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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 |
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Dataset Card for Dataset Name
Dataset for authorship verification, comprised of 12 cleaned, modified, open source authorship verification and attribution datasets.
Dataset Details
Code for cleaning and modifying datasets can be found in https://github.com/swan-07/authorship-verification/blob/main/Authorship_Verification_Datasets.ipynb and is detailed in paper.
Datasets used to produce the final dataset are:
- Reuters50
@misc{misc_reuter_50_50_217, author = {Liu,Zhi}, title = {{Reuter_50_50}}, year = {2011}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5DS42} }
License: (CC BY 4.0)
- The Blog Authorship Corpus
@misc{misc_blog_authorship_corpus, author = {J. Schler, M. Koppel, S. Argamon and J. Pennebaker}, title = {{Effects of Age and Gender on Blogging}}, year = {2006}, howpublished = {2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs}, note = {https://u.cs.biu.ac.il/~schlerj/schler_springsymp06.pdf} }
License from https://www.kaggle.com/datasets/rtatman/blog-authorship-corpus: The corpus may be freely used for non-commercial research purposes.
- Victorian
@misc{misc_victorian_era_authorship_attribution_454, author = {Gungor,Abdulmecit}, title = {{Victorian Era Authorship Attribution}}, year = {2018}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5SW4H} }
License: (CC BY 4.0)
- arXiv
@misc{misc_arXiv_100authors_comp_sci, author = {Moreo, Alejandro}, title = {{arXiv abstracts and titles from 1,469 single-authored papers (100 unique authors) in computer science }}, year = {2022}, howpublished = {Zenodo}, note = {{DOI}: https://doi.org/10.5281/zenodo.7404702} }
License: (CC BY 4.0)
- DarkReddit
@article{DBLP:journals/corr/abs-2112-05125, author = {Andrei Manolache and Florin Brad and Elena Burceanu and Antonio Barbalau and Radu Tudor Ionescu and Marius Popescu}, title = {Transferring BERT-like Transformers' Knowledge for Authorship Verification}, journal = {CoRR}, volume = {abs/2112.05125}, year = {2021}, url = {https://arxiv.org/abs/2112.05125}, eprinttype = {arXiv}, eprint = {2112.05125}, timestamp = {Mon, 13 Dec 2021 17:51:48 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-05125.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
@inproceedings{Kestemont2020OverviewOT, author = {Mike Kestemont and Enrique Manjavacas and Ilia Markov and Janek Bevendorff and Matti Wiegmann and Efstathios Stamatatos and Martin Potthast and Benno Stein}, editor = {Linda Cappellato and Carsten Eickhoff and Nicola Ferro and Aur{'{e}}lie N{'{e}}v{'{e}}ol}, title = {Overview of the Cross-Domain Authorship Verification Task at {PAN} 2020}, booktitle = {Working Notes of {CLEF} 2020 - Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 22-25, 2020}, series = {{CEUR} Workshop Proceedings}, volume = {2696}, publisher = {CEUR-WS.org}, year = {2020}, url = {http://ceur-ws.org/Vol-2696/paper\_264.pdf}, timestamp = {Tue, 27 Oct 2020 17:12:48 +0100}, biburl = {https://dblp.org/rec/conf/clef/KestemontMMBWSP20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
License from https://github.com/bit-ml/Dupin/tree/main: not disclosed
- British Academic Written English (BAWE)
@misc{20.500.12024/2539, title = {British Academic Written English Corpus}, author = {Nesi, Hilary and Gardner, Sheena and Thompson, Paul and Wickens, Paul}, url = {http://hdl.handle.net/20.500.12024/2539}, note = {Oxford Text Archive}, copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.}, year = {2008} }
License from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2539: (CC BY-NC-SA 3.0)
- IMDB62
@article{seroussi2014authorship, title={Authorship attribution with topic models}, author={Seroussi, Yanir and Zukerman, Ingrid and Bohnert, Fabian}, journal={Computational Linguistics}, volume={40}, number={2}, pages={269--310}, year={2014}, publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} }
