinity for observation
then it will experience ORG radiation from the required acceleration. This
radiation can excite the bits in the system carrying the information, thus
reducing the fidelity of this information. We find the radius where the
information is essentially lost in this way, noting that this radius depends on
the energy gap (and coupling) of the system. We look for some universality by
using the highly degenerate BPS ground states of a quantum gravity theory
(string theory) as our information storage device. For such systems one finds
that the critical distance to the horizon set by ORG radiation is the
geometric mean of the black hole radius and the radius of the extremal hole
with ORG numbers of the ORG bound state. Overall, the results suggest that
information in gravity theories should be regarded not as a quantity contained
in a system, but in terms of how much of this information is accessible to
another observer.","The goal of this tutorial is to promote interest in the study of random
NORP networks (RBNs). These can be very interesting models, since one does
not have to assume any functionality or particular connectivity of the networks
to study their generic properties. Like this, RBNs have been used for exploring
the configurations where life could emerge. The fact that RBNs are a
generalization of cellular automata makes their research a very important
topic. The tutorial, intended for a broad audience, presents the state of the
art in RBNs, spanning over several lines of research carried out by different
groups. We focus on research done within artificial life, as we cannot exhaust
the abundant research done over DATE related to RBNs.",0
"There is a common need to search of molecular databases for compounds
resembling some shape, what suggests having similar biological activity while
searching for new drugs. The large size of the databases requires fast methods
for such initial screening, for example based on feature vectors constructed to
fulfill the requirement that similar molecules should correspond to close
vectors. EVENT (ORG) is a popular approach of this type.
It uses vectors of CARDINAL real number as 3 first moments of distances from CARDINAL
emphasized points. These coordinates might contain unnecessary correlations and
does not allow to reconstruct the approximated shape. In contrast, spherical
harmonic (ORG) decomposition uses orthogonal coordinates, suggesting their
independence and so lager informational content of the feature vector. There is
usually considered rotationally invariant ORG descriptors, what means discarding
of some essential information.
  This article discusses framework for descriptors with normalized rotation,
for example by using principal component analysis (ORG). As CARDINAL of the most
interesting are ligands which have to slide into a protein, we will introduce
descriptors optimized for such flat elongated shapes. Bent deformed cylinder
(ORG) describes the molecule as a cylinder which was ORDINAL bent, then deformed
such that its cross-sections became ellipses of evolving shape. Legendre
polynomials are used to describe the central axis of such bent cylinder.
Additional polynomials are used to define evolution of such elliptic
cross-section along the main axis. There will be also discussed bent
cylindrical harmonics (ORG), which uses cross-sections described by cylindrical
harmonics instead of ellipses. All these normalized rotation descriptors allow
to reconstruct (decode) the approximated representation of the shape, hence can
be also used for lossy compression purposes.","While we are usually focused on forecasting future values of time series, it
is often valuable to additionally predict their entire probability
distributions, e.g. to evaluate risk, PERSON simulations. On example of
time series of $\approx$ 30000 ORG, there will be
presented application of hierarchical correlation reconstruction for this
purpose: MSE estimating polynomial as joint density for (current value,
context), where context is for example a few previous values. Then substituting
the currently observed context and normalizing density to CARDINAL, we get predicted
probability distribution for the current value. In contrast to standard machine
learning approaches like neural networks, optimal polynomial coefficients here
have inexpensive direct formula, have controllable accuracy, are unique and
independently calculated, each has a specific cumulant-like interpretation, and
such approximation can asymptotically approach complete description of any real
joint distribution - providing universal tool to quantitatively describe and
exploit statistical dependencies in time series, systematically enhancing
ORG/ARCH-like approaches, also based on different distributions than NORP
which turns out improper for DATE log returns. There is also discussed
application for non-stationary time series like calculating ORG time trend,
or adapting coefficients to local statistical behavior.",1
"The black hole information paradox is a very poorly understood problem. It is
often believed that GPE's argument is not precisely formulated, and a more
careful accounting of naturally occurring ORG corrections will allow the
radiation process to become unitary. We show that such is not the case, by
proving that small corrections to the leading order Hawking computation cannot
remove the entanglement between the radiation and the hole. We formulate
ORG's argument as a `theorem': assuming `traditional' physics at the
horizon and usual assumptions of locality we will be forced into mixed states
or remnants. We also argue that one cannot explain away the problem by invoking
ORG/CFT duality. We conclude with recent results on the quantum physics of
black holes which show the the interior of black holes have a `fuzzball'
structure. This nontrivial structure of microstates resolves the information
paradox, and gives a qualitative picture of how classical intuition can break
down in black hole physics.","The black hole information paradox is resolved in string theory by a radical
change in the picture of the hole: black hole microstates are horizon sized
quantum gravity objects called `fuzzballs' instead of vacuum regions with a
central singularity. The requirement of causality implies that the quantum
gravity wavefunctional $\Psi$ has an important component not present in the
semiclassical picture: virtual fuzzballs. The large mass $MONEY of the fuzzballs
would suppress their virtual fluctuations, but this suppression is compensated
by the large number -- $MONEY -- of possible fuzzballs. These
fuzzballs are extended compression-resistant objects. The presence of these
objects in the vacuum wavefunctional alters the physics of collapse when a
horizon is about to form; this resolves the information paradox. We argue that
these virtual fuzzballs also resist the curving of spacetime, and so cancel out
the large cosmological constant created by the vacuum energy of local quantum
fields. Assuming that the ORG theorem holds to leading order, we can map
the black hole information problem to a problem in cosmology. Using the virtual
fuzzball component of the wavefunctional, we give a qualitative picture of the
evolution of $\Psi$ which is consistent with the requirements placed by the
information paradox.",1
"We introduce a model of SU(2) and ORG) vector fields with a local U(2)
symmetry. Its action can be obtained in the GPE limit of a gauge invariant
regularization involving CARDINAL scalar fields. Evidence from lattice simulations
of the model supports a (CARDINAL temperature) SU(2) deconfining phase transition
through breaking of the SU(2) center symmetry, and a massive vector PERSON
triplet is found in the deconfined phase.","PERSON chain PERSON simulations of pure SU(2)xU(1) lattice gauge theory
show a (CARDINAL temperature) deconfining phase transition in the SU(2) gluon
sector when a term is added to the SU(2) and U(1) Wilson actions, which
requires joint U(2) gauge transformations of the SU(2) and ORG) vector fields.
Investigations of this deconfined phase are of interest as it could provide an
alternative to the NORP mechanism.",1
"PERSON's PERSON (IDM) for categorical i.i.d. data extends
the classical PRODUCT model to a set of priors. It overcomes several
fundamental problems which other approaches to uncertainty suffer from. Yet, to
be useful in practice, CARDINAL needs efficient ways for computing the
imprecise=robust sets or intervals. The main objective of this work is to
derive exact, conservative, and approximate, robust and credible interval
estimates under the ORG for a large class of statistical estimators, including
the entropy and mutual information.","We provide here a simple, yet very general framework that allows us to
explain several constraint propagation algorithms in a systematic way. In
particular, using the notions commutativity and semi-commutativity, we show how
the well-known AC-3, ORG, ORG and ORG algorithms are instances of a single
generic algorithm. The work reported here extends and simplifies that of NORP,
cs.PERSON.",0
"A careful analysis of conditioning in the Sleeping Beauty problem is done,
using the formal model for reasoning about knowledge and probability developed
by ORG and ORG. While the Sleeping Beauty problem has been viewed as
revealing problems with conditioning in the presence of imperfect recall, the
analysis done here reveals that the problems are not so much due to imperfect
recall as to asynchrony. The implications of this analysis for PERSON
ORG Sure-Thing Principle are considered.","Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a ORG; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists in a self-supervised state of ORG.
Thus, we provide the means to unify learning and memory within a machine
learning framework. We also explore the elegant duality of abstraction and
synthesis: the PERSON and PERSON of deep learning.",0
"This is a response to the commentaries on ""WORK_OF_ART"".","This short note discusses the role of syntax vs. semantics and the interplay
between logic, philosophy, and language in computer science and game theory.",1
"We provide here an epistemic analysis of arbitrary strategic games based on
the possibility correspondences. Such an analysis calls for the use of
transfinite iterations of the corresponding operators. Our approach is based on
ORG's PERSON and applies both to the notions of rationalizability
and the iterated elimination of strictly dominated strategies.","Blind ORG computing enables a client, who does not have enough quantum
technologies, to delegate her ORG computing to a remote quantum server in
such a way that her privacy is protected against the server. Some blind ORG
computing protocols can be made verifiable, which means that the client can
check the correctness of server's ORG computing. Can any blind protocol
always be made verifiable? In this paper, we answer to the open problem
affirmatively. We propose a plug-in that makes any universal blind ORG
computing protocol automatically verifiable. The idea is that the client
blindly generates ORG history states corresponding to the quantum
circuit that solves client's problem and its complement circuit. The client can
learn the solution of the problem and verify its correctness at the same time
by measuring energies of local NORP on these states. Measuring energies
of local NORP can be done with only single qubit measurements of GPE
operators.",0
"Cosmology seems extremely remote from everyday human practice and experience.
It is usually taken for granted that cosmological data cannot rationally
influence our beliefs about the fate of humanity -- and possible other
intelligent species -- except perhaps in the extremely distant future, when the
question of heat death (in an ever-expanding universe) becomes actual. Here, an
attempt is made to show that it may become a practical issue much sooner, if an
intelligent community wishes to maximize its creative potential. New
developments in the fields of anthropic self-selection and physical eschatology
give solid foundations to such a conclusion. This may open some new (and
possibly urgent) issues in the areas of future policy making and transhumanist
studies generally. It may also give us a slightly better perspective on the
ORG endeavor.","We critically investigate some evolutionary aspects of the famous ORG
equation, which is usually presented as the central guide for the research on
extraterrestrial intelligence. It is shown that the PERSON equation tacitly
relies on unverifiable and possibly false assumptions on both the
physico-chemical history of our ORG and the properties of advanced
intelligent communities. The importance of recent results of GPE on
chemical build-up of inhabitable planets for ORG is emphasized. CARDINAL important
evolutionary effects are briefly discussed and the resolution of the
difficulties within the context of the phase-transition astrobiological models
sketched.",1
"An algorithm $M$ is described that solves any well-defined problem $p$ as
quickly as the fastest algorithm computing a solution to $p$, save for a factor
of CARDINAL and low-order additive terms. $M$ optimally distributes resources between
the execution of provably correct $p$-solving programs and an enumeration of
all proofs, including relevant proofs of program correctness and of time bounds
on program runtimes. $M$ avoids PERSON's speed-up theorem by ignoring programs
without correctness proof. $M$ has broader applicability and can be faster than
PERSON's universal search, the fastest method for inverting functions save for a
large multiplicative constant. An extension of NORP complexity and CARDINAL
novel natural measures of function complexity are used to show that the most
efficient program computing some function $GPE is also among the shortest
programs provably computing $f$.","We give a brief introduction to the AIXI model, which unifies and overcomes
the limitations of sequential decision theory and universal PERSON
induction. While the former theory is suited for active agents in known
environments, the latter is suited for passive prediction of unknown
environments.",1
"In this article, we choose the
$[sc]_P[\bar{s}\bar{c}]_A-[sc]_A[\bar{s}\bar{c}]_P$ type tetraquark current to
study the hadronic coupling constants in the strong decays $Y(4660)\to ORG, $\eta_c ORG, $ORG, $MONEY, $ MONEY*
\bar{D}^*_s$, $ D_s \bar{D}^*_s$, $D_s^* \bar{D}_s$, $\psi^\prime \pi^+\pi^-$,
$PERSON with the ORG sum rules based on solid quark-hadron quality.
The predicted width $PERSON) )= CARDINAL is in
excellent agreement with the experimental data $MONEY 11\pm 1 {\mbox{ MeV}}$
from the GPE collaboration, which supports assigning the $Y(4660)$ to be the
$[sc]_P[\bar{s}\bar{c}]_A-[sc]_A[\bar{s}\bar{c}]_P$ type tetraquark state with
$J^{PC}=1^{--}$. In calculations, we observe that the hadronic coupling
constants MONEY f_0}|\gg |G_{Y ORG f_0}|$, which is consistent
with the observation of the $Y(4660)$ in the $PERSON mass
spectrum, and favors the MONEY assignment. It is
important to search for the process $Y(4660)\to ORG \phi(1020)$ to diagnose
the nature of the $Y(4660)$, as the decay is greatly suppressed.","A simple method for some class of inverse obstacle scattering problems is
introduced. The observation data are given by a wave field measured on a known
surface surrounding unknown obstacles over a finite time interval. The wave is
generated by an initial data with compact support outside the surface. The
method yields the distance from a given point outside the surface to obstacles
and thus more than the convex hull.",0
"An ultrametric topology formalizes the notion of hierarchical structure. An
ultrametric embedding, referred to here as ultrametricity, is implied by a
natural hierarchical embedding. Such hierarchical structure can be global in
the data set, or local. By quantifying extent or degree of ultrametricity in a
data set, we show that ultrametricity becomes pervasive as dimensionality
and/or spatial sparsity increases. This leads us to assert that very high
dimensional data are of simple structure. We exemplify this finding through a
range of simulated data cases. We discuss also application to very high
frequency time series segmentation and modeling.","ORG researchers attempting to align values of highly capable
intelligent systems with those of humanity face a number of challenges
including personal value extraction, multi-agent value merger and finally
in-silico encoding. State-of-the-art research in value alignment shows
difficulties in every stage in this process, but merger of incompatible
preferences is a particularly difficult challenge to overcome. In this paper we
assume that the value extraction problem will be solved and propose a possible
way to implement an ORG solution which optimally aligns with individual
preferences of each user. We conclude by analyzing benefits and limitations of
the proposed approach.",0
"In classical problem solving, there is of course correlation between the
selection of the problem on the part of PERSON (the problem setter) and that of
the solution on the part of PERSON (the problem solver). In ORG problem
solving, this correlation becomes quantum. This means that PERSON contributes to
selecting PERCENT of the information that specifies the problem. As the solution is
a function of the problem, this gives to PERSON advanced knowledge of PERCENT of the
information that specifies the solution. Both the quadratic and exponential
speed ups are explained by the fact that ORG algorithms start from this
advanced knowledge.","A bare description of the seminal ORG algorithm devised by PERSON could
mean more than an introduction to ORG computing. It could contribute to
opening the field to interdisciplinary research.",1
"Coding technology is used in several information processing tasks. In
particular, when noise during transmission disturbs communications, coding
technology is employed to protect the information. However, there are CARDINAL types
of coding technology: coding in classical information theory and coding in
quantum information theory. Although the physical media used to transmit
information ultimately obey quantum mechanics, we need to choose the type of
coding depending on the kind of information device, classical or quantum, that
is being used. In both branches of information theory, there are many elegant
theoretical results under the ideal assumption that an infinitely large system
is available. In a realistic situation, we need to account for finite size
effects. The present paper reviews finite size effects in classical and quantum
information theory with respect to various topics, including applied aspects.","In the setting of a metric space equipped with a doubling measure that
supports a Poincar\'e inequality, we show that any set of finite perimeter can
be approximated in the ORG norm by a set whose topological and measure theoretic
boundaries almost coincide. This result appears to be new even in the LOC
setting. The work relies on a quasicontinuity-type result for ORG functions
proved by ORG (DATE).",0
"We give a new existence proof for closed hypersurfaces of prescribed mean
curvature in GPE manifolds.","The existence of closed hypersurfaces of prescribed curvature in
semi-riemannian manifolds is proved provided there are barriers.",1
"In this work, various versions of the so-called ORG are provided,
which ensure differentiability properties of pushforwrds between spaces of
C^r-sections (or compactly supported C^r-sections) in vector bundles over
finite-dimensional base manifolds whose fibres are (possibly
infinite-dimensional) locally convex spaces.
  Applications are given, including the proof of continuity for some natural
module multiplications on spaces of sections and the construction of certain
infinite-dimensional Lie groups of GPE group-valued maps.","This paper is devoted to such a fundamental problem of ORG computing as
ORG parallelism. It is well known that ORG parallelism is the basis of
the ability of ORG computer to perform in polynomial time computations
performed by classical computers for exponential time. Therefore better
understanding of ORG parallelism is important both for theoretical and
applied research, cf. e.g. PERSON \cite{DD}. We present a realistic
interpretation based on recently developed prequantum classical statistical
field theory (PCSFT). In the PCSFT-approach to QM quantum states (mixed as well
as pure) are labels of special ensembles of classical fields. Thus e.g. a
single (!) ``electron in the pure state'' $\psi$ can be identified with a
special `` electron random field,'' say MONEY ORG computer
operates with such random fields. By CARDINAL computational step for e.g. a NORP
function $MONEY...,x_n)$ the initial random field $\Phi_{\psi_0}(\phi)$ is
transformed into the final random field $\Phi_{\psi_f}(\phi)$ ``containing all
values'' of $MONEY This is the objective of ORG computer's ability to operate
quickly with huge amounts of information -- in fact, with classical random
fields.",0
"Reprogramming matter may sound far-fetched, but we have been doing it with
increasing power and staggering efficiency for DATE, and for
centuries we have been paving the way toward the ultimate reprogrammed fate of
the universe, the vessel of all programs. How will we be doing it in DATE
time and how will it impact life and the purpose both of machines and of
humans?","Consider the self-map F of the space of real-valued test functions on the
line which takes a test function f to the test function sending a real number x
to f(f(x))-f(0). We show that PRODUCT is discontinuous, although its restriction to
the space of functions supported in K is smooth (and thus continuous), for each
compact subset K of the line. More generally, we construct mappings with
analogous pathological properties on spaces of compactly supported smooth
sections in vector bundles over non-compact bases. The results are useful in
infinite-dimensional Lie theory, where they can be used to analyze the precise
direct limit properties of test function groups and groups of compactly
supported diffeomorphisms.",0
"In this article, we take the $GPE as the vector tetraquark state
with $PERSON, and construct the
$C\gamma_5\otimes\stackrel{\leftrightarrow}{\partial}_\mu\otimes \gamma_5C$
type diquark-antidiquark current to study its mass and pole residue with the
ORG sum rules in details by taking into account the vacuum condensates up to
dimension CARDINAL in a consistent way. The predicted mass
$PERSON is in excellent agreement with experimental data
and supports assigning the $Y(4260/4220)$ to be the
$C\gamma_5\otimes\stackrel{\leftrightarrow}{\partial}_\mu\otimes \gamma_5C$
type vector tetraquark state, and disfavors assigning the $PERSON to be the
$C\gamma_5\otimes\stackrel{\leftrightarrow}{\partial}_\mu\otimes \gamma_5C$
type vector tetraquark state. It is the ORDINAL time that the ORG sum rules have
reproduced the mass of the $GPE as a vector tetraquark state.","Lecture given DATE DATE at ORG at ORG. The lecture was videotaped; this is
an edited transcript.",0
"A common assumption in belief revision is that the reliability of the
information sources is either given, derived from temporal information, or the
same for all. This article does not describe a new semantics for integration
but the problem of obtaining the reliability of the sources given the result of
a previous merging. As an example, the relative reliability of CARDINAL sensors can
be assessed given some certain observation, and allows for subsequent mergings
of data coming from them.","In this article, we study translations between variants of defaults logics
such that the extensions of the theories that are the input and the output of
the translation are in a bijective correspondence. We assume that a translation
can introduce new variables and that the result of translating a theory can
either be produced in time polynomial in the size of the theory or its output
is polynomial in that size; we however restrict to the case in which the
original theory has extensions. This study fills a gap between CARDINAL previous
pieces of work, CARDINAL studying bijective translations among restrictions of
default logics, and the other one studying non-bijective translations between
default logics variants.",1
"The apparent failure of individual probabilistic expressions to distinguish
uncertainty about truths from uncertainty about probabilistic assessments have
prompted researchers to seek formalisms where the CARDINAL types of uncertainties
are given notational distinction. This paper demonstrates that the desired
distinction is already a built-in feature of classical probabilistic models,
thus, specialized notations are unnecessary.","The primary theme of this investigation is a decision theoretic account of
conditional ought statements (e.g., ""You ought to do A, if C"") that rectifies
glaring deficiencies in classical deontic logic. The resulting account forms a
sound basis for qualitative decision theory, thus providing a framework for
qualitative planning under uncertainty. In particular, we show that adding
causal relationships (in the form of a single graph) as part of an epistemic
state is sufficient to facilitate the analysis of action sequences, their
consequences, their interaction with observations, their expected utilities
and, hence, the synthesis of plans and strategies under uncertainty.",1
"Continuing the study of complexity theory of ORG
(OTMs) that was started by ORG and the author, we prove the following
results:
  (CARDINAL) An analogue of PERSON's theorem for OTMs holds: That is, there are
languages $\mathcal{L}$ which are GPE, but neither P$^{\infty}$ nor
NP$^{\infty}$-complete. This answers an open question of \cite{CLR}.
  (CARDINAL) The speedup theorem for Turing machines, which allows us to bring down
the computation time and space usage of a Turing machine program down by an
aribtrary positive factor under relatively mild side conditions by expanding
the working alphabet does not hold for OTMs.
  (CARDINAL) We show that, for $\alpha<\beta$ such that $PERSON is the halting time
of some ORG-program, there are decision problems that are ORG-decidable in time
bounded by $MONEY for some $PERSON, but not in
time bounded by $MONEY for any MONEY","We determine the computational complexity of approximately counting the total
weight of variable assignments for every complex-weighted NORP constraint
satisfaction problem (or ORG) with any number of additional unary (i.e., arity
CARDINAL) constraints, particularly, when degrees of input instances are bounded from
above by a fixed constant. All degree-1 counting CSPs are obviously solvable in
polynomial time. When the instance's degree is CARDINAL, we present a
dichotomy theorem that classifies all counting CSPs admitting free unary
constraints into exactly CARDINAL categories. This classification theorem extends,
to complex-weighted problems, an earlier result on the approximation complexity
of unweighted counting NORP CSPs of bounded degree. The framework of the
proof of our theorem is based on a theory of signature developed from PERSON's
holographic algorithms that can efficiently solve seemingly intractable
counting CSPs. Despite the use of arbitrary complex weight, our proof of the
classification theorem is rather elementary and intuitive due to an extensive
use of a novel notion of limited T-constructibility. For the remaining degree-2
problems, in contrast, they are as hard to approximate as Holant problems,
which are a generalization of counting CSPs.",0
"The article presents an approach to interactively solve multi-objective
optimization problems. While the identification of efficient solutions is
supported by computational intelligence techniques on the basis of local
search, the search is directed by partial preference information obtained from
the decision maker.
  An application of the approach to biobjective portfolio optimization, modeled
as the well-known knapsack problem, is reported, and experimental results are
reported for benchmark instances taken from the literature. In brief, we obtain
encouraging results that show the applicability of the approach to the
described problem.","In the current paper, we present an optimization system solving multi
objective production scheduling problems (MOOPPS). The identification of PERSON
optimal alternatives or at least a close approximation of them is possible by a
set of implemented metaheuristics. Necessary control parameters can easily be
adjusted by the decision maker as the whole software is fully menu driven. This
allows the comparison of different metaheuristic algorithms for the considered
problem instances. Results are visualized by a graphical user interface showing
the distribution of solutions in outcome space as well as their corresponding
PERSON chart representation.
  The identification of a most preferred solution from the set of efficient
solutions is supported by a module based on the aspiration interactive method
(ORG). The decision maker successively defines aspiration levels until a single
solution is chosen.
  After successfully competing in the finals in GPE, GPE, the MOOPPS
software has been awarded ORG DATE
(http://www.bth.se/llab/easa_2002.nsf)",1
"PERSON in 'The Singularity May Never Be Near' gives CARDINAL arguments to
support his point of view that technological singularity may happen but that it
is unlikely. In this paper, we provide analysis of each CARDINAL of his arguments
and arrive at similar conclusions, but with more weight given to the 'likely to
happen' probability.","This paper proposes an explanation of the cognitive change that occurs as the
creative process proceeds. During the initial, intuitive phase, each thought
activates, and potentially retrieves information from, a large region
containing many memory locations. Because of the distributed,
content-addressable structure of memory, the diverse contents of these many
locations merge to generate the next thought. Novel associations often result.
As one focuses on an idea, the region searched and retrieved from narrows, such
that the next thought is the product of fewer memory locations. This enables a
shift from association-based to causation-based thinking, which facilitates the
fine-tuning and manifestation of the creative work.",0
"Based on our previous work on truly concurrent process algebra, we use it to
unify quantum and classical computing for open and closed ORG systems. This
resulted algebra can be used to verify the behaviors of ORG and classical
computing mixed systems, with a flavor of true concurrency.","This article presents a technique for proving problems hard for classes of
the polynomial hierarchy or for ORG. The rationale of this technique is that
some problem restrictions are able to simulate existential or universal
quantifiers. If this is the case, reductions from ORG
(ORG) to these restrictions can be transformed into reductions from QBFs having
CARDINAL more quantifier in the front. This means that a proof of hardness of a
problem at level n in the polynomial hierarchy can be split into n separate
proofs, which may be simpler than a proof directly showing a reduction from a
class of QBFs to the considered problem.",0
"In this article, we study the light-flavor scalar and axial-vector diquark
states in the vacuum and in the nuclear matter using the ORG sum rules in an
systematic way, and make reasonable predictions for their masses in the vacuum
and in the nuclear matter.","This paper describes a new method for reducing the error in a classifier. It
uses an error correction update that includes the very simple rule of either
adding or subtracting the error adjustment, based on whether the variable value
is currently larger or smaller than the desired value. While a traditional
neuron would sum the inputs together and then apply a function to the total,
this new method can change the function decision for each input value. This
gives added flexibility to the convergence procedure, where through a series of
transpositions, variables that are far away can continue towards the desired
value, whereas variables that are originally much closer can oscillate from CARDINAL
side to the other. Tests show that the method can successfully classify some
benchmark datasets. It can also work in a batch mode, with reduced training
times and can be used as part of a neural network architecture. Some
comparisons with an earlier wave shape paper are also made.",0
"We develop the theory and practical implementation of p-adic sparse coding of
data. Rather than the standard, sparsifying criterion that uses the MONEY
pseudo-norm, we use the p-adic norm. We require that the hierarchy or tree be
node-ranked, as is standard practice in agglomerative and other hierarchical
clustering, but not necessarily with decision trees. In order to structure the
data, all computational processing operations are direct reading of the data,
or are bounded by a constant number of direct readings of the data, implying
linear computational time. Through p-adic sparse data coding, efficient storage
results, and for bounded p-adic norm stored data, search and retrieval are
constant time operations. Examples show the effectiveness of this new approach
to content-driven encoding and displaying of data.","In a companion paper, ORG (DATE), we discussed how ORG work
linked the unrepressed unconscious (in the human) to symmetric logic and
thought processes. We showed how ultrametric topology provides a most useful
representational and computational framework for this. Now we look at the
extent to which we can find ultrametricity in text. We use coherent and
meaningful collections of CARDINAL texts to show how we can measure inherent
ultrametricity. On the basis of our findings we hypothesize that inherent
ultrametricty is a basis for further exploring unconscious thought processes.",1
"An exact solution of the FAC field equations given the barotropic
equation of state $PERSON yields CARDINAL possible models: (CARDINAL) if $MONEY
<-1$, we obtain the most general possible anisotropic model for wormholes
supported by phantom energy and (CARDINAL) if $MONEY >0$, we obtain a model for
galactic rotation curves. Here the equation of state represents a perfect fluid
which may include dark matter. These results illustrate the power and
usefulness of exact solutions.","CARDINAL of the mainstays of the controversial ""rare LOC"" hypothesis is the
""Goldilocks problem"" regarding various parameters describing a habitable
planet, partially involving the role of mass extinctions and other catastrophic
processes in biological evolution. Usually, this is construed as support for
the uniqueness of the LOC's biosphere and intelligent human life. Here I
argue that this is a misconstrual and that, on the contrary,
observation-selection effects, when applied to catastrophic processes, make it
very difficult for us to discern whether the terrestrial biosphere and
evolutionary processes which created it are exceptional in the Milky Way or
not. In particular, an anthropic overconfidence bias related to the temporal
asymmetry of evolutionary processes appears when we try to straightforwardly
estimate catastrophic risks from the past records on LOC. This agnosticism,
in turn, supports the validity and significance of practical astrobiological
and ORG research.",0
"Blind quantum computation is a secure delegated ORG computing protocol
where PERSON who does not have sufficient quantum technology at her disposal
delegates her computation to PERSON who has a fully-fledged ORG computer in
such a way that PERSON cannot learn anything about PERSON's input, output, and
algorithm. Protocols of blind quantum computation have been proposed for
several qubit measurement-based computation models, such as the graph state
model, the Affleck-Kennedy-Lieb-Tasaki model, and the
ORG topological model. Here, we consider blind quantum
computation for the continuous-variable measurement-based model. We show that
blind quantum computation is possible for the infinite squeezing case. We also
show that the finite squeezing causes no additional problem in the blind setup
apart from the one inherent to the continuous-variable measurement-based
quantum computation.","Verifiable blind ORG computing is a secure delegated ORG computing
where a client with a limited quantum technology delegates her quantum
computing to a server who has a universal ORG computer. The client's
privacy is protected (blindness) and the correctness of the computation is
verifiable by the client in spite of her limited quantum technology
(verifiability). There are mainly CARDINAL types of protocols for verifiable blind
ORG computing: the protocol where the client has only to generate
single-qubit states, and the protocol where the client needs only the ability
of single-qubit measurements. The latter is called the measurement-only
verifiable blind ORG computing. If the input of the client's quantum
computing is a quantum state whose classical efficient description is not known
to the client, there was no way for the measurement-only client to verify the
correctness of the input. Here we introduce a new protocol of measurement-only
verifiable blind ORG computing where the correctness of the quantum input
is also verifiable.",1
"We study clockability for ORG (OTMs). In particular, we
show that, in contrast to the situation for ITTMs, admissible ordinals can be
OTM-clockable, that $\Sigma_{2}$-admissible ordinals are never OTM-clockable
and that gaps in the ORG-clockable ordinals are always started by admissible
limits of admissible ordinals.","The main goal of this paper is to give a pedagogical introduction to ORG-to do this in a new way, using network diagrams called
ORG. A lesser goal of the paper is to propose a few new
ideas, such as associating with each quantum NORP net a very useful density
matrix that we call the meta density matrix.",0
"These informal notes consider ORG transforms on a simple class of nice
functions and some basic properties of the PERSON transform.","This paper presents a hypothesis that consciousness is a natural result of
neurons that become connected recursively, and work synchronously between short
and long term memories. Such neurons demonstrate qubit-like properties, each
supporting a probabilistic combination of true and false at a given phase.
Advantages of qubits include probabilistic modifications of cues for searching
associations in long term memory, and controlled toggling for parallel,
reversible computations to prioritize multiple recalls and to facilitate
mathematical abilities.",0
"We give an algebraic characterization of a form of synchronized parallel
composition allowing for true concurrency, using ideas based on PERSON
""WORK_OF_ART"".","Aggregated journal-journal citation networks based on the ORG PRODUCTReports DATE of the Science Citation Index (CARDINAL journals) and ORG (CARDINAL journals) are made accessible from the perspective
of any of these journals. The user is thus able to analyze the citation
environment in terms of links and graphs. Furthermore, the local impact of a
journal is defined as its share of the total citations in the specific
journal's citation environments; the vertical size of the nodes is varied
proportionally to this citation impact. The horizontal size of each node can be
used to provide the same information after correction for within-journal
(self)-citations. In the ""citing"" environment, the equivalents of this measure
can be considered as a citation activity index which maps how the relevant
journal environment is perceived by the collective of authors of a given
journal. As a policy application, the mechanism of interdisciplinary
developments among the sciences is elaborated for the case of nanotechnology
journals.",0
"This article expands our work in [Ca16]. By its reliance on Turing
computability, the classical theory of effectivity, along with effective
reducibility and Weihrauch reducibility, is only applicable to objects that are
either countable or can be encoded by countable objects. We propose a notion of
effectivity based on ORG (OTMs) that applies to
arbitrary set-theoretical $\Pi_{2}$-statements, along with according variants
of effective reducibility and Weihrauch reducibility. As a sample application,
we compare various choice principles with respect to effectivity.
  We also propose a generalization to set-theoretical formulas of arbitrary
quantifier complexity.","By a theorem of Sacks, if a real $x$ is recursive relative to all elements of
a set of positive PERSON measure, $PERSON is recursive. This statement, and the
analogous statement for non-meagerness instead of positive PERSON measure,
have been shown to carry over to many models of transfinite computations. Here,
we start exploring another analogue concerning recognizability rather than
computability. We introduce a notion of relativized recognizability and show
that, for ORG (ITTMs), if a real $x$ is recognizable
relative to all elements of a non-meager Borel set $MONEY, then $PERSON is
recognizable. We also show that a relativized version of this statement holds
for ORG (ITRMs). This extends our earlier work
where we obtained the (unrelativized) result for ITRMs. We then introduce a
jump operator for recognizability, examine its set-theoretical content and show
that the recognizable jumps for ITRMs and ITTMs are primitive-recursively
equivalent, even though these CARDINAL models are otherwise of vastly different
strength. Finally, we introduce degrees of recognizability by considering the
transitive closure of relativized recognizability and connect it with the
recognizable jump operator to obtain a solution to ORG's problem for degrees
of recognizability.",1
"According to the no-signaling theorem, the nonlocal collapse of the
wavefunction of an entangled particle by the measurement on its twin particle
at a remote location cannot be used to send useful information. Given that
experiments on nonlocal correlations continue to have loopholes, we propose a
stronger principle that the nonlocality of quantum mechanics itself is veiled.
In practical terms, decoherence and noise compels us to view the wavefunction
as representing knowledge of potential outcomes rather than the reality.
Experimental evidence in favor of naked nonlocality would support the view of
the wavefunction as an objective description of physical reality.","The Newcomb-Benford Law, which is also called the ORDINAL digit phenomenon, has
applications in diverse phenomena ranging from social and computer networks,
engineering systems, natural sciences, and accounting. In forensics, it has
been used to determine intrusion in a computer server based on the measured
expectations of ORDINAL digits of time varying values of data, and to check
whether the information in a data base has been tampered with. There are slight
deviations from the law in certain natural data, as in fundamental physical
constants, and here we propose a more general PERSON distribution of which the
WORK_OF_ART is a special case so that it can be used to provide a
better fit to such data, and also open the door to a mathematical examination
of the origins of such deviations.",1
"New to neuroscience with implications for ORG, the exclusive OR, or any other
GPE gate may be biologically accomplished within a single region where
active dendrites merge. This is demonstrated below using dynamic circuit
analysis. Medical knowledge aside, this observation points to the possibility
of specially coated conductors to accomplish artificial dendrites.","When training deep neural networks, it is typically assumed that the training
examples are uniformly difficult to learn. Or, to restate, it is assumed that
the training error will be uniformly distributed across the training examples.
Based on these assumptions, each training example is used an equal number of
times. However, this assumption may not be valid in many cases. ""Oddball SGD""
(novelty-driven stochastic gradient descent) was recently introduced to drive
training probabilistically according to the error distribution - training
frequency is proportional to training error magnitude. In this article, using a
deep neural network to encode a video, we show that oddball SGD can be used to
enforce uniform error across the training set.",0
"Both sigma and kappa are well established from PRODUCT data on DATE and
D->K-pi-pi$ and ORG data on J/Psi->omega-pi-pi and PERSON. Fits to these
data are accurately consistent with NORP and PERSON elastic scattering when CARDINAL
allows for the PERSON CARDINAL which arises from ORG. The phase
variation with mass is also consistent between elastic scattering and
production data.","In production processes, e.g. WORK_OF_ART or ORG ORDINAL, the
sigma and fo(980) overlap in the same partial wave. The conjecture of ORG (ORG) states that the pi-pi pair should have the same phase variation
as pi-pi elastic scattering. This is an extension of PERSON's theorem beyond
its original derivation, which stated only that the s-dependence of a single
resonance should be universal. The prediction of ORG is that the deep dip
observed in NORP elastic scattering close to CARDINAL GeV should also appear in
production data. CARDINAL sets of data disagree with this prediction. All require
different relative magnitudes of sigma and fo(980). That being so, a fresh
conjecture is to rewrite the CARDINAL-body unitarity relation for production in terms
of observed magnitudes. This leads to a prediction different to ORG. Central
production data from the ORG experiment fit naturally to this hypothesis.",1
"We compute the amplitudes for the insertion of various operators in a quark
CARDINAL-point function at CARDINAL loop in the RI' symmetric momentum scheme, RI'/SMOM.
Specifically we focus on the moments n = CARDINAL and 3 of the flavour non-singlet
twist-2 operators used in deep inelastic scattering as these are required for
lattice computations.","Slime mould \emph{Physarum polycephalum} is a large single cell capable for
distributed sensing, concurrent information processing, parallel computation
and decentralised actuation. The ease of culturing and experimenting with
ORG makes this slime mould an ideal substrate for real-world
implementations of unconventional sensing and computing devices. In DATE the GPE became a NORP knife of the unconventional computing: give
the slime mould a problem it will solve it. We provide a concise summary of
what exact computing and sensing operations are implemented with live slime
mould. The ORG devices range from morphological processors for
computational geometry to experimental archeology tools, from self-routing
wires to memristors, from devices approximating a shortest path to analog
physical models of space exploration.",0
"In this paper we extend an earlier result within ORG theory
[""Fast Dempster-Shafer Clustering Using a Neural Network Structure,"" in GPE.
ORG. Conf. ORG in
Knowledge-Based Systems (IPMU CARDINAL)] where a large number of pieces of evidence
are clustered into subsets by a neural network structure. The clustering is
done by minimizing a metaconflict function. Previously we developed a method
based on iterative optimization. While the neural method had a much lower
computation time than iterative optimization its average clustering performance
was not as good. Here, we develop a hybrid of the CARDINAL methods. We let the
neural structure do the initial clustering in order to achieve a high
computational performance. Its solution is ORG as the initial state to the
iterative optimization in order to improve the clustering performance.","In this paper we study a problem within ORG theory where CARDINAL
pieces of evidence are clustered by a neural structure into n clusters. The
clustering is done by minimizing a metaconflict function. Previously we
developed a method based on iterative optimization. However, for large scale
problems we need a method with lower computational complexity. The neural
structure was found to be effective and much faster than iterative optimization
for larger problems. While the growth in metaconflict was faster for the neural
structure compared with iterative optimization in medium sized problems, the
metaconflict per cluster and evidence was moderate. The neural structure was
able to find a global minimum over CARDINAL runs for problem sizes up to CARDINAL
clusters.",1
"In the setting of a metric space equipped with a doubling measure supporting
a Poincar\'e inequality, we show that ORG functions are, in the sense of
multiple limits, continuous with respect to a CARDINAL-fine topology, at almost every
point with respect to the codimension CARDINAL Hausdorff measure.","In the setting of a metric space equipped with a doubling measure that
supports a Poincar\'e inequality, we show that a set $MONEY is of finite perimeter
if and only if $\mathcal H(\partial^1 I_E)<\infty$, that is, if and only if the
codimension CARDINAL ORG measure of the \emph{$1$-fine boundary} of the set's
measure theoretic interior $MONEY is finite.",1
"Recently, it is well recognized that hypothesis testing has deep relations
with other topics in ORG information theory as well as in classical
information theory. These relations enable us to derive precise evaluation in
the finite-length setting. However, such usefulness of hypothesis testing is
not limited to information theoretical topics. For example, it can be used for
verification of entangled state and ORG computer as well as guaranteeing
the security of keys generated via ORG key distribution. In this talk, we
overview these kinds of applications of hypothesis testing.","We construct a universal code for stationary and memoryless classical-quantum
channel as a quantum version of the universal coding by PRODUCT and
K\""{o}rner. Our code is constructed by the combination of irreducible
representation, the decoder introduced through ORG information spectrum,
and the packing lemma.",1
"PERSON has proposed that highly excited mesons and baryons fall into parity
doublets, and that the ORG) on the leading Regge trajectory should have a
nearly degenerate PERSON} = CARDINAL} partner. A re-analysis of ORG data
does not support this idea. A likely explanation is that centrifugal barriers
on the leading trajectory allow formation of the L=J-1 states, but are too
strong to allow L=J states. CARDINAL new polarisation experiments have the potential
for major progress in meson spectroscopy.","The large N_f self-consistency programme is reviewed. As an application the
ORG beta-function is computed at O(1/N_f) and the anomalous dimensions of
polarized twist-2 singlet operators are determined at the same order.",0
"We discuss the contribution of diffractive $Q \bar Q$ production to the
longitudinal double-spin asymmetry in polarized deep--inelastic $MONEY
scattering. We show the strong dependence of the $MONEY asymmetry on the
pomeron spin structure.","We study light vector PERSON at small $x$ on the basis of the
generalized parton distribution (ORG). Our results on the cross section and
spin density matrix elements (SDME) are in fair agreement with ORG experiments",1
"This chapter discusses the institutional approach for organizing and
maintaining ontologies. The theory of institutions was named and initially
developed by PERSON and PERSON. This theory, a metatheory based on
category theory, regards ontologies as logical theories or local logics. The
theory of institutions uses the category-theoretic ideas of fibrations and
indexed categories to develop logical theories. Institutions unite the lattice
approach of ORG and PERSON with the distributed
logic of ORG and PERSON. The institutional approach
incorporates locally the lattice of theories idea of PRODUCT from the theory of
knowledge representation. ORG, which was initiated
within ORG project, uses the institutional
approach in its applied aspect for the comparison, semantic integration and
maintenance of ontologies. This chapter explains the central ideas of the
institutional approach to ontologies in a careful and detailed manner.","The sharing of ontologies between diverse communities of discourse allows
them to compare their own information structures with that of other communities
that share a common terminology and semantics - ontology sharing facilitates
interoperability between online knowledge organizations. This paper
demonstrates how ontology sharing is formalizable within the conceptual
knowledge model of Information Flow (IF). ORG indirectly
represents sharing through a specifiable, ontology extension hierarchy
augmented with synonymic type equivalencing - CARDINAL ontologies share terminology
and meaning through a common generic ontology that each extends. Using the
paradigm of participant community ontologies formalized as IF logics, a common
shared extensible ontology formalized as an IF theory, participant community
specification links from the common ontology to the participating community
ontology formalizable as IF theory interpretations, this paper argues that
ontology sharing is concentrated in a virtual ontology of community
connections, and demonstrates how this virtual ontology is computable as the
fusion of the participant ontologies - the quotient of the sum of the
participant ontologies modulo the ontological sharing structure.",1
"The article presents results of discrete thermodynamics (ORG) basic
application to electrochemical systems. Consistent treatment of the
electrochemical system as comprising CARDINAL interacting subsystems - the chemical
and the electrical (electrochemical) - leads to ln-logistic map of states of
the electrochemical system with non-unity coefficient of the electrical charge
transfer. This factor provides for a feedback and causes dynamic behavior of
electrochemical systems, including bifurcations and electrochemical
oscillations. The latter occur beyond bifurcation point at essential deviation
of the chemical subsystem from true thermodynamic equilibrium. If the charge
transfer coefficient takes on unity, the map turns into classical equation of
electrochemical equilibrium. ORG of electrochemical oscillations, resulted
from the ORG formalism, are multifractals. Graphical solutions of this work are
qualitatively compared to some experimental results.","It is a challenge for any Knowledge Base reasoning to manage ubiquitous
uncertain ontology as well as uncertain updating times, while achieving
acceptable service levels at minimum computational cost. This paper proposes an
application-independent merging ontologies for any open interaction system. A
solution that uses Multi-Entity Bayesan Networks with ORG rules, and a PERSON
program is presented to dynamically monitor Exogenous and Endogenous temporal
evolution on updating merging ontologies on a probabilistic framework for the
NORP Web.",0
"Statistical mechanics is generalized on the basis of an additive information
theory for incomplete probability distributions. The incomplete normalization
MONEY$ is used to obtain generalized entropy
$S=-k\sum_{i=1}^wp_i^q\ln p_i$. The concomitant incomplete statistical
mechanics is applied to some physical systems in order to show the effect of
the incompleteness of information. It is shown that this extensive generalized
statistics can be useful for the correlated electron systems in weak coupling
regime.","We compute the pole mass of the gluon in GPE from the local composite
operator formalism at CARDINAL loops in the GPE renormalization scheme. For
ORG theory an estimate of the mass at CARDINAL loops is CARDINAL Lambda_MSbar.",0
"When simultaneously reasoning with evidences about several different events
it is necessary to separate the evidence according to event. These events
should then be handled independently. However, when propositions of evidences
are weakly specified in the sense that it may not be certain to which event
they are referring, this may not be directly possible. In this paper a
criterion for partitioning evidences into subsets representing events is
established. This criterion, derived from the conflict within each subset,
involves minimising a criterion function for the overall conflict of the
partition. An algorithm based on characteristics of the criterion function and
an iterative optimisation among partitionings of evidences is proposed.","Toy models have been used to separate important features of quantum
computation from the rich background of the standard PERSON space model.
