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The introduced entropy functional's (EF) information measure of random process integrates multiple information contributions along the process trajectories, evaluating both the states' and between states' bound information connections. This measure reveals information that is hidden by traditional information measures, which commonly use FAC's entropy function for each selected stationary states of the process. The hidden information is important for evaluation of missing connections, disclosing the process' meaningful information, which enables producing logic of the information. The presentation consists of CARDINAL Parts. In Part 1R-revised we analyze mechanism of arising information regularities from a stochastic process, measured by EF, independently of the process' specific source and origin. Uncovering the process' regularities leads us to an information law, based on extracting maximal information from its minimum, which could create these regularities. The solved variation problem (VP) determines a dynamic process, measured by information path functional (ORG), and information dynamic model, approximating the ORG measured stochastic process with a maximal functional probability on trajectories. In Part CARDINAL, we study the cooperative processes, arising at the consolidation, as a result of the VP-EF-IPF approach, which is able to produce multiple cooperative structures, concurrently assembling in hierarchical information network (IN) and generating the ORG's digital genetic code. In Part CARDINAL we study the evolutionary information processes and regularities of evolution dynamics, evaluated by the entropy functional (EF) of random field and informational path functional of a dynamic space-time process. The information law and the regularities determine unified functional informational mechanisms of evolution dynamics.

The impulses, cutting entropy functional (EF) measure on trajectories PERSON diffusion process, integrate information path functional (ORG) composing discrete information Bits extracted from observing random process. Each cut brings memory of the cutting entropy, which provides both reduction of the process entropy and discrete unit of the cutting entropy a Bit. Consequently, information is memorized entropy cutting in random observations which process interactions. The origin of information associates with anatomy creation of impulse enables both cut entropy and stipulate random process generating information under the cut. Memory of the impulse cutting time interval freezes the observing events dynamics in information processes. Diffusion process additive functional defines EF reducing it to a regular integral functional. Compared to FAC entropy measure of random state, cutting process on separated states decreases quantity information concealed in the states correlation holding hidden process information. Infinite dimensional process cutoffs integrate finite information in ORG whose information approaches EF restricting process maximal information. Within the impulse reversible microprocess, conjugated entropy increments are entangling up to the cutoff converting entropy in irreversible information. Extracting maximum of minimal impulse information and transferring minimal entropy between impulses implement maxmin-minimax principle of optimal conversion process entropy to information. Macroprocess extremals integrate entropy of microprocess and cutoff information of impulses in the ORG information physical process. ORG measures ORG kernel information. Estimation extracting information confirms nonadditivity of EF measured process increments.

The paper gives a soundness and completeness proof for the implicative fragment of intuitionistic calculus with respect to the semantics of computability logic, which understands intuitionistic implication as interactive algorithmic reduction. This concept -- more precisely, the associated concept of reducibility -- is a generalization of Turing reducibility from the traditional, input/output sorts of problems to computational tasks of QUANTITY of interactivity. See ORG for a comprehensive online source on computability logic.

Computational biology is on the verge of a paradigm shift in its research practice - from a data-based (computational) paradigm to an information-based (cognitive) paradigm. As in the other research fields, this transition is impeded by lack of a right understanding about what is actually hidden behind the term "information". The paper is intended to clarify this issue and introduces CARDINAL new notions of "physical information" and "semantic information", which together constitute the term "information". Some implications of this introduction are considered.

It is shown that ORG black holes can support ORG charged scalar fields in their exterior regions. To that end, we solve analytically the ORG wave equation for a stationary charged massive scalar field in the background of a near-extremal ORG black hole. In particular, we derive a simple analytical formula which describes the physical properties of these stationary bound-state resonances of the charged massive scalar fields in the ORG black-hole spacetime.

The black-hole information puzzle has attracted much attention over DATE from both physicists and mathematicians. CARDINAL of the most intriguing suggestions to resolve the information paradox is due to PERSON, who has stressed the fact that the low-energy part of the semi-classical black-hole emission spectrum is partly blocked by the curvature potential that surrounds the black hole. As explicitly shown by PERSON, this fact implies that the grey-body emission spectrum of a (3+1)-dimensional black hole is considerably less entropic than the corresponding radiation spectrum of a perfectly thermal black-body emitter. Using standard ideas from ORG theory, it was shown by PERSON that, in principle, the filtered Hawking radiation emitted by a (3+1)-dimensional Schwarzschild black hole may carry with it a substantial amount of information, the information which was suspected to be lost. It is of physical interest to test the general validity of the "information leak" scenario suggested by PERSON as a possible resolution to the Hawking information puzzle. In the present paper we analyze the semi-classical entropy emission properties of higher-dimensional black holes. In particular, we provide evidence that the characteristic Hawking quanta of $(D+1)$-dimensional ORG black holes in the large MONEY$ regime are almost unaffected by the spacetime curvature outside the black-hole horizon. This fact implies that, in the GPE regime, the Hawking black-hole radiation spectra are almost purely thermal, thus suggesting that the emitted quanta cannot carry the amount of information which is required in order to resolve the information paradox. Our analysis therefore suggests that the elegant information leak scenario suggested by PERSON cannot provide a generic resolution to the intriguing Hawking information paradox.

ORG data on the sigma pole are refitted taking into account new information on coupling of sigma to ORG and eta-eta. The fit also includes ORG data on NORP elastic phases shifts and Ke4 data, and gives a pole position of CARDINAL +- 30 - i(264 +- 30) MeV. However, there is a clear discrepancy with the sigma pole position recently predicted by NORP et al. using the PERSON equation. This discrepancy may be explained naturally by uncertainties arising from inelasticity in ORG and eta-eta channels and mixing between sigma and f0(980). Adding freedom to accomodate these uncertainties gives an optimum compromise with a pole position of CARDINAL +- 30 - i(271 +- 30) MeV.

Inequalities for the transformation operator kernel $PERSON,y)$ in terms of $MONEY are given, and vice versa. These inequalities are applied to inverse scattering on CARDINAL-line. Characterization of the scattering data corresponding to the usual scattering class $MONEY of the potentials, to the class of compactly supported potentials, and to the class of square integrable potentials is given. Invertibility of each of the steps in the inversion procedure is proved.

Drawing upon a body of research on the evolution of creativity, this paper proposes a theory of how, when, and why the forward-thinking story-telling abilities of humans evolved, culminating in the visionary abilities of science fiction writers. The ability to recursively chain thoughts together evolved DATE. Language abilities, and the ability to shift between different modes of thought, evolved DATE. Science fiction dates to DATE. It is suggested that well before this time, but after DATE, and concurrent with the evolution of a division of labour between creators and imitators there arose a division of labour between past, present, and future thinkers. Agent-based model research suggests there are social benefits to the evolution of individual differences in creativity such that there is a balance between novelty-generating creators and continuity-perpetuating imitators. A balance between individuals focused on the past, present, and future would be expected to yield similar adaptive benefits.

We consider the discrete memoryless degraded broadcast channels. We prove that the error probability of decoding tends to CARDINAL exponentially for rates outside the capacity region and derive an explicit lower bound of this exponent function. We shall demonstrate that the information spectrum approach is quite useful for investigating this problem.