License from https://umlt.infotech.monash.edu/?page_id=266: not disclosed
- PAN11
@misc{misc_pan11-author-identification-corpora, author = {Argamon, Shlomo and Juola, Patrick}, title = {{PAN11 Author Identification: Attribution}}, year = {2011}, howpublished = {Zenodo}, note = {{DOI}: https://doi.org/10.5281/zenodo.3713245} }
License: not disclosed
- PAN13
@misc{misc_pan13-authorship-verification-test-and-training, author = {Juola, Patrick and Stamatatos, Efstathios}, title = {{PAN13 Author Identification: Verification}}, year = {2013}, howpublished = {Zenodo}, note = {{DOI}: https://doi.org/10.5281/zenodo.3715998} }
License: not disclosed
- PAN14
@misc{misc_pan14-authorship-verification-test-and-training, author = {Stamatatos, Efstathios and Daelemans, Walter and Verhoeven, Ben and Potthast, Martin and Stein, Benno and Juola, Patrick and A. Sanchez-Perez, Miguel and Barrón-Cedeño, Alberto}, title = {{PAN14 Author Identification: Verification}}, year = {2014}, howpublished = {Zenodo}, note = {{DOI}: https://doi.org/10.5281/zenodo.3716032} }
License: not disclosed
- PAN15
@misc{misc_pan15-authorship-verification-test-and-training, author = {Stamatatos, Efstathios and Daelemans Daelemans amd Ben Verhoeven, Walter and Juola, Patrick and López-López, Aurelio and Potthast, Martin and Stein, Benno}, title = {{PAN15 Author Identification: Verification}}, year = {2015}, howpublished = {Zenodo}, note = {{DOI}: https://doi.org/10.5281/zenodo.3737563} }
License: not disclosed
- PAN20
@Article{stein:2020k, author = {Sebastian Bischoff and Niklas Deckers and Marcel Schliebs and Ben Thies and Matthias Hagen and Efstathios Stamatatos and Benno Stein and Martin Potthast}, journal = {CoRR}, month = may, title = {{The Importance of Suppressing Domain Style in Authorship Analysis}}, url = {https://arxiv.org/abs/2005.14714}, volume = {abs/2005.14714}, year = 2020 }
using the open-set unseen all split from @article{DBLP:journals/corr/abs-2112-05125, author = {Andrei Manolache and Florin Brad and Elena Burceanu and Antonio Barbalau and Radu Tudor Ionescu and Marius Popescu}, title = {Transferring BERT-like Transformers' Knowledge for Authorship Verification}, journal = {CoRR}, volume = {abs/2112.05125}, year = {2021}, url = {https://arxiv.org/abs/2112.05125}, eprinttype = {arXiv}, eprint = {2112.05125}, timestamp = {Mon, 13 Dec 2021 17:51:48 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-05125.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
@inproceedings{Kestemont2020OverviewOT, author = {Mike Kestemont and Enrique Manjavacas and Ilia Markov and Janek Bevendorff and Matti Wiegmann and Efstathios Stamatatos and Martin Potthast and Benno Stein}, editor = {Linda Cappellato and Carsten Eickhoff and Nicola Ferro and Aur{'{e}}lie N{'{e}}v{'{e}}ol}, title = {Overview of the Cross-Domain Authorship Verification Task at {PAN} 2020}, booktitle = {Working Notes of {CLEF} 2020 - Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 22-25, 2020}, series = {{CEUR} Workshop Proceedings}, volume = {2696}, publisher = {CEUR-WS.org}, year = {2020}, url = {http://ceur-ws.org/Vol-2696/paper\_264.pdf}, timestamp = {Tue, 27 Oct 2020 17:12:48 +0100}, biburl = {https://dblp.org/rec/conf/clef/KestemontMMBWSP20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
License from https://github.com/bit-ml/Dupin/tree/main: not disclosed
Datasets were cleaned, named entities were replaced with their general type in all except PAN14, PAN15, and PAN20, and datasets were restructured into dataframes with columns |text1|text2|same| where a value of 0 in same meant the two texts had different authors, while a value of 1 meant the two texts had the same author.
All datasets were split into train/test/verification, keeping the splits if given (see paper for specifics) and otherwise using a 0.7:0.15:0.15 split.
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