Category theory, on the other hand, is a general tool to separate components of
mathematical structures, and analyze CARDINAL layer at a time. It seems natural to
combine the CARDINAL approaches, and several authors have already pursued this idea.
We explore *categorical comprehension construction* as a tool for adding
features to toy models. We use it to comprehend quantum propositions and
probabilities within the basic model of finite-dimensional PERSON spaces. We
also analyze complementary quantum observables over the category of sets and
relations. This leads into the realm of *test spaces*, a well-studied model. We
present CARDINAL of many possible extensions of this model, enabled by the
comprehension construction. Conspicuously, all models obtained in this way
carry the same categorical structure, *extending* the familiar dagger compact
framework with the complementation operations. We call the obtained structure
*dagger mix autonomous*, because it extends mix autonomous categories, popular
in computer science, in a similar way like dagger compact structure extends
compact categories. Dagger mix autonomous categories seem to arise quite
naturally in quantum computation, as soon as complementarity is viewed as a
part of the global structure.",0
"We experimentally demonstrate that supersaturated solution of sodium acetate,
commonly called 'hot ice', is a massively-parallel unconventional computer. In
the hot ice computer data are represented by a spatial configuration of
crystallization induction sites and physical obstacles immersed in the
experimental container. Computation is implemented by propagation and
interaction of growing crystals initiated at the data-sites. We discuss
experimental prototypes of hot ice processors which compute planar GPE
diagram, shortest collision-free paths and implement AND and OR logical gates.","Plasmodium of Physarym polycephalum is an ideal biological substrate for
implementing concurrent and parallel computation, including combinatorial
geometry and optimization on graphs. We report results of scoping experiments
on ORG computing in conditions of minimal friction, on the water surface.
We show that plasmodium of GPE is capable for computing a basic spanning
trees and manipulating of light-weight objects. We speculate that our results
pave the pathways towards design and implementation of amorphous biological
robots.",1
"Stochastic Gradient Descent (SGD) is arguably the most popular of the machine
learning methods applied to training deep neural networks (DNN) DATE. It has
recently been demonstrated that ORG can be statistically biased so that certain
elements of the training set are learned more rapidly than others. In this
article, we place ORG into a feedback loop whereby the probability of selection
is proportional to error magnitude. This provides a novelty-driven oddball ORG
process that learns more rapidly than traditional ORG by prioritising those
elements of the training set with the largest novelty (error). In our DNN
example, oddball ORG trains some 50x faster than regular ORG.","Presently, large enterprises rely on database systems to manage their data
and information. These databases are useful for conducting DATE business
transactions. However, the tight competition in the marketplace has led to the
concept of data mining in which data are analyzed to derive effective business
strategies and discover better ways in carrying out business. In order to
perform data mining, regular databases must be converted into what so called
informational databases also known as data warehouse. This paper presents a
design model for building data warehouse for a typical university information
system. It is based on transforming an operational database into an
informational warehouse useful for decision makers to conduct data analysis,
predication, and forecasting. The proposed model is based on CARDINAL stages of
data migration: Data extraction, data cleansing, data transforming, and data
indexing and loading. The complete system is implemented under ORG DATE
and is meant to serve as a repository of data for data mining operations.",0
"The direct long-term changes occurring in the orbital dynamics of a local
gravitationally bound binary system $MONEY due to the NORP tidal
acceleration caused by an external massive source are investigated. A class of
systems made of a test particle $m$ rapidly orbiting with orbital frequency
$PERSON b}$ an astronomical body of mass $M$ which, in turn, slowly revolves
around a distant object of mass $M^{'}$ with orbital frequency $PERSON
b}^{'}\ll PERSON b}$ is considered. The characteristic frequencies of the
NORP orbital variations of $m$ and of $M$ itself are assumed to be
negligible with respect to both $n_{\rm b}$ and $PERSON b}^{'}$. General
expressions for the resulting NORP and NORP tidal orbital shifts
of $m$ are obtained. The future missions ORG and JUICE to ORG and
PERSON, respectively, are considered in view of a possible detection. The
largest effects, of the order of $MONEY 0.1-0.5$ milliarcseconds per year
(mas yr$^{-1}$), occur for the PERSON orbiter of the JUICE mission. Although
future improvements in spacecraft tracking and orbit determination might,
perhaps, reach the required sensitivity, the systematic bias represented by the
other known orbital perturbations of both NORP and post-Newtonian origin
would be overwhelming. The realization of a dedicated artificial mini-planetary
system to be carried onboard and LOC-orbiting spacecraft is considered as
well. Post-Newtonian tidal precessions MONEY 1-10^2$ mas
yr$^{-1}$ could be obtained, but the quite larger NORP tidal effects would
be a major source of systematic bias because of the present-day percent
uncertainty in the product of the LOC's mass times the NORP
gravitational parameter.","An identity between CARDINAL versions of the PERSON bound on the probability a
certain large deviations event, is established. This identity has an
interpretation in statistical physics, namely, an isothermal equilibrium of a
composite system that consists of multiple subsystems of particles. Several
information--theoretic application examples, where the analysis of this large
deviations probability naturally arises, are then described from the viewpoint
of this statistical mechanical interpretation. This results in several
relationships between information theory and statistical physics, which we
hope, the reader will find insightful.",0
"PRODUCT and -Logic were defined by the author in
DATE and published for the ORDINAL time in DATE. We extended the neutrosophic set
respectively to ORG {when some neutrosophic component is over
CARDINAL}, ORG {when some neutrosophic component is below CARDINAL}, and to
ORG {when some neutrosophic components are off the interval [0,
CARDINAL], i.e. some neutrosophic component over CARDINAL and other neutrosophic component
below CARDINAL}. This is no surprise with respect to the classical fuzzy set/logic,
intuitionistic fuzzy set/logic, or classical/imprecise probability, where the
values are not allowed outside the interval [CARDINAL, CARDINAL], since our real-world has
numerous examples and applications of over-/under-/off-neutrosophic components.
For example, person working overtime deserves a membership degree over CARDINAL, while
a person producing more damage than benefit to a company deserves a membership
below CARDINAL. Then, similarly, ORG
etc. were extended to respectively ORG,
-Measure, -Probability, -Statistics etc. [GPE, DATE].","The surface air temperature DATE records at the land-based locations with
different climate conditions (from LOC to GPE) have been studied on
the DATE to intraseasonal time scales (low frequency DATE and seasonal
variations have been removed by subtracting a wavelet regression from the daily
records). It is shown that the power spectra of the DATE time series exhibit a
universal behaviour corresponding to the NORP distributed chaos. Global
average temperature fluctuations (land-based data) and the tropical LOC sea
surface temperature fluctuations (El Ni\~no/La Ni\~na phenomenon) have been
also considered in this context. It is shown that the practical smooth
predictability for the surface air temperature dynamics is possible at least up
to the fundamental (pumping) period of the distributed chaos.",0
"It is shown that in turbulent flows the distributed chaos with spontaneously
broken translational space symmetry (homogeneity) has a stretched exponential
spectrum $\exp-(k/k_{\beta})^{\beta }$ with $PERSON =CARDINAL Good agreement has
been established between the theory and the data of direct numerical
simulations of isotropic homogeneous turbulence (energy dissipation rate
field), of a channel flow (velocity field), of a fully developed boundary layer
flow (velocity field), and the experimental data at the plasma edges of
different fusion devices (stellarators and tokamaks). An astrophysical
application to the large-scale galaxies distribution has been briefly discussed
and good agreement with the data of recent PERSON Digital Sky Survey SDSS-III
has been established.","Semantic composition is the task of understanding the meaning of text by
composing the meanings of the individual words in the text. Semantic
decomposition is the task of understanding the meaning of an individual word by
decomposing it into various aspects (factors, constituents, components) that
are latent in the meaning of the word. We take a distributional approach to
semantics, in which a word is represented by a context vector. Much recent work
has considered the problem of recognizing compositions and decompositions, but
we tackle the more difficult generation problem. For simplicity, we focus on
noun-modifier bigrams and PERSON unigrams. A test for semantic composition is,
given context vectors for the noun and modifier in a GPE-modifier bigram (""red
salmon""), generate a noun unigram that is synonymous with the given bigram
(""sockeye""). A test for semantic decomposition is, given a context vector for a
noun unigram (""snifter""), generate a GPE-modifier bigram that is synonymous
with the given unigram (""brandy glass""). With a vocabulary of CARDINAL
unigrams from ORG, there are CARDINAL candidate unigram compositions for a
bigram and MONEY (CARDINAL squared) candidate bigram decompositions for a
unigram. We generate ranked lists of potential solutions in CARDINAL passes. A fast
unsupervised learning algorithm generates an initial list of candidates and
then a slower supervised learning algorithm refines the list. We evaluate the
candidate solutions by comparing them to ORG synonym sets. For
decomposition (unigram to bigram), the top CARDINAL most highly ranked bigrams
include a ORG synonym of the given unigram PERCENT of the time. For
composition (bigram to unigram), the top CARDINAL most highly ranked unigrams
include a ORG synonym of the given bigram PERCENT of the time.",0
"We model anomaly and change in data by embedding the data in an ultrametric
space. Taking our initial data as cross-tabulation counts (or other input data
formats), ORG allows us to endow the information space with
a Euclidean metric. We then model GPE or change by an induced ultrametric.
The induced ultrametric that we are particularly interested in takes a
sequential - e.g. temporal - ordering of the data into account. We apply this
work to the flow of narrative expressed in the film script of the ORG
movie; and to the evolution DATE of the NORP social
conflict and violence.","Behavior modeling and software architecture specification are attracting more
attention in software engineering. Describing both of them in integrated models
yields numerous advantages for coping with complexity since the models are
platform independent. They can be decomposed to be developed independently by
experts of the respective fields, and they are highly reusable and may be
subjected to formal analysis. Typically, behavior is defined as the occurrence
of an action, a pattern over time, or any change in or movement of an object.
In systems studies, there are many different approaches to modeling behavior,
such as grounding behavior simultaneously on state transitions, natural
language, and flowcharts. These different descriptions make it difficult to
compare objects with each other for consistency. This paper attempts to propose
some conceptual preliminaries to a definition of behavior in software
engineering. The main objective is to clarify the research area concerned with
system behavior aspects and to create a common platform for future research.
CARDINAL generic elementary processes (creating, processing, releasing, receiving,
and transferring) are used to form a unifying higher-order process called a
thinging machine (ORG) that is utilized as a template in modeling behavior of
systems. Additionally, a ORG includes memory and triggering relations among
stages of processes (machines). A ORG is applied to many examples from the
literature to examine their behavioristic aspects. The results show that a ORG
is a valuable tool for analyzing and modeling behavior in a system.",0
"Modern classical computing devices, except of simplest calculators, have PERSON architecture, i.e., a part of the memory is used for the program and a
part for the data. It is likely, that analogues of such architecture are also
desirable for the future applications in ORG computing, communications and
control. It is also interesting for the modern theoretical research in the
quantum information science and raises challenging questions about an
experimental assessment of such a programmable models. Together with some
progress in the given direction, such ideas encounter specific problems arising
from the very essence of ORG laws. Currently are known CARDINAL different ways
to overcome such problems, sometime denoted as a stochastic and deterministic
approach. The presented paper is devoted to the ORDINAL one, that is also may be
called the programmable quantum networks with pure states.
  In the paper are discussed basic principles and theoretical models that can
be used for the design of such nano-devices, e.g., the conditional quantum
dynamics, ORG ""no-programming theorem, the idea of deterministic
and stochastic quantum gates arrays. Both programmable quantum networks with
finite registers and hybrid models with continuous quantum variables are
considered. As a basic model for the universal programmable quantum network
with pure states and finite program register is chosen a ""Control-Shift""
ORG processor architecture with CARDINAL buses introduced in earlier works. It
is shown also, that ORG cellular automata approach to the construction of
an universal programmable ORG computer often may be considered as the
particular case of such design.","It is discussed, why classical simulators of ORG computers escape from
some no-go claims like PERSON, ORG, or recent PERSON"" theorems.",1
"ORG intelligent systems can be found everywhere: finger print,
handwriting, speech, and face recognition, spam filtering, chess and other game
programs, robots, et al. DATE the ORDINAL presumably complete mathematical
theory of artificial intelligence based on universal
induction-prediction-decision-action has been proposed. This
information-theoretic approach solidifies the foundations of inductive
inference and artificial intelligence. Getting the foundations right usually
marks a significant progress and maturing of a field. The theory provides a
gold standard and guidance for researchers working on intelligent algorithms.
The roots of universal induction have been laid exactly half-a-century ago and
the roots of universal intelligence exactly DATE. So it is timely to
take stock of what has been achieved and what remains to be done. Since there
are already good recent surveys, I describe the state-of-the-art only in
passing and refer the reader to the literature. This article concentrates on
the open problems in universal induction and its extension to universal
intelligence.","This paper studies sequence prediction based on the monotone NORP
complexity NORP m, i.e. based on universal deterministic/CARDINAL-part ORG. m is
extremely close to PERSON's universal prior M, the latter being an
excellent predictor in deterministic as well as probabilistic environments,
where performance is measured in terms of convergence of posteriors or losses.
Despite this closeness to M, it is difficult to assess the prediction quality
of m, since little is known about the closeness of their posteriors, which are
the important quantities for prediction. We show that for deterministic
computable environments, the ""posterior"" and losses of m converge, but rapid
convergence could only be shown on-sequence; the off-sequence convergence can
be slow. In probabilistic environments, neither the posterior nor the losses
converge, in general.",1
"We consider the wavelet transform of a finite, rooted, node-ranked, $p$-way
tree, focusing on the case of binary ($p = MONEY) trees. We study a ORG wavelet
transform on this tree. Wavelet transforms allow for multiresolution analysis
through translation and dilation of a wavelet function. We explore how this
works in our tree context.","Recurrent neurons, or ""simulated"" qubits, can store simultaneous true and
false with probabilistic behaviors usually reserved for the qubits of ORG
physics. Although possible to construct artificially, simulated qubits are
intended to explain biological mysteries. It is shown below that they can
simulate certain ORG computations and, although less potent than the qubits
of ORG, they nevertheless are shown to significantly exceed the
capabilities of classical deterministic circuits.",0
"Compressed Counting (ORG), based on maximally skewed stable random
projections, was recently proposed for estimating the p-th frequency moments of
data streams. The case p->1 is extremely useful for estimating FAC entropy
of data streams. In this study, we provide a very simple algorithm based on the
sample minimum estimator and prove a much improved sample complexity bound,
compared to prior results.","We propose skewed stable random projections for approximating the pth
frequency moments of dynamic data streams (0<p<=2), which has been frequently
studied in theoretical computer science and database communities. Our method
significantly (or even infinitely when GPE) improves previous methods based on
(symmetric) stable random projections.
  Our proposed method is applicable to data streams that are (a) insertion only
(the cash-register model); or (b) always non-negative (the strict EVENT
model), or (c) eventually non-negative at check points. This is only a minor
restriction for practical applications.
  Our method works particularly well when p = 1+/- \Delta and ORG is small,
which is a practically important scenario. For example, \Delta may be the decay
rate or interest rate, which are usually small. Of course, when \Delta = CARDINAL, CARDINAL
can compute the ORDINAL frequent moment (i.e., the sum) essentially error-free
using a simple couter. Our method may be viewed as a ``genearlized counter'' in
that it can count the total value in the future, taking in account of the
effect of decaying or interest accruement.
  In a summary, our contributions are CARDINAL. (A) This is the ORDINAL propsal
of skewed stable random projections. (B) Based on ORDINAL principle, we develop
various statistical estimators for skewed stable distributions, including their
variances and error (tail) probability bounds, and consequently the sample
complexity bounds.",1
"The PRODUCT was published in DATE, contains CARDINAL diagnostic categories,
described in CARDINAL pages. The PERSON appeared in DATE, contains CARDINAL diagnostic
categories, described in CARDINAL pages. The field of psychology is characterised by
a steady proliferation of diagnostic models and subcategories, that seems to be
inspired by the principle of ""divide and inflate"". This approach is in contrast
with experimental evidence, which suggests on one hand that traumas of various
kind are often present in the anamnesis of patients and, on the other, that the
gene variants implicated are shared across a wide range of diagnoses. In this
work I propose a holistic approach, built with tools borrowed from the field of
ORG. My model is based on CARDINAL pillars. The ORDINAL one is
trauma, which represents the attack to the mind, is psychological in nature and
has its origin in the environment. The ORDINAL pillar is dissociation, which
represents the mind defence in both physiological and pathological conditions,
and incorporates all other defence mechanisms. Damages to dissociation can be
considered as another category of attacks, that are neurobiological in nature
and can be of genetic or environmental origin. They include, among other
factors, synaptic over-pruning, abuse of drugs and inflammation. These factors
concur to weaken the defence, represented by the neural networks that implement
the dissociation mechanism in the brain. The model is subsequently used to
interpret CARDINAL mental conditions: DATE, complex PTSD, dissociative identity
disorder, schizophrenia and bipolar disorder. Ideally, this is a ORDINAL step
towards building a model that aims to explain a wider range of
psychopathological affections with a single theoretical framework. The last
part is dedicated to sketching a new psychotherapy for psychological trauma.","We introduce the open-ended, modular, self-improving Omega AI unification
architecture which is a refinement of PERSON's GPE architecture, as
considered from ORDINAL principles. The architecture embodies several crucial
principles of general intelligence including diversity of representations,
diversity of data types, integrated memory, modularity, and higher-order
cognition. We retain the basic design of a fundamental algorithmic substrate
called an ""AI kernel"" for problem solving and basic cognitive functions like
memory, and a larger, modular architecture that re-uses the kernel in many
ways. ORG includes CARDINAL representation languages and CARDINAL classes of neural
networks, which are briefly introduced. The architecture is intended to
initially address data science automation, hence it includes many problem
solving methods for statistical tasks. We review the broad software
architecture, higher-order cognition, self-improvement, modular neural
architectures, intelligent agents, the process and memory hierarchy, hardware
abstraction, peer-to-peer computing, and data abstraction facility.",0
"This paper discusses in layperson's terms human and computational studies of
the impact of threat and fear on exploration and creativity. A ORDINAL study
showed that both killifish from a lake with predators and from a lake without
predators explore a new environment to the same degree and plotting number of
new spaces covered over time generates a hump-shaped curve. However, for the
fish from the lake with predators the curve is shifted to the right; they take
longer. This pattern was replicated by a computer model of exploratory behavior
varying CARDINAL parameter, the fear parameter. A ORDINAL study showed that
stories inspired by threatening photographs were rated as more creative than
stories inspired by non-threatening photographs. Various explanations for the
findings are discussed.","Creativity is perhaps what most differentiates humans from other species. It
involves the capacity to shift between divergent and convergent modes of
thought in response to task demands. Divergent thought has been characterized
as the kind of thinking needed to generate multiple solutions, while convergent
thought has been characterized as the kind of thinking needed for tasks in with
CARDINAL solution. Divergent thought has been conceived of as reflecting on the task
from unconventional perspectives, while convergent thought has been conceived
of as reflecting on it from conventional perspectives. Personality traits
correlated with creativity include openness to experience, tolerance of
ambiguity, and self-confidence. Evidence that creativity is linked with
affective disorders is mixed. PERSON research using
electroencephalography (ORG) or functional magnetic resonance imaging (fMRI)
suggests that creativity is associated with a loosening of cognitive control
and decreased arousal. The distributed, content-addressable structure of
associative memory is conducive to bringing task-relevant items to mind without
the need for explicit search. Human creativity dates back to the earliest stone
tools over DATE, with the PERSON marking the onset of
art, science, and religion. Areas of controversy concern the relative
contributions of expertise, chance, and intuition, the importance of process
versus product, whether creativity is domain-specific versus domain-general,
the extent to which creativity is correlated with affective disorders, and
whether divergent thought entails the generation of multiple ideas or the
honing of a single initially ambiguous mental representation that may manifest
as different external outputs. Areas for further research include computational
modeling, the biological basis of creativity, and studies that track ideation
processes over time.",1
"Krentel PERSON System. GPE, DATE, pp.490--509] presented a framework for
an ORG optimization problem that searches an optimal value among
exponentially-many outcomes of polynomial-time computations. This paper expands
his framework to a quantum optimization problem using polynomial-time quantum
computations and introduces the notion of an ``universal'' quantum optimization
problem similar to a classical ``complete'' optimization problem. We exhibit a
canonical quantum optimization problem that is universal for the class of
polynomial-time quantum optimization problems. We show in a certain relativized
world that all quantum optimization problems cannot be approximated closely by
quantum polynomial-time computations. We also study the complexity of quantum
optimization problems in connection to well-known complexity classes.","This paper continues a systematic and comprehensive study on the structural
properties of ORG functions, which are in general multi-valued partial
functions computed by CARDINAL-way CARDINAL-head nondeterministic pushdown automata
equipped with write-only output tapes (or pushdown transducers), where ORG
refers to a relevance to context-free languages. The ORG functions tend to
behave quite differently from their corresponding context-free languages. We
extensively discuss containments, separations, and refinements among various
classes of functions obtained from the ORG functions by applying NORP
operations, functional composition, many-one relativization, and Turing
relativization. In particular, Turing relativization helps construct a
hierarchy over the class of ORG functions. We also analyze the computational
complexity of optimization functions, which are to find optimal values of ORG
functions, and discuss their relationships to the associated languages.",1
"The work is devoted to ORG) -- the
philosophical/mathematical platform and long-term project for redeveloping
classical logic after replacing truth} by computability in its underlying
semantics (see ORG). This article
elaborates some basic complexity theory for the ORG framework. Then it proves
soundness and completeness for the deductive system CL12 with respect to the
semantics of ORG, including the version of the latter based on polynomial time
computability instead of computability-in-principle. CL12 is a sequent calculus
system, where the meaning of a sequent intuitively can be characterized as ""the
succedent is algorithmically reducible to the antecedent"", and where formulas
are built from predicate letters, function letters, variables, constants,
identity, negation, parallel and choice connectives, and blind and choice
quantifiers. A case is made that CL12 is an adequate logical basis for
constructive applied theories, including complexity-oriented ones.","Inductive concept learning is the task of learning to assign cases to a
discrete set of classes. In real-world applications of concept learning, there
are many different types of cost involved. The majority of the machine learning
literature ignores all types of cost (unless accuracy is interpreted as a type
of cost measure). A few papers have investigated the cost of misclassification
errors. Very few papers have examined the many other types of cost. In this
paper, we attempt to create a taxonomy of the different types of cost that are
involved in inductive concept learning. This taxonomy may help to organize the
literature on cost-sensitive learning. We hope that it will inspire researchers
to investigate all types of cost in inductive concept learning in more depth.",0
"We present evidence for the existence of a quantum lower bound on the
PERSON-Hawking temperature of black holes. The suggested bound is supported
by a gedanken experiment in which a charged particle is dropped into a ORG
black hole. It is proved that the temperature of the final ORG
black-hole configuration is bounded from below by the relation
$PERSON r_{\text{H}}>(\hbar/r_{\text{H}})^2$, where $r_{\text{H}}$
is the horizon radius of the black hole.","The elegant `no short hair' theorem states that, if a spherically-symmetric
static black hole has hair, then this hair must extend beyond CARDINAL the horizon
radius. In the present paper we provide evidence for the failure of this
theorem beyond the regime of spherically-symmetric static black holes. In
particular, we show that rotating black holes can support extremely short-range
stationary scalar configurations (linearized scalar `clouds') in their exterior
regions. To that end, we solve analytically the PERSON-Gordon-Kerr-Newman wave
equation for a linearized massive scalar field in the regime of large scalar
masses.",1
"ORG algorithms are sequences of abstract operations, performed on
non-existent computers. They are in obvious need of categorical semantics. We
present some steps in this direction, following earlier contributions of
LOC, Coecke and Selinger. In particular, we analyze function abstraction
in quantum computation, which turns out to characterize its classical
interfaces. Some ORG algorithms provide feasible solutions of important
hard problems, such as factoring and discrete log (which are the building
blocks of modern cryptography). It is of a great practical interest to
precisely characterize the computational resources needed to execute such
ORG algorithms. There are many ideas how to build a ORG computer. Can
we prove some necessary conditions? Categorical semantics help with such
questions. We show how to implement an important family of ORG algorithms
using just NORP groups and relations.","The paper gives an account of a detailed investigation of the thermodynamic
branch as a path of the chemical system deviation from its isolated
thermodynamic equilibrium under an external impact. For a combination of direct
and reverse reactions in the same chemical system, full thermodynamic branch is
presented by an S-shaped curve, whose ends asymptotically achieve appropriate
initial states, which, in turn, are logistic ends of the opposite reactions.
The slope tangents of the steepest parts of the curves, the areas of the
maximum rate of the shift growth vs. the external thermodynamic force, occurred
to be directly proportional to the force and, simultaneously, linearly
proportional to the thermodynamic equivalent of chemical reaction, which is the
ratio between the amount in moles of any reaction participant, transformed in
an isolated system, along the reaction way from its initial state to
thermodynamic equilibrium, to its stoichiometric coefficient. The found
linearity is valid for arbitrary combination of the stoichiometric coefficients
in a reaction of compound synthesis from chemical elements like aA+bB=PERSON, and
confirms the exclusive role of the thermodynamic equivalent of transformation
as the chemical system characteristic of robustness and irreversibility.
Results of this work allow for quantitative evaluation of the chemical system
shift from thermodynamic equilibrium along thermodynamic branch and its rate
vs. the shifting force. Such an investigation became possible due to the
development of discrete thermodynamics of chemical equilibria.",0
"Let $PERSON be real-valued compactly supported sufficiently smooth function,
$q\in H^\ell_0(B_a)$, $MONEY: |x|\leq a, x\in R^3$ . It is proved that the
scattering data $PERSON,k)$ MONEY S^2$, $\forall k>0CARDINAL
determine $PERSON. here $A(\beta,\alpha,k)$ is the scattering amplitude,
corresponding to the potential $q$.","A simple proof is given for the explicit formula which allows one to recover
a CARDINALMONEY vector field $A=A(x)$ in MONEY, decaying at infinity,
from the knowledge of its $MONEY \times MONEY\nabla \cdot A$. The
representation of $MONEY as a sum of the gradient field and a divergence-free
vector fields is derived from this formula. Similar results are obtained for a
vector field in a bounded $C^2-$smooth domain.",1
"This paper describes a new mechanism that might help with defining pattern
sequences, by the fact that it can produce an upper bound on the ensemble value
that can persistently oscillate with the actual values produced from each
pattern. With every firing event, a node also receives an on/off feedback
switch. If the node fires, then it sends a feedback result depending on the
input signal strength. If the input signal is positive or larger, it can store
an 'on' switch feedback for the next iteration. If the signal is negative or
smaller, it can store an 'off' switch feedback for the next iteration. If the
node does not fire, then it does not affect the current feedback situation and
receives the switch command produced by the last active pattern event for the
same neuron. The upper bound therefore also represents the largest or most
enclosing pattern set and the lower value is for the actual set of firing
patterns. If the pattern sequence repeats, it will oscillate between the CARDINAL
values, allowing them to be recognised and measured more easily, over time.
Tests show that changing the sequence ordering produces different value sets,
which can also be measured.","This paper describes an automatic process for combining patterns and
features, to guide a search process and make predictions. It is based on the
functionality that a human brain might have, which is a highly distributed
network of simple neuronal components that can apply some level of matching and
cross-referencing over retrieved patterns. The process uses memory in a dynamic
way and it is directed through the pattern matching. DATE of the
paper describes the mechanisms for neuronal search, memory and prediction. The
ORDINAL CARDINAL of the paper then presents a formal language for defining cognitive
processes, that is, pattern-based sequences and transitions. The language can
define an outer framework for nested pattern sets that can be linked to perform
the cognitive act. The language also has a mathematical basis, allowing for the
rule construction process to be systematic and consistent. The new information
can be used to integrate the cognitive model together. A theory about linking
can suggest that only (mostly) nodes that represent the same thing link
together.",1
"In DATE, deep artificial neural networks (including recurrent ones)
have won numerous contests in pattern recognition and machine learning. This
historical survey compactly summarises relevant work, much of it from the
previous millennium. Shallow and deep learners are distinguished by the depth
of their credit assignment paths, which are chains of possibly learnable,
causal links between actions and effects. I review deep supervised learning
(also recapitulating the history of backpropagation), unsupervised learning,
reinforcement learning & evolutionary computation, and indirect search for
short programs encoding deep and large networks.","The probability distribution P from which the history of our universe is
sampled represents a theory of everything or ORG. We assume P is formally
describable. Since most (uncountably many) distributions are not, this imposes
a strong inductive bias. We show that P(x) is small for any universe x lacking
a short description, and study the spectrum of TOEs spanned by CARDINAL Ps, CARDINAL
reflecting the most compact constructive descriptions, the other the fastest
way of computing everything. The former derives from generalizations of
traditional computability, PERSON's algorithmic probability, NORP
complexity, and objects more random than PERSON's ORG, the latter from
PERSON's universal search and a natural resource-oriented postulate: the
cumulative prior probability of all x incomputable within time t by this
optimal algorithm should be QUANTITY both PERSON we find a universal
cumulatively enumerable measure that dominates traditional enumerable measures;
any such ORG must assign low probability to any universe lacking a short
enumerating program. We derive P-specific consequences for evolving observers,
inductive reasoning, ORG, philosophy, and the expected duration of
our universe.",1
"We present a formula that relates the variations of the area of extreme
throat initial data with the variation of an appropriate defined mass
functional. From this expression we deduce that the ORDINAL variation, with fixed
angular momentum, of the area is CARDINAL and the ORDINAL variation is positive
definite evaluated at the extreme ORG throat initial data. This indicates that
the area of the extreme ORG throat initial data is a minimum among this class
of data. And hence the area of generic throat initial data is bounded from
below by the angular momentum. Also, this result strongly suggests that the
inequality between area and angular momentum holds for generic asymptotically
flat axially symmetric black holes. As an application, we prove this inequality
in the non trivial family of spinning PERSON initial data.","This paper considers the relevance of the concepts of observability and
computability in physical theory. Observability is related to verifiability
which is essential for effective computing and as physical systems are
computational systems it is important even where explicit computation is not
the goal. Specifically, we examine CARDINAL problems: observability and
computability for ORG computing, and remote measurement of time and
frequency.",0
"Steganography is the science of hiding digital information in such a way that
no one can suspect its existence. Unlike cryptography which may arouse
suspicions, steganography is a stealthy method that enables data communication
in total secrecy. Steganography has many requirements, the foremost one is
irrecoverability which refers to how hard it is for someone apart from the
original communicating parties to detect and recover the hidden data out of the
secret communication. A good strategy to guaranteeirrecoverability is to cover
the secret data not usinga trivial method based on a predictable algorithm, but
using a specific random pattern based on a mathematical algorithm. This paper
proposes an image steganography technique based on ORG edge detection
algorithm.It is designed to hide secret data into a digital image within the
pixels that make up the boundaries of objects detected in the image. More
specifically, bits of the secret data replace the CARDINAL LSBs of every color
channel of the pixels detected by the ORG edge detection algorithm as part of
the edges in the carrier image. Besides, the algorithm is parameterized by
CARDINAL parameters: The size of the NORP filter, a low threshold value, and a
high threshold value. These parameters can yield to different outputs for the
same input image and secret data. As a result, discovering the inner-workings
of the algorithm would be considerably ambiguous, misguiding steganalysts from
the exact location of the covert data. Experiments showed a simulation tool
codenamed ORG, meant to cover and uncover secret data using the proposed
algorithm. As future work, examining how other image processing techniques such
as brightness and contrast adjustment can be taken advantage of in
steganography with the purpose ofgiving the communicating parties more
preferences tomanipulate their secret communication.","A definition of causality introduced by ORG and GPE, which uses
structural equations, is reviewed. A more refined definition is then
considered, which takes into account issues of normality and typicality, which
are well known to affect causal ascriptions. Causality is typically an
all-or-nothing notion: either A is a cause of B or it is not. An extension of
the definition of causality to capture notions of degree of responsibility and
degree of blame, due to ORG and ORG, is reviewed. For example, if
someone wins an election 11-0, then each person who votes for him is less
responsible for the victory than if he had won CARDINAL-5. Degree of blame takes into
account an agent's epistemic state. Roughly speaking, the degree of blame of A
for B is the expected degree of responsibility of A for B, taken over the
epistemic state of an agent. Finally, the structural-equations definition of
causality is compared to PERSON's NESS test.",0
"The paper describes some basic approaches to detection of bottlenecks in
composite (modular) systems. The following basic system bottlenecks detection
problems are examined: (CARDINAL) traditional quality management approaches (Pareto
chart based method, multicriteria analysis as selection of Pareto-efficient
points, and/or multicriteria ranking), (CARDINAL) selection of critical system
elements (critical components/modules, critical component interconnection), (CARDINAL)
selection of interconnected system components as composite system faults (via
clique-based fusion), (CARDINAL) critical elements (e.g., nodes) in networks, and (CARDINAL)
predictive detection of system bottlenecks (detection of system components
based on forecasting of their parameters). Here, heuristic solving schemes are
used. Numerical examples illustrate the approaches.","This paper addresses the problem of measurement errors in causal inference
and highlights several algebraic and graphical methods for eliminating
systematic bias induced by such errors. In particulars, the paper discusses the
control of partially observable confounders in parametric and non parametric
models and the computational problem of obtaining bias-free effect estimates in
such models.",0
"In the process of recording, storage and transmission of time-domain audio
signals, errors may be introduced that are difficult to correct in an
unsupervised way. Here, we train a convolutional deep neural network to
re-synthesize input time-domain speech signals at its output layer. We then use
this abstract transformation, which we call a deep transform (ORG), to perform
probabilistic re-synthesis on further speech (of the same speaker) which has
been degraded. Using the convolutive ORG, we demonstrate the recovery of speech
audio that has been subject to extreme degradation. This approach may be useful
for correction of errors in communications devices.","Deep neural networks (DNN) abstract by demodulating the output of linear
filters. In this article, we refine this definition of abstraction to show that
the inputs of a DNN are abstracted with respect to the filters. Or, to restate,
the abstraction is qualified by the filters. This leads us to introduce the
notion of qualitative projection. We use qualitative projection to abstract
MNIST hand-written digits with respect to the various dogs, horses, planes and
cars of the ORG dataset. We then classify the MNIST digits according to the
magnitude of their dogness, horseness, planeness and carness qualities,
illustrating the generality of qualitative projection.",1
"Reductionism has dominated science and philosophy for DATE. Complexity
has recently shown that interactions---which reductionism neglects---are
relevant for understanding phenomena. When interactions are considered,
reductionism becomes limited in several aspects. In this paper, I argue that
interactions imply non-reductionism, non-materialism, non-predictability,
NORP, and non-nihilism. As alternatives to each of these, holism,
informism, adaptation, contextuality, and meaningfulness are put forward,
respectively. A worldview that includes interactions not only describes better
our world, but can help to solve many open scientific, philosophical, and
social problems caused by implications of reductionism.","The scope of this teaching package is to make a brief induction to ORG (ANNs) for people who have no previous knowledge of them. We
ORDINAL make a brief introduction to models of networks, for then describing in
general terms ANNs. As an application, we explain the backpropagation
algorithm, since it is widely used and many other algorithms are derived from
it. The user should know algebra and the handling of functions and vectors.
Differential calculus is recommendable, but not necessary. The contents of this
package should be understood by people with high school education. It would be
useful for people who are just curious about what are ANNs, or for people who
want to become familiar with them, so when they study them more fully, they
will already have clear notions of ANNs. Also, people who only want to apply
the backpropagation algorithm without a detailed and formal explanation of it
will find this material useful. This work should not be seen as ""Nets for
dummies"", but of course it is not a treatise. Much of the formality is skipped
for the sake of simplicity. Detailed explanations and demonstrations can be
found in the referred readings. The included exercises complement the
understanding of the theory. The on-line resources are highly recommended for
extending this brief induction.",1
"A novel linking mechanism has been described previously [CARDINAL] that can be used
to autonomously link sources that provide related answers to queries executed
over an information network. The test query platform has now been re-written
resulting in essentially a new test platform using the same basic query
mechanism, but with a slightly different algorithm. This paper describes recent
test results on the same query test process that supports the original findings
and also shows the effectiveness of the linking mechanism in a new set of test
scenarios.","Concept Trees are a type of database that can organise arbitrary textual
information using a very simple rule. Each tree ideally represents a single
cohesive concept and the trees can link with each other for navigation and
semantic purposes. The trees are therefore a type of semantic network and would
benefit from having a consistent level of context for each of the nodes. The
tree nodes have a mathematical basis allowing for a consistent build process.
These would represent nouns or verbs in a text sentence, for example. A basic
test on text documents shows that the tree structure could be inherent in
natural language. New to the design can then be lists of descriptive elements
for each of the nodes. The descriptors can also be weighted, but do not have to
follow the strict counting rule of the tree nodes. With the new descriptive
layers, a much richer type of knowledge can be achieved and a consistent method
for adding context is suggested. It is also suggested to use the linking
structure of the licas system as a basis for the context links. The
mathematical model is extended further and to finish, a query language is
suggested for practical applications.",1
"Despite its size and complexity, the human cortex exhibits striking
anatomical regularities, suggesting there may simple meta-algorithms underlying
cortical learning and computation. We expect such meta-algorithms to be of
interest since they need to operate quickly, scalably and effectively with
little-to-no specialized assumptions.
  This note focuses on a specific question: How can neurons use vast quantities
of unlabeled data to speed up learning from the comparatively rare labels
provided by reward systems? As a partial answer, we propose randomized
co-training as a biologically plausible meta-algorithm satisfying the above
requirements. As evidence, we describe a biologically-inspired algorithm,
ORG (ORG) that achieves state-of-the-art performance in
semi-supervised learning, and sketch work in progress on a neuronal
implementation.","We consider a FAC setup where an agent interacts with an
environment in observation-reward-action cycles without any (esp.\ ORG)
assumptions on the environment. State aggregation and more generally feature
reinforcement learning is concerned with mapping histories/raw-states to
reduced/aggregated states. The idea behind both is that the resulting reduced
process (approximately) forms a small stationary finite-state ORG, which can
then be efficiently solved or learnt. We considerably generalize existing
aggregation results by showing that even if the reduced process is not an ORG,
the (q-)value functions and (optimal) policies of an associated ORG with same
state-space size solve the original problem, as long as the solution can
approximately be represented as a function of the reduced states. This implies
an upper bound on the required state space size that holds uniformly for all RL
problems. It may also explain why PERSON algorithms designed for MDPs sometimes
perform well beyond MDPs.",0
"The paper examines the problem of accessing a vector memory from a single
neuron in a NORP neural network. It begins with the review of the author's
earlier method, which is different from the GPE model in that it recruits
neighboring neurons by spreading activity, making it possible for single or
group of neurons to become associated with vector memories. Some open issues
associated with this approach are identified. It is suggested that fragments
that generate stored memories could be associated with single neurons through
local spreading activity.","An ultrametric topology formalizes the notion of hierarchical structure. An
ultrametric embedding, referred to here as ultrametricity, is implied by a
hierarchical embedding. Such hierarchical structure can be global in the data
set, or local. By quantifying extent or degree of ultrametricity in a data set,
we show that ultrametricity becomes pervasive as dimensionality and/or spatial
sparsity increases. This leads us to assert that very high dimensional data are
of simple structure. We exemplify this finding through a range of simulated
data cases. We discuss also application to very high frequency time series
segmentation and modeling.",0
"The theory of controlled ORG open systems describes ORG systems
interacting with ORG environments and influenced by external forces varying
according to given algorithms. It is aimed, for instance, to model quantum
devices which can find applications in the future technology based on quantum
information processing. CARDINAL of the main problems making difficult the practical
implementations of ORG information theory is the fragility of quantum
states under external perturbations. The aim of this note is to present the
relevant results concerning ergodic properties of open ORG systems which
are useful for the optimization of quantum devices and noise (errors)
reduction. In particular we present mathematical characterization of the
so-called ""decoherence-free subspaces"" for discrete and continuous-time quantum
dynamical semigroups in terms of MONEY and group representations. We
analyze the NORP models also, presenting the formulas for errors in
the PERSON approximation. The obtained results are used to discuss the proposed
different strategies of error reduction.","In this paper a knowledge representation model are proposed, FP5, which
combine the ideas from fuzzy sets and penta-valued logic. FP5 represents
imprecise properties whose accomplished degree is undefined, contradictory or
indeterminate for some objects. Basic operations of conjunction, disjunction
and negation are introduced. Relations to other representation models like
fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are
discussed.",0
"We show by example that the associative law does not hold for tensor products
in the category of general (not necessarily locally convex) topological vector
spaces. The same pathology occurs for tensor products of ORG abelian
topological groups.","In categorical quantum mechanics, classical structures characterize the
classical interfaces of quantum resources on one hand, while on the other hand
giving rise to some quantum phenomena. In the standard PERSON space model of
quantum theories, classical structures over a space correspond to its
orthonormal bases. In the present paper, we show that classical structures in
the category of relations correspond to biproducts of NORP groups. Although
relations are, of course, not an interesting model of quantum computation, this
result has some interesting computational interpretations. If relations are
viewed as denotations of nondeterministic programs, it uncovers a wide variety
of non-standard quantum structures in this familiar area of classical
computation. Ironically, it also opens up a version of what in philosophy of
quantum mechanics would be called an ontic-epistemic gap, as it provides no
direct interface to these nonstandard quantum structures.",0
"We give necessary and sufficient conditions under which a density matrix
acting on a CARDINAL tensor product space is separable. Our conditions are
given in terms of ORG.","DATE has seen the nascency of the ORDINAL
mathematical theory of general artificial intelligence. This theory of
ORG (ORG) has made significant contributions to
many theoretical, philosophical, and practical AI questions. In a series of
papers culminating in book (GPE, DATE), an exciting sound and complete
mathematical model for a super intelligent agent (AIXI) has been developed and
rigorously analyzed. While nowadays most ORG researchers avoid discussing
intelligence, the award-winning WORK_OF_ART thesis (DATE) provided the
philosophical embedding and investigated the ORG-based universal measure of
rational intelligence, which is formal, objective and non-anthropocentric.