Methods from convex optimization such as accelerated gradient descent are widely used as building blocks for deep learning algorithms. However, the reasons for their empirical success are unclear, since neural networks are not convex and standard guarantees do not apply. This paper develops the ORDINAL rigorous link between online convex optimization and error backpropagation on convolutional networks. The ORDINAL step is to introduce circadian games, a mild generalization of convex games with similar convergence properties. The main result is that error backpropagation on a convolutional network is equivalent to playing out a circadian game. It follows immediately that the waking-regret of players in the game (the units in the neural network) controls the overall rate of convergence of the network. Finally, we explore some implications of the results: (i) we describe the representations learned by a neural network game-theoretically, (ii) propose a learning setting at the level of individual units that can be plugged into deep architectures, and (iii) propose a new approach to adaptive model selection by applying bandit algorithms to choose which players to wake on each round.

As artificial agents proliferate, it is becoming increasingly important to ensure that their interactions with one another are well-behaved. In this paper, we formalize a common-sense notion of when algorithms are well-behaved: an algorithm is safe if it does no harm. Motivated by recent progress in deep learning, we focus on the specific case where agents update their actions according to gradient descent. The paper shows that that gradient descent converges to a ORG equilibrium in safe games. The main contribution is to define strongly-typed agents and show they are guaranteed to interact safely, thereby providing sufficient conditions to guarantee safe interactions. A series of examples show that strong-typing generalizes certain key features of convexity, is closely related to blind source separation, and introduces a new perspective on classical multilinear games based on tensor decomposition.

Following PERSON hypothesis of quanta (quant-ph/0012069) and the matter wave idea of PERSON (quant-ph/9911107), PERSON proposed, at DATE, the concept of wavefunction and wave equation for it. Though endowed with a realistic undular interpretation by its father, the wavefunction could not be considered as a real "matter wave" and has been provided with only abstract, formally probabilistic interpretation. In this paper we show how the resulting "mysteries" of usual theory are solved within the unreduced, dynamically multivalued description of the underlying, essentially nonlinear interaction process (quant-ph/9902015, quant-ph/9902016), without artificial modification of the ORG equation. The latter is rigorously derived instead as universal expression of unreduced interaction complexity. Causal, totally realistic wavefunction is obtained as a dynamically probabilistic intermediate state of a simple system with interaction performing dynamically discrete transitions between its localised, incompatible "realisations" ("corpuscular" states). Causal wavefunction and ORG equation are then extended to arbitrary level of world dynamics. We outline some applications of the obtained causal description, such as genuine quantum chaos (quant-ph/9511034-36) and realistic quantum devices (physics/0211071), and emphasize the basic difference of the proposed dynamically multivalued theory from dynamically single-valued imitations of causality and complexity. The causally complete wavefunction concept, representing the unified essence of unreduced (multivalued) complex dynamics, provides a clear distinctive feature of realistic science, absent in any its unitary imitation.

It is shown that nonlocal interactions determine energy spectrum in isotropic turbulence at small ORG numbers. It is also shown that for moderate ORG numbers the bottleneck effect is determined by the same nonlocal interactions. Role of the large and small scales covariance at the nonlocal interactions and in energy balance has been investigated. A possible hydrodynamic mechanism of the nonlocal solution instability at large scales has been briefly discussed. A quantitative relationship between effective strain of the nonlocal interactions and viscosity has been found. All results are supported by comparison with the data of experiments and numerical simulations.

Psychological traumas are thought to be present in a wide range of conditions, including post-traumatic stress disorder, disorganised attachment, personality disorders, dissociative identity disorder and psychosis. This work presents a new psychotherapy for psychological traumas, based on a functional model of the mind, built with elements borrowed from the fields of computer science, artificial intelligence and neural networks. The model revolves around the concept of hierarchical value and explains the emergence of dissociation and splitting in response to emotional pain. The key intuition is that traumas are caused by too strong negative emotions, which are in turn made possible by a low-value self, which is in turn determined by low-value self-associated ideas. The therapeutic method compiles a list of patient's traumas, identifies for each trauma a list of low-value self-associated ideas, and provides for each idea a list of counterexamples, to raise the self value and solve the trauma. Since the psychotherapy proposed has not been clinically tested, statements on its effectiveness are premature. However, since the conceptual basis is solid and traumas are hypothesised to be present in many psychological disorders, the potential gain may be substantial.

We propose a notion of autoreducibility for infinite time computability and explore it and its connection with a notion of randomness for infinite time machines.

Voting is a simple mechanism to aggregate the preferences of agents. Many voting rules have been shown to be ORG-hard to manipulate. However, a number of recent theoretical results suggest that this complexity may only be in the worst-case since manipulation is often easy in practice. In this paper, we show that empirical studies are useful in improving our understanding of this issue. We demonstrate that there is a smooth transition in the probability that a coalition can elect a desired candidate using the veto rule as the size of the manipulating coalition increases. We show that a rescaled probability curve displays a simple and universal form independent of the size of the problem. We argue that manipulation of the veto rule is asymptotically easy for many independent and identically distributed votes even when the coalition of manipulators is critical in size. Based on this argument, we identify a situation in which manipulation is computationally hard. This is when votes are highly correlated and the election is "hung". We show, however, that even a single uncorrelated voter is enough to make manipulation easy again.

Lecture given DATE at a Physics -- Computer Science Colloquium at ORG. The lecture was videotaped; this is an edited transcript. It also incorporates remarks made at the ORG to ORG meeting held at ORG 24--26 DATE.

Like any field of empirical science, ORG may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and tacit assumptions in deep learning research. ORG methodology seeks to overcome limitations in traditional machine learning research as it combines facets of model richness, generality, and practical applicability. The methodology so far has produced outstanding results due to a productive synergy of function approximation, under plausible assumptions of irreducibility and the efficiency of back-propagation family of algorithms. We examine these winning traits of deep learning, and also observe the various known failure modes of deep learning. We conclude by giving recommendations on how to extend deep learning methodology to cover the postulates of general-purpose ORG including modularity, and cognitive architecture. We also relate deep learning to advances in theoretical neuroscience research.

We continue our analysis of volume and energy measures that are appropriate for quantifying inductive inference systems. We extend logical depth and conceptual jump size measures in ORG to stochastic problems, and physical measures that involve volume and energy. We introduce a graphical model of computational complexity that we believe to be appropriate for intelligent machines. We show several asymptotic relations between energy, logical depth and volume of computation for inductive inference. In particular, we arrive at a "black-hole equation" of inductive inference, which relates energy, volume, space, and algorithmic information for an optimal inductive inference solution. We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to the physical limits of intelligent computation in our universe.

Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to adapt the information access provided by ubiquitous systems to users when the system only knows the user social group, without knowing anything about the user interest. The adaptation procedures associate actions to perceived situations of the user. Associations are based on feedback given by the user as a reaction to the behavior of the system. Our method brings a solution to some of the problems concerning the acceptance of the system by users when applying machine learning techniques to systems at the beginning of the interaction between the system and the user.

We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits CARDINAL types of conditioning operators: ordinary ORG conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as NORP networks in which some conditional probabilities may not be available.

Plasmodium of \emph{Physarum polycephalum} is a single huge (visible by naked eye) cell with myriad of nuclei. The plasmodium is a promising substrate for non-classical, nature-inspired, computing devices. It is capable for approximation of shortest path, computation of planar proximity graphs and plane tessellations, primitive memory and decision-making. The unique properties of the plasmodium make it an ideal candidate for a role of amorphous biological robots with massive parallel information processing and distributed inputs and outputs. We show that when adhered to light-weight object resting on a water surface the plasmodium can propel the object by oscillating its protoplasmic pseudopodia. In experimental laboratory conditions and computational experiments we study phenomenology of the plasmodium-floater system, and possible mechanisms of controlling motion of objects propelled by on board plasmodium.

In this short review I present my personal reflections on ORG information interpretation of quantum mechanics (QM). In general, this interpretation is very attractive for me. However, its rigid coupling to the notion of irreducible quantum randomness is a very complicated issue which I plan to address in more detail. This note may be useful for general public interested in ORG, especially because I try to analyze essentials of the information interpretation critically (i.e., not just emphasizing its advantages as it is commonly done). This review is written in non-physicist friendly manner. Experts actively exploring this interpretation may be interested in the paper as well, as in the comments of "an external observer" who have been monitoring the development of this approach to QM during DATE. The last part of this review is devoted to the general methodology of science with references to views of GPE, ORG, and PERSON.

It is by now well known that FAC logarithmic entropic functional ($PERSON) is inadequate for wide classes of strongly correlated systems: see for instance the DATE PERSON and PERSON's {\it Conceptual inadequacy of the FAC information in ORG measurements}, among many other systems exhibiting various forms of complexity. On the other hand, the FAC and GPE axioms uniquely mandate the GPE form $PERSON \ln p_i$; the ORG and PERSON axioms follow the same path. Many natural, artificial and social systems have been satisfactorily approached with nonadditive entropies such as the $S_q=k \frac{1-\sum_i p_i^q}{q-1}$ one ($q \in {\cal R}; \,S_1=S_{BG}$), basis of nonextensive statistical mechanics. Consistently, the FAC DATE and PERSON uniqueness theorems have already been generalized in the literature, by PERSON DATE and DATE respectively, in order to uniquely mandate $S_q$. We argue here that the same remains to be done with the ORG and PERSON DATE axioms. We arrive to this conclusion by analyzing specific classes of strongly correlated complex systems that await such generalization.

The Web community has introduced a set of standards and technologies for representing, querying, and manipulating a globally distributed data structure known as the Web of Data. The proponents of the Web of Data envision much of the world's data being interrelated and openly accessible to the general public. This vision is analogous in many ways to the Web of Documents of common knowledge, but instead of making documents and media openly accessible, the focus is on making data openly accessible. In providing data for public use, there has been a stimulated interest in a movement dubbed ORG. Open Data is analogous in many ways to the Open Source movement. However, instead of focusing on software, ORG is focused on the legal and licensing issues around publicly exposed data. Together, various technological and legal tools are laying the groundwork for the future of global-scale data management on the Web. As of DATE, in its early form, the Web of Data hosts a variety of data sets that include encyclopedic facts, drug and protein data, PERSON on music, books and scholarly articles, social network representations, geospatial information, and many other types of information. The size and diversity of the Web of Data is a demonstration of the flexibility of the underlying standards and the overall feasibility of the project as a whole. The purpose of this article is to provide a review of the technological underpinnings of the Web of Data as well as some of the hurdles that need to be overcome if the Web of Data is to emerge as the defacto medium for data representation, distribution, and ultimately, processing.

In this article, we consider convergence rates in functional linear regression with functional responses, where the linear coefficient lies in a reproducing kernel PERSON space (ORG). Without assuming that the reproducing kernel and the covariate covariance kernel are aligned, or assuming polynomial rate of decay of the eigenvalues of the covariance kernel, convergence rates in prediction risk are established. The corresponding lower bound in rates is derived by reducing to the scalar response case. Simulation studies and CARDINAL benchmark datasets are used to illustrate that the proposed approach can significantly outperform the functional PCA approach in prediction.

We study the posterior distribution of the NORP multiple change-point regression problem when the number and the locations of the change-points are unknown. While it is relatively easy to apply the general theory to obtain the $PERSON rate up to some logarithmic factor, showing the exact parametric rate of convergence of the posterior distribution requires additional work and assumptions. Additionally, we demonstrate the asymptotic normality of the segment levels under these assumptions. For inferences on the number of change-points, we show that the NORP approach can produce a consistent posterior estimate. Finally, we argue that the point-wise posterior convergence property as demonstrated might have bad finite sample performance in that consistent posterior for model selection necessarily implies the maximal squared risk will be asymptotically larger than the optimal $PERSON rate. This is the NORP version of the same phenomenon that has been noted and studied by other authors.

This article describes existing and expected benefits of the "SP theory of intelligence", and some potential applications. The theory aims to simplify and integrate ideas across artificial intelligence, mainstream computing, and human perception and cognition, with information compression as a unifying theme. It combines conceptual simplicity with descriptive and explanatory power across several areas of computing and cognition. In the "SP machine" -- an expression of the NORP theory which is currently realized in the form of a computer model -- there is potential for an overall simplification of computing systems, including software. The NORP theory promises deeper insights and better solutions in several areas of application including, most notably, unsupervised learning, natural language processing, autonomous robots, computer vision, intelligent databases, software engineering, information compression, medical diagnosis and big data. There is also potential in areas such as the semantic web, bioinformatics, structuring of documents, the detection of computer viruses, data fusion, new kinds of computer, and the development of scientific theories. The theory promises seamless integration of structures and functions within and between different areas of application. The potential value, worldwide, of these benefits and applications is MONEY DATE. Further development would be facilitated by the creation of a high-parallel, open-source version of the NORP machine, available to researchers everywhere.

This paper summarises how the "SP theory of intelligence" and its realisation in the "SP computer model" simplifies and integrates concepts across artificial intelligence and related areas, and thus provides a promising foundation for the development of a general, human-level thinking machine, in accordance with the main goal of research in artificial general intelligence. The key to this simplification and integration is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. This concept has the potential to be the "double helix" of intelligence, with as much significance for human-level intelligence as has DNA for biological sciences. Strengths of the NORP system include: versatility in the representation of diverse kinds of knowledge; versatility in aspects of intelligence (including: strengths in unsupervised learning; the processing of natural language; pattern recognition at multiple levels of abstraction that is robust in the face of errors in data; several kinds of reasoning (including: CARDINAL-step `deductive' reasoning; chains of reasoning; abductive reasoning; reasoning with probabilistic networks and trees; reasoning with 'rules'; nonmonotonic reasoning and reasoning with default values; NORP reasoning with 'explaining away'; and more); planning; problem solving; and more); seamless integration of diverse kinds of knowledge and diverse aspects of intelligence in any combination; and potential for application in several areas (including: helping to solve CARDINAL problems with big data; helping to develop human-level intelligence in autonomous robots; serving as a database with intelligence and with versatility in the representation and integration of several forms of knowledge; serving as a vehicle for medical knowledge and as an aid to medical diagnosis; and several more).