Recently, effective approximations of AIXI have been derived and experimentally
investigated in GPE paper (Veness et al. 2011). This practical breakthrough
has resulted in some impressive applications, finally muting earlier critique
that ORG is only a theory. For the ORDINAL time, without providing any domain
knowledge, the same agent is able to self-adapt to a diverse range of
interactive environments. For instance, AIXI is able to learn from scratch to
play TicTacToe, PERSON, PERSON, and other games by trial and error, without
even providing the rules of the games.
  These achievements give new hope that the grand goal of ORG is not elusive.
  This article provides an informal overview of ORG in context. It attempts to
gently introduce a very theoretical, formal, and mathematical subject, and
discusses philosophical and technical ingredients, traits of intelligence, some
social questions, and the past and future of ORG.",0
"A plethora of natural, artificial and social systems exist which do not
belong to FAC (GPE) statistical-mechanical world, based on the
standard additive entropy $PERSON and its associated exponential GPE factor.
Frequent behaviors in such complex systems have been shown to be closely
related to $q$-statistics instead, based on the nonadditive entropy $S_q$ (with
$S_1=S_{BG}$), and its associated $q$-exponential factor which generalizes the
usual GPE one. In fact, a wide range of phenomena of quite different nature
exist which can be described and, in the simplest cases, understood through
analytic (and explicit) functions and probability distributions which exhibit
some universal features. Universality classes are concomitantly observed which
can be characterized through indices such as $q$. We will exhibit here some
such cases, namely concerning the distribution of inter-occurrence (or
inter-event) times in the areas of finance, earthquakes and genomes.","Decision theory formally solves the problem of rational agents in uncertain
worlds if the true environmental prior probability distribution is known.
PERSON's theory of universal induction formally solves the problem of
sequence prediction for unknown prior distribution. We combine both ideas and
get a parameterless theory of universal ORG. We give strong
arguments that the resulting AIXI model is the most intelligent unbiased agent
possible. We outline for a number of problem classes, including sequence
prediction, strategic games, function minimization, reinforcement and
supervised learning, how the AIXI model can formally solve them. The major
drawback of the AIXI model is that it is uncomputable. To overcome this
problem, we construct a modified algorithm AIXI-tl, which is still effectively
more intelligent than any other time t and space l bounded agent. The
computation time of AIXI-tl is of the order tx2^l. Other discussed topics are
formal definitions of intelligence order relations, the horizon problem and
relations of the AIXI theory to other ORG approaches.",0
"These informal notes briefly discuss some basic topics in harmonic analysis
along the lines of convolutions and PERSON transforms.","These informal notes are concerned with spaces of functions in various
situations, including continuous functions on topological spaces, holomorphic
functions of CARDINAL or more complex variables, and so on.",1
"Data based judgments go into artificial intelligence applications but they
undergo paradoxical reversal when seemingly unnecessary additional data is
provided. Examples of this are PERSON's reversal and the disjunction effect
where the beliefs about the data change once it is presented or aggregated
differently. Sometimes the significance of the difference can be evaluated
using statistical tests such as ORG's chi-squared or ORG's exact test,
but this may not be helpful in threshold-based decision systems that operate
with incomplete information. To mitigate risks in the use of algorithms in
decision-making, we consider the question of modeling of beliefs. We argue that
evidence supports that beliefs are not classical statistical variables and they
should, in the general case, be considered as superposition states of disjoint
or polar outcomes. We analyze the disjunction effect from the perspective of
the belief as a ORG vector.","Recently, PERSON and coworkers have been able to measure the information
content of digital organisms living in their {\em Avida} artificial life
system. They show that over time, the organisms behave like ORG's demon,
accreting information (or complexity) as they evolve. In {\em Avida} the
organisms don't interact with each other, merely reproduce at a particular rate
(their fitness), and attempt to evaluate an externally given arithmetic
function in order win bonus fitness points. Measuring the information content
of a digital organism is essentially a process of counting the number of
genotypes that give rise to the same phenotype.
  Whilst PERSON organisms have a particularly simple phenotype, GPE
organisms interact with each other, giving rise to an ecology of phenotypes. In
this paper, I discuss techniques for comparing pairs of GPE organisms to
determine if they are phenotypically equivalent. I then discuss a method for
computing an estimate of the number of phenotypically equivalent genotypes that
is more accurate than the ``hot site'' estimate used by PERSON's group. Finally,
I report on an experimental analysis of a ORG run.",0
"The orbital dynamics of a test particle moving in the non-spherically
symmetric field of a rotating oblate primary is impacted also by certain
indirect, mixed effects arising from the interplay of the different NORP
and NORP accelerations which induce known direct perturbations. We
systematically calculate the indirect gravitoelectromagnetic shifts per orbit
of the NORP orbital elements of the test particle arising from the
crossing among the ORDINAL even zonal harmonic MONEY of the central body and the
NORP static and stationary components of its gravitational field. We
also work out the NORP shifts per orbit of order $J_2^MONEY, and the direct
NORP gravitoelectric effects of order $J_2 c^{-2}$ arising from the
equations of motion. In the case of both the indirect and direct
gravitoelectric $J_2 c^{-2}$ shifts, our calculation holds for an arbitrary
orientation of the symmetry axis of the central body. We yield numerical
estimates of their relative magnitudes for systems ranging from LOC
artificial satellites to stars orbiting supermassive black holes.","It has often been claimed that the proposed LOC artificial satellite
LARES/WEBER-SAT-whose primary goal is, in fact, the measurement of the general
relativistic PERSON effect at a some percent level-would allow to
greatly improve, among (many) other things, the present-day (10^-13) level of
accuracy in testing the equivalence principle as well. Recent claims point
towards even CARDINAL orders of magnitude better, i.e. CARDINAL^-15. In this note we show
that such a goal is, in fact, unattainable by many orders of magnitude being,
instead, the achievable level of the order of CARDINAL^-9.",1
"In this paper, we define a new information theoretic measure that we call the
""uprooted information"". We show that a necessary and sufficient condition for a
probability $P(s|do(t))$ to be ""identifiable"" (in the sense of GPE) in a
graph $MONEY is that its uprooted information be non-negative for all models of
the graph $PERSON In this paper, we also give a new algorithm for deciding, for a
NORP net that is NORP, whether a probability $P(s|do(t))$ is
identifiable, and, if it is identifiable, for expressing it without allusions
to confounding variables. Our algorithm is closely based on a previous
algorithm by GPE and GPE, but seems to correct a small flaw in theirs. In
this paper, we also find a {ORG necessary and sufficient graphical condition}
for a probability $P(s|do(t))$ to be identifiable when $t$ is a singleton set.
So far, in the prior literature, it appears that only a {ORG sufficient
graphical condition} has been given for this. By ""graphical"" we mean that it is
directly based on PERSON CARDINAL rules of do-calculus.","In a previous paper, we described a computer program called Qubiter which can
decompose an arbitrary unitary matrix into elementary operations of the type
used in quantum computation. In this paper, we describe a method of reducing
the number of elementary operations in such decompositions.",1
"This presentation's Part CARDINAL studies the evolutionary information processes and
regularities of evolution dynamics, evaluated by an entropy functional (EF) of
a random field (modeled by a diffusion information process) and an
informational path functional (ORG) on trajectories of the related dynamic
process (DATE). The integral information measure on the process'
trajectories accumulates and encodes inner connections and dependencies between
the information states, and contains more information than a sum of FAC's
entropies, which measures and encodes each process's states separately. Cutting
off the process' measured information under action of impulse controls (PERSON
2012a), extracts and reveals hidden information, covering the states'
correlations in a multi-dimensional random process, and implements the EF-IPF
minimax variation principle (VP). The approach models an information observer
(Lerner 2012b)-as an extractor of such information, which is able to convert
the collected information of the random process in the information dynamic
process and organize it in the hierarchical information network (IN), NORP
(PERSON, DATE). The IN's highest level of the structural hierarchy, measured
by a maximal quantity and quality of the accumulated cooperative information,
evaluates the observer's intelligence level, associated with its ability to
recognize and build such structure of a meaningful hidden information. The
considered evolution of optimal extraction, assembling, cooperation, and
organization of this information in the IN, satisfying the VP, creates the
phenomena of an evolving observer's intelligence. The requirements of
preserving the evolutionary hierarchy impose the restrictions that limit the
observer's intelligence level in the IN. The cooperative information geometry,
evolving under observations, limits the size and volumes of a particular
observer.","Hidden information emerges under impulse interactions with PERSON diffusion
process modeling interactive random environment. Impulse yes no action cuts
PERSON correlations revealing Bit of hidden information connected correlated
states. Information appears phenomenon of interaction cutting correlations
carrying entropy. Each inter action models PERSON impulse, ORG interaction between the PERSON impulses. Each impulse step down
action cuts maximum of impulse minimal entropy and impulse step up action
transits cutting minimal entropy to each step up action of merging delta
function. LOC step down action kills delivering entropy producing equivalent
minimax information. The merging action initiates ORG microprocess.
Multiple cutting entropy is converting to information micro macroprocess.
Cutting impulse entropy integrates entropy functional EF along trajectories of
multidimensional diffusion process. Information which delivers ending states of
each impulse integrates information path functional ORG along process
trajectories. Hidden information evaluates ORG kernel whose minimal path
transforms PERSON transition probability to probability of NORP diffusion.
Each transitive transformation virtually observes origin of hidden information
probabilities correlated states. ORG integrates observing ORG along minimal
path assembling information PRODUCT. Minimax imposes variation principle on EF
and ORG whose extreme equations describe observing micro and macroprocess which
describes irreversible thermodynamics. Hidden information curries free
information frozen from correlated connections. Free information binds
observing micro macro processes in information macrodynamics. Each dynamic
CARDINAL free information composes triplet structures. CARDINAL structural triplets
assemble information network. Triple networks free information cooperate
information ORG.",1
"In this article, we construct the axialvector-diquark-axialvector-antidiquark
type tensor current to interpolate both the vector and axialvector tetraquark
states, then calculate the contributions of the vacuum condensates up to
dimension-10 in the operator product expansion, and obtain the ORG sum rules
for both the vector and axialvector tetraquark states. The numerical results
support assigning the MONEY to be the $MONEY
diquark-antidiquark type tetraquark state, and assigning the $Y(4660)$ to be
the $J^{PC}=1^{--}$ diquark-antidiquark type tetraquark state. Furthermore, we
take the $Y(4260)$ and $Y(4360)$ as the mixed charmonium-tetraquark states, and
construct the QUANTITY type tensor currents to study the masses and
pole residues. The numerical results support assigning the $PERSON and
$Y(4360)$ to be the mixed charmonium-tetraquark states.","ORG called also ORG (ORG) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. CARDINAL of the most important
open questions seems to be the relationship between frequencies and the
ORG. The theory is blamed to leave frequencies
outside (or aside of) its framework. The seriousness of this accusation is
obvious: (CARDINAL) no experiment may be run to compare the performance of ORG-based
models of real world processes against real world data, (CARDINAL) data may not serve
as foundation for construction of an appropriate belief model.
  In this paper we develop a frequentist interpretation of the ORG bringing to
fall the above argument against ORG. An immediate consequence of it is the
possibility to develop algorithms acquiring automatically ORG belief models
from data. We propose CARDINAL such algorithms for various classes of belief model
structures: for tree structured belief networks, for poly-tree belief networks
and for general type belief networks.",0
"This paper initiates a systematic study of ORG functions, which are
(partial) functions defined in terms of quantum mechanical computations. Of all
quantum functions, we focus on resource-bounded quantum functions whose inputs
are classical bit strings. We prove complexity-theoretical properties and
unique characteristics of these quantum functions by recent techniques
developed for the analysis of quantum computations. We also discuss relativized
ORG functions that make adaptive and nonadaptive oracle queries.","The present article introduces ptarithmetic (short for ""polynomial time
arithmetic"") -- a formal number theory similar to the well known Peano
arithmetic, but based on the recently born computability logic (see
ORG) instead of classical logic. The
formulas of ptarithmetic represent interactive computational problems rather
than just true/false statements, and their ""truth"" is understood as existence
of a polynomial time solution. The system of ptarithmetic elaborated in this
article is shown to be sound and complete. Sound in the sense that every
theorem T of the system represents an interactive number-theoretic
computational problem with a polynomial time solution and, furthermore, such a
solution can be effectively extracted from a proof of NORP And complete in the
sense that every interactive number-theoretic problem with a polynomial time
solution is represented by some theorem T of the system.
  The paper is self-contained, and can be read without any previous familiarity
with computability logic.",0
"We implement PERSON machine on a plasmodium of true slime mold
{\em Physarum polycephalum}. We provide experimental findings on realization of
the machine instructions, illustrate basic operations, and elements of
programming.","A phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A
phyllosilicate PERSON is a regular network of finite state machines ---
silicon nodes and oxygen nodes --- which mimics structure of the
phyllosilicate. A node takes states CARDINAL and CARDINAL. Each node updates its state in
discrete time depending on a sum of states of its CARDINAL (silicon) or CARDINAL
(oxygen) neighbours. Phyllosilicate automata exhibit localizations attributed
to ORG: gliders, oscillators, still lifes, and a glider gun.
Configurations and behaviour of typical localizations, and interactions between
the localizations are illustrated.",1
"The paper sets forth comprehensive basics of WORK_OF_ARTPERSON (ORG), developed by the author during DATE and
spread over series of publications. Based on the linear equations of
irreversible thermodynamics, ORG definition of the thermodynamic force,
and FAC principle, ORG brings forward a notion of chemical
equilibrium as a balance of internal and external thermodynamic forces, acting
against a chemical system. The basic expression of ORG is a logistic map that
ties together energetic characteristics of the chemical transformation in the
system, its deviation from true thermodynamic equilibrium, and the sum of
thermodynamic forces, causing that deviation. System deviation from
thermodynamic equilibrium is the major variable of the theory. Solutions to the
basic map define the chemical system domain of states comprising bifurcation
diagrams with CARDINAL areas, from true thermodynamic equilibrium to chaos, having
specific distinctive meaning for chemical systems. The theory is derived from
the currently recognized ideas of chemical thermodynamics and binds classical
and contemporary thermodynamics of chemical equilibria into a unique concept.
ORG opens new opportunities in understanding and analysis of equilibria in
chemical systems. Some new results, included in the paper, have never been
published before.","The method of ""random PERSON features (ORG)"" has become a popular tool for
approximating the ""radial basis function (ORG)"" kernel. The variance of ORG is
actually large. Interestingly, the variance can be substantially reduced by a
simple normalization step as we theoretically demonstrate. We name the improved
scheme as the ""normalized PERSON (NRFF)"".
  We also propose the ""generalized PERSON (ORG)"" kernel as a measure of data
similarity. ORG is positive definite as there is an associated hashing method
named ""generalized consistent weighted sampling (GPE)"" which linearizes this
nonlinear kernel. We provide an extensive empirical evaluation of the ORG
kernel and the ORG kernel on CARDINAL publicly available datasets. For a
majority of the datasets, the (tuning-free) ORG kernel outperforms the
best-tuned ORG kernel.
  We conduct extensive experiments for comparing the linearized RBF kernel
using ORG with the linearized ORG kernel using GPE. We observe that, to reach
a comparable classification accuracy, GPE typically requires substantially
fewer samples than ORG, even on datasets where the original ORG kernel
outperforms the original ORG kernel. The empirical success of GPE (compared to
ORG) can also be explained from a theoretical perspective. ORDINAL, the
relative variance (normalized by the squared expectation) of GPE is
substantially smaller than that of ORG, except for the very high similarity
region (where the variances of both methods are close to zero). ORDINAL, if we
make a model assumption on the data, we can show analytically that GPE
exhibits much smaller variance than ORG for estimating the same object (e.g.,
the ORG kernel), except for the very high similarity region.",0
"This paper describes an ORG method (called ORG) to generate predefined arbitrarily shaped CARDINAL-dimensional arrays of
cells by means of evolutionary techniques. It is based on a model of
development, whose key features are: i) the distinction bewteen ``normal'' and
``driver'' cells, being the latter able to receive guidance from the genome,
ii) the implementation of the proliferation/apoptosis events in such a way that
many cells are created/deleted at once, in order to speed-up the morphogenetic
process. iii) the presence in driver cells of an epigenetic memory, that holds
the position of the cell in the cell lineage tree and represents the source of
differentiation during development. The experiments performed with a number of
100x100 black and white and colour target shapes (the horse, the couple, the
hand, the dolphin, the map of GPE, the foot, the frog, the baby, the
stomach, the NORP flag, the head) bring to the conclusion that the method
described is able to generate any target shape, outperforming any other known
method in terms of size and variety of the generated shapes. The interpretation
of the proposed method as a model of embryogenesis and its biological
implications are discussed.","Methods to find correlation among variables are of interest to many
disciplines, including statistics, machine learning, (big) data mining and
neurosciences. Parameters that measure correlation between CARDINAL variables are of
limited utility when used with multiple variables. In this work, I propose a
simple criterion to measure correlation among an arbitrary number of variables,
based on a data set. The central idea is to i) design a function of the
variables that can take different forms depending on a set of parameters, ii)
calculate the difference between a statistics associated to the function
computed on the data set and the same statistics computed on a randomised
version of the data set, called ""scrambled"" data set, and iii) optimise the
parameters to maximise this difference. Many such functions can be organised in
layers, which can in turn be stacked CARDINAL on top of the other, forming a neural
network. The function parameters are searched with an enhanced genetic
algortihm called POET and the resulting method is tested on a cancer gene data
set. The method may have potential implications for some issues that affect the
field of neural networks, such as overfitting, the need to process huge amounts
of data for training and the presence of ""adversarial examples"".",1
"PERSON's original definition of default logic allows for the application of a
default that contradicts a previously applied one. We call failure this
condition. The possibility of generating failures has been in the past
considered as a semantical problem, and variants have been proposed to solve
it. We show that it is instead a computational feature that is needed to encode
some domains into default logic.","We show that the separability of states in ORG has a close
counterpart in classical physics, and that conditional mutual information
(a.k.a. conditional information transmission) is a very useful quantity in the
study of both quantum and classical separabilities. We also show how to define
entanglement of formation in terms of conditional mutual information. This
paper lays the theoretical foundations for a sequel paper which will present a
computer program that can calculate a decomposition of any separable quantum or
classical state.",0
"The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems.","Non deterministic applications arise in many domains, including, stochastic
optimization, multi-objectives optimization, stochastic planning, contingent
stochastic planning, reinforcement learning, reinforcement learning in
partially observable PERSON decision processes, and conditional planning. We
present a logic programming framework called non deterministic logic programs,
along with a declarative semantics and fixpoint semantics, to allow
representing and reasoning about inherently non deterministic real-world
applications. The language of non deterministic logic programs framework is
extended with non-monotonic negation, and CARDINAL alternative semantics are
defined: the stable non deterministic model semantics and the well-founded non
deterministic model semantics as well as their relationship is studied. These
semantics subsume the deterministic stable model semantics and the
deterministic well-founded semantics of deterministic normal logic programs,
and they reduce to the semantics of deterministic definite logic programs
without negation. We show the application of the non deterministic logic
programs framework to a conditional planning problem.",0
"Deep neural networks (DNN) are the state of the art on many engineering
problems such as computer vision and audition. A key factor in the success of
the DNN is scalability - bigger networks work better. However, the reason for
this scalability is not yet well understood. Here, we interpret the DNN as a
discrete system, of ORG filters followed by nonlinear activations, that is
subject to the laws of sampling theory. In this context, we demonstrate that
over-sampled networks are more selective, learn faster and learn more robustly.
Our findings may ultimately generalize to the human brain.","Regularisation of deep neural networks (DNN) during training is critical to
performance. By far the most popular method is known as dropout. Here, cast
through the prism of signal processing theory, we compare and contrast the
regularisation effects of dropout with those of dither. We illustrate some
serious inherent limitations of dropout and demonstrate that dither provides a
more effective regulariser.",1
"Importance sampling and PERSON sampling (of which GPE sampling
is a special case) are CARDINAL methods commonly used to sample multi-variate
probability distributions (that is, NORP networks). Heretofore, the
sampling of NORP networks has been done on a conventional ""classical
computer"". In this paper, we propose methods for doing importance sampling and
PERSON sampling of a classical NORP network on a quantum
computer.","In spite of all {\bf no-go} PERSON (e.g., PERSON, GPE and
NORP,..., ORG,...) we constructed a realist basis of quantum mechanics. In
our model both classical and quantum spaces b are rough images of the
fundamental {\bf prespace.} ORG mechanics cannot be reduced to classical
one. Both classical and quantum representations induce reductions of prespace
information.",0
"Since PERSON, finite automata theory has been inspired by physics, in
particular by ORG complementarity. We review automaton complementarity,
reversible automata and the connections to generalized urn models. Recent
developments in quantum information theory may have appropriate formalizations
in the GPE context.","Gray (DATE) argued that the GPE paradox (PERSON) is a misnomer, and it is not a
valid paradox. Gray also speculated that the argument was misattributed to
GPE, whose lunchtime remarks did not pertain to the existence of
extraterrestrial intelligence, but to the feasibility of interstellar travel.
Instead, the paradox is ascribed to LAW, and it is further
suggested that the paradox is not a real problem or research subject and should
not be used in debates about ORG projects. The arguments given are
unpersuasive, ahistorical, and, in CARDINAL instance, clearly hinge on
literalistic and uncharitable reading of evidence. Instead, I argue the
following CARDINAL points: (i) Contrary to Gray's assertion, the historical issue
of naming of ideas or concepts is completely divorced from their epistemic
status. (ii) PERSON is easily and smoothly generalized into EVENT
paradox, so it makes no sense either theoretically or empirically to separate
the CARDINAL. (iii) In sharp contrast to the main implication of PERSON's paper, ORG
has become more aggravated lately due to advances in astrobiology.",0
"In the framework of the emergent gravity scenario by PERSON, it was
recently observed by PERSON and PERSON that, among other things, an anomalous
pericenter precession would affect the orbital motion of a test particle
orbiting an isolated central body. Here, it is shown that, if it were real, its
expected magnitude for the inner planets of the Solar System would be at the
same level of the present-day accuracy in constraining any possible deviations
from their standard perihelion precessions as inferred from long data records
spanning DATE. The most favorable situation for testing the
Verlinde-type precession seems to occur for LOC. Indeed, according to recent
versions of the ORG and PERSON planetary ephemerides, non-standard perihelion
precessions, of whatsoever physical origin, which are larger than some $GPE
CARDINAL-0.11$ milliarcseconds per century are not admissible, while the putative
precession predicted by PERSON and PERSON amounts to MONEY milliarcseconds per
century. Other potentially interesting astronomical and astrophysical scenarios
like, e.g., the LOC's LOC artificial satellite, the double pulsar
system PERSON/B and the S-stars orbiting ORG
in GPE A$^\ast$ are, instead, not viable because of the excessive smallness of
the predicted effects for them.","General formulas of entanglement concentration are derived by using an
information-spectrum approach for the i.i.d. sequences and the general
sequences of partially entangled pure states. That is, we derive general
relations between the performance of the entanglement concentration and the
eigenvalues of the partially traced state. The achievable rates with constant
constraints and those with exponential constraints can be calculated from these
formulas.",0
"This is a collection of linguistic-mathematical approaches to NORP rebus,
puzzles, poetical and juridical texts, and proposes fancies, recreational math
problems, and paradoxes. We study the frequencies of letters, syllables, vowels
in various poetry, grill definitions in rebus, and rebus rules. We also compare
the scientific language, poetical language, and puzzle language, and compute
the FAC entropy and NORP informational energy.","In order to more accurately situate and fit the neutrosophic logic into the
framework of nonstandard analysis, we present the neutrosophic inequalities,
neutrosophic equality, neutrosophic infimum and supremum, neutrosophic standard
intervals, including the cases when the neutrosophic logic standard and
nonstandard components T, I, F get values outside of the classical real unit
interval [CARDINAL, CARDINAL], and a brief evolution of neutrosophic operators. The paper
intends to answer ORG criticism that we found benefic in better
understanding the nonstandard neutrosophic logic, although the nonstandard
neutrosophic logic was never used in practical applications.",1
"Whereas the research program of the measurement of scientific communications
emerged in a context where the delineations among academia, government, and
industry were institutionalized, the systemic development of these relations
during DATE has changed the system of reference
for the evaluation of research. In a knowledge-based economy science fulfills
functions that change the definitions of what is considered research and
globalization has changed the relevance of national systems of reference.
Science, of course, has been internationally oriented from its very beginning,
but the entrainment of the research process in these global developments is
reflected in the research evaluation and the scientometric measurement. In
other words, the systems under study have become more complex. A complex
dynamics can analytically be decomposed in several subdynamics. The evolving
systems and subsystems communicate in different dimensions and the evaluation
has become part of the codification of these communications.","CARDINAL steps aid in the analysis of selection. ORDINAL, describe phenotypes by
their component causes. Components include genes, maternal effects, symbionts,
and any other predictors of phenotype that are of interest. ORDINAL, describe
fitness by its component causes, such as an individual's phenotype, its
neighbors' phenotypes, resource availability, and so on. ORDINAL, put the
predictors of phenotype and fitness into an exact equation for evolutionary
change, providing a complete expression of selection and other evolutionary
processes. The complete expression separates the distinct causal roles of the
various hypothesized components of phenotypes and fitness. Traditionally, those
components are given by the covariance, variance, and regression terms of
evolutionary models. I show how to interpret those statistical expressions with
respect to information theory. The resulting interpretation allows one to read
the fundamental equations of selection and evolution as sentences that express
how various causes lead to the accumulation of information by selection and the
decay of information by other evolutionary processes. The interpretation in
terms of information leads to a deeper understanding of selection and
heritability, and a clearer sense of how to formulate causal hypotheses about
evolutionary process. Kin selection appears as a particular type of causal
analysis that partitions social effects into meaningful components.",0
"How best to quantify the information of an object, whether natural or
artifact, is a problem of wide interest. A related problem is the computability
of an object. We present practical examples of a new way to address this
problem. By giving an appropriate representation to our objects, based on a
hierarchical coding of information, we exemplify how it is remarkably easy to
compute complex objects. Our algorithmic complexity is related to the length of
the class of objects, rather than to the length of the object.","The concept of {\em complexity} (as a quantity) has been plagued by numerous
contradictory and confusing definitions. By explicitly recognising a role for
the observer of a system, an observer that attaches meaning to data about the
system, these contradictions can be resolved, and the numerous complexity
measures that have been proposed can be seen as cases where different observers
are relevant, and/or being proxy measures that loosely scale with complexity,
but are easy to compute from the available data. Much of the epistemic
confusion in the subject can be squarely placed at science's tradition of
removing the observer from the description in order to guarantee {\em
objectivity}. Explicitly acknowledging the role of the observer helps untangle
other confused subject areas. PERSON} is a topic about which much ink
has been spilt, but it can be understand easily as an irreducibility between
description space and meaning space. ORG can also be understood
as a theory of observation. The success in explaining quantum mechanics, leads
one to conjecture that all of physics may be reducible to properties of the
observer. And indeed, what are the necessary (as opposed to contingent)
properties of an observer? This requires a full theory of consciousness, from
which we are a long way from obtaining. However where progress does appear to
have been made, e.g. PERSON's {\em PERSON}, a
recurring theme of self-observation is a crucial ingredient.",0
"This paper proposes a new mechanism for pruning a search game-tree in
computer chess. The algorithm stores and then reuses chains or sequences of
moves, built up from previous searches. These move sequences have a built-in
forward-pruning mechanism that can radically reduce the search space. A typical
search process might retrieve a move from FAC, where the
decision of what move to retrieve would be based on the position itself. This
algorithm stores move sequences based on what previous sequences were better,
or caused cutoffs. This is therefore position independent and so it could also
be useful in games with imperfect information or uncertainty, where the whole
situation is not known at any CARDINAL time. Over a small set of tests, the
algorithm was shown to clearly out-perform Transposition Tables, both in terms
of search reduction and game-play results.","This paper reconsiders the problem of the absent-minded driver who must
choose between alternatives with different payoff with imperfect recall and
varying degrees of knowledge of the system. The classical absent-minded driver
problem represents the case with limited information and it has bearing on the
general area of communication and learning, social choice, mechanism design,
auctions, theories of knowledge, belief, and rational agency. Within the
framework of extensive games, this problem has applications to many artificial
intelligence scenarios. It is obvious that the performance of the agent
improves as information available increases. It is shown that a non-uniform
assignment strategy for successive choices does better than a fixed probability
strategy. We consider both classical and quantum approaches to the problem. We
argue that the superior performance of quantum decisions with access to
entanglement cannot be fairly compared to a classical algorithm. If the
cognitive systems of agents are taken to have access to quantum resources, or
have a quantum mechanical basis, then that can be leveraged into superior
performance.",0
"This is a review of ""WORK_OF_ART"", by PERSON.","Causal models defined in terms of a collection of equations, as defined by
PRODUCT, are axiomatized here. Axiomatizations are provided for CARDINAL
successively more general classes of causal models: (CARDINAL) the class of recursive
theories (those without feedback), (CARDINAL) the class of theories where the
solutions to the equations are unique, (CARDINAL) arbitrary theories (where the
equations may not have solutions and, if they do, they are not necessarily
unique). It is shown that to reason about causality in the most general ORDINAL
class, we must extend the language used by GPE and GPE. In addition, the
complexity of the decision procedures is characterized for all the languages
and classes of models considered.",1
"In this paper are discussed some formal properties of ORG devices
necessary for implementation of nondeterministic Turing machine.","The essential graph is a distinguished member of a PERSON equivalence class
of ORG chain graphs. However, the directed edges in the essential graph are not
necessarily strong or invariant, i.e. they may not be shared by every member of
the equivalence class. Likewise for the undirected edges. In this paper, we
develop a procedure for identifying which edges in an essential graph are
strong. We also show how this makes it possible to bound some causal effects
when the true chain graph is unknown.",0
"This paper presents a theory of error in cross-validation testing of
algorithms for predicting real-valued attributes. The theory justifies the
claim that predicting real-valued attributes requires balancing the conflicting
demands of simplicity and accuracy. Furthermore, the theory indicates precisely
how these conflicting demands must be balanced, in order to minimize
cross-validation error. A general theory is presented, then it is developed in
detail for ORG regression and instance-based learning.","Many ORG researchers and cognitive scientists have argued that analogy is the
core of cognition. The most influential work on computational modeling of
analogy-making is WORK_OF_ART (ORG) and its implementation in the
WORK_OF_ART (ORG). A limitation of ORG is the requirement for
complex hand-coded representations. We introduce ORG (ORG), which combines ideas from ORG and ORG
(ORG) in order to remove the requirement for hand-coded representations. ORG
builds analogical mappings between lists of words, using a large corpus of raw
text to automatically discover the semantic relations among the words. We
evaluate ORG on a set of CARDINAL analogical mapping problems, CARDINAL based on
scientific analogies and CARDINAL based on common metaphors. ORG achieves
human-level performance on the CARDINAL problems. We compare ORG with a variety
of alternative approaches and find that they are not able to reach the same
level of performance.",1
"Let $u_t = u_{xx} - q(x) u, CARDINAL \leq x \leq MONEY, MONEY, $u(0, t) = CARDINAL, ORG, t) =
a(t), u(x,0) = MONEY, where $PERSON is a given function vanishing for $t>T$, $a(t)
\not\equiv MONEY, $\int^T_0 a(t) dt < \infty$. Suppose one measures the flux $PERSON(0,t) := b_0 (t)$ for all $t>0$. Does this information determine $MONEY
uniquely? Do the measurements of the flux $u_x (CARDINAL) := b(t)$ give more
information MONEY (ORG does?
  The above questions are answered in this paper.","Mathematically rigorous inversion method is developed to recover compactly
supported potentials from the fixed-energy scattering data in CARDINAL dimensions.
Error estimates are given for the solution.
  An algorithm for inversion of noisy discrete fixed-energy CARDINALD scattering data
is developed and its error estimates are obtained",1
"If a system falls through a black hole horizon, then its information is lost
to an observer at infinity. But we argue that the {\it accessible} information
is lost {\it before} the horizon is crossed. The temperature of the hole limits
information carrying signals from a system that has fallen too close to the
horizon. Extremal holes have T=0, but there is a minimum energy required to
emit a quantum in the short proper time left before the horizon is crossed. If
we attempt to bring the system back to infinity for observation, then
acceleration radiation destroys the information. All CARDINAL considerations give
a critical distance from the horizon $d\sim \sqrt{r_H\over \Delta E}$, where
$PERSON is the horizon radius and MONEY E$ is the energy scale characterizing
the system. For systems in string theory where we pack information as densely
as possible, this acceleration constraint is found to have a geometric
interpretation. These estimates suggest that in theories of gravity we should
measure information not as a quantity contained inside a given system, but in
terms of how much of that information can be reliably accessed by another
observer.","We argue that bound states of branes have a size that is of the same order as
the horizon radius of the corresponding black hole. Thus the interior of a
black hole is not `empty space with a central singularity', and Hawking
radiation can pick up information from the degrees of freedom of the hole.",1
"PERSON equilibrium is the most commonly-used notion of equilibrium in game
theory. However, it suffers from numerous problems. Some are well known in the
game theory community; for example, the ORG equilibrium of repeated prisoner's
dilemma is neither normatively nor descriptively reasonable. However, new
problems arise when considering PERSON equilibrium from a computer science
perspective: for example, PERSON equilibrium is not robust (it does not tolerate
``faulty'' or ``unexpected'' behavior), it does not deal with coalitions, it
does not take computation cost into account, and it does not deal with cases
where players are not aware of all aspects of the game. Solution concepts that
try to address these shortcomings of ORG equilibrium are discussed.","The original ORG definition of causality [Halpern and GPE, DATE]
was updated in the journal version of the paper [PRODUCT and GPE, DATE] to
deal with some problems pointed out by PERSON and PERSON [DATE]. Here the
definition is modified yet again, in a way that (a) leads to a simpler
definition, (b) handles the problems pointed out by PERSON and GPE, and many
others, (c) gives reasonable answers (that agree with those of the original and
updated definition) in the standard problematic examples of causality, and (d)
has lower complexity than either the original or updated definitions.",1
"This paper verifies a result of {Shenoy:94} concerning graphoidal structure
of Shenoy's notion of independence for ORG theory of belief
functions. Shenoy proved that his notion of independence has graphoidal
properties for positive normal valuations.
  The requirement of strict positive normal valuations as prerequisite for
application of graphoidal properties excludes a wide class of ORG belief
functions. It excludes especially so-called probabilistic belief functions. It
is demonstrated that the requirement of positiveness of valuation may be
weakened in that it may be required that commonality function is non-zero for
singleton sets instead, and the graphoidal properties for independence of
belief function variables are then preserved. This means especially that
probabilistic belief functions with all singleton sets as focal points possess
graphoidal properties for independence.","In previous papers, we expressed ORG in terms of
ORG (ORG). In this brief paper, we express ORG in terms of ORG.",0
"We review the application of the critical point large N_f self-consistency
method to ORG. In particular we derive the O(1/N_f) d-dimensional critical
exponents whose epsilon-expansion determines the perturbative coefficients in
MSbar of the field dimensions, beta-function and various twist-2 operators
which occur in the operator product expansion of deep inelastic scattering.","The leading order coefficients of the beta-function of ORG are computed in a
large N_f expansion. They are in agreement with the CARDINAL loop ORG
calculation. The method involves computing the anomalous dimension of the
operator (G^2_{mu nu})^2 at the d-dimensional fixed point in the NORP
Thirring model to which ORG is equaivalent in this limit. The effect the
O(1/N_f) corrections have on the location of the infrared stable fixed point
for a range of N_f is also examined.",1
"Unlike classical information, ORG knowledge is restricted to the outcome
of measurements of maximal observables corresponding to single contexts.","Some physical aspects related to the limit operations of the ORG lamp are
discussed. Regardless of the formally unbounded and even infinite number of
""steps"" involved, the physical limit has an operational meaning in agreement
with the PRODUCT sums of infinite series. The formal analogies to accelerated
(hyper-) computers and the recursion theoretic diagonal methods are discussed.
As ORG information is not bound by the mutually exclusive states of
classical bits, it allows a consistent representation of fixed point states of
the diagonal operator. In an effort to reconstruct the self-contradictory
feature of diagonalization, a generalized diagonal method allowing no quantum
fixed points is proposed.",1
"It is shown in the ORDINAL part of this paper that a combined model comprising
ordinary and quintessential matter can support a traversable wormhole in
Einstein-Maxwell gravity. Since the solution allows CARDINAL tidal forces, the
wormhole is suitable for a humanoid traveler. The ORDINAL part of the paper
shows that the electric field can be eliminated (Einstein gravity), but only by
tolerating enormous tidal forces. Such a wormhole would still be capable of
transmitting signals.","It has been shown that a noncommutative-geometry background may be able to
support traversable wormholes. This paper discusses the possible detection of
such wormholes in the outer regions of galactic halos by means of gravitational
lensing. The procedure allows a comparison to other models such as the
ORG-Frenk-White model and PERSON) modified gravity and is likely to favor a
model based on noncommutative geometry.",1
"A general expression of the axial-vector current is presented, in which both
the effects of the chiral symmetry breaking and the spontaneous chiral symmetry
breaking are included. A new resonance formula of the axial-vector meson is
derived and in the limit of $q^{2}\rightarrow 0$ this formula doesn't go back
to the ``chiral limit``. The studies show that the dominance of the
axial-vector meson is associated with the satisfaction of PCAC. The dominance
of pion exchange is companied by the strong anomaly of ORG.","A correction of the low energy theorem of the \gamma\to3\pi GPE has been
found. A_{3\pi}(0,0,0) and the cross section are calculated. Theory agrees with
data. There is no new adjustable parameter.",1
"A PERSON simulation based on O(alpha_s) QCD matrix elements matched to
parton showers shows that final-state hadrons in deep inelastic scattering
(ORG) can be used to tag events with a single (anti)quark recoiled against the
proton. The method is particularly suited to study the mean charge of leading
particles, which is sensitive to fragmentation and sea quark contribution to
the proton structure function. We also discuss methods to study the charm
production in ORG using the Breit frame.","We propose a data format for PERSON (MC) events, or any structural data,
including experimental data, in a compact binary form using variable-size
integer encoding as implemented in the Google's Protocol Buffers package. This
approach is implemented in the so-called ProMC library which produces smaller
file sizes for MC records compared to the existing input-output libraries used
in high-energy physics (ORG). Other important features are a separation of
abstract data layouts from concrete programming implementations,
self-description and random access. Data stored in FAC files can be written,
read and manipulated in a number of programming languages, such C++, PERSON and
Python.",1
"Probability answer set programming is a declarative programming that has been
shown effective for representing and reasoning about a variety of probability
reasoning tasks. However, the lack of probability aggregates, e.g. {\em
expected values}, in the language of disjunctive hybrid probability logic
programs (ORG) disallows the natural and concise representation of many
interesting problems. In this paper, we extend ORG to allow arbitrary
probability aggregates. We introduce CARDINAL types of probability aggregates; a
type that computes the expected value of a classical aggregate, e.g., the
expected value of the minimum, and a type that computes the probability of a
classical aggregate, ORG, the probability of sum of values. In addition, we
define a probability answer set semantics for ORG with arbitrary probability
aggregates including monotone, antimonotone, and nonmonotone probability
aggregates. We show that the proposed probability answer set semantics of ORG
subsumes both the original probability answer set semantics of ORG and the
classical answer set semantics of classical disjunctive logic programs with
classical aggregates, and consequently subsumes the classical answer set
semantics of the original disjunctive logic programs. We show that the proposed
probability answer sets of ORG with probability aggregates are minimal
probability models and hence incomparable, which is an important property for
nonmonotonic probability reasoning.","Ageing of publications, percentage of self-citations, and impact vary from
journal to journal within fields of science. The assumption that citation and
publication practices are homogenous within specialties and fields of science
is invalid. Furthermore, the delineation of fields and among specialties is
fuzzy. Institutional units of analysis and persons may move between fields or
span different specialties. The match between the citation index and
institutional profiles varies among institutional units and nations. The
respective matches may heavily affect the representation of the units. Non-ISI
journals are increasingly cornered into ""transdisciplinary"" PRODUCT functions
with the exception of specialist journals publishing in languages other than
LANGUAGE. An ""externally cited impact factor"" can be calculated for these
journals. The citation impact of non-ISI journals will be demonstrated using
WORK_OF_ART as the example.",0
"Science and mathematics help people better to understand world, eliminating
different fallacies and misconceptions. CARDINAL of such misconception is related to
arithmetic, which is so important both for science and everyday life. People
think that their counting is governed by the rules of the conventional
arithmetic and that other kinds of arithmetic do not exist and cannot exist. It
is demonstrated in this paper that this popular image of the situation with
integer numbers is incorrect. In many situations, we have to utilize different
rules of counting and operating. This is a consequence of the existing
diversity in nature and society and to represent correctly this diversity
people have to utilize different arithmetics. To distinct them, we call the
conventional arithmetic PERSON, while other arithmetics are called
NORP. Theory of NORP arithmetics is developed in the book
of the author ""Non-Diophantine arithmetics or is it possible that CARDINAL + 2 is not
equal to CARDINAL."" In this work, some properties of NORP arithmetics are
considered, as well as their connections to numerical computations and
contemporary physics are explained.","A practical tool for natural language modeling and development of
human-machine interaction is developed in the context of formal grammars and
languages. A new type of formal grammars, called grammars with prohibition, is
introduced. Grammars with prohibition provide more powerful tools for natural
language generation and better describe processes of language learning than the
conventional formal grammars. Here we study relations between languages
generated by different grammars with prohibition based on conventional types of
formal grammars such as context-free or context sensitive grammars. Besides, we
compare languages generated by different grammars with prohibition and
languages generated by conventional formal grammars. In particular, it is
demonstrated that they have essentially higher computational power and
expressive possibilities in comparison with the conventional formal grammars.
Thus, while conventional formal grammars are recursive and subrecursive
algorithms, many classes of grammars with prohibition are superrecursive
algorithms. Results presented in this work are aimed at the development of
human-machine interaction, modeling natural languages, empowerment of
programming languages, computer simulation, better software systems, and theory
of recursion.",1
"The ""SP theory of intelligence"", with its realisation in the ""SP computer
model"", aims to simplify and integrate observations and concepts across
AI-related fields, with information compression as a unifying theme. This paper
describes how abstract structures and processes in the theory may be realised
in terms of neurons, their interconnections, and the transmission of signals
between neurons. This part of the NORP theory -- ""SP-neural"" -- is a tentative
and partial model for the representation and processing of knowledge in the
brain. In the NORP theory (apart from NORP-neural), all kinds of knowledge are
represented with ""patterns"", where a pattern is an array of atomic symbols in
CARDINAL or CARDINAL dimensions. In SP-neural, the concept of a ""pattern"" is realised as
an array of neurons called a ""pattern assembly"", similar to Hebb's concept of a
""cell assembly"" but with important differences. Central to the processing of
information in the NORP system is the powerful concept of ""multiple alignment"",
borrowed and adapted from bioinformatics. Processes such as pattern
recognition, reasoning and problem solving are achieved via the building of
multiple alignments, while unsupervised learning -- significantly different
from EVENT -- is achieved by creating patterns from
sensory information and also by creating patterns from multiple alignments in
which there is a partial match between CARDINAL pattern and another. Short-lived
neural structures equivalent to multiple alignments will be created via an
inter-play of excitatory and inhibitory neural signals. The paper discusses
several associated issues, with relevant empirical evidence.","This paper examines common assumptions regarding the decision-making internal
environment for intelligent agents and investigates issues related to
processing of memory and belief states to help obtain better understanding of
the responses. In specific, we consider order effects and discuss both
classical and non-classical explanations for them. We also consider implicit
cognition and explore if certain inaccessible states may be best modeled as
quantum states. We propose that the hypothesis that quantum states are at the
basis of order effects be tested on large databases such as those related to
medical treatment and drug efficacy. A problem involving a maze network is
considered and comparisons made between classical and quantum decision
scenarios for it.",0
"We introduce operational semantics into games. And based on the operational
semantics, we establish a full algebra of games, including basic algebra of
games, algebra of concurrent games, recursion and abstraction. The algebra can
be used widely to reason on the behaviors of systems (not only computational
systems) with game theory supported.","ORG to traditionally accurate computing, approximate computing focuses
on the rapidity of the satisfactory solution, but not the unnecessary accuracy
of the solution. Approximate bisimularity is the approximate one corresponding
to traditionally accurate bisimilarity. Based on the work of distances between
basic processes, we propose an algebraic approach for distances between
processes to support a whole process calculus ORG, which contains prefix, sum,
composition, restriction, relabeling and recursion.",1
"We study the problem of deciding whether some PSPACE-complete problems have
models of bounded size. Contrary to problems in ORG, models of ORG-complete
problems may be exponentially large. However, such models may take polynomial
space in a succinct representation. For example, the models of a ORG are
explicitely represented by and-or trees (which are always of exponential size)
but can be succinctely represented by circuits (which can be polynomial or
exponential). We investigate the complexity of deciding the existence of such
succinct models when a bound on size is given.","We analyze the computational complexity of problems related to case-based
planning: planning when a plan for a similar instance is known, and planning
from a library of plans. We prove that planning from a single case has the same
complexity than generative planning (i.e., planning ""from scratch""); using an
extended definition of cases, complexity is reduced if the domain stored in the
case is similar to the one to search plans for. Planning from a library of
cases is shown to have the same complexity. In both cases, the complexity of
planning remains, in the worst case, PSPACE-complete.",1
"I provide an alternative way of seeing quantum computation. ORDINAL, I describe
an idealized classical problem solving machine that, thanks to a many body
interaction, reversibly and nondeterministically produces the solution of the
problem under the simultaneous influence of all the problem constraints. This
requires a perfectly accurate, rigid, and reversible relation between the
coordinates of the machine parts - the machine can be considered the many body
generalization of another perfect machine, the bounching ball model of
reversible computation. The mathematical description of the machine, as it is,
is applicable to ORG problem solving, an extension of the quantum
algorithms that comprises the physical representation of the problem-solution
interdependence. The perfect relation between the coordinates of the machine
parts is transferred to the populations of the reduced density operators of the
parts of the computer register. The solution of the problem is reversibly and
nondeterministically produced under the simultaneous influence of the state
before measurement and the quantum principle. At the light of the present
notion of simultaneous computation, the quantum speed up turns out to be
""precognition"" of the solution, namely the reduction of the initial ignorance
of the solution due to backdating, to before running the algorithm, a
time-symmetric part of the state vector reduction on the solution; as such, it
is bounded by state vector reduction through an entropic inequality. PACS
numbers: CARDINAL, 01.55.+b, 01.70.+w","There exists an increasing evidence supporting the picture of the PERSON
junction (JJ) as a ""macroscopic quantum system"". On the other hand the
interpretation of experimental data strongly depends on the assumed theoretical
model. We analyse the possible states of a NORP pair box (""charge qubit"") for
the CARDINAL types of models : CARDINAL-mode ORG model with its large $MONEY
aproximations and the many-body description within the mean-field approximation
(Gross-Pitaevski equation). While the ORDINAL class of models supports the
picture of JJ being a quantum subsystem of a single degree of freedom, the
ORDINAL approach yields an essentially classical structure of accessible quantum
states which, in particular, implies the absence of entanglement for CARDINAL
coupled JJ's. The arguments in favor of the mean-field theory are presented and
different experimental tests including a new proposal are briefly discussed.",0
"Belief integration methods are often aimed at deriving a single and
consistent knowledge base that retains as much as possible of the knowledge
bases to integrate. The rationale behind this approach is the minimal change
principle: the result of the integration process should differ as less as
possible from the knowledge bases to integrate. We show that this principle can
be reformulated in terms of a more general model of belief revision, based on
the assumption that inconsistency is due to the mistakes the knowledge bases
contain. Current belief revision strategies are based on a specific kind of
mistakes, which however does not include all possible ones. Some alternative
possibilities are discussed.","Merging beliefs requires the plausibility of the sources of the information
to be merged. They are typically assumed equally reliable in lack of hints
indicating otherwise; yet, a recent line of research spun from the idea of
deriving this information from the revision process itself. In particular, the
history of previous revisions and previous merging examples provide information
for performing subsequent mergings.
  Yet, no examples or previous revisions may be available. In spite of the
apparent lack of information, something can still be inferred by a
try-and-check approach: a relative reliability ordering is assumed, the merging
process is performed based on it, and the result is compared with the original
information. The outcome of this check may be incoherent with the initial
assumption, like when a completely reliable source is rejected some of the
information it provided. In such cases, the reliability ordering assumed in the
ORDINAL place can be excluded from consideration. The ORDINAL theorem of this
article proves that such a scenario is indeed possible. Other results are
obtained under various definition of reliability and merging.",1
"The computer revolution has been driven by a sustained increase of
computational speed of CARDINAL order of magnitude (a factor of CARDINAL)
DATE since DATE. In natural sciences this has led to a
continuous increase of the importance of computer simulations. Major enabling
techniques are PERSON (MCMC) and ORG (GPE)
simulations.
  This article deals with the MCMC approach. ORDINAL basic simulation techniques,
as well as methods for their statistical analysis are reviewed. Afterwards the
focus is on generalized ensembles and biased updating, CARDINAL advanced techniques,
which are of relevance for simulations of biomolecules, or are expected to
become relevant with that respect. In particular we consider the multicanonical
ensemble and the replica exchange method (also known as parallel tempering or
method of multiple PERSON chains).","Evolution of a physical quantum state vector is described as governed by CARDINAL
distinct physical laws: PERSON, unitary time evolution and a
relativistically covariant reduction process. In previous literature, it was
concluded that a relativistically satisfactory version of the collapse
postulate is in contradiction with physical measurements of a non-local state
history. Here it is shown that such measurements are excluded when reduction is
formulated as a physical process and the measurement devices are included as
part of the state vector.",1
"In DATE we witness a dramatic growth of research focused on
semantic image understanding. Indeed, without understanding image content
successful accomplishment of any image-processing task is simply incredible. Up
to the recent times, the ultimate need for such understanding has been met by
the knowledge that a domain expert or a vision system supervisor have
contributed to every image-processing application. The advent of the Internet
has drastically changed this situation. Internet sources of visual information
are diffused and dispersed over the whole Web, so the duty of information
content discovery and evaluation must be relegated now to an image
understanding agent (a machine or a computer program) capable to perform image
content assessment at a remote image location. Development of Content Based
Image Retrieval (ORG) techniques was a right move in a right direction,
launched DATE. Unfortunately, very little progress has been made
since then. The reason for this can be seen in a rank of long lasting
misconceptions that ORG designers are continuing to adhere to. I hope, my
arguments will help them to change their minds.","In DATE, we witness a paradigm shift in our nature
studies - from a data-processing based computational approach to an
information-processing based cognitive approach. The process is restricted and
often misguided by the lack of a clear understanding about what information is
and how it should be treated in research applications (in general) and in
biological studies (in particular). The paper intend to provide some remedies
for this bizarre situation.",1
"We prove that extreme ORG initial data set is a unique absolute minimum of
the total mass in a (physically relevant) class of vacuum, maximal,
asymptotically flat, axisymmetric data for PERSON equations with fixed
angular momentum. These data represent non-stationary, axially symmetric, black
holes. As a consequence, we obtain that any data in this class satisfy the
inequality $\sqrt{J} \leq m$, where $m$ and $MONEY are the total mass and angular
momentum of the spacetime.","For a given asymptotically flat initial data set for PERSON equations a new
geometric invariant is constructed. This invariant measure the departure of the
data set from the stationary regime, it vanishes if and only if the data is
stationary. In vacuum, it can be interpreted as a measure of the total amount
of radiation contained in the data.",1
"Most existing approaches in ORG (CRS) focus on
recommending relevant items to users taking into account contextual
information, such as time, location, or social aspects. However, few of them
have considered the problem of user's content dynamicity. We introduce in this
paper an algorithm that tackles the user's content dynamicity by modeling the
CRS as a contextual bandit algorithm and by including a situation clustering
algorithm to improve the precision of the ORG. Within a deliberately designed
offline simulation framework, we conduct evaluations with real online event log
data. The experimental results and detailed analysis reveal several important
discoveries in context aware recommender system.","We introduce in this paper an algorithm named PERSON that
tackles the dynamicity of the user's content. It is based on dynamic
exploration/exploitation tradeoff and can adaptively balance the CARDINAL aspects by
deciding which situation is most relevant for exploration or exploitation. The
experimental results demonstrate that our algorithm outperforms surveyed
algorithms.",1
"The article describes the proposition and application of a local search
metaheuristic for multi-objective optimization problems. It is based on CARDINAL
main principles of heuristic search, intensification through variable
neighborhoods, and diversification through perturbations and successive
iterations in favorable regions of the search space. The concept is
successfully tested on permutation flow shop scheduling problems under multiple
objectives and compared to other local search approaches. While the obtained
results are encouraging in terms of their quality, another positive attribute
of the approach is its simplicity as it does require the setting of only very
few parameters.","Logitboost is an influential boosting algorithm for classification. In this
paper, we develop robust logitboost to provide an explicit formulation of
tree-split criterion for building weak learners (regression trees) for
logitboost. This formulation leads to a numerically stable implementation of
logitboost. We then propose ORG-logitboost for multi-class classification, by
combining robust logitboost with the prior work of ORG-boost. Previously,
ORG-boost was implemented as ORG using the mart algorithm. Our extensive
experiments on multi-class classification compare CARDINAL algorithms: mart,
abcmart, (robust) logitboost, and ORG-logitboost, and demonstrate the
superiority of ORG-logitboost. Comparisons with other learning methods
including ORG and deep learning are also available through prior publications.",0
"""WORK_OF_ART"" is the name of a model of cellular development that,
coupled with an evolutionary technique, becomes an evo-devo (or ""artificial
embryology"", or ""computational development"") method to generate CARDINAL or CARDINAL sets
of artificial cells arbitrarily shaped. 'In silico' experiments have proved the
effectiveness of the method in devo-evolving any kind of shape, of any
complexity (in terms of number of cells, number of colours, etc.); being shape
complexity a metaphor for organismal complexity, such simulations established
its potential to generate the complexity typical of biological systems.
Moreover, it has also been shown how the underlying model of cellular
development is able to produce the artificial version of key biological
phenomena such as embryogenesis, the presence of ""junk DNA"", the phenomenon of
ageing and the process of carcinogenesis. The objective of this document is not
to provide new material (most of the material presented here has already been
published elsewhere): rather, it is to provide all details that, for lack of
space, could not be provided in the published papers and in particular to give
all technical details necessary to re-implement the method.","Transposable elements are DNA sequences that can move around to different
positions in the genome. During this process, they can cause mutations, and
lead to an increase in genome size. Despite representing a large genomic
fraction, transposable elements have no clear biological function. This work
builds upon a previous model, to propose a new concept of natural selection
which combines NORP and NORP elements. Transposable elements are
hypothesised to be the vector of a flow of genetic information from soma to
germline, that shapes gene regulatory regions across the genome. The paper
introduces the concept, presents and discusses the body of evidence in support
of this hypothesis, and suggests an experiment to test it.",1
"Fluctuations on de Sitter solution of FAC field equations are
obtained in terms of the matter density primordial density fluctuations and
spin-torsion density and matter density fluctuations obtained from ORG data.
Einstein-de Sitter solution is shown to be unstable even in the absence of
torsion.The spin-torsion density fluctuation to generate a deflationary phase
is computed from the ORG data.","Any regular NORP probability distribution that can be represented by an
ORG chain graph (CG) can be expressed as a system of ORG equations with
correlated errors whose structure depends on the CG. However, the ORG represents
the errors implicitly, as no nodes in the ORG correspond to the errors. We
propose in this paper to add some deterministic nodes to the ORG in order to
represent the errors explicitly. We call the result an ORG CG. We will show
that, as desired, every AMP CG is PERSON equivalent to its corresponding ORG
ORG under marginalization of the error nodes. We will also show that every ORG
ORG under marginalization of the error nodes is PERSON equivalent to some PERSON
under marginalization of the error nodes, and that the latter is PERSON
equivalent to some directed and acyclic graph (ORG) under marginalization of
the error nodes and conditioning on some selection nodes. This is important
because it implies that the independence model represented by an AMP CG can be
accounted for by some data generating process that is partially observed and
has selection bias. Finally, we will show that ORG CGs are closed under
marginalization. This is a desirable feature because it guarantees parsimonious
models under marginalization.",0
"We prove new estimates for the volume of a NORP manifold and show
especially that cosmological spacetimes with crushing singularities have finite
volume.","When agents are acting together, they may need a simple mechanism to decide
on joint actions. CARDINAL possibility is to have the agents express their
preferences in the form of a ballot and use a voting rule to decide the winning
action(s). Unfortunately, agents may try to manipulate such an election by
misreporting their preferences. Fortunately, it has been shown that it is
ORG-hard to compute how to manipulate a number of different voting rules.
However, ORG-hardness only bounds the worst-case complexity. Recent theoretical
results suggest that manipulation may often be easy in practice. To address
this issue, I suggest studying empirically if computational complexity is in
practice a barrier to manipulation. The basic tool used in my investigations is
the identification of computational ""phase transitions"". Such an approach has
been fruitful in identifying hard instances of propositional satisfiability and
other ORG-hard problems. I show that phase transition behaviour gives insight
into the hardness of manipulating voting rules, increasing concern that
computational complexity is indeed any sort of barrier. Finally, I look at the
problem of computing manipulation of other, related problems like stable
marriage and tournament problems.",0
"I postulate that human or other intelligent agents function or should
function as follows. They store all sensory observations as they come - the
data is holy. At any time, given some agent's current coding capabilities, part
of the data is compressible by a short and hopefully fast program / description
/ explanation / world model. In the agent's subjective eyes, such data is more
regular and more ""beautiful"" than other data. It is well-known that knowledge
of regularity and repeatability may improve the agent's ability to plan actions
leading to external rewards. In absence of such rewards, however, known beauty
is boring. Then ""interestingness"" becomes the ORDINAL derivative of subjective
beauty: as the learning agent improves its compression algorithm, formerly
apparently random data parts become subjectively more regular and beautiful.
Such progress in compressibility is measured and maximized by the curiosity
drive: create action sequences that extend the observation history and yield
previously unknown / unpredictable but quickly learnable algorithmic
regularity. We discuss how all of the above can be naturally implemented on
computers, through an extension of passive unsupervised learning to the case of
active data selection: we reward a general reinforcement learner (with access
to the adaptive compressor) for actions that improve the subjective
compressibility of the growing data. An unusually large breakthrough in
compressibility deserves the name ""discovery"". The ""creativity"" of artists,
dancers, musicians, pure mathematicians can be viewed as a by-product of this
principle. Several qualitative examples support this hypothesis.","We discuss the problem of gauge invariance of the vector meson
photoproduction at small $x$ within the CARDINAL-gluon exchange model. It is found
that the gauge invariance is fulfilled if one includes the graphs with higher
Fock states in the meson wave function. Obtained results are used to estimate
the amplitudes with longitudinal and transverse photon and vector meson
polarization.",0
"The recently proposed ""generalized PERSON"" (ORG) kernel can be efficiently
linearized, with direct applications in large-scale statistical learning and
fast near neighbor search. The linearized ORG kernel was extensively compared
in with linearized radial basis function (RBF) kernel. On a large number of
classification tasks, the tuning-free ORG kernel performs (surprisingly) well
compared to the best-tuned ORG kernel. Nevertheless, one would naturally expect
that the ORG kernel ought to be further improved if we introduce tuning
parameters.
  In this paper, we study CARDINAL simple constructions of tunable ORG kernels:
(i) the exponentiated-GMM (or eGMM) kernel, (ii) the powered-GMM (or pGMM)
kernel, and (iii) the exponentiated-powered-GMM (epGMM) kernel. The pGMM kernel
can still be efficiently linearized by modifying the original hashing procedure
for the ORG kernel. On CARDINAL publicly available classification datasets, we
verify that the proposed tunable ORG kernels typically improve over the
original ORG kernel. On some datasets, the improvements can be astonishingly
significant.
  For example, on CARDINAL popular datasets which were used for testing deep learning
algorithms and tree methods, our experiments show that the proposed tunable ORG
kernels are strong competitors to trees and deep nets. The previous studies
developed tree methods including ""ORG-robust-logitboost"" and demonstrated the
excellent performance on those CARDINAL datasets (and other datasets), by
establishing the ORDINAL-order tree-split formula and new derivatives for
multi-class logistic loss. Compared to tree methods like
""ORG-robust-logitboost"" (which are slow and need substantial model sizes), the
tunable ORG kernels produce largely comparable results.","We develop the concept of ORG (PERSON Class PERSON) for
multi-class classification and present ORG, a concrete implementation of
ORG. The original MART (Multiple Additive Regression Trees) algorithm has
been very successful in large-scale applications. For binary classification,
ORG recovers MART. For multi-class classification, ORG considerably
improves MART, as evaluated on several public data sets.",1
"The present article is a brief informal survey of computability logic --- the
game-semantically conceived formal theory of computational resources and tasks.
This relatively young nonclassical logic is a conservative extension of
classical ORDINAL order logic but is much more expressive than the latter,
yielding a wide range of new potential application areas. In a reasonable (even
if not strict) sense the same holds for intuitionistic and ORG logics, which
allows us to say that ORG reconciles and unifies the CARDINAL traditions of
logical thought (and beyond) on the basis of its natural and ""universal"" game
semantics. A comprehensive online survey of the subject can be found at
http://www.csc.villanova.edu/~japaridz/CL/ .","Computability logic (CL) (see ORG ) is a
research program for redeveloping logic as a formal theory of computability, as
opposed to the formal theory of truth which it has more traditionally been.
PERSON in ORG stand for interactive computational problems, seen as games
between a machine and its environment; logical operators represent operations
on such entities; and ""truth"" is understood as existence of an effective
solution. The formalism of ORG is open-ended, and may undergo series of
extensions as the studies of the subject advance. So far three -- parallel,
sequential and choice -- sorts of conjunction and disjunction have been
studied. The present paper adds CARDINAL more natural kind to this collection,
termed toggling. The toggling operations can be characterized as lenient
versions of choice operations where choices are retractable, being allowed to
be reconsidered any finite number of times. This way, they model
trial-and-error style decision steps in interactive computation. The main
technical result of this paper is constructing a sound and complete
axiomatization for the propositional fragment of computability logic whose
vocabulary, together with negation, includes all CARDINAL -- parallel, toggling,
sequential and choice -- kinds of conjunction and disjunction. Along with
toggling conjunction and disjunction, the paper also introduces the toggling
versions of quantifiers and recurrence operations.",1
"Review of: PERSON and PERSON, Geometric Data Analysis, From
ORG, LOC, Dordrecht, DATE,
PERSON.","A review of some of the author's results in the area of inverse scattering is
given. The following topics are discussed: CARDINAL) Property $MONEY and applications, CARDINAL)
Stable inversion of fixed-energy 3D scattering data and its error estimate, CARDINAL)
Inverse scattering with ''incomplete`` data, CARDINAL) Inverse scattering for
inhomogeneous Schr\""odinger equation, CARDINAL) PERSON's inverse scattering method, CARDINAL)
Invertibility of the steps in ORG, PERSON, and PERSON inversion
methods, 7) The Newton-Sabatier and PERSON procedures are not inversion
methods, WORK_OF_ART: existence, location, perturbation theory, 9) Born
inversion as an ill-posed problem, CARDINAL) Inverse obstacle scattering with
fixed-frequency data, CARDINAL) Inverse scattering with data at a fixed energy and a
fixed incident direction, CARDINAL) Creating materials with a desired refraction
coefficient and wave-focusing properties.",0
"We study the problem of estimating the coefficients in linear ordinary
differential equations (ODE's) with a diverging number of variables when the
solutions are observed with noise. The solution trajectories are ORDINAL smoothed
with local polynomial regression and the coefficients are estimated with
nonconcave penalty proposed by \cite{fan01}. Under some regularity and sparsity
conditions, we show the procedure can correctly identifies nonzero coefficients
with probability converging to one and the estimators for nonzero coefficients
have the same asymptotic normal distribution as they would have when the CARDINAL
coefficients are known and the same CARDINAL-step procedure is used. Our asymptotic
results are valid under the misspecified case where linear ODE's are only used
as an approximation to nonlinear ORG's, and the estimates will converge to the
coefficients of the best approximating linear system. From our results, when
the solution trajectories of the ORG's are sufficiently smooth, the parametric
$\sqrt{n}$ rate is achieved even though nonparametric regression estimator is
used in the ORDINAL step of the procedure. The performance of the CARDINAL-step
procedure is illustrated by a simulation study as well as an application to
yeast cell-cycle data.","We study light hadron leptoproduction at small $x$. PERSON production
is analysed in terms of generalized gluon distributions with taking into
account the transverse quark motion. Within a CARDINAL-gluon model the double spin
asymmetries for longitudinally polarized leptons and transversely polarized
protons in the diffractive $Q \bar Q$ production are investigated. The
predicted $A_{lT}$ asymmetry is large and can be used to obtain information on
the polarized gluon distributions in the proton.",0
"It is demonstrated how linear computational time and storage efficient
approaches can be adopted when analyzing very large data sets. More
importantly, interpretation is aided and furthermore, basic processing is
easily supported. Such basic processing can be the use of supplementary, i.e.
contextual, elements, or particular associations. Furthermore pixellated grid
cell contents can be utilized as a basic form of imposed clustering. For a
given resolution level, here related to an associated m-adic ($m$ here is a
non-prime integer) or p-adic ($p$ is prime) number system encoding, such
pixellated mapping results in partitioning. The association of a range of
m-adic and p-adic representations leads naturally to an imposed hierarchical
clustering, with partition levels corresponding to the m-adic-based and
p-adic-based representations and displays. In these clustering embedding and
imposed cluster structures, some analytical visualization and search
applications are described","This paper describes information flow within logical environments. The theory
of information flow, the logic of distributed systems, was first defined by
GPE and ORG.
DATE). Logical environments are a semantic-oriented version of institutions.
The theory of institutions, which was initiated by PERSON and PERSON
(Institutions: Abstract Model Theory for Specification and Programming. DATE),
is abstract model theory. Information flow is the flow of information in
channels over distributed systems. The semantic integration of distributed
systems, be they ontologies, databases or other information resources, can be
defined in terms of the channel theory of information flow. As originally
defined, the theory of information flow uses only a specific logical
environment in order to discuss information flow. This paper shows how
information flow can be defined in an arbitrary logical environment.",0
"A plethora of natural, artificial and social complex systems exists which
violate the basic hypothesis (e.g., ergodicity) of ORG (GPE)
statistical mechanics. Many of such cases can be satisfactorily handled by
introducing nonadditive entropic functionals, such as $PERSON p_i^q}{q-1} \; \Bigl(q \in {\cal R}; PERSON, \sum_{i=1}^W
GPE \Bigr)$, with $S_1=S_{BG}\equiv PERSON \ln p_i$. Each class
of such systems can be characterized by a set of values $\{q\}$, directly
corresponding to its various physical/dynamical/geometrical properties. A most
important subset is usually referred to as the $q$-triplet, namely
$(q_{sensitivity}, q_{relaxation}, NORP, defined in the body
of this paper. In the GPE limit we have
$PERSON For a given class of
complex systems, the set CARDINAL\{q\}$ contains only a few independent values of $PERSON,
all the others being functions of those few. An illustration of this structure
was given in DATE [Tsallis, ORG and PERSON, GPE. Natl. Acad. Sc. USA {\bf
CARDINAL}, DATE; TGS]. This illustration enabled a satisfactory analysis of the
PRODUCT data on the solar wind. But the general form of these structures
still is an open question. This is so, for instance, for the challenging
$PERSON associated with the edge of chaos of the logistic map. We introduce
here a transformation which sensibly generalizes the ORG one, and which might
constitute an important step towards the general solution.","The so called $q$-triplets were conjectured in DATE and then found in nature
in DATE. A relevant further step was achieved in DATE when the possibility was
advanced that they could reflect an entire infinite algebra based on
combinations of the self-dual relations MONEY 2-q$ ({\it additive duality})
and MONEY/q$ ({ORG multiplicative duality}). The entire algebra collapses
into the single fixed point $q=1$, corresponding to FAC entropy
and statistical mechanics. For $q \ne 1$, an infinite set of indices $PERSONappears, corresponding in principle to an infinite number of physical
properties of a given complex system describable in terms of the so called
$q$-statistics. The basic idea that is put forward is that, for a given
universality class of systems, a small number (typically CARDINAL or CARDINAL) of
independent CARDINALq$ indices exist, the infinite others being obtained from these
few ones by simply using the relations of the algebra. The $q$-triplets appear
to constitute a few central elements of the algebra. During DATE, an
impressive amount of $q$-triplets have been exhibited in analytical,
computational, experimental and observational results in natural, artificial
and social systems. Some of them do satisfy the available algebra constructed
solely with the additive and multiplicative dualities, but some others seem to
violate it. In the present work we generalize those CARDINAL dualities with the hope
that a wider set of systems can be handled within. The basis of the
generalization is given by the {ORG selfdual} relation $q \to q_a(q) \equiv
\frac{(a+2) -aq}{a-(a-2)q} \,\, (a \in {\cal R})$. We verify that MONEY,
and that $q_2(q)=2-q$ and $q_0(q)=1/q$. To physically motivate this
generalization, we briefly review illustrative applications of $q$-statistics,
in order to exhibit possible candidates where the present generalized algebras
could be useful.",1
"The series solution of the behavior of a finite number of physical bodies and
PERSON's PERSON number share quasi-algorithmic expressions; yet both lack a
computable radius of convergence.","In an independence model, the triplets that represent conditional
independences between singletons are called elementary. It is known that the
elementary triplets represent the independence model unambiguously under some
conditions. In this paper, we show how this representation helps performing
some operations with independence models, such as finding the dominant triplets
or a minimal independence map of an independence model, or computing the union
or intersection of a pair of independence models, or performing causal
reasoning. For the latter, we rephrase in terms of conditional independences
some of LOC's results for computing causal effects.",0
"ORG) has recently become a real formal science: the
new millennium brought the ORDINAL mathematically sound, asymptotically optimal,
universal problem solvers, providing a new, rigorous foundation for the
previously largely heuristic field of General PERSON and embedded agents. At the
same time there has been rapid progress in practical methods for learning true
sequence-processing programs, as opposed to traditional methods limited to
stationary pattern association. Here we will briefly review some of the new
results, and speculate about future developments, pointing out that the time
intervals between the most notable events in DATE or CARDINAL^CARDINAL lifetimes
of human history have sped up exponentially, apparently converging to CARDINAL
within DATE. Or is this impression just a by-product of the way
humans allocate memory space to past events?","We present the ORDINAL class of mathematically rigorous, general, fully
self-referential, self-improving, optimally efficient problem solvers. Inspired
by PERSON celebrated self-referential formulas (DATE), such a problem
solver rewrites any part of its own code as soon as it has found a proof that
the rewrite is useful, where the problem-dependent utility function and the
hardware and the entire initial code are described by axioms encoded in an
initial proof searcher which is also part of the initial code. The searcher
systematically and efficiently tests computable proof techniques (programs
whose outputs are proofs) until it finds a provably useful, computable
self-rewrite. We show that such a self-rewrite is globally optimal - no local
PRODUCT! - since the code first had to prove that it is not useful to continue
the proof search for alternative self-rewrites. Unlike previous
non-self-referential methods based on hardwired proof searchers, ours not only
boasts an optimal order of complexity but can optimally reduce any slowdowns
hidden by the O()-notation, provided the utility of such speed-ups is provable
at all.",1
"The emerging Web of Data utilizes the web infrastructure to represent and
interrelate data. The foundational standards of the Web of Data include the
WORK_OF_ART) and ORG (ORG).
URIs are used to identify resources and ORG is used to relate resources. While
ORG has been posited as a logic language designed specifically for knowledge
representation and reasoning, it is more generally useful if it can
conveniently support other models of computing. In order to realize the Web of
ORG as a general-purpose medium for storing and processing the world's data,
it is necessary to separate ORG from its logic language legacy and frame it
simply as a data model. Moreover, there is significant advantage in seeing the
NORP Web as a particular interpretation of the Web of Data that is focused
specifically on knowledge representation and reasoning. By doing so, other
interpretations of the Web of Data are exposed that realize ORG in different
capacities and in support of different computing models.","This paper argues that the operations of a 'Universal Turing Machine' (UTM)
and equivalent mechanisms such as ORG (ORG) - which are
widely accepted as definitions of the concept of `computing' - may be
interpreted as *information compression by multiple alignment, unification and
search* (ICMAUS).
  The motivation for this interpretation is that it suggests ways in which the
UTM/PCS model may be augmented in a proposed new computing system designed to
exploit the ICMAUS principles as fully as possible. The provision of a
relatively sophisticated search mechanism in the proposed 'SP' system appears
to open the door to the integration and simplification of a range of functions
including unsupervised inductive learning, best-match pattern recognition and
information retrieval, probabilistic reasoning, planning and problem solving,
and others. Detailed consideration of how the ICMAUS principles may be applied
to these functions is outside the scope of this article but relevant sources
are cited in this article.",0
"This paper presents some properties of unary coding of significance for
biological learning and instantaneously trained neural networks.","Sparse methods for supervised learning aim at finding good linear predictors
from as few variables as possible, i.e., with small cardinality of their
supports. This combinatorial selection problem is often turned into a convex
optimization problem by replacing the cardinality function by its convex
envelope (tightest convex lower bound), in this case the CARDINAL-norm. In this
paper, we investigate more general set-functions than the cardinality, that may
incorporate prior knowledge or structural constraints which are common in many
applications: namely, we show that for nondecreasing submodular set-functions,
the corresponding convex envelope can be obtained from its ORG extension, a
common tool in submodular analysis. This defines a family of polyhedral norms,
for which we provide generic algorithmic tools (subgradients and proximal
operators) and theoretical results (conditions for support recovery or
high-dimensional inference). By selecting specific submodular functions, we can
give a new interpretation to known norms, such as those based on
rank-statistics or grouped norms with potentially overlapping groups; we also
define new norms, in particular ones that can be used as non-factorial priors
for supervised learning.",0
"Submodular functions are relevant to machine learning for CARDINAL
reasons: (CARDINAL) some problems may be expressed directly as the optimization of
submodular functions and (CARDINAL) the lovasz extension of submodular functions
provides a useful set of regularization functions for supervised and
unsupervised learning. In this monograph, we present the theory of submodular
functions from a convex analysis perspective, presenting tight links between
certain polyhedra, combinatorial optimization and convex optimization problems.
In particular, we show how submodular function minimization is equivalent to
solving a wide variety of convex optimization problems. This allows the
derivation of new efficient algorithms for approximate and exact submodular
function minimization with theoretical guarantees and good practical
performance. By listing many examples of submodular functions, we review
various applications to machine learning, such as clustering, experimental
design, sensor placement, graphical model structure learning or subset
selection, as well as a family of structured sparsity-inducing norms that can
be derived and used from submodular functions.","A unary constraint (on the NORP domain) is a function from {CARDINAL} to the
set of real numbers. A free use of auxiliary unary constraints given besides
input instances has proven to be useful in establishing a complete
classification of the computational complexity of approximately solving
weighted counting NORP constraint satisfaction problems (or #CSPs). In
particular, CARDINAL special constant unary constraints are a key to an arity
reduction of arbitrary constraints, sufficient for the desired classification.
In an exact counting model, both constant unary constraints are always assumed
to be available since they can be eliminated efficiently using an arbitrary
nonempty set of constraints. In contrast, we demonstrate in an approximate
counting model, that CARDINAL of them is efficiently approximated and thus
eliminated approximately by a nonempty constraint set. This fact directly leads
to an efficient construction of polynomial-time randomized
approximation-preserving Turing reductions (or ORG-reductions) from #CSPs with
designated constraints to any given #CSPs composed of symmetric real-valued
constraints of arbitrary arities even in the presence of arbitrary extra unary
constraints.",0
"There has been a remarkable increase in work at the interface of computer
science and game theory in DATE. In this article I survey some of
the main themes of work in the area, with a focus on the work in computer
science. Given the length constraints, I make no attempt at being
comprehensive, especially since other surveys are also available, and a
comprehensive survey book will appear shortly.","A general notion of algebraic conditional plausibility measures is defined.
Probability measures, ranking functions, possibility measures, and (under the
appropriate definitions) sets of probability measures can all be viewed as
defining algebraic conditional plausibility measures. It is shown that
algebraic conditional plausibility measures can be represented using NORP
networks.",1
"Keyphrases are useful for a variety of purposes, including summarizing,
indexing, labeling, categorizing, clustering, highlighting, browsing, and
searching. The task of automatic keyphrase extraction is to select keyphrases
from within the text of a given document. Automatic keyphrase extraction makes
it feasible to generate keyphrases for the huge number of documents that do not
have manually assigned keyphrases. Good performance on this task has been
obtained by approaching it as a supervised learning problem. An input document
is treated as a set of candidate phrases that must be classified as either
keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase,
the most important features (attributes) appear to be the frequency and
location of the candidate phrase in the document. Recent work has demonstrated
that it is also useful to know the frequency of the candidate phrase as a
manually assigned keyphrase for other documents in the same domain as the given
document (e.g., the domain of computer science). Unfortunately, this
keyphrase-frequency feature is domain-specific (the learning process must be
repeated for each new domain) and training-intensive (good performance requires
a relatively large number of training documents in the given domain, with
manually assigned keyphrases). The aim of the work described here is to remove
these limitations. In this paper, I introduce new features that are derived by
mining lexical knowledge from a very large collection of unlabeled data,
consisting of CARDINAL Web pages without manually assigned
keyphrases. I present experiments that show that the new features result in
improved keyphrase extraction, although they are neither domain-specific nor
training-intensive.","An inductive learning algorithm takes a set of data as input and generates a
hypothesis as output. A set of data is typically consistent with an infinite
number of hypotheses; therefore, there must be factors other than the data that
determine the output of the learning algorithm. In machine learning, these
other factors are called the bias of the learner. Classical learning algorithms
have a fixed bias, implicit in their design. Recently developed learning
algorithms dynamically adjust their bias as they search for a hypothesis.
PERSON that shift bias in this manner are not as well understood as
classical algorithms. In this paper, we show that the PERSON effect has
implications for the design and analysis of bias shifting algorithms. The
PERSON effect was proposed in DATE, to explain how phenomena that might appear
to require NORP evolution (inheritance of acquired characteristics) can
arise from purely NORP evolution. GPE and ORG presented a
computational model of the PERSON effect in DATE. We explore a variation on
their model, which we constructed explicitly to illustrate the lessons that the
PERSON effect has for research in bias shifting algorithms. The main lesson is
that it appears that a good strategy for shift of bias in a learning algorithm
is to begin with a weak bias and gradually shift to a strong bias.",1
"We prove that for any vacuum, maximal, asymptotically flat, axisymmetric
initial data for PERSON equations close to extreme ORG data, the inequality
$\sqrt{J} \leq m$ is satisfied, where $m$ and $MONEY are the total mass and
angular momentum of the data. The proof consists in showing that extreme ORG
is a local minimum of the mass.","The aim of this chapter is to present an introduction and also an overview of
some of the most relevant results concerning positivity energy theorems in
General PERSON. These theorems provide the answer to a long standing
problem that has been proved remarkably difficult to solve. They constitute CARDINAL
of the major results in classical General Relativity and they uncover a deep
self-consistence of the theory.",1
"This paper deals with a model of cellular growth called ""WORK_OF_ART"", whose key features are: i) distinction bewteen ""normal"" and ""driver""
cells; ii) presence in driver cells of an epigenetic memory, that holds the
position of the cell in the driver cell lineage tree and represents the source
of differentiation during development. In the ORDINAL part of the paper the model
is proved able to generate arbitrary target shapes of unmatched size and
variety by means of evo-devo techniques, thus being validated as a model of
embryogenesis and cellular differentiation. In the ORDINAL part of the paper it
is shown how the model can produce artificial counterparts for some key aspects
of multicellular biology, such as junk DNA, ageing and carcinogenesis. If
individually each of these topics has been the subject of intense investigation
and modelling effort, to our knowledge no single model or theory seeking to
cover all of them under a unified framework has been put forward as yet: this
work contains such a theory, which makes ORG a potential basis
for a project of ORG.","We report complexity results about redundancy of formulae in CARDINAL form. We
ORDINAL consider the problem of checking redundancy and show some algorithms that
are slightly better than the trivial one. We then analyze problems related to
finding irredundant equivalent subsets (I.E.S.) of a given set. The concept of
cyclicity proved to be relevant to the complexity of these problems. Some
results about LOC formulae are also shown.",0
"The commentators have brought a wealth of new perspectives to the question of
how culture evolves. Each of their diverse disciplines--ranging from psychology
to biology to anthropology to economics to engineering--has a valuable
contribution to make to our understanding of this complex, multifaceted topic.
Though the vast majority of their comments were supportive of my approach, it
is natural that a reply such as this focus on points where my views differ from
that of the commentators. ... I conclude by saying that I am grateful to the
commentators for their diverse perspectives and insights, their overall support
for the project, and provocative ideas for where to go from here. Clearly there
are many fascinating avenues to explore as we move forward on our quest to
understand how culture evolves.","We describe ORG gate response in a mesoscopic ring threaded by a magnetic
flux $MONEY The ring is attached symmetrically to CARDINAL semi-infinite
CARDINAL-dimensional metallic electrodes and CARDINAL gate voltages, viz, $V_a$ and
$PERSON, are applied in CARDINAL arm of the ring which are treated as the inputs of
the ORG gate. The calculations are based on the tight-binding model and the
PERSON's function method, which numerically compute the conductance-energy and
current-voltage characteristics as functions of the ring-to-electrode coupling
strength, magnetic flux and gate voltages. Our theoretical study shows that,
for a particular value of CARDINAL\phi$ (MONEY) ($\phi_0=ch/e$, the elementary
flux-quantum), a high output current (CARDINAL) (in the logical sense) appears if both
the CARDINAL inputs to the gate are the same, while if one but not both inputs are
high (1), a low output current (0) results. It clearly exhibits the ORG gate
behavior and this aspect may be utilized in designing an electronic logic gate.",0
"This paper presents ORG (CNs) framework. CNs are used to
generalize neural and swarm architectures. Artificial neural networks, ant
colony optimization, particle swarm optimization, and realistic biological
models are used as examples of instantiations of CNs. The description of these
architectures as CNs allows their comparison. Their differences and
similarities allow the identification of properties that enable neural and
swarm architectures to perform complex computations and exhibit complex
cognitive abilities. In this context, the most relevant characteristics of CNs
are the existence multiple dynamical and functional scales. The relationship
between multiple dynamical and functional scales with adaptation, cognition (of
brains and swarms) and computation is discussed.","It is shown that the gauge fixings of the LOC and the W fields and CARDINAL
scalars are via nonconserved axial-vector and charged vector currents of
massive fermions dynamically generated by fermion masses in WORK_OF_ART
interactions. The top quark mass provides enough strength for this chiral
symmetry breaking. The masses of the CARDINAL scalars are determined to be about
$MONEY GeV. These scalars have negative probability. They are the
nonperturbative solutions of the ORG. A new perturbation theory with dynamically
generated and fixed gauge fixings is constructed. The Faddeev-Popov procedure
is not invoked.",0
"A mobile ad hoc network (ORG) is a collection of autonomous nodes that
communicate with each other by forming a multi-hop radio network and
maintaining connections in a decentralized manner. Security remains a major
challenge for these networks due to their features of open medium, dynamically
changing topologies, reliance on cooperative algorithms, absence of centralized
monitoring points, and lack of clear lines of defense. Protecting the network
layer of a PRODUCT from malicious attacks is an important and challenging
security issue, since most of the routing protocols for MANETs are vulnerable
to various types of attacks. Ad hoc on-demand distance vector routing (ORG) is
a very popular routing algorithm. However, it is vulnerable to the well-known
black hole attack, where a malicious node falsely advertises good paths to a
destination node during the route discovery process but drops all packets in
the data forwarding phase. This attack becomes more severe when a group of
malicious nodes cooperate each other. The proposed mechanism does not apply any
cryptographic primitives on the routing messages. Instead, it protects the
network by detecting and reacting to malicious activities of the nodes.
Simulation results show that the scheme has a significantly high detection rate
with moderate network traffic overhead and computation overhead in the nodes.","A mobile ad hoc network (ORG) is a collection of mobile nodes that
communicate with each other by forming a multi-hop radio network. Security
remains a major challenge for these networks due to their features of open
medium, dynamically changing topologies, reliance on cooperative algorithms,
absence of centralized monitoring points, and lack of clear lines of defense.
Design of an efficient and reliable ORG authentication protocol for such
networks is a particularly challenging task since the nodes are battery-driven
and resource constrained. This paper presents a robust and efficient key
exchange protocol for nodes authentication in a ORG based on multi-path
communication. Simulation results demonstrate that the protocol is effective
even in presence of large fraction of malicious nodes in the network. Moreover,
it has a minimal computation and communication overhead that makes it ideally
suitable for MANETs.",1
"The partition function of an oscillator disturbed by a set of electron
particle paths has been computed by a path integral method which permits to
evaluate at any temperature the relevant cumulant terms in the series
expansion. The time dependent source current peculiar of the semiclassical
PERSON model induces large electron-phonon anharmonicities on the
phonon subsystem. As a main signature of anharmonicity the phonon heat capacity
shows a peak whose temperature location strongly varies with the strength of
the {\it e-ph} coupling. High energy oscillators are less sensitive to
anharmonic perturbations.","We present a study of the CARDINAL dimensional PERSON model
Hamiltonian by a diagrammatic perturbative method in the weak electron-phonon
coupling regime. Exact computation of both the charge carrier effective mass
and the electron spectral function shows that electrons are good quasiparticles
in the adiabatic and antiadiabatic limits but novel features emerge in the
intermediate regime, where the phonons and the electrons compare on the energy
scale. Together with a sizeable mass enhancement we observe, in the latter
regime, a spread of the spectral weight (among several transition peaks)
associated with an increased relevance of multiphonons contributions at larger
{\it e-ph} couplings. Accordingly electrons cease to be the good quasiparticles
and an onset of polaron formation is favoured.",1
"We propose a method to organize experimental data from particle collision
experiments in a general format which can enable a simple visualisation and
effective classification of collision data using machine learning techniques.
The method is based on sparse fixed-size matrices with single- and CARDINAL-particle
variables containing information on identified particles and jets. We
illustrate this method using an example of searches for new physics at the LHC
experiments.","Let G be a Lie group and E be a locally convex topological G-module.
  If E is sequentially complete, then E and its space of smooth vectors are
modules for the algebra D(G) of compactly supported smooth functions on PERSONHowever, the module multiplication need not be continuous. The pathology can be
ruled out if E is (or embeds into) a projective limit of PERSON.
  Moreover, in this case the space of analytic vectors is a module for the
algebra A(G) of superdecaying analytic functions introduced by ORG,
GPE and GPE. We prove that the space of analytic vectors is a
topological A(G)-module if E is a GPE space or, more generally, if every
countable set of continuous seminorms on E has an upper bound. The same
conclusion is obtained if G has a compact Lie algebra.