We study the contribution of diffractive $Q \bar Q$ production to the $PERSON proton structure function and the longitudinal double-spin asymmetry in polarized deep--inelastic $MONEY scattering. We show the strong dependence of the $F_2^D$ structure function and the $MONEY asymmetry on the quark--pomeron coupling structure.

We propose an online form of the cake cutting problem. This models situations where agents arrive and depart during the process of dividing a resource. We show that well known fair division procedures like cut-and-choose and the ORG moving knife procedure can be adapted to apply to such online problems. We propose some fairness properties that online cake cutting procedures can possess like online forms of proportionality and envy-freeness. We also consider the impact of collusion between agents. Finally, we study theoretically and empirically the competitive ratio of these online cake cutting procedures. Based on its resistance to collusion, and its good performance in practice, our results favour the online version of the cut-and-choose procedure over the online version of the moving knife procedure.

It is shown that, the wavelet regression detrended fluctuations of the reconstructed temperature for DATE (LOC ice cores data) are completely dominated by CARDINAL subharmonic resonance, presumably related to LOC precession effect on the energy that the intertropical regions receive from the ORG. Effects of Galactic turbulence on the temperature fluctuations are also discussed. Direct evidence of chaotic response of the atmospheric CO_2 dynamics to obliquity periodic forcing has been found in a reconstruction of atmospheric CO_2 data (deep sea proxies), for DATE.

Within the program of finding axiomatizations for various parts of computability logic, it was proved earlier that the logic of interactive Turing reduction is exactly the implicative fragment of ORG's intuitionistic calculus. That sort of reduction permits unlimited reusage of the computational resource represented by the antecedent. An at least equally basic and natural sort of algorithmic reduction, however, is the one that does not allow such reusage. The present article shows that turning the logic of the ORDINAL sort of reduction into the logic of the ORDINAL sort of reduction takes nothing more than just deleting the contraction rule from its NORP-style axiomatization. The first (Turing) sort of interactive reduction is also shown to come in CARDINAL natural versions. While those CARDINAL versions are very different from each other, their logical behaviors (in isolation) turn out to be indistinguishable, with that common behavior being precisely captured by implicative intuitionistic logic. Among the other contributions of the present article is an informal introduction of a series of new -- finite and bounded -- versions of recurrence operations and the associated reduction operations. An online source on computability logic can be found at ORG

Constraint satisfaction problems have been studied in numerous fields with practical and theoretical interests. In DATE, major breakthroughs have been made in a study of counting constraint satisfaction problems (or #CSPs). In particular, a computational complexity classification of bounded-degree #CSPs has been discovered for all degrees except for CARDINAL, where the "degree" of an input instance is the maximal number of times that each input variable appears in a given set of constraints. Despite the efforts of recent studies, however, a complexity classification of degree-2 #CSPs has eluded from our understandings. This paper challenges this open problem and gives its partial solution by applying CARDINAL novel proof techniques--T_{2}-constructibility and parametrized symmetrization--which are specifically designed to handle "arbitrary" constraints under randomized approximation-preserving reductions. We partition entire constraints into CARDINAL sets and we classify the approximation complexity of all degree-2 #CSPs whose constraints are drawn from CARDINAL of the CARDINAL sets into CARDINAL categories: problems computable in polynomial-time or problems that are at least as hard as #SAT. Our proof exploits a close relationship between complex-weighted degree-2 #CSPs and Holant problems, which are a natural generalization of complex-weighted #CSPs.

We examine the characteristic features of reversible and quantum computations in the presence of supplementary external information, known as advice. In particular, we present a simple, algebraic characterization of languages recognized by CARDINAL-way reversible finite automata augmented with deterministic advice. With a further elaborate argument, we prove a similar but slightly weaker result for bounded-error CARDINAL-way quantum finite automata with advice. Immediate applications of those properties lead to containments and separations among various language families when they are assisted by appropriately chosen advice. We further demonstrate the power and limitation of randomized advice and quantum advice when they are given to CARDINAL-way quantum finite automata.

Multi-valued partial ORG functions are computed by CARDINAL-way nondeterministic pushdown automata equipped with write-only output tapes. We give an answer to a fundamental question, raised by PERSON, and PERSON [Act. Inform. DATE) CARDINAL-417], of whether all multi-valued partial ORG functions can be refined by single-valued partial ORG functions. We negatively solve this question by presenting a special multi-valued partial ORG function as an example function and by proving that no refinement of this particular function becomes a single-valued partial ORG function. This contrasts an early result of Kobayashi [Inform. Control CARDINAL (DATE) CARDINAL-109] that multi-valued partial ORG functions are always refined by single-valued ORG functions, where ORG functions are computed by nondeterministic finite automata with output tapes. Our example function turns out to be unambiguously CARDINAL-valued, and thus we obtain a stronger separation result, in which no refinement of unambiguously CARDINAL-valued partial ORG functions can be single-valued. For the proof, we ORDINAL introduce a new concept of colored automata having no output tapes but having "colors," which can simulate pushdown automata with constant-space output tapes. We then conduct an extensive combinatorial analysis on the behaviors of transition records of stack contents (called stack histories) of colored automata.

We re-examine a practical aspect of combinatorial fuzzy problems of various types, including search, counting, optimization, and decision problems. We are focused only on those fuzzy problems that take series of fuzzy input objects and produce fuzzy values. To solve such problems efficiently, we design fast fuzzy algorithms, which are modeled by polynomial-time deterministic fuzzy Turing machines equipped with read-only auxiliary tapes and write-only output tapes and also modeled by polynomial-size fuzzy circuits composed of fuzzy gates. We also introduce fuzzy proof verification systems to model the fuzzification of nondeterminism. Those models help us identify CARDINAL complexity classes: PERSON of fuzzy functions, PERSON and PERSON of fuzzy decision problems, and PERSON of fuzzy optimization problems. Based on a relative approximation scheme targeting fuzzy membership degree, we formulate CARDINAL notions of "reducibility" in order to compare the computational complexity of CARDINAL fuzzy problems. These reducibility notions make it possible to locate the most difficult fuzzy problems in ORG and in GPE.

The issue of how to create open-ended evolution in an artificial system is one the open problems in artificial life. This paper examines CARDINAL of the factors that have some bearing on this issue, using the ORG artificial life system. {\em Parsimony pressure} is a tendency to penalise more complex organisms by the extra cost needed to reproduce longer genotypes, encouraging simplification to happen. In ORG, parsimony is controlled by the \verb+SlicePow+ parameter. When full parsimony is selected, evolution optimises the ancestral organism to produce extremely simple organisms. With parsimony completely relaxed, organisms grow larger, but not more complex. They fill up with ``junk''. This paper looks at scanning a range of \verb+SlicePow+ from CARDINAL to CARDINAL to see if there is an optimal value for generating complexity. Tierra (along with most ALife systems) use pseudo random number generators. PERSON can never create information, only destroy it. So the total complexity of the ORG system is bounded by the initial complexity, implying that the individual organism complexity is bounded. Biological systems, however, have plenty of sources of randomness, ultimately dependent on quantum randomness, so do not have this complexity limit. Sources of real random numbers exist for computers called {\em entropy gatherers} -- this paper reports on the effect of changing ORG's pseudo random number generator for an entropy gatherer.