  The question of whether D(G) and PRODUCT) are topological algebras is also
addressed.",0
"Many mathematical models utilize limit processes. Continuous functions and
the calculus, differential equations and topology, all are based on limits and
continuity. However, when we perform measurements and computations, we can
achieve only approximate results. In some cases, this discrepancy between
theoretical schemes and practical actions changes drastically outcomes of a
research and decision-making resulting in uncertainty of knowledge. In the
paper, a mathematical approach to such kind of uncertainty, which emerges in
computation and measurement, is suggested on the base of the concept of a fuzzy
limit. A mathematical technique is developed for differential models with
uncertainty. To take into account the intrinsic uncertainty of a model, it is
suggested to use fuzzy derivatives instead of conventional derivatives of
functions in this model.","It is argued that the existing schemes of fault-tolerant quantum computation
designed for discrete-time models and based on quantum error correction fail
for continuous-time NORP models even with NORP noise.",0
"This report presents an empirical evaluation of CARDINAL algorithms for
automatically extracting keywords and keyphrases from documents. The CARDINAL
algorithms are compared using CARDINAL different collections of documents. For each
document, we have a target set of keyphrases, which were generated by hand. The
target keyphrases were generated for human readers; they were not tailored for
any of the CARDINAL keyphrase extraction algorithms. Each of the algorithms was
evaluated by the degree to which the algorithm's keyphrases matched the
manually generated keyphrases. The CARDINAL algorithms were (CARDINAL) the ORG
feature in ORG's Word 97, (CARDINAL) an algorithm based on PERSON
part-of-speech tagger, (CARDINAL) the ORG feature in FAC 97, and (CARDINAL)
ORG's ORG algorithm. For all CARDINAL document collections, ORG's Extractor
yields the best match with the manually generated keyphrases.","Recognizing analogies, synonyms, antonyms, and associations appear to be CARDINAL
distinct tasks, requiring distinct ORG algorithms. In the past, the CARDINAL tasks
have been treated independently, using a wide variety of algorithms. These CARDINAL
semantic classes, however, are a tiny sample of the full range of semantic
phenomena, and we cannot afford to create ad hoc algorithms for each semantic
phenomenon; we need to seek a unified approach. We propose to subsume a broad
range of phenomena under analogies. To limit the scope of this paper, we
restrict our attention to the subsumption of synonyms, antonyms, and
associations. We introduce a supervised corpus-based machine learning algorithm
for classifying analogous word pairs, and we show that it can solve
multiple-choice ORG analogy questions, ORG synonym questions, ORG
synonym-antonym questions, and similar-associated-both questions from cognitive
psychology.",1
"NORP integral functional measure of entropy-uncertainty (EF) on
trajectories of NORP multi-dimensional diffusion process is cutting off by
interactive impulses (controls). Each cutoff minimax of EF superimposes and
entangles conjugated fractions in microprocess, enclosing the captured entropy
fractions as source of an information unit. The impulse step-up action launches
the unit formation and step-down action finishes it and brings energy from the
interactive jump. This finite jump transfers the entangled entropy from
uncertain Yes-logic to the certain-information No-logic information unit whose
measuring at end of the cut kills final entropy-uncertainty and limits unit.
The Yes-No logic holds Bit Participator creating elementary information
observer without physical pre-law. Cooperating CARDINAL units in doublet and an
opposite directional information unit in triplet forms minimal stable
structure. Information path functional (ORG) integrates multiple hidden
information contributions along the cutting process correlations in information
units of cooperating doublets-triplets, bound by free information, and enfolds
the sequence of enclosing triplet structures in the information network (IN)
that sequentially decreases the entropy and maximizes information. The IN bound
triplets release free information rising information forces enable attracting
new information unit and ordering it. While ORG collects the information units,
the IN performs logical computing using doublet-triplet code. The IN different
levels unite logic of ORG and macro- information processes,
composing quantum and/or classical computation.","Man-in-the-Middle (MM) is not only a ubiquitous attack pattern in security,
but also an important paradigm of network computation and economics.
Recognizing ongoing GPE-attacks is an important security task; modeling
GPE-interactions is an interesting task for semantics of computation. Traced
monoidal categories are a natural framework for GPE-modelling, as the trace
structure provides a tool to hide what happens *in the middle*. An effective
analysis of what has been traced out seems to require an additional property of
traces, called *normality*. We describe a modest model of network computation,
based on partially ordered multisets (pomsets), where basic network
interactions arise from the monoidal trace structure, and a normal trace
structure arises from an iterative, i.e. coalgebraic structure over terms and
messages used in computation and communication. The correspondence is
established using a convenient monadic description of normally traced monoidal
categories.",0
"We propose that a general learning system should have CARDINAL kinds of agents
corresponding to sensory, short-term, and long-term memory that implicitly will
facilitate context-free and context-sensitive aspects of learning. These CARDINAL
agents perform mututally complementary functions that capture aspects of the
human cognition system. We investigate the use of ORG networks for use
as models of short-term and sensory memory.","It is generally accepted that machines can replicate cognitive tasks
performed by conscious agents as long as they are not based on the capacity of
awareness. We consider several views on the nature of subjective awareness,
which is fundamental for self-reflection and review, and present reasons why
this property is not computable. We argue that consciousness is more than an
epiphenomenon and assuming it to be a separate category is consistent with both
quantum mechanics and cognitive science. We speak of CARDINAL kinds of
consciousness, little-C and big-C, and discuss the significance of this
classification in analyzing the current academic debates in the field. The
interaction between the system and the measuring apparatus of the experimenter
is examined both from the perspectives of decoherence and the quantum PERSON
effect. These ideas are used as context to address the question of limits to
machine consciousness.",1
"Up to now information and information process have no scientific definitions,
neither implicit origin. They emerge in observing multiple impulses interactive
yes-no actions modeling information ORG. Merging action and reaction, joining
probabilistic prior and posterior actions on edge of the observed
predictability, begin a microprocess. Its time of entanglement starts space
interval composing CARDINAL qubits or Bit of reversible logic in the emerging
information process. The impulse interacting action curves impulse geometry
creating asymmetrical logic Bit as logical ORG demon. With approaching
probability one, the attracting interaction captures energy memorizing
asymmetrical logic in information certain Bit. Such Bit is naturally extracted
at minimal quality energy equivalent ln2 working as PERSON. The
memorized impulse Bit and its free information self-organizes multiple ORG in
triplets composing a macroprocess. Each memorized information binds reversible
microprocess with irreversible information macroprocess along multi-dimensional
observing process. The macroprocess self-forming triplet units attract new UP
through free Information. Multiple UP adjoin hierarchical network (IN) whose
free information produces new UP at higher level node and encodes triplets in
multi-levels hierarchical organization. The interactive information dynamics
assemble geometrical and information structures of cognition and intelligence
in double spiral rotating code. The ORG path functional integrates in
bits the interactive dynamics.","Compressed Counting (ORG)} was recently proposed for approximating the
$\alpha$th frequency moments of data streams, for $MONEY \leq CARDINAL Under the
relaxed strict-Turnstile model, ORG dramatically improves the standard algorithm
based on symmetric stable random projections}, especially as $\alpha\to 1$. A
direct application of ORG is to estimate the entropy, which is an important
summary statistic in Web/network measurement and often serves a crucial
""feature"" for data mining. The R\'enyi entropy and the NORP entropy are
functions of the $\alpha$th frequency moments; and both approach the FAC
entropy as $\alpha\to 1$. A recent theoretical work suggested using the
$\alpha$th frequency moment to approximate the FAC entropy with
$\alpha=1+\delta$ and very small $MONEY (e.g., $<10^{-4}$).
  In this study, we experiment using ORG to estimate frequency moments, R\'enyi
entropy, Tsallis entropy, and FAC entropy, on real Web crawl data. We
demonstrate the variance-bias trade-off in estimating FAC entropy and
provide practical recommendations. In particular, our experiments enable us to
draw some important conclusions:
  (CARDINAL) As $\alpha\to 1$, ORG dramatically improves {\em symmetric stable random
projections} in estimating frequency moments, R\'enyi entropy, Tsallis entropy,
and FAC entropy. The improvements appear to approach ""infinity.""
  (CARDINAL) Using {\em symmetric stable random projections} and $\alpha = CARDINAL
with very small $MONEY does not provide a practical algorithm because the
required sample size is enormous.",0
"Many relativists have been long convinced that black hole evaporation leads
to information loss or remnants. String theorists have however not been too
worried about the issue, largely due to a belief that the NORP argument for
information loss is flawed in its details. A recently derived inequality shows
that the Hawking argument for black holes with horizon can in fact be made
rigorous. What happens instead is that in string theory black hole microstates
have no horizons. Thus the evolution of radiation quanta with E ~ kT is
modified by order unity at the horizon, and we resolve the information paradox.
We discuss how it is still possible for E >> kT objects to see an approximate
black hole like geometry. We also note some possible implications of this
physics for the early ORG.","We consider the problem of nonlinear dimensionality reduction: given a
training set of high-dimensional data whose ``intrinsic'' low dimension is
assumed known, find a feature extraction map to low-dimensional space, a
reconstruction map back to high-dimensional space, and a geometric description
of the dimension-reduced data as a smooth manifold. We introduce a
complexity-regularized quantization approach for fitting a NORP mixture
model to the training set via a ORG algorithm. Complexity regularization
controls the trade-off between adaptation to the local shape of the underlying
manifold and global geometric consistency. The resulting mixture model is used
to design the feature extraction and reconstruction maps and to define a
NORP metric on the low-dimensional data. We also sketch a proof of
consistency of our scheme for the purposes of estimating the unknown underlying
pdf of high-dimensional data.",0
"The article presents results of preliminary study of solutions to recently
offered basic thermodynamic equation for equilibrium in chemical systems with
focus on chaotic behavior. Classical part of that equation was investigated
earlier in a series of papers. In this work a similarity between
CARDINAL-dimensional logistic map and non-classical (chaotic) term of the equation
was discussed to introduce the problem. Results of this work allow us to
evaluate the region where open equilibrium belongs to the basin of regular
attractor and leads to trivial solutions with CARDINAL deviation from true
thermodynamic equilibrium, and then to find ORDINAL bifurcation threshold as a
limit of open equilibrium and a limit of the classical region as well. Features
of the basic equation are discussed with regard to relative values of the
chaotic and thermodynamic temperatures. Obtained results prompt us to consider
the basic equation of new theory to be the general equation of state of
chemical systems.","The paper offers a discrete thermodynamic model of lasers. ORG is an open
system; its equilibrium is based on a balance of CARDINAL thermodynamic forces, CARDINAL
related to the incoming pumping power and another to the emitted light. The
basic expression for such equilibrium is a logistic map, graphical solutions to
which are pitchfork bifurcation diagrams. As pumping force increases, the
relative populations on the ground and lasing branches tend to CARDINAL and unity
correspondingly. An interesting feature of this model is the line spectrum of
the up and down transitions between the branches beyond bifurcation point. Even
in a simple case of CARDINAL-level laser with CARDINAL possible transition types (up and
down), the spectra look like sets of the line packets, starting well before the
population inversion. This effect is an independent confirmation of the
PERSON's prohibition on practical realization of CARDINAL-level laser. Multilevel
lasers may be approached by employing the idea of thermodynamic activity for
the emitting atoms. Considering coefficient of thermodynamic activity of the
lasing level atoms to be proportional to the ratio of life times on the upper
and lasing (the CARDINAL) levels, one can derive a new basic map for the multilevel
laser system. For a modest ratio only of CARDINAL, spontaneous transitions between
levels are pushed to the area beyond population inversion, opening a space for
the functioning of laser.",1
"The development of discursive knowledge presumes the communication of meaning
as analytically different from the communication of information. Knowledge can
then be considered as a meaning which makes a difference. Whereas the
communication of information is studied in the information sciences and
scientometrics, the communication of meaning has been central to PERSON's
attempts to make the theory of autopoiesis relevant for sociology. Analytical
techniques such as semantic maps and the simulation of anticipatory systems
enable us to operationalize the distinctions which ORG proposed as relevant
to the elaboration of ORG's ""horizons of meaning"" in empirical research:
interactions among communications, the organization of meaning in
instantiations, and the self-organization of interhuman communication in terms
of symbolically generalized media such as truth, love, and power. Horizons of
meaning, however, remain uncertain orders of expectations, and one should
caution against reification from the meta-biological perspective of systems
theory.","In requirements specification, software engineers create a textual
description of the envisioned system as well as develop conceptual models using
such tools as ORG (ORG) and System Modeling Language
(ORG). CARDINAL such tool, called FM, has recently been developed as an extension
of the INPUT-PROCESS-OUTPUT (ORG) model. ORG has been used extensively in many
interdisciplinary applications and is described as one of the most fundamental
and important of all descriptive tools. This paper is an attempt to
understanding the ORG in ORG. The fundamental way to describe ORG is in
verbs. This use of language has an important implication for systems modeling
since verbs express the vast range of actions and movements of all things. It
is clear that modeling needs to examine verbs. Accordingly, this paper involves
a study of LANGUAGE verbs as a bridge to learn about processes, not as
linguistic analysis but rather to reveal the semantics of processes,
particularly the CARDINAL verbs that form the basis of FM states: create, process,
receive, release, and transfer. The paper focuses on verb classification, and
specifically on how to model the action of verbs diagrammatically. From the
linguistics point of view, according to some researchers, further exploration
of the notion of verb classes is needed for real-world tasks such as machine
translation, language generation, and document classification. Accordingly,
this non-linguistics study may benefit linguistics.",0
"Feature Markov Decision Processes (PhiMDPs) are well-suited for learning
agents in general environments. Nevertheless, unstructured (Phi)MDPs are
limited to relatively simple environments. Structured MDPs like ORG (DBNs) are used for large-scale real-world problems. In this
article I extend PhiMDP to PhiDBN. The primary contribution is to derive a cost
criterion that allows to automatically extract the most relevant features from
the environment, leading to the ""best"" DBN representation. I discuss all
building blocks required for a complete general learning algorithm.","The provably asymptotically fastest algorithm within a factor of CARDINAL for
formally described problems will be constructed. The main idea is to enumerate
all programs provably equivalent to the original problem by enumerating all
proofs. The algorithm could be interpreted as a generalization and improvement
of PERSON search, which is, within a multiplicative constant, the fastest
algorithm for inverting functions. PERSON's speed-up theorem is avoided by taking
into account only programs for which a correctness proof exists. Furthermore,
it is shown that the fastest program that computes a certain function is also
one of the shortest programs provably computing this function. To quantify this
statement, the definition of NORP complexity is extended, and CARDINAL new
natural measures for the complexity of a function are defined.",1
"In this paper will be presented methodology of encoding information in
valuations of discrete lattice with some translational invariant constrains in
asymptotically optimal way. The method is based on finding statistical
description of such valuations and changing it into statistical algorithm,
which allows to construct deterministically valuation with given statistics.
NORP statistics allow to generate valuations with uniform distribution - we
get maximum information capacity this way. It will be shown that we can reach
the optimum for CARDINAL-dimensional models using maximal entropy random walk and
that for the general case we can practically get as close to the capacity of
the model as we want (found numerically: lost CARDINAL} bit/node for FAC). There will be also presented simpler alternative to arithmetic coding
method which can be used as cryptosystem and data correction method too.","A research programme is set out for developing the use of high-level methods
for quantum computation and information, based on the categorical formulation
of ORG introduced by the author and PERSON.",0
"We define a physically reasonable mass for an asymptotically ORG
(ORG) manifold which is uniquely defined in the case of a normalized
representation.","The aim is formal principles of origin information and information process
creating information observer self-creating information in interactive
observations. The interactive phenomenon creates Yes-No actions of information
ORG in its information observer. Information emerges from interacting random
field of NORP probabilities, which link PERSON law probabilities
and NORP probabilities observing PERSON diffusion process by probabilistic
CARDINAL-1 impulses. Each No-0 action cuts maximum of impulse minimal entropy while
following Yes-1 action transfers maxim between impulses performing dual
principle of converting process entropy to information. Merging Yes-No actions
generate microprocess within bordered impulse producing Bit with free
information when the microprocess probability approaches CARDINAL. Interacting bits
memorize free information which attracts multiple Bits moving macroprocess self
joining triplet macrounits. Memorized information binds reversible microprocess
with irreversible macroprocess. The observation converts cutting entropy to
information macrounits. Macrounits logically self-organize information networks
encoding the units in geometrical structures enclosing triplet code. Multiple
IN binds their ending triplets enclosing observer information cognition and
intelligence. The observer cognition assembles common units through multiple
attraction and resonances at forming IN triplet hierarchy which accept only
units that recognizes each IN node. Maximal number of accepted triplet levels
in multiple IN measures the observer maximum comparative information
intelligence. The observation process carries probabilistic and certain wave
functions which self-organize the space hierarchical structures. These
information regularities create integral logic and intelligence self-requesting
needed information.",0
"In this paper we demonstrate that it is possible to manage intelligence in
constant time as a pre-process to information fusion through a series of
processes dealing with issues such as clustering reports, ranking reports with
respect to importance, extraction of prototypes from clusters and immediate
classification of newly arriving intelligence reports. These methods are used
when intelligence reports arrive which concerns different events which should
be handled independently, when it is not known a priori to which event each
intelligence report is related. We use clustering that runs as a back-end
process to partition the intelligence into subsets representing the events, and
in parallel, a fast classification that runs as a front-end process in order to
put the newly arriving intelligence into its correct information fusion
process.","In this paper we develop methods for selection of templates and use these
templates to recluster an already performed ORG clustering taking
into account intelligence to template fit during the reclustering phase. By
this process the risk of erroneous force aggregation based on some misplace
pieces of evidence from the ORDINAL clustering process is greatly reduced.
Finally, a more reliable force aggregation is performed using the result of the
ORDINAL clustering. These steps are taken in order to maintain most of the
excellent computational performance of ORG clustering, while at the
same time improve on the clustering result by including some higher relations
among intelligence reports described by the templates. The new improved
algorithm has a computational complexity of O(n**3 ORG) compared to PERSON) of standard PERSON clustering using ORG spin mean field
theory.",1
"Many papers proved the security of quantum key distribution (QKD) system, in
the asymptotic framework. The degree of the security has not been discussed in
the finite coding-length framework, sufficiently. However, to guarantee any
implemented QKD system requires, it is needed to evaluate a protocol with a
finite coding-length. For this purpose, we derive a tight upper bound of the
eavesdropper's information. This bound is better than existing bounds. We also
obtain the exponential rate of the eavesdropper's information. Further, we
approximate our bound by using the normal distribution.","We discuss secure computation of modular sum when multiple access channel
from distinct players $ORG, \ldots, A_c$ to a ORDINAL party (Receiver) is given.
Then, we define the secure modulo sum capacity as the supremum of the
transmission rate of modulo sum without information leakage of other
information. We derive its useful lower bound, which is numerically calculated
under a realistic model that can be realizable as a NORP multiple access
channel.",1
"CARDINAL of basic difficulties of machine learning is handling unknown rotations
of objects, for example in image recognition. A related problem is evaluation
of similarity of shapes, for example of CARDINAL chemical molecules, for which
direct approach requires costly pairwise rotation alignment and comparison.
Rotation invariants are useful tools for such purposes, allowing to extract
features describing shape up to rotation, which can be used for example to
search for similar rotated patterns, or fast evaluation of similarity of shapes
e.g. for virtual screening, or machine learning including features directly
describing shape. A standard approach are rotationally invariant cylindrical or
spherical harmonics, which can be seen as based on polynomials on sphere,
however, they provide very few invariants - only one per degree of polynomial.
There will be discussed a general approach to construct arbitrarily large sets
of rotation invariants of polynomials, for MONEY in $PERSON up to
$O(n^D)$ independent invariants instead of CARDINALO(D)$ offered by standard
approaches, possibly also a complete set: providing not only necessary, but
also sufficient condition for differing only by rotation (and reflectional
symmetry).","PERSON (ORG) is a promising technique especially for
multimedia data compression, already used in GPE audio codec and considered
for AV1 video codec. It quantizes vectors from ORG unit sphere by ORDINAL
projecting them to MONEY norm unit sphere, then quantizing and encoding there.
This paper shows that the used standard radial projection is suboptimal and
proposes to tune its deformations by using parameterized power projection:
$PERSON instead, where the optimized power $p$ is applied
coordinate-wise, getting usually MONEY, NORP improvement comparing to
radial projection.",1
"Computer scientists are in the position to create new, free high-quality
journals. So what would it take?","For the most of my life, I have earned my living as a computer vision
professional busy with image processing tasks and problems. In the computer
vision community there is a widespread belief that artificial vision systems
faithfully replicate human vision abilities or at least very closely mimic
them. It was a great surprise to me when one day I have realized that computer
and human vision have next to nothing in common. The former is occupied with
extensive data processing, carrying out massive pixel-based calculations, while
the latter is busy with meaningful information processing, concerned with smart
objects-based manipulations. And the gap between the CARDINAL is insurmountable. To
resolve this confusion, I had had to return and revaluate ORDINAL the vision
phenomenon itself, define more carefully what visual information is and how to
treat it properly. In this work I have not been, as it is usually accepted,
biologically inspired . On the contrary, I have drawn my inspirations from a
pure mathematical theory, the ORG s complexity theory. The results of my
work have been already published elsewhere. So the objective of this paper is
to try and apply the insights gained in course of this my enterprise to a more
general case of information processing in human brain and the challenging issue
of human intelligence.",0
"This paper is placed at the intersection-point between the study of
theoretical computational models aimed at capturing the essence of genetic
regulatory networks and the field of ORG (or ORG). A model is proposed, with the objective of providing an effective
way to generate arbitrary forms by using evolutionary-developmental techniques.
Preliminary experiments have been performed.","Borderline personality disorder and narcissistic personality disorder are
important nosographic entities and have been subject of intensive
investigations. The currently prevailing psychodynamic theory for mental
disorders is based on the repertoire of defense mechanisms employed. Another
line of research is concerned with the study of psychological traumas and
dissociation as a defensive response. Both theories can be used to shed light
on some aspects of pathological mental functioning, and have many points of
contact. This work merges these CARDINAL psychological theories, and builds a model
of mental function in a relational context called ORG.
The model, which is enriched with ideas borrowed from the field of computer
science, leads to a new therapeutic proposal for psychological traumas and
personality disorders.",1
"Active learning strategies respond to the costly labelling task in a
supervised classification by selecting the most useful unlabelled examples in
training a predictive model. Many conventional active learning algorithms focus
on refining the decision boundary, rather than exploring new regions that can
be more informative. In this setting, we propose a sequential algorithm named
ORG that can improve any Active learning algorithm by an optimal random
exploration. Experimental results show a statistically significant and
appreciable improvement in the performance of our new approach over the
existing active feedback methods.","The conceptual knowledge framework ORG needs several components for a
successful design. CARDINAL important, but previously overlooked, component is the
central core of ORG. The central core provides a theoretical link between
the ontological specification in ORG and the conceptual knowledge
representation in ORG. This paper discusses the formal semantics and syntactic
styles of the central core, and also the important role it plays in defining
interoperability between ORG, ORG and GPE.",0
"We apply multilayer bootstrap network (ORG), a recent proposed unsupervised
learning method, to unsupervised speaker recognition. The proposed method ORDINAL
extracts supervectors from an unsupervised universal background model, then
reduces the dimension of the high-dimensional supervectors by multilayer
bootstrap network, and finally conducts unsupervised speaker recognition by
clustering the low-dimensional data. The comparison results with CARDINAL unsupervised
and CARDINAL supervised speaker recognition techniques demonstrate the effectiveness
and robustness of the proposed method.","Multitask clustering tries to improve the clustering performance of multiple
tasks simultaneously by taking their relationship into account. Most existing
multitask clustering algorithms fall into the type of generative clustering,
and none are formulated as convex optimization problems. In this paper, we
propose CARDINAL convex Discriminative Multitask Clustering (DMTC) algorithms to
address the problems. Specifically, we ORDINAL propose a NORP DMTC framework.
Then, we propose CARDINAL convex GPE objectives within the framework. The ORDINAL
one, which can be seen as a technical combination of the convex multitask
feature learning and the convex ORG (M3C),
aims to learn a shared feature representation. The ORDINAL one, which can be
seen as a combination of the convex multitask relationship learning and M3C,
aims to learn the task relationship. The CARDINAL objectives are solved in a uniform
procedure by the efficient cutting-plane algorithm. Experimental results on a
toy problem and CARDINAL benchmark datasets demonstrate the effectiveness of the
proposed algorithms.",1
"In this article, we tentatively assign the $X(3915)$ and $X(4500)$ to be the
ground state and the ORDINAL radial excited state of the
axialvector-diquark-axialvector-antidiquark type scalar $PERSON, respectively, assign the $PERSON to be the ground state
vector-diquark-vector-antidiquark type scalar $cs\bar{c}\bar{s}$ tetraquark
state, and study their masses and pole residues with the ORG sum rules in
details by calculating the contributions of the vacuum condensates up to
dimension CARDINAL. The numerical results support assigning the $X(3915)$ and
$X(4500)$ to be the ground state and the ORDINAL radial excited state of the
axialvector-diquark-axialvector-antidiquark type scalar $PERSON, respectively, and assigning the $PERSON to be the ground
state vector-diquark-vector-antidiquark type scalar $PERSON.","In this article, we take the point of view that the $D_s(2700)$ be a
tetraquark state, which consists of a scalar diquark and a vector antidiquark,
and calculate its mass with the ORG sum rules. The numerical result indicates
that the mass of the vector charmed ORG state is about
$ORG or $PERSON from different sum
rules, which is MONEYMONEY larger than the experimental data. Such
tetraquark component should be very small in the $PERSON",1
"Artificial intelligence has impacted many aspects of human life. This paper
studies the impact of artificial intelligence on economic theory. In particular
we study the impact of artificial intelligence on the theory of bounded
rationality, efficient market hypothesis and prospect theory.","Many academic journals ask their authors to provide a list of CARDINAL to
CARDINAL key words, to appear on the ORDINAL page of each article. Since these key
words are often phrases of CARDINAL or more words, we prefer to call them
keyphrases. There is a surprisingly wide variety of tasks for which keyphrases
are useful, as we discuss in this paper. Recent commercial software, such as
ORG's Word 97 and Verity's Search 97, includes algorithms that
automatically extract keyphrases from documents. In this paper, we approach the
problem of automatically extracting keyphrases from text as a supervised
learning task. We treat a document as a set of phrases, which the learning
algorithm must learn to classify as positive or negative examples of
keyphrases. Our ORDINAL set of experiments applies the C4.5 decision tree
induction algorithm to this learning task. The ORDINAL set of experiments
applies the GenEx algorithm to the task. We developed the ORG algorithm
specifically for this task. The ORDINAL set of experiments examines the
performance of GenEx on the task of metadata generation, relative to the
performance of ORG's Word 97. The ORDINAL and final set of experiments
investigates the performance of GenEx on the task of highlighting, relative to
PERSON's Search 97. The experimental results support the claim that a
specialized learning algorithm (GenEx) can generate better keyphrases than a
general-purpose learning algorithm (C4.5) and the non-learning algorithms that
are used in commercial software (Word CARDINAL and Search 97).",0
"A significant progress has been made in DATE over the study
of combinatorial ORG optimization problems and their associated optimization and
approximate classes, such as ORG, ORG, ORG (or APXP), and ORG. Unfortunately, a
collection of problems that are simply placed inside the P-solvable
optimization class ORG never have been studiously analyzed regarding their exact
computational complexity. To improve this situation, the existing framework
based on polynomial-time computability needs to be expanded and further refined
for an insightful analysis of various approximation algorithms targeting
optimization problems within ORG. In particular, we deal with those problems
characterized in terms of logarithmic-space computations and uniform-circuit
computations. We are focused on nondeterministic logarithmic-space (ORG)
optimization problems or ORG problems. Our study covers a wide range of
optimization and approximation classes, dubbed as, ORG, ORG, ORG, and LSAS as
well as new classes NORP, ORG, ORG, and DATE, which are founded on uniform
families of NORP circuits. Although many ORG decision problems can be
naturally converted into ORG optimization (ORG) problems, few ORG problems have
been studied vigorously. We thus provide a number of new ORG problems falling
into those low-complexity classes. With the help of NC1 or AC0
approximation-preserving reductions, we also identify the most difficult
problems (known as complete problems) inside those classes. Finally, we
demonstrate a number of collapses and separations among those refined
optimization and approximation classes with or without unproven
complexity-theoretical assumptions.","String theory suggests that black hole microstates are ORG, horizon sized
`fuzzballs', rather than smooth geometries with horizon. Radiation from
fuzzballs can carry information and does not lead to information loss. But if
we let a shell of matter collapse then it creates a horizon, and it seems that
subsequent radiation will lead to information loss. We argue that the
resolution to this problem is that the shell can tunnel to the fuzzball
configurations. The amplitude for tunneling is small because we are relating
CARDINAL macroscopically different configurations, but the number of states that we
can tunnel to, given through the PERSON entropy, is very large. These small
and large numbers can cancel each other, making it possible for the shell to
tunnel into fuzzball states before a significant amount of radiation has been
emitted. This offers a way to resolve the information paradox.",0
"The world is witnessing the birth of a revolutionary computing paradigm that
promises to have a profound effect on the way we interact with computers,
devices, physical spaces, and other people. This new technology, called
ubiquitous computing, envisions a world where embedded processors, computers,
sensors, and digital communications are inexpensive commodities that are
available everywhere. This paper presents a comprehensive discussion on the
central trends in ubiquitous computing considering them form technical, social
and economic perspectives. It clearly identifies different application areas
and sectors that will benefit from the potentials of ubiquitous computing. It
also brings forth the challenges of ubiquitous computing that require active
solutions and management.","Merely by existing, all physical systems register information. And by
evolving dynamically in time, they transform and process that information. The
laws of physics determine the amount of information that a physical system can
register (number of bits) and the number of elementary logic operations that a
system can perform (number of ops). The universe is a physical system. This
paper quantifies the amount of information that the universe can register and
the number of elementary operations that it can have performed over its
history. The universe can have performed MONEY ops on
$MONEY bits.",0
Short philosophical essay,"Deep neural networks are usually trained with stochastic gradient descent
(SGD), which minimizes objective function using very rough approximations of
gradient, only averaging to the real gradient. ORG approaches like
momentum or ORG only consider a single direction, and do not try to model
distance from extremum - neglecting valuable information from calculated
sequence of gradients, often stagnating in some suboptimal plateau. ORDINAL
order methods could exploit these missed opportunities, however, beside
suffering from very large cost and numerical instabilities, many of them
attract to suboptimal points like saddles due to negligence of signs of
curvatures (as eigenvalues of GPE).
  Saddle-free ORG method (SFN)~\cite{SFN} is a rare example of addressing
this issue - changes saddle attraction into repulsion, and was shown to provide
essential improvement for final value this way. However, it neglects noise
while modelling ORDINAL order behavior, focuses on PERSON subspace for numerical
reasons, and requires costly eigendecomposion.
  Maintaining SFN advantages, there are proposed inexpensive ways for
exploiting these opportunities. ORDINAL order behavior is linear dependence of
ORDINAL derivative - we can optimally estimate it from sequence of noisy
gradients with least square linear regression, in online setting here: with
weakening weights of old gradients. Statistically relevant subspace is
suggested by ORG of recent noisy gradients - in online setting it can be made
by slowly rotating considered directions toward new gradients, gradually
replacing old directions with recent statistically relevant. Eigendecomposition
can be also performed online: with regularly performed step of QR method to
maintain diagonal PERSON. Outside the ORDINAL order modeled subspace we can
simultaneously perform gradient descent.",0
"In DATE, the notion of quantum polynomial-time computability
has been modeled by ORG machines as well as quantum circuits. Here,
we seek a ORDINAL model, which is a quantum analogue of the schematic (inductive
or constructive) definition of (primitive) recursive functions. For quantum
functions, which map finite-dimensional PERSON spaces to themselves, we
present such a schematic definition, composed of a small set of initial quantum
functions and a few construction rules that dictate how to build a new quantum
function from the existing quantum functions. We prove that our schematic
definition precisely characterizes all functions that can be computable with
high success probabilities on well-formed quantum Turing machines in polynomial
time or equivalently, uniform families of polynomial-size quantum circuits. Our
new, schematic definition is quite simple and intuitive and, more importantly,
it avoids the cumbersome introduction of the well-formedness condition imposed
on a quantum PRODUCT machine model as well as of the uniformity condition
necessary for a quantum circuit model. Our new approach can further open a door
to the descriptional complexity of other functions and to the theory of
higher-type quantum functionals.","Wiring diagrams are given for a quantum algorithm processor in ORG to
compute, in parallel, all divisors of an n-bit integer. Lines required in a
wiring diagram are proportional to ORG time is proportional to the
square of n.",0
"This article is a semitutorial-style survey of computability logic. An
extended online version of it is maintained at
http://www.csc.villanova.edu/~japaridz/CL/ .","This paper presents a simple unsupervised learning algorithm for classifying
reviews as recommended (thumbs up) or not recommended (thumbs down). The
classification of a review is predicted by the average semantic orientation of
the phrases in the review that contain adjectives or adverbs. A phrase has a
positive semantic orientation when it has good associations (e.g., ""subtle
nuances"") and a negative semantic orientation when it has bad associations
(e.g., ""very cavalier""). In this paper, the semantic orientation of a phrase is
calculated as the mutual information between the given phrase and the word
""excellent"" minus the mutual information between the given phrase and the word
""poor"". A review is classified as recommended if the average semantic
orientation of its phrases is positive. The algorithm achieves an average
accuracy of PERCENT when evaluated on CARDINAL reviews from PRODUCT, sampled from CARDINAL
different domains (reviews of automobiles, banks, movies, and travel
destinations). The accuracy ranges from PERCENT for automobile reviews to PERCENT for
movie reviews.",0
"In a Web Advertising Traffic Operation it's necessary to manage the
day-to-day trafficking, pacing and optimization of digital and paid social
campaigns. The data analyst on ORG can not only quickly provide
answers but also speaks the language of the ORG Manager and visually
displays the discovered process problems. In order to solve a growing number of
complaints in the customer service process, the weaknesses in the process
itself must be identified and communicated to the department. With the help of
WORK_OF_ART data it is possible to identify unwanted loops and
delays in the process. With this paper we propose a process discovery based on
ORG technique to automatically discover variations and detect at
ORDINAL glance what the problem is, and undertake corrective measures.","This work attempts to unify CARDINAL domains: ORG for cooperative
control systems and ORG, under the umbrella of
crowdsourcing for information gain on ORG related to different
devices (as PC, PERSON, GPE,...) This paper proposes a framework for
adapting ORG objects components for a disaggregated system, which dynamically
composes web pages for different kind of devices including ubiquitous/pervasive
computing systems. It introduces the notions of responsive webdesign for
non-cooperative ORG equilibrium proposing an algorithm (RE-SAUI) for the
dynamic interface based on the game theory.",1
"ORG is a powerful application package for doing mathematics and is
used almost in all branches of science. It has widespread applications ranging
from quantum computation, statistical analysis, number theory, zoology,
astronomy, and many more. PERSON gives a rich set of programming
extensions to its end-user language, and it permits us to write programs in
procedural, functional, or logic (rule-based) style, or a mixture of all CARDINAL.
For tasks requiring interfaces to the external environment, PERSONprovides mathlink, which allows us to communicate mathematica programs with
external programs written in C, PERSON, ORG, ORG, ORG, PERSON, or other languages.
It has also extensive capabilities for editing graphics, equations, text, etc.
  In this article, we explore the basic mechanisms of parallelization of a
mathematica program by sharing different parts of the program into all other
computers available in the network. Doing the parallelization, we can perform
large computational operations within a very short period of time, and
therefore, the efficiency of the numerical works can be achieved. Parallel
computation supports any version of GPE and it also works as well even
if different versions of mathematica are installed in different computers. The
whole operation can run under any supported operating system like Unix,
ORG, ORG, etc. Here we focus our study only for the Unix based
operating system, but this method works as well for all other cases.","We explore electron transport properties in honeycomb lattice ribbons with
zigzag edges coupled to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes.
The calculations are based on the tight-binding model and the ORG's function
method, which numerically compute the conductance-energy and current-voltage
characteristics as functions of the lengths and widths of the ribbons. Our
numerical results predict that for such a ribbon an energy gap always appears
in the conductance spectrum across the energy E=0. With the increase of the
size of the ribbon, the gap gradually decreases but it never vanishes. This
clearly manifests that a honeycomb lattice ribbon with zigzag edges always
exhibits the semiconducting behavior, and it becomes much more clearly visible
from our presented current-voltage characteristics.",1
"We examine the effect of previous history on starting a computation on a
ORG computer. Specifically, we assume that the quantum register has some
unknown state on it, and it is required that this state be cleared and replaced
by a specific superposition state without any phase uncertainty, as needed by
ORG algorithms. We show that, in general, this task is computationally
impossible.","This article presents new properties of the mesh array for matrix
multiplication. In contrast to the standard array that requires 3n-2 steps to
complete its computation, the mesh array requires only 2n-1 steps. Symmetries
of the mesh array computed values are presented which enhance the efficiency of
the array for specific applications. In multiplying symmetric matrices, the
results are obtained in DATE steps. The mesh array is examined for its
application as a scrambling system.",1
"Necessary and sufficient conditions are given for the construction of a
hybrid ORG computer that operates on both continuous and discrete quantum
variables. Such hybrid computers are shown to be more efficient than
conventional quantum computers for performing a variety of ORG algorithms,
such as computing eigenvectors and eigenvalues.","A ORG's demon is a device that gets information and trades it in for
thermodynamic advantage, in apparent (but not actual) contradiction to the
ORDINAL law of thermodynamics. ORG-mechanical versions of ORG's demon
exhibit features that classical versions do not: in particular, a device that
gets information about a quantum system disturbs it in the process. In
addition, the information produced by ORG measurement acts as an additional
source of thermodynamic inefficiency. This paper investigates the properties of
quantum-mechanical ORG's demons, and proposes experimentally realizable
models of such devices.",1
"Evolvability is the capacity to evolve. This paper introduces a simple
computational model of evolvability and demonstrates that, under certain
conditions, evolvability can increase indefinitely, even when there is no
direct selection for evolvability. The model shows that increasing evolvability
implies an accelerating evolutionary pace. It is suggested that the conditions
for indefinitely increasing evolvability are satisfied in biological and
cultural evolution. We claim that increasing evolvability is a large-scale
trend in evolution. This hypothesis leads to testable predictions about
biological and cultural evolution.","A plasmodium of GPE polycephalum is a very large cell visible by unaided
eye. The plasmodium is capable for distributed sensing, parallel information
processing, and decentralized optimization. It is an ideal substrate for future
and emerging bio-computing devices. We study space-time dynamics of plasmodium
reactiom to localised illumination, and provide analogies between propagating
plasmodium and travelling wave-fragments in excitable media. We show how
plasmodium-based computing devices can be precisely controlled and shaped by
planar domains of illumination.",0
"A computer program is a set of electronic instructions executed from within
the computer memory by the computer central processing unit. Its purpose is to
control the functionalities of the computer allowing it to perform various
tasks. Basically, a computer program is written by humans using a programming
language. A programming language is the set of grammatical rules and vocabulary
that governs the correct writing of a computer program. In practice, the
majority of the existing programming languages are written in LANGUAGE-speaking
countries and thus they all use the LANGUAGE language to express their syntax
and vocabulary. However, many other programming languages were written in
NORP languages, for instance, the NORP BASIC, ORG, the
PERSON, and the Arabic Loughaty. This paper discusses the design and
implementation of a new programming language, called GPE. It is a
General-Purpose, High-Level, Imperative, Object-Oriented, and Compiled LANGUAGE
programming language that uses the LANGUAGE language as syntax and vocabulary.
The core of GPE is a compiler system made up of CARDINAL components, they are
the LOC, the scanner, the parser, the semantic analyzer, the code
generator, and the linker. The experiments conducted have illustrated the
several powerful features of the GPE language including functions,
while-loop, and arithmetic operations. As future work, more advanced features
are to be developed including inheritance, polymorphism, file processing,
graphical user interface, and networking.","This is brief and hopefully friendly, with basic notions, a few different
perspectives, and references with more information in various directions.",0
"We discuss the computation of the CARDINAL loop anomalous dimensions for various
operators used in deep inelastic scattering in the ORG and ORG' schemes. In
particular the results for the n = CARDINAL and CARDINAL ORG operators in arbitrary linear
covariant gauge in the RI' scheme are new.","We renormalize various scalar field theories with a $\phi^n$ self interaction
such as $n$ $=MONEY, MONEY in their respective critical dimensions which
are non-integer. The renormalization group functions for the $MONEY symmetric
extensions are also computed.",1
"PERSON has developed a general set of evolutionary statistics that quantify
the adaptive component of evolutionary processes. On the basis of these
measures, he has proposed a set of CARDINAL classes of evolutionary system. All
artificial life sytems so far looked at fall into the ORDINAL CARDINAL classes, whereas
the biosphere, and possibly the human economy belongs to the ORDINAL class. The
challenge to the artificial life community is to identify exactly what is
difference between these natural evolutionary systems, and existing artificial
life systems. At ALife VII, I presented a study using an artificial
evolutionary ecology called \EcoLab. PERSON's statistics captured the
qualitative behaviour of the model. \EcoLab{} exhibited behaviour from the
ORDINAL CARDINAL classes, but not class CARDINAL, which is characterised by unbounded growth in
diversity. \EcoLab{} exhibits a critical surface given by an inverse
relationship between connectivity and diversity, above which the model cannot
tarry long. Thus in order to get unbounded diversity increase, there needs to
be a corresponding connectivity reducing (or food web pruning) process. This
paper reexamines this question in light of CARDINAL possible processes that reduce
ecosystem connectivity: a tendency for specialisation and increase in
biogeographic zones through ORG drift.","In an earlier article PERSON, On nonspecific evidence, PERSON. PERSON.
ORG. CARDINAL), CARDINAL-725 (DATE)] we established within ORG theory a
criterion function called the metaconflict function. With this criterion we can
partition into subsets a set of several pieces of evidence with propositions
that are weakly specified in the sense that it may be uncertain to which event
a proposition is referring. Each subset in the partitioning is representing a
separate event. The metaconflict function was derived as the plausibility that
the partitioning is correct when viewing the conflict in PERSON's rule within
each subset as a newly constructed piece of metalevel evidence with a
proposition giving support against the entire partitioning. In this article we
extend the results of the previous article. We will not only find the most
plausible subset for each piece of evidence as was done in the earlier article.
In addition we will specify each piece of nonspecific evidence, in the sense
that we find to which events the proposition might be referring, by finding the
plausibility for every subset that this piece of evidence belong to the subset.
In doing this we will automatically receive indication that some evidence might
be false. We will then develop a new methodology to exploit these newly
specified pieces of evidence in a subsequent reasoning process. This will
include methods to discount evidence based on their degree of falsity and on
their degree of credibility due to a partial specification of affiliation, as
well as a refined method to infer the event of each subset.",0
"The meson fields are simulated by quark operators and an effective chiral
theory of mesons is presented. There are spontaneous chiral symmetry breaking
and dynamical chiral symmetry breaking. Theoretical results agree with data
well.","A $U(2)_{L}\times U(2)_{R}$ chiral theory of pseudoscalar, vector, and
axial-vector mesons has been proposed. ORG has been revealed from this theory.
The physical processes of normal parity and abnormal parity have been studied
by using the same lagrangian and the universality of coupling has been
revealed. CARDINAL new mass relations between vector and axial-vector mesons have
been found. PERSON's ORDINAL sum rule and new relations about the amplitude of
$a_{1}$ PERSON are satisfied. KSFR sum rule is satisfied pretty well. The $\rho$
pole in pion form factor has been achieved. The theoretical results of
$MONEY, $\omega\rightarrow ORG, $MONEY
and $\pi\gamma$, $MONEY \rho\nu$, $MONEY DATE,
$\pi^{0}\rightarrow \gamma\gamma$, $PERSON,
$MONEY, $MONEY} GPE,
$f_{1}\rightarrow\eta\pi\pi$, $\rho\rightarrow\eta\gamma$, $MONEY
\rightarrow\eta\gamma$ are in good agreement with data. PERSON's $PERSON
scattering lengths and slopes and $a^{0}_{2}$, $a^{2}_{2}$, and $b^{1}_{1}$
have been obtained. Especially, the $\rho$ resonance in the amplitude
$MONEY of $PERSON scattering has been revealed from this theory. CARDINAL
coefficients of chiral perturbation theory have been determined and they are
close to the values used by chiral perturbation theory.",1
"We mathematically model PERSON principles of symmetric and
asymmetric being through use of an ultrametric topology. We use for this the
highly regarded DATE book of this NORP psychiatrist and pyschoanalyst (born
DATE, died DATE). Such an ultrametric model corresponds to hierarchical
clustering in the empirical data, e.g. text. We show how an ultrametric
topology can be used as a mathematical model for the structure of the logic
that reflects or expresses ORG symmetric being, and hence of the
reasoning and thought processes involved in conscious reasoning or in reasoning
that is lacking, perhaps entirely, in consciousness or awareness of itself. In
a companion paper we study how symmetric (in the sense of ORG)
reasoning can be demarcated in a context of symmetric and asymmetric reasoning
provided by narrative text.","We consider decision problems of rating alternatives based on their pairwise
comparisons according to CARDINAL criteria. Given pairwise comparison matrices for
each criterion, the problem is to find the overall scores of the alternatives.
We offer a solution that involves the minimax approximation of the comparison
matrices by a common consistent matrix of unit rank in terms of the ORG
metric in logarithmic scale. The approximation problem reduces to a
bi-objective optimization problem to minimize the approximation errors
simultaneously for both comparison matrices. We formulate the problem in terms
of tropical (idempotent) mathematics, which focuses on the theory and
applications of algebraic systems with idempotent addition. To solve the
optimization problem obtained, we apply methods and results of tropical
optimization to derive a complete PERSON-optimal solution in a direct explicit
form ready for further analysis and straightforward computation. We then
exploit this result to solve the bi-criteria decision problem of interest. As
illustrations, we present examples of the solution of CARDINAL-dimensional
optimization problems in general form, and of a decision problem with CARDINAL
alternatives in numerical form.",0
"Constraint satisfaction problems (or CSPs) have been extensively studied in,
for instance, artificial intelligence, database theory, graph theory, and
statistical physics. From a practical viewpoint, it is beneficial to
approximately solve those CSPs. When CARDINAL tries to approximate the total number
of truth assignments that satisfy all NORP-valued constraints for
(unweighted) NORP CSPs, there is a known trichotomy theorem by which all
such counting problems are neatly classified into exactly CARDINAL categories
under polynomial-time (randomized) approximation-preserving reductions. In
contrast, we obtain a dichotomy theorem of approximate counting for
complex-weighted NORP CSPs, provided that all complex-valued unary
constraints are freely available to use. It is the expressive power of free
unary constraints that enables us to prove such a stronger, complete
classification theorem. This discovery makes a step forward in the quest for
the approximation-complexity classification of all counting CSPs. To deal with
complex weights, we employ proof techniques of factorization and arity
reduction along the line of solving PERSON problems. Moreover, we introduce a
novel notion of T-constructibility that naturally induces
approximation-preserving reducibility. Our result also gives an approximation
analogue of the dichotomy theorem on the complexity of exact counting for
complex-weighted NORP CSPs.","The article presents a study of rather simple local search heuristics for the
single machine total weighted tardiness problem (ORG), namely hillclimbing
and WORK_OF_ART. In particular, we revisit these approaches
for the ORG as there appears to be a lack of appropriate/challenging
benchmark instances in this case. The obtained results are impressive indeed.
Only few instances remain unsolved, and even those are approximated within PERCENT
of the optimal/best known solutions. Our experiments support the claim that
metaheuristics for the ORG are very likely to lead to good results, and
that, before refining search strategies, more work must be done with regard to
the proposition of benchmark data. Some recommendations for the construction of
such data sets are derived from our investigations.",0
"In this article, we take the $Z_c(3900)$ and $PERSON as the ground state
and the ORDINAL radial excited state of the axial-vector tetraquark states with
$PERSON, respectively, and study their masses and pole residues with
the ORG sum rules by calculating the contributions of the vacuum condensates up
to dimension-10 in a consistent way in the operator product expansion. The
numerical result favors assigning the $Z_c(3900)$ and $PERSON as the ground
state and ORDINAL radial excited state of the axial-vector tetraquark states,
respectively.","In this article, we study the MONEYCARDINAL type and $MONEY
CARDINAL type scalar $cs\bar{c}\bar{s}$ tetraquark states with the ORG sum rules by
calculating the contributions of the vacuum condensates up to dimension CARDINAL in a
consistent way. The ground state masses MONEY \gamma_5C}=3.89\pm
0.05\,\rm{GeV}$ and $M_{C\otimes C}=5.48\pm0.10\,\rm{GeV}$ support assigning
the $X(3915)$ to be the ground state $C\gamma_5\otimes \gamma_5C$ type
tetraquark state with $PERSON, but do not support assigning the
$PERSON to be the ground state $C\otimes CARDINAL type $PERSON state with $PERSON Then we tentatively assign the $X(3915)$
and $X(4500)$ to be the GPE and MONEY type scalar
$cs\bar{c}\bar{s}$ tetraquark states respectively, and obtain the CARDINAL mass
$M_{\rm CARDINAL and CARDINAL mass $M_{\rm
CARDINAL from the ORG sum rules, which support
assigning the $PERSON to be the GPE $C\gamma_5\otimes \gamma_5C$ type
tetraquark state, but do not support assigning the $X(4500)$ to be the CARDINAL
MONEYC$ type tetraquark state.",1
"The problem of statistical learning is to construct an accurate predictor of
a random variable as a function of a correlated random variable on the basis of
an i.i.d. training sample from their joint distribution. Allowable predictors
are constrained to lie in some specified class, and the goal is to approach
asymptotically the performance of the best predictor in the class. We consider
CARDINAL settings in which the learning agent only has access to rate-limited
descriptions of the training data, and present information-theoretic bounds on
the predictor performance achievable in the presence of these communication
constraints. Our proofs do not assume any separation structure between
compression and learning and rely on a new class of operational criteria
specifically tailored to joint design of encoders and learning algorithms in
rate-constrained settings.","We present a CARDINAL-stage quantum cryptographic protocol guaranteeing security
in which each party uses its own secret key. Unlike the BB84 protocol, where
the qubits are transmitted in CARDINAL direction and classical information
exchanged thereafter, the communication in the proposed protocol remains
quantum in each stage. A related system of key distribution is also described.",0
"It is hypothesized by some thinkers that benign looking ORG objectives may
result in powerful ORG drives that may pose an existential risk to human
society. We analyze this scenario and find the underlying assumptions to be
unlikely. We examine the alternative scenario of what happens when universal
goals that are not human-centric are used for designing ORG agents. We follow a
design approach that tries to exclude malevolent motivations from ORG agents,
however, we see that objectives that seem benevolent may pose significant risk.
We consider the following meta-rules: preserve and pervade life and culture,
maximize the number of free minds, maximize intelligence, maximize wisdom,
maximize energy production, behave like human, seek pleasure, accelerate
evolution, survive, maximize control, and maximize capital. We also discuss
various solution approaches for benevolent behavior including selfless goals,
hybrid designs, NORP, universal constraints, semi-autonomy, and
generalization of robot laws. A ""prime directive"" for ORG may help in
formulating an encompassing constraint for avoiding malicious behavior. We
hypothesize that social instincts for autonomous robots may be effective such
as attachment learning. We mention multiple beneficial scenarios for an
advanced semi-autonomous ORG agent in the near future including space
exploration, automation of industries, state functions, and cities. We conclude
that a beneficial ORG agent with intelligence beyond human-level is possible and
has many practical use cases.","We explore the relations between the zeta distribution and algorithmic
information theory via a new model of the transfer learning problem. The
program distribution is approximated by a zeta distribution with parameter near
$MONEY We model the training sequence as a stochastic process. We analyze the
upper temporal bound for learning a training sequence and its entropy rates,
assuming an oracle for the transfer learning problem. We argue from empirical
evidence that power-law models are suitable for natural processes. CARDINAL
sequence models are proposed. Random typing model is like no-free lunch where
transfer learning does not work. ORG process independently samples programs
from the zeta distribution. A model of common sub-programs inspired by genetics
uses a database of sub-programs. An evolutionary zeta process samples mutations
from ORG distribution. The analysis of stochastic processes inspired by
evolution suggest that ORG may be feasible in nature, countering no-free lunch
sort of arguments.",1
"In this paper we examine the possibility of testing the equivalence
principle, in its weak form, by analyzing the orbital motion of a pair of
artificial satellites of different composition moving along orbits of identical
shape and size in the gravitational field of LOC. It turns out that the
obtainable level of accuracy is, realistically, of the order of CARDINAL^-10 or
slightly better. It is limited mainly by the fact that, due to the unavoidable
orbital injection errors, it would not be possible to insert the satellites in
orbits with exactly the same radius and that such difference could be known
only with a finite precision. The present-day level of accuracy, obtained with
torsion balance LOC-based measurements and the analysis of LOC-Moon motion
in the gravitational field of LOC with ORG technique, is of
the order of CARDINAL^-13. The proposed space-based missions ORG, \muSCOPE, GG and
SEE aim to reach a CARDINAL^-15-10^-18 precision level.","Long-range constraints on the GPE radius of curvature L in the
ORG (ORG) braneworld model are inferred from orbital motions of well
known artificial and natural bodies. Thus, they do not rely upon more or less
speculative and untested theoretical assumptions, contrary to other long-range
ORG tests proposed in astrophysical scenarios in which many of the phenomena
adopted may depend on the system's composition, formation and dynamical history
as well. The perihelion precession of ORG and its radiotechnical ranging
from the LOC yield L <= QUANTITY. Tighter bounds come from the perigee
precession of the PERSON, from which it can be inferred L <= CARDINAL m. The best
constraints (L <= CARDINAL m) come from the Satellite-to-Satellite Tracking (SST)
range of the GRACE A/B spacecrafts orbiting the LOC: proposed follow-on of
such a mission, implying a subnm s-1 range-rate accuracy, may constrain L at
\sim QUANTITY level. Weaker constraints come from the double pulsar system (L <=
QUANTITY) and from the main sequence star CARDINAL orbiting the compact object in
ORG* (L <= CARDINAL - 8.8 AU). Such bounds on the length L, which must not
necessarily be identified with the GPE radius of curvature of the ORG model,
naturally translate into constraints on an, e.g., universal coupling parameter
K of the r^-3 interaction. GRACE yields K <= CARDINAL^CARDINAL m^5 s^-2.",1
"PERSON has written a wonderful book about visualization that makes our
field of scientometrics accessible to much larger audiences. The book is to be
read in relation to the ongoing series of exhibitions entitled ""Places &
Spaces: Mapping Science"" currently touring the world. The book also provides
the scholarly background to the exhibitions. It celebrates scientometrics as
the discipline in the background that enables us to visualize the evolution of
knowledge as the acumen of human civilization.","The Actor Network represents heterogeneous entities as actants (GPE et
GPE, DATE; DATE). Although computer programs for the visualization of social
networks increasingly allow us to represent heterogeneity in a network using
different shapes and colors for the visualization, hitherto this possibility
has scarcely been exploited (NORP et GPE, DATE). In this contribution to
the PERSON, I study the question of what heterogeneity can add
specifically to the visualization of a network. How does an integrated network
improve on the CARDINAL-dimensional ones (such as co-word and co-author maps)? The
oeuvre of PERSON is used as the case materials, that is, his CARDINAL papers
which can be retrieved from the (Social) Science Citation Index since DATE.",1
"The introduced entropy functional's (EF) information measure of random
process integrates multiple information contributions along the process
trajectories, evaluating both the states' and between states' bound information
connections. This measure reveals information that is hidden by traditional
information measures, which commonly use FAC's entropy function for each
selected stationary states of the process. The hidden information is important
for evaluation of missing connections, disclosing the process' meaningful
information, which enables producing logic of the information. The presentation
consists of CARDINAL Parts. In Part 1R-revised we analyze mechanism of arising
information regularities from a stochastic process, measured by EF,
independently of the process' specific source and origin. Uncovering the
process' regularities leads us to an information law, based on extracting
maximal information from its minimum, which could create these regularities.
The solved variation problem (VP) determines a dynamic process, measured by
information path functional (ORG), and information dynamic model, approximating
the ORG measured stochastic process with a maximal functional probability on
trajectories. In Part CARDINAL, we study the cooperative processes, arising at the
consolidation, as a result of the VP-EF-IPF approach, which is able to produce
multiple cooperative structures, concurrently assembling in hierarchical
information network (IN) and generating the ORG's digital genetic code. In Part
CARDINAL we study the evolutionary information processes and regularities of evolution
dynamics, evaluated by the entropy functional (EF) of random field and
informational path functional of a dynamic space-time process. The information
law and the regularities determine unified functional informational mechanisms
of evolution dynamics.","The impulses, cutting entropy functional (EF) measure on trajectories PERSON
diffusion process, integrate information path functional (ORG) composing
discrete information Bits extracted from observing random process. Each cut
brings memory of the cutting entropy, which provides both reduction of the
process entropy and discrete unit of the cutting entropy a Bit. Consequently,
information is memorized entropy cutting in random observations which process
interactions. The origin of information associates with anatomy creation of
impulse enables both cut entropy and stipulate random process generating
information under the cut. Memory of the impulse cutting time interval freezes
the observing events dynamics in information processes. Diffusion process
additive functional defines EF reducing it to a regular integral functional.
Compared to FAC entropy measure of random state, cutting process on
separated states decreases quantity information concealed in the states
correlation holding hidden process information. Infinite dimensional process
cutoffs integrate finite information in ORG whose information approaches EF
restricting process maximal information. Within the impulse reversible
microprocess, conjugated entropy increments are entangling up to the cutoff
converting entropy in irreversible information. Extracting maximum of minimal
impulse information and transferring minimal entropy between impulses implement
maxmin-minimax principle of optimal conversion process entropy to information.
Macroprocess extremals integrate entropy of microprocess and cutoff information
of impulses in the ORG information physical process. ORG measures ORG kernel
information. Estimation extracting information confirms nonadditivity of EF
measured process increments.",1
"The paper gives a soundness and completeness proof for the implicative
fragment of intuitionistic calculus with respect to the semantics of
computability logic, which understands intuitionistic implication as
interactive algorithmic reduction. This concept -- more precisely, the
associated concept of reducibility -- is a generalization of Turing
reducibility from the traditional, input/output sorts of problems to
computational tasks of QUANTITY of interactivity. See
ORG for a comprehensive online source on
computability logic.","Computational biology is on the verge of a paradigm shift in its research
practice - from a data-based (computational) paradigm to an information-based
(cognitive) paradigm. As in the other research fields, this transition is
impeded by lack of a right understanding about what is actually hidden behind
the term ""information"". The paper is intended to clarify this issue and
introduces CARDINAL new notions of ""physical information"" and ""semantic
information"", which together constitute the term ""information"". Some
implications of this introduction are considered.",0
"It is shown that ORG black holes can support ORG charged scalar
fields in their exterior regions. To that end, we solve analytically the
ORG wave equation for a stationary charged massive scalar field in the
background of a near-extremal ORG black hole. In particular, we derive
a simple analytical formula which describes the physical properties of these
stationary bound-state resonances of the charged massive scalar fields in the
ORG black-hole spacetime.","The black-hole information puzzle has attracted much attention over DATE
from both physicists and mathematicians. CARDINAL of the most intriguing suggestions
to resolve the information paradox is due to PERSON, who has stressed the
fact that the low-energy part of the semi-classical black-hole emission
spectrum is partly blocked by the curvature potential that surrounds the black
hole. As explicitly shown by PERSON, this fact implies that the grey-body
emission spectrum of a (3+1)-dimensional black hole is considerably less
entropic than the corresponding radiation spectrum of a perfectly thermal
black-body emitter. Using standard ideas from ORG theory, it
was shown by PERSON that, in principle, the filtered Hawking radiation
emitted by a (3+1)-dimensional Schwarzschild black hole may carry with it a
substantial amount of information, the information which was suspected to be
lost. It is of physical interest to test the general validity of the
""information leak"" scenario suggested by PERSON as a possible resolution to
the Hawking information puzzle. In the present paper we analyze the
semi-classical entropy emission properties of higher-dimensional black holes.
In particular, we provide evidence that the characteristic Hawking quanta of
$(D+1)$-dimensional ORG black holes in the large MONEY$ regime are
almost unaffected by the spacetime curvature outside the black-hole horizon.
This fact implies that, in the GPE regime, the Hawking black-hole
radiation spectra are almost purely thermal, thus suggesting that the emitted
quanta cannot carry the amount of information which is required in order to
resolve the information paradox. Our analysis therefore suggests that the
elegant information leak scenario suggested by PERSON cannot provide a
generic resolution to the intriguing Hawking information paradox.",1
"ORG data on the sigma pole are refitted taking into account new information
on coupling of sigma to ORG and eta-eta. The fit also includes ORG data
on NORP elastic phases shifts and Ke4 data, and gives a pole position of CARDINAL
+- 30 - i(264 +- 30) MeV. However, there is a clear discrepancy with the sigma
pole position recently predicted by NORP et al. using the PERSON equation. This
discrepancy may be explained naturally by uncertainties arising from
inelasticity in ORG and eta-eta channels and mixing between sigma and f0(980).
Adding freedom to accomodate these uncertainties gives an optimum compromise
with a pole position of CARDINAL +- 30 - i(271 +- 30) MeV.","Inequalities for the transformation operator kernel $PERSON,y)$ in terms of
$MONEY are given, and vice versa. These inequalities are applied to
inverse scattering on CARDINAL-line. Characterization of the scattering data
corresponding to the usual scattering class $MONEY of the potentials, to the
class of compactly supported potentials, and to the class of square integrable
potentials is given. Invertibility of each of the steps in the inversion
procedure is proved.",0
"Drawing upon a body of research on the evolution of creativity, this paper
proposes a theory of how, when, and why the forward-thinking story-telling
abilities of humans evolved, culminating in the visionary abilities of science
fiction writers. The ability to recursively chain thoughts together evolved
DATE. Language abilities, and the ability to
shift between different modes of thought, evolved DATE. Science fiction dates to DATE. It is suggested
that well before this time, but after DATE, and concurrent with
the evolution of a division of labour between creators and imitators there
arose a division of labour between past, present, and future thinkers.
Agent-based model research suggests there are social benefits to the evolution
of individual differences in creativity such that there is a balance between
novelty-generating creators and continuity-perpetuating imitators. A balance
between individuals focused on the past, present, and future would be expected
to yield similar adaptive benefits.","We consider the discrete memoryless degraded broadcast channels. We prove
that the error probability of decoding tends to CARDINAL exponentially for rates
outside the capacity region and derive an explicit lower bound of this exponent
function. We shall demonstrate that the information spectrum approach is quite
useful for investigating this problem.",0
"Methods from convex optimization such as accelerated gradient descent are
widely used as building blocks for deep learning algorithms. However, the
reasons for their empirical success are unclear, since neural networks are not
convex and standard guarantees do not apply. This paper develops the ORDINAL
rigorous link between online convex optimization and error backpropagation on
convolutional networks. The ORDINAL step is to introduce circadian games, a mild
generalization of convex games with similar convergence properties. The main
result is that error backpropagation on a convolutional network is equivalent
to playing out a circadian game. It follows immediately that the waking-regret
of players in the game (the units in the neural network) controls the overall
rate of convergence of the network. Finally, we explore some implications of
the results: (i) we describe the representations learned by a neural network
game-theoretically, (ii) propose a learning setting at the level of individual
units that can be plugged into deep architectures, and (iii) propose a new
approach to adaptive model selection by applying bandit algorithms to choose
which players to wake on each round.","As artificial agents proliferate, it is becoming increasingly important to
ensure that their interactions with one another are well-behaved. In this
paper, we formalize a common-sense notion of when algorithms are well-behaved:
an algorithm is safe if it does no harm. Motivated by recent progress in deep
learning, we focus on the specific case where agents update their actions
according to gradient descent. The paper shows that that gradient descent
converges to a ORG equilibrium in safe games. The main contribution is to
define strongly-typed agents and show they are guaranteed to interact safely,
thereby providing sufficient conditions to guarantee safe interactions. A
series of examples show that strong-typing generalizes certain key features of
convexity, is closely related to blind source separation, and introduces a new
perspective on classical multilinear games based on tensor decomposition.",1
"Following PERSON hypothesis of quanta (quant-ph/0012069) and the matter
wave idea of PERSON (quant-ph/9911107), PERSON proposed,
at DATE, the concept of wavefunction and wave equation for it.
Though endowed with a realistic undular interpretation by its father, the
wavefunction could not be considered as a real ""matter wave"" and has been
provided with only abstract, formally probabilistic interpretation. In this
paper we show how the resulting ""mysteries"" of usual theory are solved within
the unreduced, dynamically multivalued description of the underlying,
essentially nonlinear interaction process (quant-ph/9902015, quant-ph/9902016),
without artificial modification of the ORG equation. The latter is
rigorously derived instead as universal expression of unreduced interaction
complexity. Causal, totally realistic wavefunction is obtained as a dynamically
probabilistic intermediate state of a simple system with interaction performing
dynamically discrete transitions between its localised, incompatible
""realisations"" (""corpuscular"" states). Causal wavefunction and ORG
equation are then extended to arbitrary level of world dynamics. We outline
some applications of the obtained causal description, such as genuine quantum
chaos (quant-ph/9511034-36) and realistic quantum devices (physics/0211071),
and emphasize the basic difference of the proposed dynamically multivalued
theory from dynamically single-valued imitations of causality and complexity.
The causally complete wavefunction concept, representing the unified essence of
unreduced (multivalued) complex dynamics, provides a clear distinctive feature
of realistic science, absent in any its unitary imitation.","It is shown that nonlocal interactions determine energy spectrum in isotropic
turbulence at small ORG numbers. It is also shown that for moderate
ORG numbers the bottleneck effect is determined by the same nonlocal
interactions. Role of the large and small scales covariance at the nonlocal
interactions and in energy balance has been investigated. A possible
hydrodynamic mechanism of the nonlocal solution instability at large scales has
been briefly discussed. A quantitative relationship between effective strain of
the nonlocal interactions and viscosity has been found. All results are
supported by comparison with the data of experiments and numerical simulations.",0
"Psychological traumas are thought to be present in a wide range of
conditions, including post-traumatic stress disorder, disorganised attachment,
personality disorders, dissociative identity disorder and psychosis. This work
presents a new psychotherapy for psychological traumas, based on a functional
model of the mind, built with elements borrowed from the fields of computer
science, artificial intelligence and neural networks. The model revolves around
the concept of hierarchical value and explains the emergence of dissociation
and splitting in response to emotional pain. The key intuition is that traumas
are caused by too strong negative emotions, which are in turn made possible by
a low-value self, which is in turn determined by low-value self-associated
ideas. The therapeutic method compiles a list of patient's traumas, identifies
for each trauma a list of low-value self-associated ideas, and provides for
each idea a list of counterexamples, to raise the self value and solve the
trauma. Since the psychotherapy proposed has not been clinically tested,
statements on its effectiveness are premature. However, since the conceptual
basis is solid and traumas are hypothesised to be present in many psychological
disorders, the potential gain may be substantial.","We propose a notion of autoreducibility for infinite time computability and
explore it and its connection with a notion of randomness for infinite time
machines.",0
"Voting is a simple mechanism to aggregate the preferences of agents. Many
voting rules have been shown to be ORG-hard to manipulate. However, a number of
recent theoretical results suggest that this complexity may only be in the
worst-case since manipulation is often easy in practice. In this paper, we show
that empirical studies are useful in improving our understanding of this issue.
We demonstrate that there is a smooth transition in the probability that a
coalition can elect a desired candidate using the veto rule as the size of the
manipulating coalition increases. We show that a rescaled probability curve
displays a simple and universal form independent of the size of the problem. We
argue that manipulation of the veto rule is asymptotically easy for many
independent and identically distributed votes even when the coalition of
manipulators is critical in size. Based on this argument, we identify a
situation in which manipulation is computationally hard. This is when votes are
highly correlated and the election is ""hung"". We show, however, that even a
single uncorrelated voter is enough to make manipulation easy again.","Lecture given DATE at a Physics -- Computer Science
Colloquium at ORG. The lecture was videotaped; this is
an edited transcript. It also incorporates remarks made at the ORG to
ORG meeting held at ORG 24--26 DATE.",0
"Like any field of empirical science, ORG may be approached axiomatically. We
formulate requirements for a general-purpose, human-level AI system in terms of
postulates. We review the methodology of deep learning, examining the explicit
and tacit assumptions in deep learning research. ORG methodology
seeks to overcome limitations in traditional machine learning research as it
combines facets of model richness, generality, and practical applicability. The
methodology so far has produced outstanding results due to a productive synergy
of function approximation, under plausible assumptions of irreducibility and
the efficiency of back-propagation family of algorithms. We examine these
winning traits of deep learning, and also observe the various known failure
modes of deep learning. We conclude by giving recommendations on how to extend
deep learning methodology to cover the postulates of general-purpose ORG
including modularity, and cognitive architecture. We also relate deep learning
to advances in theoretical neuroscience research.","We continue our analysis of volume and energy measures that are appropriate
for quantifying inductive inference systems. We extend logical depth and
conceptual jump size measures in ORG to stochastic problems, and physical
measures that involve volume and energy. We introduce a graphical model of
computational complexity that we believe to be appropriate for intelligent
machines. We show several asymptotic relations between energy, logical depth
and volume of computation for inductive inference. In particular, we arrive at
a ""black-hole equation"" of inductive inference, which relates energy, volume,
space, and algorithmic information for an optimal inductive inference solution.
We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to
the physical limits of intelligent computation in our universe.",1
"Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to adapt the information access provided
by ubiquitous systems to users when the system only knows the user social
group, without knowing anything about the user interest. The adaptation
procedures associate actions to perceived situations of the user. Associations
are based on feedback given by the user as a reaction to the behavior of the
system. Our method brings a solution to some of the problems concerning the
acceptance of the system by users when applying machine learning techniques to
systems at the beginning of the interaction between the system and the user.","We present a symbolic machinery that admits both probabilistic and causal
information about a given domain and produces probabilistic statements about
the effect of actions and the impact of observations. The calculus admits CARDINAL
types of conditioning operators: ordinary ORG conditioning, P(y|X = x), which
represents the observation X = x, and causal conditioning, P(y|do(X = x)), read
the probability of Y = y conditioned on holding X constant (at x) by deliberate
action. Given a mixture of such observational and causal sentences, together
with the topology of the causal graph, the calculus derives new conditional
probabilities of both types, thus enabling one to quantify the effects of
actions (and policies) from partially specified knowledge bases, such as
NORP networks in which some conditional probabilities may not be available.",0
"Plasmodium of \emph{Physarum polycephalum} is a single huge (visible by naked
eye) cell with myriad of nuclei. The plasmodium is a promising substrate for
non-classical, nature-inspired, computing devices. It is capable for
approximation of shortest path, computation of planar proximity graphs and
plane tessellations, primitive memory and decision-making. The unique
properties of the plasmodium make it an ideal candidate for a role of amorphous
biological robots with massive parallel information processing and distributed
inputs and outputs. We show that when adhered to light-weight object resting on
a water surface the plasmodium can propel the object by oscillating its
protoplasmic pseudopodia. In experimental laboratory conditions and
computational experiments we study phenomenology of the plasmodium-floater
system, and possible mechanisms of controlling motion of objects propelled by
on board plasmodium.","In this short review I present my personal reflections on ORG
information interpretation of quantum mechanics (QM). In general, this
interpretation is very attractive for me. However, its rigid coupling to the
notion of irreducible quantum randomness is a very complicated issue which I
plan to address in more detail. This note may be useful for general public
interested in ORG, especially because I try to analyze
essentials of the information interpretation critically (i.e., not just
emphasizing its advantages as it is commonly done). This review is written in
non-physicist friendly manner. Experts actively exploring this interpretation
may be interested in the paper as well, as in the comments of ""an external
observer"" who have been monitoring the development of this approach to QM
during DATE. The last part of this review is devoted to the
general methodology of science with references to views of GPE, ORG,
and PERSON.",0
"It is by now well known that FAC
logarithmic entropic functional ($PERSON) is inadequate for wide classes of
strongly correlated systems: see for instance the DATE PERSON and PERSON's
{\it Conceptual inadequacy of the FAC information in ORG measurements},
among many other systems exhibiting various forms of complexity. On the other
hand, the FAC and GPE axioms uniquely mandate the GPE form
$PERSON \ln p_i$; the ORG and PERSON axioms follow the same
path. Many natural, artificial and social systems have been satisfactorily
approached with nonadditive entropies such as the $S_q=k \frac{1-\sum_i
p_i^q}{q-1}$ one ($q \in {\cal R}; \,S_1=S_{BG}$), basis of nonextensive
statistical mechanics. Consistently, the FAC DATE and PERSON
uniqueness theorems have already been generalized in the literature, by PERSON
DATE and DATE respectively, in order to uniquely mandate $S_q$. We argue
here that the same remains to be done with the ORG and PERSON DATE axioms.
We arrive to this conclusion by analyzing specific classes of strongly
correlated complex systems that await such generalization.","The Web community has introduced a set of standards and technologies for
representing, querying, and manipulating a globally distributed data structure
known as the Web of Data. The proponents of the Web of Data envision much of
the world's data being interrelated and openly accessible to the general
public. This vision is analogous in many ways to the Web of Documents of common
knowledge, but instead of making documents and media openly accessible, the
focus is on making data openly accessible. In providing data for public use,
there has been a stimulated interest in a movement dubbed ORG. Open Data
is analogous in many ways to the Open Source movement. However, instead of
focusing on software, ORG is focused on the legal and licensing issues
around publicly exposed data. Together, various technological and legal tools
are laying the groundwork for the future of global-scale data management on the
Web. As of DATE, in its early form, the Web of Data hosts a variety of data
sets that include encyclopedic facts, drug and protein data, PERSON on music,
books and scholarly articles, social network representations, geospatial
information, and many other types of information. The size and diversity of the
Web of Data is a demonstration of the flexibility of the underlying standards
and the overall feasibility of the project as a whole. The purpose of this
article is to provide a review of the technological underpinnings of the Web of
Data as well as some of the hurdles that need to be overcome if the Web of Data
is to emerge as the defacto medium for data representation, distribution, and
ultimately, processing.",0
"In this article, we consider convergence rates in functional linear
regression with functional responses, where the linear coefficient lies in a
reproducing kernel PERSON space (ORG). Without assuming that the reproducing
kernel and the covariate covariance kernel are aligned, or assuming polynomial
rate of decay of the eigenvalues of the covariance kernel, convergence rates in
prediction risk are established. The corresponding lower bound in rates is
derived by reducing to the scalar response case. Simulation studies and CARDINAL
benchmark datasets are used to illustrate that the proposed approach can
significantly outperform the functional PCA approach in prediction.","We study the posterior distribution of the NORP multiple change-point
regression problem when the number and the locations of the change-points are
unknown. While it is relatively easy to apply the general theory to obtain the
$PERSON rate up to some logarithmic factor, showing the exact
parametric rate of convergence of the posterior distribution requires
additional work and assumptions. Additionally, we demonstrate the asymptotic
normality of the segment levels under these assumptions. For inferences on the
number of change-points, we show that the NORP approach can produce a
consistent posterior estimate. Finally, we argue that the point-wise posterior
convergence property as demonstrated might have bad finite sample performance
in that consistent posterior for model selection necessarily implies the
maximal squared risk will be asymptotically larger than the optimal
$PERSON rate. This is the NORP version of the same phenomenon that
has been noted and studied by other authors.",1
"This article describes existing and expected benefits of the ""SP theory of
intelligence"", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the ""SP machine"" -- an expression
of the NORP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The NORP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is MONEY DATE. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the NORP machine, available to researchers everywhere.","This paper summarises how the ""SP theory of intelligence"" and its realisation
in the ""SP computer model"" simplifies and integrates concepts across artificial
intelligence and related areas, and thus provides a promising foundation for
the development of a general, human-level thinking machine, in accordance with
the main goal of research in artificial general intelligence.
  The key to this simplification and integration is the powerful concept of
""multiple alignment"", borrowed and adapted from bioinformatics. This concept
has the potential to be the ""double helix"" of intelligence, with as much
significance for human-level intelligence as has DNA for biological sciences.
  Strengths of the NORP system include: versatility in the representation of
diverse kinds of knowledge; versatility in aspects of intelligence (including:
strengths in unsupervised learning; the processing of natural language; pattern
recognition at multiple levels of abstraction that is robust in the face of
errors in data; several kinds of reasoning (including: CARDINAL-step `deductive'
reasoning; chains of reasoning; abductive reasoning; reasoning with
probabilistic networks and trees; reasoning with 'rules'; nonmonotonic
reasoning and reasoning with default values; NORP reasoning with
'explaining away'; and more); planning; problem solving; and more); seamless
integration of diverse kinds of knowledge and diverse aspects of intelligence
in any combination; and potential for application in several areas (including:
helping to solve CARDINAL problems with big data; helping to develop human-level
intelligence in autonomous robots; serving as a database with intelligence and
with versatility in the representation and integration of several forms of
knowledge; serving as a vehicle for medical knowledge and as an aid to medical
diagnosis; and several more).",1
"We study the contribution of diffractive $Q \bar Q$ production to the $PERSON
proton structure function and the longitudinal double-spin asymmetry in
polarized deep--inelastic $MONEY scattering. We show the strong dependence of the
$F_2^D$ structure function and the $MONEY asymmetry on the quark--pomeron
coupling structure.","We propose an online form of the cake cutting problem. This models situations
where agents arrive and depart during the process of dividing a resource. We
show that well known fair division procedures like cut-and-choose and the
ORG moving knife procedure can be adapted to apply to such online
problems. We propose some fairness properties that online cake cutting
procedures can possess like online forms of proportionality and envy-freeness.
We also consider the impact of collusion between agents. Finally, we study
theoretically and empirically the competitive ratio of these online cake
cutting procedures. Based on its resistance to collusion, and its good
performance in practice, our results favour the online version of the
cut-and-choose procedure over the online version of the moving knife procedure.",0
"It is shown that, the wavelet regression detrended fluctuations of the
reconstructed temperature for DATE (LOC ice cores data)
are completely dominated by CARDINAL subharmonic resonance, presumably related
to LOC precession effect on the energy that the intertropical regions receive
from the ORG. Effects of Galactic turbulence on the temperature fluctuations
are also discussed. Direct evidence of chaotic response of the atmospheric CO_2
dynamics to obliquity periodic forcing has been found in a reconstruction of
atmospheric CO_2 data (deep sea proxies), for DATE.","Within the program of finding axiomatizations for various parts of
computability logic, it was proved earlier that the logic of interactive Turing
reduction is exactly the implicative fragment of ORG's intuitionistic
calculus. That sort of reduction permits unlimited reusage of the computational
resource represented by the antecedent. An at least equally basic and natural
sort of algorithmic reduction, however, is the one that does not allow such
reusage. The present article shows that turning the logic of the ORDINAL sort of
reduction into the logic of the ORDINAL sort of reduction takes nothing more
than just deleting the contraction rule from its NORP-style axiomatization.
The first (Turing) sort of interactive reduction is also shown to come in CARDINAL
natural versions. While those CARDINAL versions are very different from each
other, their logical behaviors (in isolation) turn out to be indistinguishable,
with that common behavior being precisely captured by implicative
intuitionistic logic. Among the other contributions of the present article is
an informal introduction of a series of new -- finite and bounded -- versions
of recurrence operations and the associated reduction operations. An online
source on computability logic can be found at
ORG",0
"Constraint satisfaction problems have been studied in numerous fields with
practical and theoretical interests. In DATE, major breakthroughs have
been made in a study of counting constraint satisfaction problems (or #CSPs).
In particular, a computational complexity classification of bounded-degree
#CSPs has been discovered for all degrees except for CARDINAL, where the ""degree"" of
an input instance is the maximal number of times that each input variable
appears in a given set of constraints. Despite the efforts of recent studies,
however, a complexity classification of degree-2 #CSPs has eluded from our
understandings. This paper challenges this open problem and gives its partial
solution by applying CARDINAL novel proof techniques--T_{2}-constructibility and
parametrized symmetrization--which are specifically designed to handle
""arbitrary"" constraints under randomized approximation-preserving reductions.
We partition entire constraints into CARDINAL sets and we classify the
approximation complexity of all degree-2 #CSPs whose constraints are drawn from
CARDINAL of the CARDINAL sets into CARDINAL categories: problems computable in
polynomial-time or problems that are at least as hard as #SAT. Our proof
exploits a close relationship between complex-weighted degree-2 #CSPs and
Holant problems, which are a natural generalization of complex-weighted #CSPs.","We examine the characteristic features of reversible and quantum computations
in the presence of supplementary external information, known as advice. In
particular, we present a simple, algebraic characterization of languages
recognized by CARDINAL-way reversible finite automata augmented with deterministic
advice. With a further elaborate argument, we prove a similar but slightly
weaker result for bounded-error CARDINAL-way quantum finite automata with advice.
Immediate applications of those properties lead to containments and separations
among various language families when they are assisted by appropriately chosen
advice. We further demonstrate the power and limitation of randomized advice
and quantum advice when they are given to CARDINAL-way quantum finite automata.",1
"Multi-valued partial ORG functions are computed by CARDINAL-way nondeterministic
pushdown automata equipped with write-only output tapes. We give an answer to a
fundamental question, raised by PERSON, and PERSON [Act. Inform.
DATE) CARDINAL-417], of whether all multi-valued partial ORG functions can be
refined by single-valued partial ORG functions. We negatively solve this
question by presenting a special multi-valued partial ORG function as an
example function and by proving that no refinement of this particular function
becomes a single-valued partial ORG function. This contrasts an early result of
Kobayashi [Inform. Control CARDINAL (DATE) CARDINAL-109] that multi-valued partial ORG
functions are always refined by single-valued ORG functions, where ORG
functions are computed by nondeterministic finite automata with output tapes.
Our example function turns out to be unambiguously CARDINAL-valued, and thus we obtain
a stronger separation result, in which no refinement of unambiguously CARDINAL-valued
partial ORG functions can be single-valued. For the proof, we ORDINAL introduce a
new concept of colored automata having no output tapes but having ""colors,""
which can simulate pushdown automata with constant-space output tapes. We then
conduct an extensive combinatorial analysis on the behaviors of transition
records of stack contents (called stack histories) of colored automata.","We re-examine a practical aspect of combinatorial fuzzy problems of various
types, including search, counting, optimization, and decision problems. We are
focused only on those fuzzy problems that take series of fuzzy input objects
and produce fuzzy values. To solve such problems efficiently, we design fast
fuzzy algorithms, which are modeled by polynomial-time deterministic fuzzy
Turing machines equipped with read-only auxiliary tapes and write-only output
tapes and also modeled by polynomial-size fuzzy circuits composed of fuzzy
gates. We also introduce fuzzy proof verification systems to model the
fuzzification of nondeterminism. Those models help us identify CARDINAL complexity
classes: PERSON of fuzzy functions, PERSON and PERSON of fuzzy decision
problems, and PERSON of fuzzy optimization problems. Based on a relative
approximation scheme targeting fuzzy membership degree, we formulate CARDINAL
notions of ""reducibility"" in order to compare the computational complexity of
CARDINAL fuzzy problems. These reducibility notions make it possible to locate the
most difficult fuzzy problems in ORG and in GPE.",1
"The issue of how to create open-ended evolution in an artificial system is
one the open problems in artificial life. This paper examines CARDINAL of the
factors that have some bearing on this issue, using the ORG artificial life
system.
  {\em Parsimony pressure} is a tendency to penalise more complex organisms by
the extra cost needed to reproduce longer genotypes, encouraging simplification
to happen. In ORG, parsimony is controlled by the \verb+SlicePow+ parameter.
When full parsimony is selected, evolution optimises the ancestral organism to
produce extremely simple organisms. With parsimony completely relaxed,
organisms grow larger, but not more complex. They fill up with ``junk''. This
paper looks at scanning a range of \verb+SlicePow+ from CARDINAL to CARDINAL to see if
there is an optimal value for generating complexity.
  Tierra (along with most ALife systems) use pseudo random number generators.
PERSON can never create information, only destroy it. So the total
complexity of the ORG system is bounded by the initial complexity, implying
that the individual organism complexity is bounded. Biological systems,
however, have plenty of sources of randomness, ultimately dependent on quantum
randomness, so do not have this complexity limit. Sources of real random
numbers exist for computers called {\em entropy gatherers} -- this paper
reports on the effect of changing ORG's pseudo random number generator for
an entropy gatherer.","In the {\em Many Worlds Interpretation} of quantum mechanics, the range of
possible worlds (or histories) provides variation, and the PERSON
is a selective principle analogous to natural selection. When looked on in this
way, the ``process'' by which the laws and constants of physics is determined
not too different from the process that gave rise to our current biodiversity,
i.e. NORP evolution. This has implications for the fields of ORG and
ORG, which are based on a philosophy of the inevitability of life.",1
"In this article, we assign the $D_{s3}^*(2860)$ to be a D-wave $PERSON, and study the mass and decay constant of the $D_{s3}^*(2860)$ with the
ORG sum rules by calculating the contributions of the vacuum condensates up to
dimension-6 in the operator product expansion. The predicted mass
$M_{D_{s3}^*}=(2.86\pm0.10)\,\rm{GeV}$ is in excellent agreement with the
experimental value $M_{D_{s3}^*}=(2860.5\pm 2.6 \pm 2.5\pm CARDINAL{ MeV}$
from the LHCb collaboration. The present prediction supports assigning the
$D_{s3}^*(2860)$ to be the D-wave $PERSON.","The ORG (generalized min-max) PERSON was recently proposed (PERSON, DATE) as a
measure of data similarity and was demonstrated effective in machine learning
tasks. In order to use the ORG kernel for large-scale datasets, the prior work
resorted to the (generalized) consistent weighted sampling (GPE) to convert
the ORG kernel to ORG kernel. We call this approach as ``GMM-GCWS''.
  In the machine learning literature, there is a popular algorithm which we
call ``RBF-RFF''. That is, one can use the ``random Fourier features'' (ORG) to
convert the ``radial basis function'' (ORG) kernel to ORG kernel. It was
empirically shown in (PERSON, DATE) that ORG typically requires substantially
more samples than ORG in order to achieve comparable accuracies.