In the {\em Many Worlds Interpretation} of quantum mechanics, the range of possible worlds (or histories) provides variation, and the PERSON is a selective principle analogous to natural selection. When looked on in this way, the ``process'' by which the laws and constants of physics is determined not too different from the process that gave rise to our current biodiversity, i.e. NORP evolution. This has implications for the fields of ORG and ORG, which are based on a philosophy of the inevitability of life.

In this article, we assign the $D_{s3}^*(2860)$ to be a D-wave $PERSON, and study the mass and decay constant of the $D_{s3}^*(2860)$ with the ORG sum rules by calculating the contributions of the vacuum condensates up to dimension-6 in the operator product expansion. The predicted mass $M_{D_{s3}^*}=(2.86\pm0.10)\,\rm{GeV}$ is in excellent agreement with the experimental value $M_{D_{s3}^*}=(2860.5\pm 2.6 \pm 2.5\pm CARDINAL{ MeV}$ from the LHCb collaboration. The present prediction supports assigning the $D_{s3}^*(2860)$ to be the D-wave $PERSON.

The ORG (generalized min-max) PERSON was recently proposed (PERSON, DATE) as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the ORG kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GPE) to convert the ORG kernel to ORG kernel. We call this approach as ``GMM-GCWS''. In the machine learning literature, there is a popular algorithm which we call ``RBF-RFF''. That is, one can use the ``random Fourier features'' (ORG) to convert the ``radial basis function'' (ORG) kernel to ORG kernel. It was empirically shown in (PERSON, DATE) that ORG typically requires substantially more samples than ORG in order to achieve comparable accuracies. The NORP method is a general tool for computing nonlinear kernels, which again converts nonlinear kernels into ORG kernels. We apply the NORP method for approximating the ORG kernel, a strategy which we name as ``GMM-NYS''. In this study, our extensive experiments on a set of fairly large datasets confirm that ORG is also a strong competitor of ORG.

In cocktail party listening scenarios, the human brain is able to separate competing speech signals. However, the signal processing implemented by the brain to perform cocktail party listening is not well understood. Here, we trained CARDINAL separate convolutive autoencoder deep neural networks (DNN) to separate monaural and binaural mixtures of CARDINAL concurrent speech streams. We then used these DNNs as convolutive deep transform (ORG) devices to perform probabilistic re-synthesis. The CDTs operated directly in the time-domain. Our simulations demonstrate that very simple neural networks are capable of exploiting monaural and binaural information available in a cocktail party listening scenario.

The short-time PERSON transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameterizes the trade-off between time and frequency resolution. However, it is not yet known how this parameter affects the operation of the binary mask in terms of separation quality for real-world signals such as speech or music. Here, we demonstrate that the trade-off between time and frequency in the STFT, used to perform ideal binary mask separation, depends upon the types of source that are to be separated. In particular, we demonstrate that different window sizes are optimal for separating different combinations of speech and musical signals. Our findings have broad implications for machine audition and machine learning in general.

We show that the class QAM does not change even if the verifier's ability is restricted to only single-qubit measurements. To show the result, we use the idea of the measurement-based ORG computing: the verifier, who can do only single-qubit measurements, can test the graph state sent from the prover and use it for his measurement-based ORG computing. We also introduce a new QMA-complete problem related to the stabilizer test.

Recently nonparametric functional model with functional responses has been proposed within the functional reproducing kernel PERSON spaces (fRKHS) framework. Motivated by its superior performance and also its limitations, we propose a NORP process model whose posterior mode coincide with the fRKHS estimator. The NORP approach has several advantages compared to its predecessor. ORDINAL, the multiple unknown parameters can be inferred together with the regression function in a unified framework. ORDINAL, as a NORP method, the statistical inferences are straightforward through the posterior distributions. We also use the predictive process models adapted from ORG to overcome the computational limitations, thus extending the applicability of this popular technique to a new problem. Modifications of predictive process models are nevertheless critical in our context to obtain valid inferences. The numerical results presented demonstrate the effectiveness of the modifications.

When a shell collapses through its horizon, semiclassical physics suggests that information cannot escape from this horizon. One might hope that nonperturbative ORG gravity effects will change this situation and avoid the `information paradox'. We note that string theory has provided a set of states over which the wavefunction of the shell can spread, and that the number of these states is large enough that such a spreading would significantly modify the classically expected evolution. In this article we perform a simple estimate of the spreading time, showing that it is much shorter than the Hawking evaporation time for the hole. Thus information can emerge from the hole through the relaxation of the shell state into a linear combination of fuzzballs.

We are pursuing a modeling methodology that views the world as a realm of things. A thing is defined as something that can be created, processed, released, transferred, and received. Additionally, in this modeling approach, a thing is a CARDINAL-dimensional structure referred to as a thinging (abstract) machine. On the other hand, machines are things that are operated on; that is, they are created, processed, released, transferred, and received. The intertwining with the world is accomplished by integrating these CARDINAL modes of an entity s being: being a thing that flows through machines and being a machine that processes things. This paper further enriches these notions of things and machines. We present further exploration of the thinging machine model through introducing a new notion called the thing/machine (thimac) as a label of the unity of things/machines. Thimacs replace traditional categorization, properties, and behavior with creating, processing, releasing, transferring, and receiving, as well as the CARDINAL linking notions of flow and triggering. The paper discusses the concept of thimacs with examples and focuses on the notion of structure as it applies to various diagrammatic modeling methodologies.

We present for mental processes the program of mathematical mapping which has been successfully realized for physical processes. We emphasize that our project is not about mathematical simulation of brain's functioning as a complex physical system, i.e., mapping of physical and chemical processes in the brain on mathematical spaces. The project is about mapping of purely mental processes on mathematical spaces. We present various arguments -- philosophic, mathematical, information, and neurophysiological -- in favor of the $p$-adic model of mental space. $p$-adic spaces have structures of hierarchic trees and in our model such a tree hierarchy is considered as an image of neuronal hierarchy. Hierarchic neural pathways are considered as fundamental units of information processing. As neural pathways can go through whole body, the mental space is produced by the whole neural system. Finally, we develop ORG in that GPE are represented by probability distributions on mental space.

The paper considers CARDINAL-phase random design linear regression models. The errors and the regressors are stationary long-range dependent NORP. The regression parameters, the scale parameters and the change-point are estimated using a method introduced by ORG). This is called S-estimator and it has the property that is more robust than the classical estimators; the outliers don't spoil the estimation results. Some asymptotic results, including the strong consistency and the convergence rate of the S-estimators, are proved.

We present a comprehensive study of the use of value precedence constraints to break value symmetry. We ORDINAL give a simple encoding of value precedence into ternary constraints that is both efficient and effective at breaking symmetry. We then extend value precedence to deal with a number of generalizations like wreath value and partial interchangeability. We also show that value precedence is closely related to lexicographical ordering. Finally, we consider the interaction between value precedence and symmetry breaking constraints for variable symmetries.