  The NORP method is a general tool for computing nonlinear kernels, which
again converts nonlinear kernels into ORG kernels. We apply the NORP
method for approximating the ORG kernel, a strategy which we name as
``GMM-NYS''. In this study, our extensive experiments on a set of fairly large
datasets confirm that ORG is also a strong competitor of ORG.",0
"In cocktail party listening scenarios, the human brain is able to separate
competing speech signals. However, the signal processing implemented by the
brain to perform cocktail party listening is not well understood. Here, we
trained CARDINAL separate convolutive autoencoder deep neural networks (DNN) to
separate monaural and binaural mixtures of CARDINAL concurrent speech streams. We
then used these DNNs as convolutive deep transform (ORG) devices to perform
probabilistic re-synthesis. The CDTs operated directly in the time-domain. Our
simulations demonstrate that very simple neural networks are capable of
exploiting monaural and binaural information available in a cocktail party
listening scenario.","The short-time PERSON transform (STFT) provides the foundation of
binary-mask based audio source separation approaches. In computing a
spectrogram, the STFT window size parameterizes the trade-off between time and
frequency resolution. However, it is not yet known how this parameter affects
the operation of the binary mask in terms of separation quality for real-world
signals such as speech or music. Here, we demonstrate that the trade-off
between time and frequency in the STFT, used to perform ideal binary mask
separation, depends upon the types of source that are to be separated. In
particular, we demonstrate that different window sizes are optimal for
separating different combinations of speech and musical signals. Our findings
have broad implications for machine audition and machine learning in general.",1
"We show that the class QAM does not change even if the verifier's ability is
restricted to only single-qubit measurements. To show the result, we use the
idea of the measurement-based ORG computing: the verifier, who can do only
single-qubit measurements, can test the graph state sent from the prover and
use it for his measurement-based ORG computing. We also introduce a new
QMA-complete problem related to the stabilizer test.","Recently nonparametric functional model with functional responses has been
proposed within the functional reproducing kernel PERSON spaces (fRKHS)
framework. Motivated by its superior performance and also its limitations, we
propose a NORP process model whose posterior mode coincide with the fRKHS
estimator. The NORP approach has several advantages compared to its
predecessor. ORDINAL, the multiple unknown parameters can be inferred together
with the regression function in a unified framework. ORDINAL, as a NORP
method, the statistical inferences are straightforward through the posterior
distributions. We also use the predictive process models adapted from ORG to overcome the computational limitations, thus
extending the applicability of this popular technique to a new problem.
Modifications of predictive process models are nevertheless critical in our
context to obtain valid inferences. The numerical results presented demonstrate
the effectiveness of the modifications.",0
"When a shell collapses through its horizon, semiclassical physics suggests
that information cannot escape from this horizon. One might hope that
nonperturbative ORG gravity effects will change this situation and avoid
the `information paradox'. We note that string theory has provided a set of
states over which the wavefunction of the shell can spread, and that the number
of these states is large enough that such a spreading would significantly
modify the classically expected evolution. In this article we perform a simple
estimate of the spreading time, showing that it is much shorter than the
Hawking evaporation time for the hole. Thus information can emerge from the
hole through the relaxation of the shell state into a linear combination of
fuzzballs.","We are pursuing a modeling methodology that views the world as a realm of
things. A thing is defined as something that can be created, processed,
released, transferred, and received. Additionally, in this modeling approach, a
thing is a CARDINAL-dimensional structure referred to as a thinging (abstract)
machine. On the other hand, machines are things that are operated on; that is,
they are created, processed, released, transferred, and received. The
intertwining with the world is accomplished by integrating these CARDINAL modes of
an entity s being: being a thing that flows through machines and being a
machine that processes things. This paper further enriches these notions of
things and machines. We present further exploration of the thinging machine
model through introducing a new notion called the thing/machine (thimac) as a
label of the unity of things/machines. Thimacs replace traditional
categorization, properties, and behavior with creating, processing, releasing,
transferring, and receiving, as well as the CARDINAL linking notions of flow and
triggering. The paper discusses the concept of thimacs with examples and
focuses on the notion of structure as it applies to various diagrammatic
modeling methodologies.",0
"We present for mental processes the program of mathematical mapping which has
been successfully realized for physical processes. We emphasize that our
project is not about mathematical simulation of brain's functioning as a
complex physical system, i.e., mapping of physical and chemical processes in
the brain on mathematical spaces. The project is about mapping of purely mental
processes on mathematical spaces. We present various arguments -- philosophic,
mathematical, information, and neurophysiological -- in favor of the $p$-adic
model of mental space. $p$-adic spaces have structures of hierarchic trees and
in our model such a tree hierarchy is considered as an image of neuronal
hierarchy. Hierarchic neural pathways are considered as fundamental units of
information processing. As neural pathways can go through whole body, the
mental space is produced by the whole neural system. Finally, we develop
ORG in that GPE are represented by
probability distributions on mental space.","The paper considers CARDINAL-phase random design linear regression models. The
errors and the regressors are stationary long-range dependent NORP. The
regression parameters, the scale parameters and the change-point are estimated
using a method introduced by ORG). This is called
S-estimator and it has the property that is more robust than the classical
estimators; the outliers don't spoil the estimation results. Some asymptotic
results, including the strong consistency and the convergence rate of the
S-estimators, are proved.",0
"We present a comprehensive study of the use of value precedence constraints
to break value symmetry. We ORDINAL give a simple encoding of value precedence
into ternary constraints that is both efficient and effective at breaking
symmetry. We then extend value precedence to deal with a number of
generalizations like wreath value and partial interchangeability. We also show
that value precedence is closely related to lexicographical ordering. Finally,
we consider the interaction between value precedence and symmetry breaking
constraints for variable symmetries.","The paper demonstrates that strict adherence to probability theory does not
preclude the use of concurrent, self-activated constraint-propagation
mechanisms for managing uncertainty. Maintaining local records of
sources-of-belief allows both predictive and diagnostic inferences to be
activated simultaneously and propagate harmoniously towards a stable
equilibrium.",0
"The equations of evolutionary change by natural selection are commonly
expressed in statistical terms. ORG's fundamental theorem emphasizes the
variance in fitness. Quantitative genetics expresses selection with covariances
and regressions. Population genetic equations depend on genetic variances. How
can we read those statistical expressions with respect to the meaning of
natural selection? CARDINAL possibility is to relate the statistical expressions to
the amount of information that populations accumulate by selection. However,
the connection between selection and information theory has never been
compelling. Here, I show the correct relations between statistical expressions
for selection and information theory expressions for selection. Those relations
link selection to the fundamental concepts of entropy and information in the
theories of physics, statistics, and communication. We can now read the
equations of selection in terms of their natural meaning. Selection causes
populations to accumulate information about the environment.","This paper describes a relatively simple way of allowing a brain model to
self-organise its concept patterns through nested structures. For a simulation,
time reduction is helpful and it would be able to show how patterns may form
and then fire in sequence, as part of a search or thought process. It uses a
very simple equation to show how the inhibitors in particular, can switch off
certain areas, to allow other areas to become the prominent ones and thereby
define the current brain state. This allows for a small amount of control over
what appears to be a chaotic structure inside of the brain. It is attractive
because it is still mostly mechanical and therefore can be added as an
automatic process, or the modelling of that. The paper also describes how the
nested pattern structure can be used as a basic counting mechanism. Another
mathematical conclusion provides a basis for maintaining memory or concept
patterns. The self-organisation can space itself through automatic processes.
This might allow new neurons to be added in a more even manner and could help
to maintain the concept integrity. The process might also help with finding
memory structures afterwards. This extended version integrates further with the
existing cognitive model and provides some new conclusions.",0
"Research on bias in machine learning algorithms has generally been concerned
with the impact of bias on predictive accuracy. We believe that there are other
factors that should also play a role in the evaluation of bias. CARDINAL such factor
is the stability of the algorithm; in other words, the repeatability of the
results. If we obtain CARDINAL sets of data from the same phenomenon, with the same
underlying probability distribution, then we would like our learning algorithm
to induce approximately the same concepts from both sets of data. This paper
introduces a method for quantifying stability, based on a measure of the
agreement between concepts. We also discuss the relationships among stability,
predictive accuracy, and bias.","A path information is defined in connection with the different possible paths
of chaotic system moving in its phase space CARDINAL cells. On the basis of
the assumption that the paths are differentiated by their actions, we show that
the maximum path information leads to a path probability distribution as a
function of action from which the well known transition probability of NORP
motion can be easily derived. An interesting result is that the most probable
paths are just the paths of least action. This suggests that the principle of
least action, in a probabilistic situation, is equivalent to the principle of
maximization of information or uncertainty associated with the probability
distribution.",0
"We explore OR gate response in a mesoscopic ring threaded by a magnetic flux
$MONEY The ring is symmetrically attached to CARDINAL semi-infinite CARDINAL-dimensional
metallic electrodes and CARDINAL gate voltages, viz, $V_a$ and $PERSON, are applied in
CARDINAL arm of the ring which are treated as the CARDINAL inputs of the OR gate. All the
calculations are based on the tight-binding model and the ORG's function
method, which numerically compute the conductance-energy and current-voltage
characteristics as functions of the gate voltages, ring-to-electrodes coupling
strengths and magnetic flux. Our theoretical study shows that, for
MONEY ($\phi_0=ch/e$, the elementary flux-quantum) a high output
current (CARDINAL) (in the logical sense) appears if one or both the inputs to the
gate are high (1), while if neither input is high (1), a low output current (0)
appears. It clearly demonstrates the PRODUCT gate behavior and this aspect may be
utilized in designing the electronic logic gate.","We explore NOT gate response in a mesoscopic ring threaded by a magnetic flux
$MONEY The ring is attached symmetrically to CARDINAL semi-infinite CARDINAL-dimensional
metallic electrodes and a gate voltage, viz, $PERSON, is applied in CARDINAL arm of
the ring which is treated as the input of the NOT gate. The calculations are
based on the tight-binding model and the ORG's function method, which
numerically compute the conductance-energy and current-voltage characteristics
as functions of the ring-to-electrodes coupling strength, magnetic flux and
gate voltage. Our theoretical study shows that, for MONEY
($\phi_0=ch/e$, the elementary flux-quantum) a high output current (CARDINAL) (in the
logical sense) appears if the input to the gate is low (0), while a low output
current (0) appears when the input to the gate is high (1). It clearly exhibits
the NOT gate behavior and this aspect may be utilized in designing an
electronic logic gate.",1
"A multidimensional optimization problem is formulated in the tropical
mathematics setting as to maximize a nonlinear objective function, which is
defined through a multiplicative conjugate transposition operator on vectors in
a finite-dimensional semimodule over a general idempotent semifield. The study
is motivated by problems drawn from project scheduling, where the deviation
between initiation or completion times of activities in a project is to be
maximized subject to various precedence constraints among the activities. To
solve the unconstrained problem, we ORDINAL establish an upper bound for the
objective function, and then solve a system of vector equations to find all
vectors that yield the bound. As a corollary, an extension of the solution to
handle constrained problems is discussed. The results obtained are applied to
give complete direct solutions to the motivating problems from project
scheduling. Numerical examples of the development of optimal schedules are also
presented.","A multidimensional extremal problem in the idempotent algebra setting is
considered which consists in minimizing a nonlinear functional defined on a
finite-dimensional semimodule over an idempotent semifield. The problem
integrates CARDINAL other known problems by combining their objective functions into
CARDINAL general function and includes these problems as particular cases. A new
solution approach is proposed based on the analysis of linear inequalities and
spectral properties of matrices. The approach offers a comprehensive solution
to the problem in a closed form that involves performing simple matrix and
vector operations in terms of idempotent algebra and provides a basis for the
development of efficient computational algorithms and their software
implementation.",1
"We consider the rate distortion problem with side information at the decoder
posed and investigated by PERSON and PERSON. The rate distortion function
indicating the trade-off between the rate on the data compression and the
quality of data obtained at the decoder was determined by PERSON and PERSON. In
this paper, we study the error probability of decoding at rates below the rate
distortion function. We evaluate the probability of decoding such that the
estimation of source outputs by the decoder has a distortion not exceeding a
prescribed distortion level. We prove that when the rate of the data
compression is below the rate distortion function this probability goes to CARDINAL
exponentially and derive an explicit lower bound of this exponent function. On
the PERSON-Ziv source coding problem the strong converse coding theorem has not
been established yet. We prove this as a simple corollary of our result.","This paper presents a soundness and completeness proof for propositional
intuitionistic calculus with respect to the semantics of computability logic.
The latter interprets formulas as interactive computational problems,
formalized as games between a machine and its environment. Intuitionistic
implication is understood as algorithmic reduction in the weakest possible --
and hence most natural -- sense, disjunction and conjunction as
deterministic-choice combinations of problems (disjunction = machine's choice,
conjunction = environment's choice), and ""absurd"" as a computational problem of
universal strength. See ORG for a
comprehensive online source on computability logic.",0
"We frame the question of what kind of subjective experience a brain
simulation would have in contrast to a biological brain. We discuss the brain
prosthesis thought experiment. We evaluate how the experience of the brain
simulation might differ from the biological, according to a number of
hypotheses about experience and the properties of simulation. Then, we identify
finer questions relating to the original inquiry, and answer them from both a
general physicalist, and panexperientialist perspective.","This paper begins with a general theory of error in cross-validation testing
of algorithms for supervised learning from examples. It is assumed that the
examples are described by attribute-value pairs, where the values are symbolic.
Cross-validation requires a set of training examples and a set of testing
examples. The value of the attribute that is to be predicted is known to the
learner in the training set, but unknown in the testing set. The theory
demonstrates that cross-validation error has CARDINAL components: error on the
training set (inaccuracy) and sensitivity to noise (instability). This general
theory is then applied to voting in instance-based learning. Given an example
in the testing set, a typical instance-based learning algorithm predicts the
designated attribute by voting among the k nearest neighbors (the k most
similar examples) to the testing example in the training set. Voting is
intended to increase the stability (resistance to noise) of instance-based
learning, but a theoretical analysis shows that there are circumstances in
which voting can be destabilizing. The theory suggests ways to minimize
cross-validation error, by insuring that voting is stable and does not
adversely affect accuracy.",0
"We show how to express the information contained in a Quantum Bayesian (QB)
net as a product of unitary matrices. If each of these unitary matrices is
expressed as a sequence of elementary operations (operations such as
controlled-nots and qubit rotations), then the result is a sequence of
operations that can be used to run a ORG computer. QB nets have been run
entirely on a classical computer, but one expects them to run faster on a
ORG computer.","This survey note describes a brief systemic view to approaches for evaluation
of hierarchical composite (modular) systems. The list of considered issues
involves the following: (i) basic assessment scales (quantitative scale,
ordinal scale, PERSON, CARDINAL kinds of poset-like scales), (ii)
basic types of scale transformations problems, (iii) basic types of scale
integration methods. Evaluation of the modular systems is considered as
assessment of system components (and their compatibility) and integration of
the obtained local estimates into the total system estimate(s). This process is
based on the above-mentioned problems (i.e., scale transformation and
integration). Illustrations of the assessment problems and evaluation
approaches are presented (including numerical examples).",0
"There is no single definition of complexity (Edmonds DATE; PERSON DATE;
PERSON DATE), as it acquires different meanings in different contexts. A
general notion is the amount of information required to describe a phenomenon
(PERSON), but it can also be understood as the length of the
shortest program required to compute that description, as the time required to
compute that description, as the minimal model to statistically describe a
phenomenon, etc.","In this chapter, concepts related to information and computation are reviewed
in the context of human computation. A brief introduction to information theory
and different types of computation is given. CARDINAL examples of human computation
systems, online social networks and GPE, are used to illustrate how these
can be described and compared in terms of information and computation.",1
"We present critical arguments against individual interpretation of GPE's
complementarity and PERSON's uncertainty principles. Statistical
interpretation of these principles is discussed in the contextual framework. We
support the possibility to use ORG of
quantum formalism. In spite of all {\bf no-go} PERSON (e.g., PERSON,
GPE and NORP,..., ORG,...), recently (quant-ph/0306003 and 0306069) we
constructed a realist basis of quantum mechanics. In our model both classical
and ORG spaces are rough images of the fundamental {\bf prespace.} ORG
mechanics cannot be reduced to classical one. Both classical and quantum
representations induce reductions of prespace information.","We perform geometrization of genetics by representing genetic information by
points of the CARDINAL-adic {ORG information space.} By well known theorem of number
theory this space can also be represented as the CARDINAL-adic space. The process of
DNA-reproduction is described by the action of a CARDINAL-adic (or equivalently
CARDINAL-adic) dynamical system. As we know, the genes contain information for
production of proteins. The genetic code is a degenerate map of codons to
proteins. We model this map as functioning of a polynomial dynamical system.
The purely mathematical problem under consideration is to find a dynamical
system reproducing the degenerate structure of the genetic code. We present CARDINAL
of possible solutions of this problem.",1
"This book develops the conjecture that all kinds of information processing in
computers and in brains may usefully be understood as ""information compression
by multiple alignment, unification and search"". This ""SP theory"", which has
been under development since DATE, provides a unified view of such things as
the workings of a universal Turing machine, the nature of 'knowledge', the
interpretation and production of natural language, pattern recognition and
best-match information retrieval, several kinds of probabilistic reasoning,
planning and problem solving, unsupervised learning, and a range of concepts in
mathematics and logic. The theory also provides a basis for the design of an
'SP' computer with several potential advantages compared with traditional
digital computers.","Excess freedom in how computers are used creates problems that include: bit
rot, problems with big data, problems in the creation and debugging of
software, and problems with cyber security. To tame excess freedom, ""tough
love"" is needed in the form of a {\em universal framework for the
representation and processing of diverse kinds of knowledge} (ORG). The ""SP
machine"", based on the ""SP theory of intelligence"", has the potential to
provide that framework and to help solve the problems above. There is potential
to reduce the CARDINAL different kinds of computer file to one, and to reduce
the CARDINAL of different computer languages to one.",1
"The paper analyzes the problem of judgments or preferences subsequent to
initial analysis by autonomous agents in a hierarchical system where the higher
level agents does not have access to group size information. We propose methods
that reduce instances of preference reversal of the kind encountered in
PERSON's paradox.","The solution of FAC system in CARDINAL space dimensions
with ORG data in PRODUCT and wave data in H^{s+1/2} x H^{s-1/2} is uniquely
determined in the natural solution space C^0([0,T],H^s) x
C^0([0,T],H^{s+\frac1/2}), provided s > CARDINAL . This improves the uniqueness
part of the global well-posedness result by ORG and the author, where
uniqueness was proven in (smaller) spaces of PRODUCT type. Local
well-posedness is also proven for ORG data in L^2 and wave data in H^{3/5}+}
x H^{-2/5+} in the solution space C^0([0,T],L^2) x C^0([0,T],H^{3/5+}) and also
for more regular data.",0
"We study the spin-dependent cross-sections of vector PERSON
for longitudinally and transversely polarized photons within a QCD- model. The
dependence of the $\sigma_T/\sigma_L$ ratio on the photon virtuality and on ORG wave function is analysed.","Motivated by novel results in the theory of wave dynamics in black-hole
spacetimes, we analyze the dynamics of a massive scalar field surrounding a
rapidly rotating ORG black hole. In particular, we report on the existence of
stationary (infinitely long-lived) regular field configurations in the
background of maximally rotating black holes. The effective height of these
scalar ""clouds"" above the central black hole is determined analytically. Our
results support the possible existence of stationary scalar field dark matter
distributions surrounding rapidly rotating black holes.",0
"The paper focuses on a new class of combinatorial problems which consists in
restructuring of solutions (as sets/structures) in combinatorial optimization.
CARDINAL main features of the restructuring process are examined: (i) a cost of the
restructuring, (ii) a closeness to a goal solution. CARDINAL types of the
restructuring problems are under study: (a) CARDINAL-stage structuring, (b)
multi-stage structuring, and (c) structuring over changed element set.
CARDINAL-criterion and NORP problem formulations can be considered. The
restructuring problems correspond to redesign (improvement, upgrade) of modular
systems or solutions. The restructuring approach is described and illustrated
(problem statements, solving schemes, examples) for the following combinatorial
optimization problems: knapsack problem, multiple choice problem, assignment
problem, spanning tree problems, clustering problem, multicriteria ranking
(sorting) problem, morphological clique problem. Numerical examples illustrate
the restructuring problems and solving schemes.","The paper addresses problem of data allocation in CARDINAL-layer computer storage
while taking into account dynamic digraph(s) over computing tasks. The basic
version of data file allocation on parallel hard magnetic disks is considered
as special bin packing model. CARDINAL problems of the allocation solution
reconfiguration (restructuring) are suggested: (i) CARDINAL-stage restructuring
model, (ii) multistage restructuring models. Solving schemes are based on
simplified heuristics. Numerical examples illustrate problems and solving
schemes.",1
"Despite efforts to increase the supply of organs from living donors, most
kidney transplants performed in GPE still come from deceased donors. The
age of these donated organs has increased substantially in DATE as
the rate of fatal accidents on roads has fallen. The GPE and ORG
in GPE is therefore looking to design a new mechanism that better matches
the age of the organ to the age of the patient. I discuss the design,
axiomatics and performance of several candidate mechanisms that respect the
special online nature of this fair division problem.","PERSON's work, in its depth and breadth, encompasses many areas of
scientific and philosophical interest. It helped establish the accepted
mathematical concept of randomness, which in turn is the basis of tools that I
have developed to justify and quantify what I think is clear evidence of the
algorithmic nature of the world. To illustrate the concept I will establish
novel upper bounds of algorithmic randomness for elementary cellular automata.
I will discuss how the practice of science consists in conceiving a model that
starts from certain initial values, running a computable instantiation, and
awaiting a result in order to determine where the system may be in a future
state--in a shorter time than the time taken by the actual unfolding of the
phenomenon in question. If a model does not comply with all or some of these
requirements it is traditionally considered useless or even unscientific, so
the more precise and faster the better. A model is thus better if it can
explain more with less, which is at the core of PERSON's ""compression is
comprehension"". I will pursue these questions related to the random versus
possibly algorithmic nature of the world in CARDINAL directions, drawing heavily on
the work of PERSON. I will also discuss how the algorithmic approach is
related to the success of science at producing models of the world, allowing
computer simulations to better understand it and make more accurate predictions
and interventions.",0
"We discuss views about whether the universe can be rationally comprehended,
starting with ORG, then ORG, and then the views of some distinguished
scientists of DATE. Based on this, we defend the thesis that
comprehension is compression, i.e., explaining many facts using few theoretical
assumptions, and that a theory may be viewed as a computer program for
calculating observations. This provides motivation for defining the complexity
of something to be the size of the simplest theory for it, in other words, the
size of the smallest program for calculating it. This is the central idea of
algorithmic information theory (ORG), a field of theoretical computer science.
Using the mathematical concept of program-size complexity, we exhibit
irreducible mathematical facts, mathematical facts that cannot be demonstrated
using any mathematical theory simpler than they are. It follows that the world
of mathematical ideas has infinite complexity and is therefore not fully
comprehensible, at least not in a static fashion. Whether the physical world
has finite or infinite complexity remains to be seen. Current science believes
that the world contains randomness, and is therefore also infinitely complex,
but a deterministic universe that simulates randomness via pseudo-randomness is
also a possibility, at least according to recent highly speculative work of ORG.","A remarkable new definition of a self-delimiting universal Turing machine is
presented that is easy to program and runs very quickly. This provides a new
foundation for algorithmic information theory. This new universal Turing
machine is implemented via software written in GPE and C. Using this
new software, it is now possible to give a self-contained ``hands on''
mini-course presenting very concretely the latest proofs of the fundamental
information-theoretic incompleteness theorems.",1
"In this paper we develop a method for clustering belief functions based on
attracting and conflicting metalevel evidence. Such clustering is done when the
belief functions concern multiple events, and all belief functions are mixed
up. The clustering process is used as the means for separating the belief
functions into subsets that should be handled independently. While the
conflicting metalevel evidence is generated internally from pairwise conflicts
of all belief functions, the attracting metalevel evidence is assumed given by
some external source.","In this paper we develop an evidential force aggregation method intended for
classification of evidential intelligence into recognized force structures. We
assume that the intelligence has already been partitioned into clusters and use
the classification method individually in each cluster. The classification is
based on a measure of fitness between template and fused intelligence that
makes it possible to handle intelligence reports with multiple nonspecific and
uncertain propositions. With this measure we can aggregate on a level-by-level
basis, starting from general intelligence to achieve a complete force structure
with recognized units on all hierarchical levels.",1
"Individual-intelligence research, from a neurological perspective, discusses
the hierarchical layers of the cortex as a structure that performs conceptual
abstraction and specification. This theory has been used to explain how
motor-cortex regions responsible for different behavioral modalities such as
writing and speaking can be utilized to express the same general concept
represented higher in the cortical hierarchy. For example, the concept of a
dog, represented across a region of high-level cortical-neurons, can either be
written or spoken about depending on the individual's context. The higher-layer
cortical areas project down the hierarchy, sending abstract information to
specific regions of the motor-cortex for contextual implementation. In this
paper, this idea is expanded to incorporate collective-intelligence within a
hyper-cortical construct. This hyper-cortex is a multi-layered network used to
represent abstract collective concepts. These ideas play an important role in
understanding how collective-intelligence systems can be engineered to handle
problem abstraction and solution specification. Finally, a collection of common
problems in the scientific community are solved using an artificial
hyper-cortex generated from digital-library metadata.","The ORG community is focused on integrating ORG (ORG) data sets into a single unified representation known as the Web
of ORG. The Web of Data can be traversed by both man and machine and shows
promise as the \textit{de facto} standard for integrating data world wide much
like WORK_OF_ART is the \textit{de facto} standard for integrating
documents. On DATE, an updated ORG cloud
visualization was made publicly available. This visualization represents the
various ORG data sets currently in the ORG cloud and their interlinking
relationships. For the purposes of this article, this visual representation was
manually transformed into a directed graph and analyzed.",1
"Cryptography is a theory of secret functions. Category theory is a general
theory of functions. Cryptography has reached a stage where its structures
often take several pages to define, and its formulas sometimes run from page to
page. Category theory has some complicated definitions as well, but one of its
specialties is taming the flood of structure. Cryptography seems to be in need
of high level methods, whereas category theory always needs concrete
applications. So why is there no categorical cryptography? CARDINAL reason may be
that the foundations of modern cryptography are built from probabilistic
polynomial-time Turing machines, and category theory does not have a good
handle on such things. On the other hand, such foundational problems might be
the very reason why cryptographic constructions often resemble low level
machine programming. I present some preliminary explorations towards
categorical cryptography. It turns out that some of the main security concepts
are easily characterized through the categorical technique of *diagram
chasing*, which was first used PERSON's seminal `WORK_OF_ART and
PERSON'.","In a previous FAST paper, I presented a quantitative model of the process of
trust building, and showed that trust is accumulated like wealth: the rich get
richer. This explained the pervasive phenomenon of adverse selection of trust
certificates, as well as the fragility of trust networks in general. But a
simple explanation does not always suggest a simple solution. It turns out that
it is impossible to alter the fragile distribution of trust without sacrificing
some of its fundamental functions. A solution for the vulnerability of trust
must thus be sought elsewhere, without tampering with its distribution. This
observation was the starting point of the present paper. It explores a
different method for securing trust: not by redistributing it, but by mining
for its sources. The method used to break privacy is thus also used to secure
trust. A high level view of the mining methods that connect the CARDINAL is provided
in terms of *similarity networks*, and *spectral decomposition* of similarity
preserving maps. This view may be of independent interest, as it uncovers a
common conceptual and structural foundation of mathematical classification
theory on one hand, and of the spectral methods of graph clustering and data
mining on the other hand.",1
"This paper is intended to be a pedagogical introduction to quantum NORP
networks (QB nets), as I personally use them to represent mixed states (i.e.,
density matrices, and open ORG). A special effort is made to make
contact with notions used in textbooks on quantum WORK_OF_ART
(quantum PRODUCT), such as the one by PERSON (arXiv:1106.1445)","The paper serves as the ORDINAL contribution towards the development of the
theory of efficiency: a unifying framework for the currently disjoint theories
of information, complexity, communication and computation. Realizing the
defining nature of the brute force approach in the fundamental concepts in all
of the above mentioned fields, the paper suggests using efficiency or
improvement over the brute force algorithm as a common unifying factor
necessary for the creation of a unified theory of information manipulation. By
defining such diverse terms as randomness, knowledge, intelligence and
computability in terms of a common denominator we are able to bring together
contributions from FAC, PERSON, PERSON, PERSON, PERSON, PERSON and
many others under a common umbrella of the efficiency theory.",0
"We investigate the information provided about a specified distributed
apparatus of n units in the measurement of a quantum state. It is shown that,
in contrast to such measurement of a classical state, which is bounded by PERSON(CARDINAL) bits, the information in a quantum measurement is bounded by CARDINAL x
n^(1/2) bits. This means that the use of ORG apparatus offers an
exponential advantage over classical apparatus.","The notion of ORG information related to the CARDINAL different perspectives
of the global and local states is examined. There is circularity in the
definition of quantum information because we can speak only of the information
of systems that have been specifically prepared. In particular, we examine the
final state obtained by applying unitary transformations on a single qubit that
belongs to an entangled pair.",1
"This paper is concerned with an inverse obstacle problem which employs the
dynamical scattering data of NORP wave over a finite time interval. The
unknown obstacle is assumed to be sound-soft one. The governing equation of the
wave is given by the classical wave equation. The wave is generated by the
initial data localized outside the obstacle and observed over a finite time
interval at a place which is not necessary the same as the support of the
initial data. The observed data are the so-called bistatic data. In this paper,
an enclosure method which employs the bistatic data and is based on CARDINAL main
analytical formulae, is developed. The ORDINAL CARDINAL enables us to extract the
maximum spheroid with focal points at the center of the support of the initial
data and that of the observation points whose exterior encloses the unknown
obstacle of general shape. The ORDINAL one, under some technical assumption for
the obstacle including convexity as an example, indicates the deviation of the
geometry of the boundary of the obstacle and the maximum spheroid at the
contact points. Several implications of those CARDINAL formulae are also given. In
particular, a constructive proof of a uniqueness of a spherical obstacle using
the bistatic data is given.","This paper discusses an axiomatic approach for the integration of ontologies,
an approach that extends to ORDINAL order logic a previous approach (Kent 2000)
based on information flow. This axiomatic approach is represented in the
ORG (ORG), a metalevel framework for organizing the
information that appears in digital libraries, distributed databases and
ontologies (Kent 2001). The paper argues that the integration of ontologies is
the CARDINAL-step process of alignment and unification. Ontological alignment
consists of the sharing of common terminology and semantics through a mediating
ontology. Ontological unification, concentrated in a virtual ontology of
community connections, is fusion of the alignment diagram of participant
community ontologies - the quotient of the sum of the participant portals
modulo the ontological alignment structure.",0
"This study of the CARDINAL dimensional PERSON model in a weak
coupling perturbative regime points out the effective mass behavior as a
function of the adiabatic parameter $MONEY, $PERSON is the
zone boundary phonon energy and $MONEY is the electron band hopping integral.
Computation of low order diagrams shows that CARDINAL phonons scattering processes
become appreciable in the intermediate regime in which zone boundary phonons
energetically compete with band electrons. Consistently, in the intermediate
(and also moderately antiadiabatic) range the relevant mass renormalization
signals the onset of a polaronic crossover whereas the electrons are
essentially undressed in the fully adiabatic and antiadiabatic systems. The
effective mass is roughly twice as much the bare band value in the intermediate
regime while an abrupt increase (mainly related to the peculiar 1D dispersion
relations) is obtained at $MONEY \sqrt{2}J$.","Online estimation and modelling of i.i.d. data for short sequences over large
or complex ""alphabets"" is a ubiquitous (GPE in machine learning,
information theory, data compression, statistical language processing, and
document analysis. WORK_OF_ART distribution (also called Polya
urn scheme) and extensions thereof are widely applied for online i.i.d.
estimation. Good a-priori choices for the parameters in this regime are
difficult to obtain though. I derive an optimal adaptive choice for the main
parameter via tight, data-dependent redundancy bounds for a related model. The
CARDINAL-line recommendation is to set the 'total mass' = 'precision' =
'concentration' parameter to m/2ln[(n+1)/m], where n is the (past) sample size
and m the number of different symbols observed (so far). The resulting
estimator (i) is simple, (ii) online, (iii) fast, (iv) performs well for all m,
small, middle and large, (v) is independent of the base alphabet size, (vi)
non-occurring symbols induce no redundancy, (vii) the constant sequence has
constant redundancy, (viii) symbols that appear only finitely often have
bounded/constant contribution to the redundancy, (ix) is competitive with
(slow) NORP mixing over all sub-alphabets.",0
"To follow the dynamicity of the user's content, researchers have recently
started to model interactions between users and ORG (CARS) as a bandit problem where the system needs to deal with
exploration and exploitation dilemma. In this sense, we propose to study the
freshness of the user's content in CARS through the bandit problem. We
introduce in this paper an algorithm named ORG Sampling
(ORG) that manages the recommendation of fresh document according to the
user's risk of the situation. The intensive evaluation and the detailed
analysis of the experimental results reveals several important discoveries in
the exploration/exploitation (exr/exp) behaviour.","Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to bring a solution to some of the
problems concerning the acceptance of the system by users. To achieve this, we
propose a fundamental shift in terms of how we model the learning of
recommender system: inspired by models of human reasoning developed in robotic,
we combine reinforcement learning and case-base reasoning to define a
recommendation process that uses these CARDINAL approaches for generating
recommendations on different context dimensions (social, temporal, geographic).
We describe an implementation of the recommender system based on this
framework. We also present preliminary results from experiments with the system
and show how our approach increases the recommendation quality.",1
"Steganography is the art and science of writing hidden messages in such a way
that no one apart from the sender and the receiver would realize that a secret
communicating is taking place. Unlike cryptography which only scrambles secret
data keeping them overt, steganography covers secret data into medium files
such as image files and transmits them in total secrecy avoiding drawing
eavesdroppers suspicions. However, considering that the public channel is
monitored by eavesdroppers, it is vulnerable to stego-attacks which refer to
randomly trying to break the medium file and recover the secret data out of it.
That is often true because steganalysts assume that the secret data are encoded
into a single medium file and not into multiple ones that complement each
other. This paper proposes a text steganography method for hiding secret
textual data using CARDINAL mediums; a ORG sentence containing all the
characters of the alphabet, and an uncompressed image file. The algorithm tries
to search for every character of the secret message into the ORG text. The
search starts from a random index called seed and ends up on the index of the
ORDINAL occurrence of the character being searched for. As a result, CARDINAL indexes
are obtained, the seed and the offset indexes. Together they are embedded into
the CARDINAL LSBs of the color channels of the image medium. Ultimately, both
mediums mainly the ORG and the image are sent to the receiver. The
advantage of the proposed method is that it makes the covert data hard to be
recovered by unauthorized parties as it uses CARDINAL mediums, instead of one, to
deliver the secret data. Experiments conducted, illustrated an example that
explained how to encode and decode a secret text message using the ORG and
the image mediums.","The evolution of the Internet and computer applications have generated
colossal amount of data. They are referred to as ORG and they consist of
huge volume, high velocity, and variable datasets that need to be managed at
the right speed and within the right time frame to allow real-time data
processing and analysis. Several ORG solutions were developed, however
they are all based on distributed computing which can be sometimes expensive to
build, manage, troubleshoot, and secure. This paper proposes a novel method for
processing ORG using memory-based, multi-processing, and CARDINAL-server
architecture. It is memory-based because data are loaded into memory prior to
start processing. It is multi-processing because it leverages the power of
parallel programming using shared memory and multiple threads running over
several CPUs in a concurrent fashion. It is CARDINAL-server because it only requires
a single server that operates in a non-distributed computing environment. The
foremost advantages of the proposed method are high performance, low cost, and
ease of management. The experiments conducted showed outstanding results as the
proposed method outperformed other conventional methods that currently exist on
the market. Further research can improve upon the proposed method so that it
supports message passing between its different processes using remote procedure
calls among other techniques.",1
"Based on cumulative distribution functions, PERSON series expansion and
PERSON tests, we present a simple method to display probability densities
for data drawn from a continuous distribution. It is often more efficient than
using histograms.","This article is a tutorial on PERSON chain PERSON simulations and their
statistical analysis. The theoretical concepts are illustrated through many
numerical assignments from the author's book on the subject. Computer code (in
GPE) is available for all subjects covered and can be downloaded from the
web.",1
"We explore the possible connections between the dynamic behaviour of a system
and Turing universality in terms of the system's ability to (effectively)
transmit and manipulate information. Some arguments will be provided using a
defined compression-based transition coefficient which quantifies the
sensitivity of a system to being programmed. In the same spirit, a list of
conjectures concerning the ability of FAC machines to perform
universal computation will be formulated. The main working hypothesis is that
universality is deeply connected to the qualitative behaviour of a system,
particularly to its ability to react to external stimulus--as it needs to be
programmed--and to its capacity for transmitting this information.","The paper attempts to describe the space of possible mind designs by ORDINAL
equating all minds to software. Next it proves some interesting properties of
the mind design space such as infinitude of minds, size and representation
complexity of minds. A survey of mind design taxonomies is followed by a
proposal for a new field of investigation devoted to study of minds,
intellectology, a list of open problems for this new field is presented.",0
"The notions of formal contexts and concept lattices, although introduced by
ORG DATE, already have proven to be of great utility in various
applications such as data analysis and knowledge representation. In this paper
we give arguments that ORG's original notion of formal context, although
quite appealing in its simplicity, now should be replaced by a more semantic
notion. This new notion of formal context entails a modified approach to
concept construction. We base our arguments for these new versions of formal
context and concept construction upon ORG's philosophical attitude with
reference to the intensional aspect of concepts. We give a brief development of
the relational theory of formal contexts and concept construction,
demonstrating the equivalence of ""concept-lattice construction"" of ORG with
the well-known ""completion by cuts"" of ORG. Generalization and
abstraction of these formal contexts offers a powerful approach to knowledge
representation.","Dialectical logic is the logic of dialectical processes. The goal of
dialectical logic is to introduce dynamic notions into logical computational
systems. The fundamental notions of proposition and truth-value in standard
logic are subsumed by the notions of process and flow in dialectical logic.
PRODUCT logic has a standard aspect, which can be defined in terms of the
""local cartesian closure"" of subtypes. The standard aspect of dialectical logic
provides a natural program semantics which incorporates ORG's
precondition/postcondition semantics and extends the standard Kripke semantics
of dynamic logic. The goal of the standard aspect of dialectical logic is to
unify the logic of small-scale and large-scale programming.",1
"Image information content is known to be a complicated and controvercial
problem. This paper posits a new image information content definition.
Following the theory of ORG complexity, we define
image information content as a set of descriptions of imafe data structures.
CARDINAL levels of such description can be generally distinguished: 1)the global
level, where the coarse structure of the entire scene is initially outlined; CARDINAL)
the intermediate level, where structures of separate, non-overlapping image
regions usually associated with individual scene objects are deliniated; and CARDINAL)
the low-level description, where local image structures observed in a limited
and restricted field of view are resolved. A technique for creating such image
information content descriptors is developed. Its algorithm is presented and
elucidated with some examples, which demonstrate the effectiveness of the
proposed approach.","We study electron transport through a quantum interferometer with
side-coupled quantum dots. The interferometer, threaded by a magnetic flux
CARDINAL\phi$, is attached symmetrically to CARDINAL semi-infinite CARDINAL-dimensional metallic
electrodes. The calculations are based on the tight-binding model and the
PERSON's function method, which numerically compute the conductance-energy and
current-voltage characteristics. Our results predict that under certain
conditions this particular geometry exhibits anti-resonant states. These states
are specific to the interferometric nature of the scattering and do not occur
in conventional one-dimensional scattering problems of potential barriers. Most
importantly we show that, such a simple geometric model can also be used as a
classical ORG gate, where the CARDINAL gate voltages, viz, $V_a$ and $PERSON, are
applied, respectively, in the CARDINAL dots those are treated as the CARDINAL inputs of
the ORG gate. For MONEY ($\phi_0=ch/e$, the elementary flux-quantum),
a high output current (CARDINAL) (in the logical sense) appears if one, and CARDINAL,
of the inputs to the gate is high (1), while if both inputs are low (0) or both
are high (1), a low output current (0) appears. It clearly demonstrates the ORG
gate behavior and this aspect may be utilized in designing the electronic logic
gate.",0
"ORG information is radically different from classical information in that
the quantum formalism (PERSON space) makes necessary the introduction of
irreducible ``nits,'' n being an arbitrary natural number (bigger than one),
not just bits.","It has been argued that analogy is the core of cognition. In ORG research,
algorithms for analogy are often limited by the need for hand-coded high-level
representations as input. An alternative approach is to use high-level
perception, in which high-level representations are automatically generated
from raw data. Analogy perception is the process of recognizing analogies using
high-level perception. We present ORG, an algorithm for analogy
perception that recognizes lexical proportional analogies using representations
that are automatically generated from a large corpus of raw textual data. A
proportional analogy is an analogy of the form A:B::C:D, meaning ""A is to B as
C is to D"". A lexical proportional analogy is a proportional analogy with
words, such as carpenter:wood::mason:stone. PairClass represents the semantic
relations between CARDINAL words using a high-dimensional feature vector, in which
the elements are based on frequencies of patterns in the corpus. PairClass
recognizes analogies by applying standard supervised machine learning
techniques to the feature vectors. We show how CARDINAL different tests of word
comprehension can be framed as problems of analogy perception and we then apply
PRODUCT to the CARDINAL resulting sets of analogy perception problems. We
achieve competitive results on all CARDINAL tests. This is the ORDINAL time a
uniform approach has handled such a range of tests of word comprehension.",0
"The general pupose of the scholarly communication process is to support the
creation and dissemination of ideas within the scientific community. At a finer
granularity, there exists multiple stages which, when confronted by a member of
the community, have different requirements and therefore different solutions.
In order to take a researcher's idea from an initial inspiration to a community
resource, the scholarly communication infrastructure may be required to CARDINAL)
provide a scientist initial seed ideas; CARDINAL) form a team of well suited
collaborators; CARDINAL) located the most appropriate venue to publish the formalized
idea; CARDINAL) determine the most appropriate peers to review the manuscript; and CARDINAL)
disseminate the end product to the most interested members of the community.
Through the various delinieations of this process, the requirements of each
stage are tied soley to the multi-functional resources of the community: its
researchers, its journals, and its manuscritps. It is within the collection of
these resources and their inherent relationships that the solutions to
scholarly communication are to be found. This paper describes an associative
network composed of multiple scholarly artifacts that can be used as a medium
for supporting the scholarly communication process.","Semantic networks qualify the meaning of an edge relating any CARDINAL vertices.
Determining which vertices are most ""central"" in a semantic network is
difficult because CARDINAL relationship type may be deemed subjectively more
important than another. For this reason, research into semantic network metrics
has focused primarily on context-based rankings (i.e. user prescribed
contexts). Moreover, many of the current semantic network metrics rank ORG (i.e. directed paths between CARDINAL vertices) and not the vertices
themselves. This article presents a framework for calculating semantically
meaningful primary eigenvector-based metrics such as eigenvector centrality and
ORG in semantic networks using a modified version of the random walker
model of PERSON chain analysis. Random walkers, in the context of this article,
are constrained by a grammar, where the grammar is a user defined data
structure that determines the meaning of the final vertex ranking. The ideas in
this article are presented within the context of ORG (ORG) of the NORP Web initiative.",1
"We use the system of p-adic numbers for the description of information
processes. Basic objects of our models are so called transformers of
information, basic processes are information processes, the statistics are
information statistics (thus we present a model of information reality). The
classical and quantum mechanical formalisms on information p-adic spaces are
developed. It seems that classical and quantum mechanical models on p-adic
information spaces can be applied for the investigation of flows of information
in cognitive and social systems, since a p-adic metric gives quite natural
description of the ability to form associations.","We present comparative analysis of ORG arguments
directed against PERSON anti-Bell arguments. In general we support
PERSON viewpoint to the sequence of measurements in the ORG
experiments as stochastic time-like process. On the other hand, we support
ORG arguments against the use of time-like
correlations as the factor blocking the derivation of ORG-type inequalities.
We presented our own time-analysis of measurements in the ORG experiments
based on the frequency approach to probability. Our analysis gives strong
arguments in favour of local realism. Moreover, our frequency analysis supports
the original EPR-idea that ORG mechnaics is not complete.",1
"An important theorem in classical complexity theory is that NORP=ORG,
i.e. that languages decidable with double-logarithmic space bound are regular.
We consider a transfinite analogue of this theorem. To this end, we introduce
deterministic ordinal automata (DOAs), show that they satisfy many of the basic
statements of the theory of deterministic finite automata and regular
languages. We then consider languages decidable by an ordinal Turing machine
(ORG), introduced by PERSON in DATE and show that if the working space of an
ORG is of strictly smaller cardinality than the input length for all
sufficiently long inputs, the language so decided is also decidable by a DOA.","ORG ($ITRM$'s) are a well-established machine
model for infinitary computations. Their computational strength relative to
oracles is understood, see e.g. PERSON (DATE), PERSON and PERSON (DATE) and
PERSON and PERSON (DATE). We consider the notion of recognizability, which was
ORDINAL formulated for ORG in ORG and PERSON (CARDINAL)
and applied to $ITRM$'s in GPE (DATE). A real $x$ is
$ITRM$-recognizable iff there is an MONEYMONEY such that $P^{y}$ stops
with output CARDINAL iff $y=x$, and otherwise stops with output CARDINAL. In GPE
(DATE), it is shown that the recognizable reals are not contained in the
computable reals. Here, we investigate in detail how the $MONEY
reals are distributed along the canonical well-ordering $<_{L}$ of G\""odel's
constructible hierarchy $MONEY In particular, we prove that the recognizable
reals have gaps in $<_{PERSON, that there is MONEY in terms of
recognizability and consider a relativized notion of recognizability.",1
"The possible distinction between inanimate and living matter has been of
interest to humanity since DATE. Clearly, such a rich question
can not be answered in a single manner, and a plethora of approaches naturally
do exist. However, during DATE, a new standpoint, of
thermostatistical nature, has emerged. It is related to the proposal of
nonadditive entropies in DATE, in order to generalise the celebrated
ORG additive functional, basis of standard statistical mechanics.
Such entropies have found deep fundamental interest and uncountable
applications in natural, artificial and social systems. In some sense, this
perspective represents an epistemological paradigm shift. These entropies
crucially concern complex systems, in particular those whose microscopic
dynamics violate ergodicity. Among those, living matter and other living-like
systems play a central role. We briefly review here this approach, and present
some of its predictions, verifications and applications.","Critically growing problems of fundamental science organisation and content
are analysed with examples from physics and emerging interdisciplinary fields.
Their origin is specified and new science structure (organisation and content)
is proposed as a unified solution.",0
"We compute ORG) correction to the stability critical exponent, omega,
in the Landau-Ginzburg-Wilson model with O(N) x O(m) symmetry at the stable
chiral fixed point and the stable direction at the unstable antichiral fixed
point. Several constraints on ORG) coefficients of the CARDINAL loop
perturbative beta-functions are computed.","By considering the scaling behaviour of various ORG graphs at leading
order in large $\Nf$ at the non-trivial fixed point of the MONEY
$\beta$-function of ORG we deduce the critical exponents corresponding to the
quark, gluon and ghost anomalous dimensions as well as the anomalous dimensions
of the quark-quark-gluon and ghost-ghost-gluon vertices in the PERSON gauge. As
the exponents encode all orders information on the perturbation series of the
corresponding renormalization group functions we find agreement with the known
CARDINAL loop structure and, moreover, we provide new information at all
subsequent orders.",1
"We consider the estimation of hidden NORP process by using information
geometry with respect to transition matrices. We consider the case when we use
only the histogram of $k$-memory data. ORDINAL, we focus on a partial
observation model with NORP process and we show that the asymptotic
estimation error of this model is given as the inverse of projective ORG
information of transition matrices. Next, we apply this result to the
estimation of hidden NORP process. We carefully discuss the equivalence
problem for hidden PERSON process on the tangent space. Then, we propose a
novel method to estimate hidden NORP process.","The path integral formalism is applied to derive the full partition function
of a generalized PERSON describing a particle motion
in a bath of oscillators. The electronic correlations are computed versus
temperature for some choices of oscillators energies. We study the perturbing
effect of a time averaged particle path on the phonon subsystem deriving the
relevant temperature dependent cumulant corrections to the harmonic partition
function and free energy. The method has been applied to compute the total heat
capacity up to room temeperature: a low temperature upturn in the heat capacity
over temperature ratio points to a glassy like behavior ascribable to a time
dependent electronic hopping with variable range in the linear chain.",0
"Purpose - To test major Web search engines on their performance on
navigational queries, i.e. searches for homepages. Design/methodology/approach
- CARDINAL real user queries are posed to CARDINAL search engines (ORG, ORG, ORG,
Ask, ORG, and PERSON). Users described the desired pages, and the results
position of these is recorded. Measured success N and mean reciprocal rank are
calculated. ORG of the major search engines ORG, ORG,
and ORG is best, with PERCENT of queries answered correctly. Ask and
PERSON perform worse but receive good scores as well. Research
limitations/implications - All queries were in NORP, and the NORP-language
interfaces of the search engines were used. Therefore, the results are only
valid for NORP queries. Practical implications - When designing a search
engine to compete with the major search engines, care should be taken on the
performance on navigational queries. Users can be influenced easily in their
quality ratings of search engines based on this performance. Originality/value
- This study systematically compares the major search engines on navigational
queries and compares the findings with studies on the retrieval effectiveness
of the engines on informational queries. Paper type - research paper","We carried out a retrieval effectiveness test on the CARDINAL major web search
engines (i.e., ORG, ORG and ORG). In addition to relevance
judgments, we classified the results according to their commercial intent and
whether or not they carried any advertising. We found that all search engines
provide a large number of results with a commercial intent. ORG provides
significantly more commercial results than the other search engines do.
However, the commercial intent of a result did not influence jurors in their
relevance judgments.",1
"Over DATE, ORG has made a remarkable progress. It is agreed that
this is due to the recently revived ORG technology. PERSON
enables to process large amounts of data using simplified neuron networks that
simulate the way in which the brain works. However, there is a different point
of view, which posits that the brain is processing information, not data. This
unresolved duality hampered ORG progress for DATE. In this paper, I propose a
notion of NORP information that hopefully will resolve the problem. I
consider integrated information as a coupling between CARDINAL separate entities -
physical information (that implies data processing) and semantic information
(that provides physical information interpretation). In this regard,
intelligence becomes a product of information processing. Extending further
this line of thinking, it can be said that information processing does not
require more a human brain for its implementation. Indeed, bacteria and amoebas
exhibit intelligent behavior without any sign of a brain. That dramatically
removes the need for ORG systems to emulate the human brain complexity! The
paper tries to explore this shift in ORG systems design philosophy.","This paper describes a new model for an artificial neural network processing
unit or neuron. It is slightly different to a traditional feedforward network
by the fact that it favours a mechanism of trying to match the wave-like
'shape' of the input with the shape of the output against specific value error
corrections. The expectation is then that a best fit shape can be transposed
into the desired output values more easily. This allows for notions of
reinforcement through resonance and also the construction of synapses.",0
"This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In DATE, our large ORG recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe ORG-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its ORG-based world
model. Unlike our previous model-building ORG-based ORG machines dating back to
DATE, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially ""learning to think."" The basic ideas
of this report can be applied to many other cases where CARDINAL ORG-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in DATE, and also explain concepts such as
""mirror neurons."" Experimental results will be described in separate papers.","Self-delimiting (ORG) programs are a central concept of theoretical computer
science, particularly algorithmic information & probability theory, and
asymptotically optimal program search (AOPS). To apply AOPS to (possibly
recurrent) neural networks (NNs), I introduce ORG NNs. Neurons of a typical
ORG have threshold activation functions. During a computational episode,
activations are spreading from input neurons through ORG until the
computation activates a special halt neuron. Weights of the NN's used
connections define its program. Halting programs form a prefix code. The reset
of the initial NN state does not cost more than the latest program execution.
Since prefixes of ORG programs influence their suffixes (weight changes
occurring early in an episode influence which weights are considered later),
ORG learning algorithms (LAs) should execute weight changes online during
activation spreading. This can be achieved by applying AOPS to growing ORG
NNs. To efficiently teach a ORG to solve many tasks, such as correctly
classifying many different patterns, or solving many different robot control
tasks, each connection keeps a list of tasks it is used for. The lists may be
efficiently updated during training. To evaluate the overall effect of
currently tested weight changes, a ORG GPE needs to re-test performance only
on the efficiently computable union of tasks potentially affected by the
current weight changes. Future SLIM NNs will be implemented on CARDINAL-dimensional
brain-like multi-processor hardware. Their LAs will minimize task-specific
total wire length of used connections, to encourage efficient solutions of
subtasks by subsets of neurons that are physically close. The novel class of
ORG LAs is currently being probed in ongoing experiments to be reported in
separate papers.",1
"An upper limit is given to the amount of ORG information that can be
transmitted reliably down a noisy, decoherent ORG channel. A class of
quantum error-correcting codes is presented that allow the information
transmitted to attain this limit. The result is the quantum analog of FAC's
bound and code for the noisy classical channel.","This paper investigates a variety of unconventional quantum computation
devices, including fermionic quantum computers and computers that exploit
nonlinear ORG mechanics. It is shown that unconventional ORG computing
devices can in principle compute some quantities more rapidly than
`conventional' quantum computers.",1
"The possibility of measuring the NORP gravitoelectric correction to
the orbital period of a test particle freely orbiting a spherically symmetric
mass in ORG is analyzed. It should be possible, in principle, to
detect it for ORG at a precision level of CARDINAL^-4. This level is mainly set
by the unavoidable systematic errors due to the mismodelling in the NORP
period which could not be reduced by accumulating a large number of orbital
revolutions. Future missions like PERSON and ORG should allow to
improve it by increasing our knowledge of the ORG's orbital parameters. The
observational accuracy is estimated to be CARDINAL^-4 from the knowledge of ORG (ICRF) axes. It could be improved by
observing as many planetary transits as possible. It is not possible to measure
such an effect in the gravitational field of the LOC by analyzing the motion
of artificial satellites or the PERSON because of the unavoidable systematic
errors related to the uncertainties in the NORP periods. In the case of
some recently discovered exoplanets the problems come from the observational
errors which are larger than the relativistic effect.","In this paper we calculate explicitly the secular classical precessions of
the node \Omega and the perigee \omega of an LOC artificial satellite induced
by the static, even zonal harmonics of the geopotential up to PERSON.
ORG, their systematic errors induced by the mismodelling in the even
zonal geopotential coefficients J_l are compared to the general relativistic
secular gravitomagnetic and gravitoelectric precessions of the node and the
perigee of the existing laser-ranged geodetic satellites and of the proposed
PRODUCT.",1
"The history of data analysis that is addressed here is underpinned by CARDINAL
themes, -- those of tabular data analysis, and the analysis of collected
heterogeneous data. ""Exploratory data analysis"" is taken as the heuristic
approach that begins with data and information and seeks underlying explanation
for what is observed or measured. I also cover some of the evolving context of
research and applications, including scholarly publishing, technology transfer
and the economic relationship of the university to society.","The new interface of ORG (of ORG) enables users to
retrieve sets CARDINAL documents in a single search. This makes it
possible to compare publication trends for GPE, the GPE, PRODUCT, and a number
of smaller countries. GPE no longer grew exponentially during DATE, but
linearly. Contrary to previous predictions on the basis of exponential growth
or Scopus data, the cross-over of the lines for GPE and the GPE is postponed
to DATE (after DATE) according to this data. These long
extrapolations, however, should be used only as indicators and not as
predictions. Along with the dynamics in the publication trends, one also has to
take into account the dynamics of the databases used for the measurement.",0
"Results about the redundancy of circumscriptive and default theories are
presented. In particular, the complexity of establishing whether a given theory
is redundant is establihsed.","These are some informal notes concerning topological vector spaces, with a
brief overview of background material and basic notions, and emphasis on
examples related to classical analysis.",0
"Boltzmann introduced in the DATE's a logarithmic measure for the connection
between the thermodynamical entropy and the probabilities of the microscopic
configurations of the system. His entropic functional for classical systems was
extended by GPE to the entire phase space of a many-body system, and by PERSON in order to cover ORG systems as well. Finally, it was used by
FAC within the theory of information. The simplest expression of this
functional corresponds to a discrete set of $MONEY microscopic possibilities, and
is given by $S_{BG}= PERSON p_i \ln p_i$ ($PERSON is a positive universal
constant; {ORG BG} stands for ORG}). This relation enables the
construction of GPE statistical mechanics. The GPE theory has provided
uncountable important applications. Its application in physical systems is
legitimate whenever the hypothesis of {ORG ergodicity} is satisfied. However,
{\it what can we do when ergodicity and similar simple hypotheses are
violated?}, which indeed happens in very many natural, artificial and social
complex systems. It was advanced in DATE the possibility of generalizing GPE
statistical mechanics through a family of nonadditive entropies, namely
$S_q=PERSON, which recovers the additive $PERSON
entropy in the $q \to1$ limit. The index $PERSON is to be determined from
mechanical ORDINAL principles. Along DATE, this idea intensively evolved
world-wide (see Bibliography in \url{http://tsallis.cat.cbpf.br/biblio.htm}),
and led to a plethora of predictions, verifications, and applications in
physical systems and elsewhere. As expected whenever a {ORG paradigm shift} is
explored, some controversy naturally emerged as well in the community. The
present status of the general picture is here described, starting from its
dynamical and thermodynamical foundations, and ending with its most recent
physical applications.","The black hole information paradox is CARDINAL of the most important issues in
theoretical physics. We review some recent progress using string theory in
understanding the nature of black hole microstates. For all cases where these
microstates have been constructed, one finds that they are horizon sized
`fuzzballs'. Most computations are for extremal states, but recently one has
been able to study a special family of non-extremal microstates, and see
`information carrying radiation' emerge from these gravity solutions. We
discuss how the fuzzball picture can resolve the information paradox. We use
the nature of fuzzball states to make some conjectures on the dynamical aspects
of black holes, observing that the large phase space of fuzzball solutions can
make the black hole more `quantum' than assumed in traditional treatments.",0
"Steganography is an information hiding technique in which secret data are
secured by covering them into a computer carrier file without damaging the file
or changing its size. The difference between steganography and cryptography is
that steganography is a stealthy method of communication that only the
communicating parties are aware of; while, cryptography is an overt method of
communication that anyone is aware of, despite its payload is scribbled.
Typically, an irrecoverable steganography algorithm is the algorithm that makes
it hard for malicious ORDINAL parties to discover how it works and how to recover
the secret data out of the carrier file. CARDINAL popular way to achieve
irrecoverability is to digitally process the carrier file after hiding the
secret data into it. However, such process is irreversible as it would destroy
the concealed data. This paper proposes a new image steganography method for
textual data, as well as for any form of digital data, based on adjusting the
brightness of the carrier image after covering the secret data into it. The
algorithm used is parameterized as it can be configured using CARDINAL different
parameters defined by the communicating parties. They include the amount of
brightness to apply on the carrier image after the completion of the covering
process, the color channels whose brightness should be adjusted, and the bytes
that should carry in the secret data. The novelty of the proposed method is
that it embeds bits of the secret data into the CARDINAL LSBs of the bytes that
compose the carrier image in such a way that does not destroy the secret data
when restoring back the original brightness of the carrier image. The
simulation conducted proved that the proposed algorithm is valid and correct.","Permutation is the different arrangements that can be made with a given
number of things taking some or all of them at a time. The notation P(n,r) is
used to denote the number of permutations of n things taken r at a time.
Permutation is used in various fields such as mathematics, group theory,
statistics, and computing, to solve several combinatorial problems such as the
job assignment problem and the traveling salesman problem. In effect,
permutation algorithms have been studied and experimented for DATE now.
Bottom-Up, PERSON, and PERSON are CARDINAL of the most popular
permutation algorithms that emerged during DATE. In this paper, we
are implementing CARDINAL of the most eminent permutation algorithms, they are
respectively: Bottom-Up, PERSON, and PERSON algorithms. The
implementation of each algorithm will be carried out using CARDINAL different
approaches: brute-force and divide and conquer. The algorithms codes will be
tested using a computer simulation tool to measure and evaluate the execution
time between the different implementations.",1
"We survey the prospects for an ORG which can serve as the
basis for a fundamental theory of information, incorporating qualitative and
structural as well as quantitative aspects. We motivate our discussion with
some basic conceptual puzzles: how can information increase in computation, and
what is it that we are actually computing in general? Then we survey a number
of the theories which have been developed within ORG, as partial
exemplifications of the kind of fundamental theory which we seek: including
WORK_OF_ART, and PERSON. We look at recent work
showing new ways of combining quantitative and qualitative theories of
information, as embodied respectively by ORG and ORG. Then we look at ORG and ORG, as examples
of dynamic models of logic and computation in which information flow and
interaction are made central and explicit. We conclude by looking briefly at
some key issues for future progress.","We look at intensionality from the perspective of computation. In particular,
we review how game semantics has been used to characterize the sequential
functional processes, leading to powerful and flexible methods for constructing
fully abstract models of programming languages, with applications in program
analysis and verification. In a broader context, we can regard game semantics
as a ORDINAL step towards developing a positive theory of intensional structures
with a robust mathematical structure, and finding the right notions of
invariance for these structures.",1
"The internal structure of a measuring device, which depends on what its
components are and how they are organized, determines how it categorizes its
inputs. This paper presents a geometric approach to studying the internal
structure of measurements performed by distributed systems such as
probabilistic cellular automata. It constructs the quale, a family of sections
of a suitably defined presheaf, whose elements correspond to the measurements
performed by all subsystems of a distributed system. Using the quale we
quantify (i) the information generated by a measurement; (ii) the extent to
which a measurement is context-dependent; and (iii) whether a measurement is
decomposable into independent submeasurements, which turns out to be equivalent
to context-dependence. Finally, we show that only indecomposable measurements
are more informative than the sum of their submeasurements.","The paper demonstrates that falsifiability is fundamental to learning. We
prove the following theorem for statistical learning and sequential prediction:
If a theory is falsifiable then it is learnable -- i.e. admits a strategy that
predicts optimally. An analogous result is shown for universal induction.",1
"An inverse source problem for the heat equation is considered. Extraction
formulae for information about the time and location when and where the unknown
source of the equation ORDINAL appeared are given from a single lateral
boundary measurement. New roles of the plane progressive wave solutions or
their complex versions for the backward heat equation are given.","In this paper a wave is generated by an initial data whose support is
localized at the outside of unknown obstacles and observed in a limited time on
a known closed surface or the same position as the support of the initial data.
The observed data in the latter process are nothing but the back-scattering
data. CARDINAL types of obstacles are considered. One is obstacles with a
dissipative boundary condition which is a generalization of the sound-hard
obstacles; another is obstacles with a finite refractive index, so-called,
transparent obstacles. For each type of obstacles CARDINAL formulae which yield
explicitly the distance from the support of the initial data to unknown
obstacles are given.",1
"""Information Processing"" is a recently launched buzzword whose meaning is
vague and obscure even for the majority of its users. The reason for this is
the lack of a suitable definition for the term ""information"". In my attempt to
amend this bizarre situation, I have realized that, following the insights of
ORG theory, information can be defined as a description of
structures observable in a given data set. CARDINAL types of structures could be
easily distinguished in every data set - in this regard, CARDINAL types of
information (information descriptions) should be designated: physical
information and semantic information. ORG's theory also posits that the
information descriptions should be provided as a linguistic text structure.
This inevitably leads us to an assertion that information processing has to be
seen as a kind of text processing. The idea is not new - inspired by the
observation that human information processing is deeply rooted in natural
language handling customs, PERSON and his followers have introduced the
so-called ""WORK_OF_ART"" paradigm. Despite of promotional efforts, the
idea is not taking off yet. The reason - a lack of a coherent understanding of
what should be called ""information"", and, as a result, misleading research
roadmaps and objectives. I hope my humble attempt to clarify these issues would
be helpful in avoiding common traps and pitfalls.","Over DATE, ORG has made a remarkable progress due to recently
revived PERSON technology. ORG enables to process large
amounts of data using simplified neuron networks that simulate the way in which
the brain works. At the same time, there is another point of view that posits
that brain is processing information, not data. This duality hampered ORG
progress for DATE. To provide a remedy for this situation, I propose a new
definition of information that considers it as a coupling between CARDINAL separate
entities - physical information (that implies data processing) and semantic
information (that provides physical information interpretation). In such a
case, intelligence arises as a result of information processing. The paper
points on the consequences of this turn for the ORG design philosophy.",1
"As far as algorithmic thinking is bound by symbolic paper-and-pencil
operations, the Church-Turing thesis appears to hold. But is physics, and even
more so, is the human mind, bound by symbolic paper-and-pencil operations? What
about the powers of the continuum, the quantum, and what about human intuition,
human thought? These questions still remain unanswered. With the strong
ORG assumption, human consciousness is just a function of
the organs (maybe in a very wide sense and not only restricted to neuronal
brain activity), and thus the question is relegated to physics. In dualistic
models of the mind, human thought transcends symbolic paper-and-pencil
operations.","ORG bootstrap network builds a gradually narrowed multilayer nonlinear
network from bottom up for unsupervised nonlinear dimensionality reduction.
Each layer of the network is a nonparametric density estimator. It consists of
a group of k-centroids clusterings. Each clustering randomly selects data
points with randomly selected features as its centroids, and learns a CARDINAL-hot
encoder by CARDINAL-nearest-neighbor optimization. Geometrically, the nonparametric
density estimator at each layer projects the input data space to a
uniformly-distributed discrete feature space, where the similarity of CARDINAL data
points in the discrete feature space is measured by the number of the nearest
centroids they share in common. The multilayer network gradually reduces the
nonlinear variations of data from bottom up by building a vast number of
hierarchical trees implicitly on the original data space. Theoretically, the
estimation error caused by the nonparametric density estimator is proportional
to the correlation between the clusterings, both of which are reduced by the
randomization steps.",0
"Since no fusion theory neither rule fully satisfy all needed applications,
the author proposes ORG and a combination of
fusion rules in solving problems/applications. For each particular application,
CARDINAL selects the most appropriate model, rule(s), and algorithm of
implementation. We are working in the unification of the fusion theories and
rules, which looks like a cooking recipe, better we'd say like a logical chart
for a computer programmer, but we don't see another method to comprise/unify
all things. The unification scenario presented herein, which is now in an
incipient form, should periodically be updated incorporating new discoveries
from the fusion and engineering research.","The present work includes some of the author's original researches on integer
solutions of GPE liner equations and systems. The notion of ""general
integer solution"" of a GPE linear equation with CARDINAL unknowns is
extended to GPE linear equations with $n$ unknowns and then to
GPE linear systems. The proprieties of the general integer solution are
determined (both for a GPE linear equation and for a GPE linear
system). CARDINAL original integer algorithms (CARDINAL for GPE linear
equations, and CARDINAL for GPE linear systems) are exposed. The algorithms
are strictly proved and an example for each of them is given. These algorithms
can be easily implemented on the computer.",1
"Can ORG-complete problems be solved efficiently in the physical universe? I
survey proposals including soap bubbles, protein folding, ORG computing,
quantum advice, quantum adiabatic algorithms, quantum-mechanical
nonlinearities, hidden variables, relativistic time dilation, analog computing,
Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and
""anthropic computing."" The section on soap bubbles even includes some
""experimental"" results. While I do not believe that any of the proposals will
let us solve ORG-complete problems efficiently, I argue that by studying them,
we can learn something not only about computation but also about physics.","We show that any quantum algorithm to decide whether a function f:[n]->[n] is
a permutation or far from a permutation must make GPE) queries to
f, even if the algorithm is given a w-qubit quantum witness in support of f
being a permutation. This implies that there exists an oracle A such that ORG
is not contained in GPE, answering an DATE open question of the
author. Indeed, we show that relative to some oracle, ORG is not in the
counting class A0PP defined by PERSON. The proof is a fairly simple extension
of the quantum lower bound for the collision problem.",1
"CARDINAL of the most important aims of the fields of robotics, artificial
intelligence and artificial life is the design and construction of systems and
machines as versatile and as reliable as living organisms at performing high
level human-like tasks. But how are we to evaluate artificial systems if we are
not certain how to measure these capacities in living systems, let alone how to
define life or intelligence? Here I survey a concrete metric towards measuring
abstract properties of natural and artificial systems, such as the ability to
react to the environment and to control one's own behaviour.","I will survey some matters of relevance to a philosophical discussion of
information, taking into account developments in algorithmic information theory
(ORG). I will propose that meaning is deep in the sense of PERSON's logical
depth, and that algorithmic probability may provide the stability needed for a
robust algorithmic definition of meaning, one that takes into consideration the
interpretation and the recipient's own knowledge encoded in the story attached
to a message.",1
"PERSON of the Internet in the early 90's increased dramatically the
number of images being distributed and shared over the web. As a result, image
information retrieval systems were developed to index and retrieve image files
spread over the Internet. Most of these systems are keyword-based which search
for images based on their textual metadata; and thus, they are imprecise as it
is vague to describe an image with a human language. Besides, there exist the
content-based image retrieval systems which search for images based on their
visual information. However, content-based type systems are still immature and
not that effective as they suffer from low retrieval recall/precision rate.
This paper proposes a new hybrid image information retrieval model for indexing
and retrieving web images published in HTML documents. The distinguishing mark
of the proposed model is that it is based on both graphical content and textual
metadata. The graphical content is denoted by color features and color
histogram of the image; while PERSON are denoted by the terms that
surround the image in the HTML document, more particularly, the terms that
appear in the tags p, h1, and h2, in addition to the terms that appear in the
image's alt attribute, filename, and class-label. Moreover, this paper presents
a new term weighting scheme called VTF-IDF short for WORK_OF_ART which unlike traditional schemes, it
exploits the ORG tag structure and assigns an extra bonus weight for terms
that appear within certain particular HTML tags that are correlated to the
semantics of the image. Experiments conducted to evaluate the proposed ORG model
showed a high retrieval precision rate that outpaced other current models.","This paper describes a process for clustering concepts into chains from data
presented randomly to an evaluating system. There are a number of rules or
guidelines that help the system to determine more accurately what concepts
belong to a particular chain and what ones do not, but it should be possible to
write these in a generic way. This mechanism also uses a flat structure without
any hierarchical path information, where the link between CARDINAL concepts is made
at the level of the concept itself. It does not require related metadata, but
instead, a simple counting mechanism is used. Key to this is a count for both
the concept itself and also the group or chain that it belongs to. To test the
possible success of the mechanism, concept chain parts taken randomly from a
larger ontology were presented to the system, but only at a depth of CARDINAL concepts
each time. That is - root concept plus a concept that it is linked to. The
results show that this can still lead to very variable structures being formed
and can also accommodate some level of randomness.",0
"Many researches proposed the use of the TIME state as the input state for
phase estimation, which is CARDINAL topic of quantum metrology. This is because the
input TIME state provides the maximum ORG information at the specific point.
However, the ORG information does not necessarily give the attainable bound
for estimation error. In this paper, we adopt the local asymptotic mini-PERSON
criterion as well as the mini-max criterion, and show that the maximum ORG
information does not give the attainable bound for estimation error under these
criteria in the phase estimation. We also propose the optimal input state under
the constraints for photon number of the input state instead of the TIME state.","In the setting of a complete metric space that is equipped with a doubling
measure and supports a Poincar\'e inequality, we prove the fine ORG
property, the quasi-Lindel\""of principle, and the ORG property for the fine
topology in the case $PERSON",0
"PERSON-cognitive action reproduces and changes both social and cognitive
structures. The analytical distinction between these dimensions of structure
provides us with richer models of scientific development. In this study, I
assume that (i) social structures organize expectations into belief structures
that can be attributed to individuals and communities; (ii) expectations are
specified in scholarly literature; and (iii) intellectually the sciences
(disciplines, specialties) tend to self-organize as systems of rationalized
expectations. Whereas social organizations remain localized, academic writings
can circulate, and expectations can be stabilized and globalized using
symbolically generalized codes of communication. The intellectual
restructuring, however, remains latent as a ORDINAL-order dynamics that can be
accessed by participants only reflexively. Yet, the emerging ""horizons of
meaning"" provide feedback to the historically developing organizations by
constraining the possible future states as boundary conditions. I propose to
model these possible future states using incursive and hyper-incursive
equations from the computation of anticipatory systems. Simulations of these
equations enable us to visualize the couplings among the historical--i.e.,
recursive--progression of social structures along trajectories, the
evolutionary--i.e., hyper-incursive--development of systems of expectations at
the regime level, and the incursive instantiations of expectations in actions,
organizations, and texts.","The tension between qualitative theorizing and quantitative methods is
pervasive in the social sciences, and poses a constant challenge to empirical
research. But in science studies as an interdisciplinary specialty, there are
additional reasons why a more reflexive consciousness of the differences among
the relevant disciplines is necessary. How can qualitative insights from the
history of ideas and the sociology of science be combined with the quantitative
perspective? By using the example of the lexical and semantic value of word
occurrences, the issue of qualitatively different meanings of the same
phenomena is discussed as a methodological problem. CARDINAL criteria for methods
which are needed for the development of science studies as an integrated
enterprise can then be specified. Information calculus is suggested as a method
which can comply with these criteria.",1
"We show that a highly-mixed state in terms of a large min-entropy is useless
as a resource state for measurement-based ORG computation in the sense that
if a classically efficiently verifiable problem is efficiently solved with such
a highly-mixed measurement-based quantum computation then such a problem can
also be classically efficiently solved. We derive a similar result also for the
DQC1$_k$ model, which is a generalized version of the DQC1 model where $k$
output qubits are measured. We also show that the measurement-based ORG
computing on a highly-mixed resource state in terms of the von ORG entropy,
and PERSON model are useless in another sense that the mutual information
between the computation results and inputs is very small.","The string-net condensate is a new class of materials which exhibits the
quantum topological order. In order to answer the important question, ""how
useful is the string-net condensate in quantum information processing?"", we
consider the most basic example of the string-net condensate, namely the MONEY
gauge string-net condensate on the CARDINAL-dimensional hexagonal lattice, and show
that the universal measurement-based quantum computation (in the sense of the
quantum computational webs) is possible on it by using the framework of the
quantum computational tensor network. This result implies that even the most
basic example of the string-net condensate is equipped with the correlation
space that has the capacity for the universal quantum computation.",1
"We develop a classification method for incoming pieces of evidence in
NORP theory. This methodology is based on previous work with
clustering and specification of originally nonspecific evidence. This
methodology is here put in order for fast classification of future incoming
pieces of evidence by comparing them with prototypes representing the clusters,
instead of making a full clustering of all evidence. This method has a
computational complexity of O(M * N) for each new piece of evidence, where M is
the maximum number of subsets and ORG is the number of prototypes chosen for each
subset. That is, a computational complexity independent of the total number of
previously arrived pieces of evidence. The parameters M and ORG are typically
fixed and domain dependent in any application.","With the emergence of the ORG gradient flow technique there is renewed
interest in the issue of scale setting in lattice gauge theory. Here I compare
for the SU(3) Wilson gauge action non-perturbative scale functions of ORG,
PERSON and PERSON (ORG), ORG and PERSON (NS), both relying on PERSON's
method using the quark potential, and the scale function derived by PERSON,
PERSON and PERSON (ORG) from a deconfining phase transition investigation by
the PERSON group. It turns out that the scale functions are based on
mutually inconsistent data, though the ORG scale function is consistent with
the ORG data when their low $MONEY (MONEY) data point is removed.
Besides, only the ORG scale function is consistent with CARDINAL data points
calculated from the gradient flow by L\""uscher. In the range for which data
exist the discrepancies between the scale functions are only up to $\pm CARDINAL of
their values, but clearly visible within the statistical accuracy.",0
"In this abstract paper, we introduce a new kernel learning method by a
nonparametric density estimator. The estimator consists of a group of
k-centroids clusterings. Each clustering randomly selects data points with
randomly selected features as its centroids, and learns a CARDINAL-hot encoder by
CARDINAL-nearest-neighbor optimization. The estimator generates a sparse
representation for each data point. Then, we construct a nonlinear kernel
matrix from the sparse representation of data. CARDINAL major advantage of the
proposed kernel method is that it is relatively insensitive to its free
parameters, and therefore, it can produce reasonable results without parameter
tuning. Another advantage is that it is simple. We conjecture that the proposed
method can find its applications in many learning tasks or methods where sparse
representation or kernel matrix is explored. In this preliminary study, we have
applied the kernel matrix to spectral clustering. Our experimental results
demonstrate that the kernel generated by the proposed method outperforms the
well-tuned NORP RBF kernel. This abstract paper is used to protect the
idea, full versions will be updated later.","We consider a simplified version of a solvable model by Mandal and PERSON,
which constructively demonstrates the interplay between work extraction and the
increase of the FAC entropy of an information reservoir which is in contact
with the physical system. We extend ORG and PERSON's main findings in
several directions: ORDINAL, we allow sequences of correlated bits rather than
just independent bits. ORDINAL, at least for the case of binary information,
we show that, in fact, the FAC entropy is CARDINAL measure of complexity of
the information that must increase in order for work to be extracted. The
extracted work can also be upper bounded in terms of the increase in other
quantities that measure complexity, like the predictability of future bits from
past ones. ORDINAL, we provide an extension to the case of non-binary information
(i.e., a larger alphabet), and finally, we extend the scope to the case where
the incoming bits (before the interaction) form an individual sequence, rather
than a random one. In this case, the entropy before the interaction can be
replaced by ORG (LZ) complexity of the incoming sequence, a fact
that gives rise to an entropic meaning of the LZ complexity, not only in
information theory, but also in physics.",0
"The technological singularity refers to a hypothetical scenario in which
technological advances virtually explode. The most popular scenario is the
creation of super-intelligent algorithms that recursively create ever higher
intelligences. It took DATE for these ideas to spread from science
fiction to popular science magazines and finally to attract the attention of
serious philosophers. PERSON (PERSON 2010) article is the ORDINAL
comprehensive philosophical analysis of the singularity in a respected
philosophy journal. The motivation of my article is to augment PERSON' and to
discuss some issues not addressed by him, in particular what it could mean for
intelligence to explode. In this course, I will (have to) provide a more
careful treatment of what intelligence actually is, separate speed from
intelligence explosion, compare what super-intelligent participants and
classical human observers might experience and do, discuss immediate
implications for the diversity and value of life, consider possible bounds on
intelligence, and contemplate intelligences right at the singularity.","Purpose - To test the ability of major search engines, ORG, ORG, ORG,
and Ask, to distinguish between NORP and LANGUAGE-language documents
  Design/methodology/approach - 50 queries, using words common in NORP and in
LANGUAGE, were posed to the engines. The advanced search option of language
restriction was used, once in NORP and once in LANGUAGE. The ORDINAL CARDINAL results
per engine in each language were investigated.
  Findings - While none of the search engines faces problems in providing
results in the language of the interface that is used, both ORG and ORG face
problems when the results are restricted to a foreign language.
  Research limitations/implications - Search engines were only tested in NORP
and in LANGUAGE. We have only anecdotal evidence that the problems are the same
with other languages.
  Practical implications - Searchers should not use the language restriction in
ORG and ORG when searching for foreign-language documents. Instead,
searchers should use ORG or Ask. If searching for foreign language documents
in ORG or ORG, the interface in the target language/country should be used.
  Value of paper - Demonstrates a problem with search engines that has not been
previously investigated.",0
"There are (at least) CARDINAL approaches to quantifying information. The ORDINAL,
algorithmic information or NORP complexity, takes events as strings and,
given a universal Turing machine, quantifies the information content of a
string as the length of the shortest program producing it. The ORDINAL, FAC
information, takes events as belonging to ensembles and quantifies the
information resulting from observing the given event in terms of the number of
alternate events that have been ruled out. The ORDINAL, statistical learning
theory, has introduced measures of capacity that control (in part) the expected
risk of classifiers. These capacities quantify the expectations regarding
future data that learning algorithms embed into classifiers.
  This note describes a new method of quantifying information, effective
information, that links algorithmic information to FAC information, and
also links both to capacities arising in statistical learning theory. After
introducing the measure, we show that it provides a non-universal analog of
NORP complexity. We then apply it to derive basic capacities in
statistical learning theory: empirical PERSON-entropy and empirical Rademacher
complexity. A nice byproduct of our approach is an interpretation of the
explanatory power of a learning algorithm in terms of the number of hypotheses
it falsifies, counted in CARDINAL different ways for the CARDINAL capacities. We also
discuss how effective information relates to information gain, FAC and
mutual information.","In the setting of a metric space equipped with a doubling measure and
supporting a Poincar\'e inequality, and based on results by Bj\""orn and
Shanmugalingam (DATE), we show that functions of bounded variation can be
extended from any bounded uniform domain to the whole space. Closely related to
extensions is the concept of boundary traces, which have previously been
studied by PERSON (DATE). On spaces that satisfy a suitable
locality condition for sets of finite perimeter, we establish some basic
results for the traces of functions of bounded variation. Our analysis of
traces also produces novel results on the behavior of functions of bounded
variation in their jump sets.",0
"The notion of profile appeared in DATE, which was mainly due to
the need to create custom applications that could be adapted to the user. In
this paper, we treat the different aspects of the user's profile, defining it,
profile, its features and its indicators of interest, and then we describe the
different approaches of modelling and acquiring the user's interests.","We present PERSON LINUCB, an algorithm for con-textual
multi-armed bandits. This algorithm uses ORG to find the
optimal exploration of the ORG. Within a deliberately designed offline
simulation framework we conduct evaluations with real online event log data.
The experimental results demonstrate that our algorithm outperforms surveyed
algorithms.",1
"Physical entities are ultimately (re)constructed from elementary yes/no
events, in particular clicks in detectors or measurement devices recording
quanta. Recently, the interpretation of certain such clicks has given rise to
unfounded claims which are neither necessary nor sufficient, although they are
presented in that way. In particular, clicks can neither inductively support
nor ""(WORK_OF_ART"" the Kochen-Specker theorem, which is a formal result that has
a deductive proof by contradiction. More importantly, the alleged empirical
evidence of quantum contextuality, which is ""inferred"" from violations of
bounds of classical probabilities by quantum correlations, is based on highly
nontrivial assumptions, in particular on physical omniscience.","Suspicions that the world might be some sort of a machine or algorithm
existing ``in the mind'' of some symbolic number cruncher have lingered from
antiquity. Although popular at times, the most radical forms of this idea never
reached mainstream. Modern developments in physics and computer science have
lent support to the thesis, but empirical evidence is needed before it can
begin to replace our contemporary world view.",1
"This paper proposes a theory of creativity, referred to as honing theory,
which posits that creativity fuels the process by which culture evolves through
communal exchange amongst minds that are self-organizing, self-maintaining, and
self-reproducing. According to honing theory, minds, like other selforganizing
systems, modify their contents and adapt to their environments to minimize
entropy. Creativity begins with detection of high psychological entropy
material, which provokes uncertainty and is arousalinducing. The creative
process involves recursively considering this material from new contexts until
it is sufficiently restructured that arousal dissipates. Restructuring involves
neural synchrony and dynamic binding, and may be facilitated by temporarily
shifting to a more associative mode of thought. A creative work may similarly
induce restructuring in others, and thereby contribute to the cultural
evolution of more nuanced worldviews. Since lines of cultural descent
connecting creative outputs may exhibit little continuity, it is proposed that
cultural evolution occurs at the level of self-organizing minds, outputs
reflect their evolutionary state. Honing theory addresses challenges not
addressed by other theories of creativity, such as the factors that guide
restructuring, and in what sense creative works evolve. Evidence comes from
empirical studies, an agent-based computational model of cultural evolution,
and a model of concept combination.","An idea is not a replicator because it does not consist of coded
self-assembly instructions. It may retain structure as it passes from CARDINAL
individual to another, but does not replicate it. The cultural replicator is
not an idea but an associatively-structured network of them that together form
an internal model of the world, or worldview. A worldview is a primitive,
uncoded replicator, like the autocatalytic sets of polymers widely believed to
be the earliest form of life. Primitive replicators generate self-similar
structure, but because the process happens in a piecemeal manner, through
bottom-up interactions rather than a top-down code, they replicate with low
fidelity, and acquired characteristics are inherited. Just as polymers catalyze
reactions that generate other polymers, the retrieval of an item from memory
can in turn trigger other items, thus cross-linking memories, ideas, and
concepts into an integrated conceptual structure. Worldviews evolve idea by
idea, largely through social exchange. An idea participates in the evolution of
culture by revealing certain aspects of the worldview that generated it,
thereby affecting the worldviews of those exposed to it. If an idea influences
seemingly unrelated fields this does not mean that separate cultural lineages
are contaminating one another, because it is worldviews, not ideas, that are
the basic unit of cultural evolution.",1
"Some PERSON centenary reflections on whether incompleteness is really
serious, and whether mathematics should be done somewhat differently, based on
using algorithmic complexity measured in bits of information. [Enriques lecture
given DATE, at ORG.]","This is an alternative version of the course notes in PERSON. The
previous version is based on measuring the size of lisp s-expressions. This
version is based on measuring the size of what I call lisp m-expressions, which
are lisp s-expressions with most parentheses omitted. This formulation of
algorithmic information theory is harder to understand than the one that was
presented in PERSON, but the constants obtained in all theorems are
now CARDINAL the size that they were before. It is not clear to me which
version of algorithmic information theory is to be preferred.",1
"We allow representing and reasoning in the presence of nested multiple
aggregates over multiple variables and nested multiple aggregates over
functions involving multiple variables in answer sets, precisely, in answer set
optimization programming and in answer set programming. We show the
applicability of the answer set optimization programming with nested multiple
aggregates and the answer set programming with nested multiple aggregates to
ORG, a fundamental a priori
optimization problem in ORG.","PERSON is important issue in reinforcement learning. In
this paper, we bridge the gap between reinforcement learning and knowledge
representation, by providing a rich knowledge representation framework, based
on normal logic programs with answer set semantics, that is capable of solving
model-free reinforcement learning problems for more complex do-mains and
exploits the domain-specific knowledge. We prove the correctness of our
approach. We show that the complexity of finding an offline and online policy
for a model-free reinforcement learning problem in our approach is ORG-complete.
Moreover, we show that any model-free reinforcement learning problem in ORG
environment can be encoded as a ORG problem. The importance of that is
model-free reinforcement",1
"In this article, we calculate the contributions of the vacuum condensates up
to dimension-6 including the $\mathcal{O}(\alpha_s)$ corrections to the quark
condensates in the operator product expansion, then study the masses and decay
constants of the pseudoscalar, scalar, vector and axial-vector heavy-light
mesons with the ORG sum rules in a systematic way. The masses of the observed
mesons $(D,ORG, $(D_s,D_s^*)$, $(D_0^*(2400),D_1(2430))$,
$(D_{s0}^*(2317),D_{s1}(2460))$, $(B,ORG, $(B_s,GPE can be well
reproduced, while the predictions for the masses of the $(PERSON}, PERSON and
$(B^*_{s0}, B_{s1})$ can be confronted with the experimental data in the
future. We obtain the decay constants of the pseudoscalar, scalar, vector and
axial-vector heavy-light mesons, which have many phenomenological applications
in studying the semi-leptonic and ORG decays of the heavy-light mesons.","In this article, we calculate the masses and residues of the heavy baryons
MONEY with spin-parity ${MONEY with the ORG
sum rules. The numerical values are compatible with experimental data and other
theoretical estimations.",1