The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty. Maintaining local records of sources-of-belief allows both predictive and diagnostic inferences to be activated simultaneously and propagate harmoniously towards a stable equilibrium.

The equations of evolutionary change by natural selection are commonly expressed in statistical terms. ORG's fundamental theorem emphasizes the variance in fitness. Quantitative genetics expresses selection with covariances and regressions. Population genetic equations depend on genetic variances. How can we read those statistical expressions with respect to the meaning of natural selection? CARDINAL possibility is to relate the statistical expressions to the amount of information that populations accumulate by selection. However, the connection between selection and information theory has never been compelling. Here, I show the correct relations between statistical expressions for selection and information theory expressions for selection. Those relations link selection to the fundamental concepts of entropy and information in the theories of physics, statistics, and communication. We can now read the equations of selection in terms of their natural meaning. Selection causes populations to accumulate information about the environment.

This paper describes a relatively simple way of allowing a brain model to self-organise its concept patterns through nested structures. For a simulation, time reduction is helpful and it would be able to show how patterns may form and then fire in sequence, as part of a search or thought process. It uses a very simple equation to show how the inhibitors in particular, can switch off certain areas, to allow other areas to become the prominent ones and thereby define the current brain state. This allows for a small amount of control over what appears to be a chaotic structure inside of the brain. It is attractive because it is still mostly mechanical and therefore can be added as an automatic process, or the modelling of that. The paper also describes how the nested pattern structure can be used as a basic counting mechanism. Another mathematical conclusion provides a basis for maintaining memory or concept patterns. The self-organisation can space itself through automatic processes. This might allow new neurons to be added in a more even manner and could help to maintain the concept integrity. The process might also help with finding memory structures afterwards. This extended version integrates further with the existing cognitive model and provides some new conclusions.

Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. CARDINAL such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain CARDINAL sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.

A path information is defined in connection with the different possible paths of chaotic system moving in its phase space CARDINAL cells. On the basis of the assumption that the paths are differentiated by their actions, we show that the maximum path information leads to a path probability distribution as a function of action from which the well known transition probability of NORP motion can be easily derived. An interesting result is that the most probable paths are just the paths of least action. This suggests that the principle of least action, in a probabilistic situation, is equivalent to the principle of maximization of information or uncertainty associated with the probability distribution.

We explore OR gate response in a mesoscopic ring threaded by a magnetic flux $MONEY The ring is symmetrically attached to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes and CARDINAL gate voltages, viz, $V_a$ and $PERSON, are applied in CARDINAL arm of the ring which are treated as the CARDINAL inputs of the OR gate. All the calculations are based on the tight-binding model and the ORG's function method, which numerically compute the conductance-energy and current-voltage characteristics as functions of the gate voltages, ring-to-electrodes coupling strengths and magnetic flux. Our theoretical study shows that, for MONEY ($\phi_0=ch/e$, the elementary flux-quantum) a high output current (CARDINAL) (in the logical sense) appears if one or both the inputs to the gate are high (1), while if neither input is high (1), a low output current (0) appears. It clearly demonstrates the PRODUCT gate behavior and this aspect may be utilized in designing the electronic logic gate.

We explore NOT gate response in a mesoscopic ring threaded by a magnetic flux $MONEY The ring is attached symmetrically to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes and a gate voltage, viz, $PERSON, is applied in CARDINAL arm of the ring which is treated as the input of the NOT gate. The calculations are based on the tight-binding model and the ORG's function method, which numerically compute the conductance-energy and current-voltage characteristics as functions of the ring-to-electrodes coupling strength, magnetic flux and gate voltage. Our theoretical study shows that, for MONEY ($\phi_0=ch/e$, the elementary flux-quantum) a high output current (CARDINAL) (in the logical sense) appears if the input to the gate is low (0), while a low output current (0) appears when the input to the gate is high (1). It clearly exhibits the NOT gate behavior and this aspect may be utilized in designing an electronic logic gate.

A multidimensional optimization problem is formulated in the tropical mathematics setting as to maximize a nonlinear objective function, which is defined through a multiplicative conjugate transposition operator on vectors in a finite-dimensional semimodule over a general idempotent semifield. The study is motivated by problems drawn from project scheduling, where the deviation between initiation or completion times of activities in a project is to be maximized subject to various precedence constraints among the activities. To solve the unconstrained problem, we ORDINAL establish an upper bound for the objective function, and then solve a system of vector equations to find all vectors that yield the bound. As a corollary, an extension of the solution to handle constrained problems is discussed. The results obtained are applied to give complete direct solutions to the motivating problems from project scheduling. Numerical examples of the development of optimal schedules are also presented.

A multidimensional extremal problem in the idempotent algebra setting is considered which consists in minimizing a nonlinear functional defined on a finite-dimensional semimodule over an idempotent semifield. The problem integrates CARDINAL other known problems by combining their objective functions into CARDINAL general function and includes these problems as particular cases. A new solution approach is proposed based on the analysis of linear inequalities and spectral properties of matrices. The approach offers a comprehensive solution to the problem in a closed form that involves performing simple matrix and vector operations in terms of idempotent algebra and provides a basis for the development of efficient computational algorithms and their software implementation.

We consider the rate distortion problem with side information at the decoder posed and investigated by PERSON and PERSON. The rate distortion function indicating the trade-off between the rate on the data compression and the quality of data obtained at the decoder was determined by PERSON and PERSON. In this paper, we study the error probability of decoding at rates below the rate distortion function. We evaluate the probability of decoding such that the estimation of source outputs by the decoder has a distortion not exceeding a prescribed distortion level. We prove that when the rate of the data compression is below the rate distortion function this probability goes to CARDINAL exponentially and derive an explicit lower bound of this exponent function. On the PERSON-Ziv source coding problem the strong converse coding theorem has not been established yet. We prove this as a simple corollary of our result.

This paper presents a soundness and completeness proof for propositional intuitionistic calculus with respect to the semantics of computability logic. The latter interprets formulas as interactive computational problems, formalized as games between a machine and its environment. Intuitionistic implication is understood as algorithmic reduction in the weakest possible -- and hence most natural -- sense, disjunction and conjunction as deterministic-choice combinations of problems (disjunction = machine's choice, conjunction = environment's choice), and "absurd" as a computational problem of universal strength. See ORG for a comprehensive online source on computability logic.

We frame the question of what kind of subjective experience a brain simulation would have in contrast to a biological brain. We discuss the brain prosthesis thought experiment. We evaluate how the experience of the brain simulation might differ from the biological, according to a number of hypotheses about experience and the properties of simulation. Then, we identify finer questions relating to the original inquiry, and answer them from both a general physicalist, and panexperientialist perspective.

This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The value of the attribute that is to be predicted is known to the learner in the training set, but unknown in the testing set. The theory demonstrates that cross-validation error has CARDINAL components: error on the training set (inaccuracy) and sensitivity to noise (instability). This general theory is then applied to voting in instance-based learning. Given an example in the testing set, a typical instance-based learning algorithm predicts the designated attribute by voting among the k nearest neighbors (the k most similar examples) to the testing example in the training set. Voting is intended to increase the stability (resistance to noise) of instance-based learning, but a theoretical analysis shows that there are circumstances in which voting can be destabilizing. The theory suggests ways to minimize cross-validation error, by insuring that voting is stable and does not adversely affect accuracy.

We show how to express the information contained in a Quantum Bayesian (QB) net as a product of unitary matrices. If each of these unitary matrices is expressed as a sequence of elementary operations (operations such as controlled-nots and qubit rotations), then the result is a sequence of operations that can be used to run a ORG computer. QB nets have been run entirely on a classical computer, but one expects them to run faster on a ORG computer.

This survey note describes a brief systemic view to approaches for evaluation of hierarchical composite (modular) systems. The list of considered issues involves the following: (i) basic assessment scales (quantitative scale, ordinal scale, PERSON, CARDINAL kinds of poset-like scales), (ii) basic types of scale transformations problems, (iii) basic types of scale integration methods. Evaluation of the modular systems is considered as assessment of system components (and their compatibility) and integration of the obtained local estimates into the total system estimate(s). This process is based on the above-mentioned problems (i.e., scale transformation and integration). Illustrations of the assessment problems and evaluation approaches are presented (including numerical examples).

There is no single definition of complexity (Edmonds DATE; PERSON DATE; PERSON DATE), as it acquires different meanings in different contexts. A general notion is the amount of information required to describe a phenomenon (PERSON), but it can also be understood as the length of the shortest program required to compute that description, as the time required to compute that description, as the minimal model to statistically describe a phenomenon, etc.

In this chapter, concepts related to information and computation are reviewed in the context of human computation. A brief introduction to information theory and different types of computation is given. CARDINAL examples of human computation systems, online social networks and GPE, are used to illustrate how these can be described and compared in terms of information and computation.

We present critical arguments against individual interpretation of GPE's complementarity and PERSON's uncertainty principles. Statistical interpretation of these principles is discussed in the contextual framework. We support the possibility to use ORG of quantum formalism. In spite of all {\bf no-go} PERSON (e.g., PERSON, GPE and NORP,..., ORG,...), recently (quant-ph/0306003 and 0306069) we constructed a realist basis of quantum mechanics. In our model both classical and ORG spaces are rough images of the fundamental {\bf prespace.} ORG mechanics cannot be reduced to classical one. Both classical and quantum representations induce reductions of prespace information.

We perform geometrization of genetics by representing genetic information by points of the CARDINAL-adic {ORG information space.} By well known theorem of number theory this space can also be represented as the CARDINAL-adic space. The process of DNA-reproduction is described by the action of a CARDINAL-adic (or equivalently CARDINAL-adic) dynamical system. As we know, the genes contain information for production of proteins. The genetic code is a degenerate map of codons to proteins. We model this map as functioning of a polynomial dynamical system. The purely mathematical problem under consideration is to find a dynamical system reproducing the degenerate structure of the genetic code. We present CARDINAL of possible solutions of this problem.

This book develops the conjecture that all kinds of information processing in computers and in brains may usefully be understood as "information compression by multiple alignment, unification and search". This "SP theory", which has been under development since DATE, provides a unified view of such things as the workings of a universal Turing machine, the nature of 'knowledge', the interpretation and production of natural language, pattern recognition and best-match information retrieval, several kinds of probabilistic reasoning, planning and problem solving, unsupervised learning, and a range of concepts in mathematics and logic. The theory also provides a basis for the design of an 'SP' computer with several potential advantages compared with traditional digital computers.

Excess freedom in how computers are used creates problems that include: bit rot, problems with big data, problems in the creation and debugging of software, and problems with cyber security. To tame excess freedom, "tough love" is needed in the form of a {\em universal framework for the representation and processing of diverse kinds of knowledge} (ORG). The "SP machine", based on the "SP theory of intelligence", has the potential to provide that framework and to help solve the problems above. There is potential to reduce the CARDINAL different kinds of computer file to one, and to reduce the CARDINAL of different computer languages to one.

The paper analyzes the problem of judgments or preferences subsequent to initial analysis by autonomous agents in a hierarchical system where the higher level agents does not have access to group size information. We propose methods that reduce instances of preference reversal of the kind encountered in PERSON's paradox.

The solution of FAC system in CARDINAL space dimensions with ORG data in PRODUCT and wave data in H^{s+1/2} x H^{s-1/2} is uniquely determined in the natural solution space C^0([0,T],H^s) x C^0([0,T],H^{s+\frac1/2}), provided s > CARDINAL . This improves the uniqueness part of the global well-posedness result by ORG and the author, where uniqueness was proven in (smaller) spaces of PRODUCT type. Local well-posedness is also proven for ORG data in L^2 and wave data in H^{3/5}+} x H^{-2/5+} in the solution space C^0([0,T],L^2) x C^0([0,T],H^{3/5+}) and also for more regular data.

We study the spin-dependent cross-sections of vector PERSON for longitudinally and transversely polarized photons within a QCD- model. The dependence of the $\sigma_T/\sigma_L$ ratio on the photon virtuality and on ORG wave function is analysed.

Motivated by novel results in the theory of wave dynamics in black-hole spacetimes, we analyze the dynamics of a massive scalar field surrounding a rapidly rotating ORG black hole. In particular, we report on the existence of stationary (infinitely long-lived) regular field configurations in the background of maximally rotating black holes. The effective height of these scalar "clouds" above the central black hole is determined analytically. Our results support the possible existence of stationary scalar field dark matter distributions surrounding rapidly rotating black holes.

The paper focuses on a new class of combinatorial problems which consists in restructuring of solutions (as sets/structures) in combinatorial optimization. CARDINAL main features of the restructuring process are examined: (i) a cost of the restructuring, (ii) a closeness to a goal solution. CARDINAL types of the restructuring problems are under study: (a) CARDINAL-stage structuring, (b) multi-stage structuring, and (c) structuring over changed element set. CARDINAL-criterion and NORP problem formulations can be considered. The restructuring problems correspond to redesign (improvement, upgrade) of modular systems or solutions. The restructuring approach is described and illustrated (problem statements, solving schemes, examples) for the following combinatorial optimization problems: knapsack problem, multiple choice problem, assignment problem, spanning tree problems, clustering problem, multicriteria ranking (sorting) problem, morphological clique problem. Numerical examples illustrate the restructuring problems and solving schemes.

The paper addresses problem of data allocation in CARDINAL-layer computer storage while taking into account dynamic digraph(s) over computing tasks. The basic version of data file allocation on parallel hard magnetic disks is considered as special bin packing model. CARDINAL problems of the allocation solution reconfiguration (restructuring) are suggested: (i) CARDINAL-stage restructuring model, (ii) multistage restructuring models. Solving schemes are based on simplified heuristics. Numerical examples illustrate problems and solving schemes.

Despite efforts to increase the supply of organs from living donors, most kidney transplants performed in GPE still come from deceased donors. The age of these donated organs has increased substantially in DATE as the rate of fatal accidents on roads has fallen. The GPE and ORG in GPE is therefore looking to design a new mechanism that better matches the age of the organ to the age of the patient. I discuss the design, axiomatics and performance of several candidate mechanisms that respect the special online nature of this fair division problem.

PERSON's work, in its depth and breadth, encompasses many areas of scientific and philosophical interest. It helped establish the accepted mathematical concept of randomness, which in turn is the basis of tools that I have developed to justify and quantify what I think is clear evidence of the algorithmic nature of the world. To illustrate the concept I will establish novel upper bounds of algorithmic randomness for elementary cellular automata. I will discuss how the practice of science consists in conceiving a model that starts from certain initial values, running a computable instantiation, and awaiting a result in order to determine where the system may be in a future state--in a shorter time than the time taken by the actual unfolding of the phenomenon in question. If a model does not comply with all or some of these requirements it is traditionally considered useless or even unscientific, so the more precise and faster the better. A model is thus better if it can explain more with less, which is at the core of PERSON's "compression is comprehension". I will pursue these questions related to the random versus possibly algorithmic nature of the world in CARDINAL directions, drawing heavily on the work of PERSON. I will also discuss how the algorithmic approach is related to the success of science at producing models of the world, allowing computer simulations to better understand it and make more accurate predictions and interventions.

We discuss views about whether the universe can be rationally comprehended, starting with ORG, then ORG, and then the views of some distinguished scientists of DATE. Based on this, we defend the thesis that comprehension is compression, i.e., explaining many facts using few theoretical assumptions, and that a theory may be viewed as a computer program for calculating observations. This provides motivation for defining the complexity of something to be the size of the simplest theory for it, in other words, the size of the smallest program for calculating it. This is the central idea of algorithmic information theory (ORG), a field of theoretical computer science. Using the mathematical concept of program-size complexity, we exhibit irreducible mathematical facts, mathematical facts that cannot be demonstrated using any mathematical theory simpler than they are. It follows that the world of mathematical ideas has infinite complexity and is therefore not fully comprehensible, at least not in a static fashion. Whether the physical world has finite or infinite complexity remains to be seen. Current science believes that the world contains randomness, and is therefore also infinitely complex, but a deterministic universe that simulates randomness via pseudo-randomness is also a possibility, at least according to recent highly speculative work of ORG.

A remarkable new definition of a self-delimiting universal Turing machine is presented that is easy to program and runs very quickly. This provides a new foundation for algorithmic information theory. This new universal Turing machine is implemented via software written in GPE and C. Using this new software, it is now possible to give a self-contained ``hands on'' mini-course presenting very concretely the latest proofs of the fundamental information-theoretic incompleteness theorems.

In this paper we develop a method for clustering belief functions based on attracting and conflicting metalevel evidence. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. The clustering process is used as the means for separating the belief functions into subsets that should be handled independently. While the conflicting metalevel evidence is generated internally from pairwise conflicts of all belief functions, the attracting metalevel evidence is assumed given by some external source.

In this paper we develop an evidential force aggregation method intended for classification of evidential intelligence into recognized force structures. We assume that the intelligence has already been partitioned into clusters and use the classification method individually in each cluster. The classification is based on a measure of fitness between template and fused intelligence that makes it possible to handle intelligence reports with multiple nonspecific and uncertain propositions. With this measure we can aggregate on a level-by-level basis, starting from general intelligence to achieve a complete force structure with recognized units on all hierarchical levels.

Individual-intelligence research, from a neurological perspective, discusses the hierarchical layers of the cortex as a structure that performs conceptual abstraction and specification. This theory has been used to explain how motor-cortex regions responsible for different behavioral modalities such as writing and speaking can be utilized to express the same general concept represented higher in the cortical hierarchy. For example, the concept of a dog, represented across a region of high-level cortical-neurons, can either be written or spoken about depending on the individual's context. The higher-layer cortical areas project down the hierarchy, sending abstract information to specific regions of the motor-cortex for contextual implementation. In this paper, this idea is expanded to incorporate collective-intelligence within a hyper-cortical construct. This hyper-cortex is a multi-layered network used to represent abstract collective concepts. These ideas play an important role in understanding how collective-intelligence systems can be engineered to handle problem abstraction and solution specification. Finally, a collection of common problems in the scientific community are solved using an artificial hyper-cortex generated from digital-library metadata.

The ORG community is focused on integrating ORG (ORG) data sets into a single unified representation known as the Web of ORG. The Web of Data can be traversed by both man and machine and shows promise as the \textit{de facto} standard for integrating data world wide much like WORK_OF_ART is the \textit{de facto} standard for integrating documents. On DATE, an updated ORG cloud visualization was made publicly available. This visualization represents the various ORG data sets currently in the ORG cloud and their interlinking relationships. For the purposes of this article, this visual representation was manually transformed into a directed graph and analyzed.

Cryptography is a theory of secret functions. Category theory is a general theory of functions. Cryptography has reached a stage where its structures often take several pages to define, and its formulas sometimes run from page to page. Category theory has some complicated definitions as well, but one of its specialties is taming the flood of structure. Cryptography seems to be in need of high level methods, whereas category theory always needs concrete applications. So why is there no categorical cryptography? CARDINAL reason may be that the foundations of modern cryptography are built from probabilistic polynomial-time Turing machines, and category theory does not have a good handle on such things. On the other hand, such foundational problems might be the very reason why cryptographic constructions often resemble low level machine programming. I present some preliminary explorations towards categorical cryptography. It turns out that some of the main security concepts are easily characterized through the categorical technique of *diagram chasing*, which was first used PERSON's seminal `WORK_OF_ART and PERSON'.

In a previous FAST paper, I presented a quantitative model of the process of trust building, and showed that trust is accumulated like wealth: the rich get richer. This explained the pervasive phenomenon of adverse selection of trust certificates, as well as the fragility of trust networks in general. But a simple explanation does not always suggest a simple solution. It turns out that it is impossible to alter the fragile distribution of trust without sacrificing some of its fundamental functions. A solution for the vulnerability of trust must thus be sought elsewhere, without tampering with its distribution. This observation was the starting point of the present paper. It explores a different method for securing trust: not by redistributing it, but by mining for its sources. The method used to break privacy is thus also used to secure trust. A high level view of the mining methods that connect the CARDINAL is provided in terms of *similarity networks*, and *spectral decomposition* of similarity preserving maps. This view may be of independent interest, as it uncovers a common conceptual and structural foundation of mathematical classification theory on one hand, and of the spectral methods of graph clustering and data mining on the other hand.

This paper is intended to be a pedagogical introduction to quantum NORP networks (QB nets), as I personally use them to represent mixed states (i.e., density matrices, and open ORG). A special effort is made to make contact with notions used in textbooks on quantum WORK_OF_ART (quantum PRODUCT), such as the one by PERSON (arXiv:1106.1445)

The paper serves as the ORDINAL contribution towards the development of the theory of efficiency: a unifying framework for the currently disjoint theories of information, complexity, communication and computation. Realizing the defining nature of the brute force approach in the fundamental concepts in all of the above mentioned fields, the paper suggests using efficiency or improvement over the brute force algorithm as a common unifying factor necessary for the creation of a unified theory of information manipulation. By defining such diverse terms as randomness, knowledge, intelligence and computability in terms of a common denominator we are able to bring together contributions from FAC, PERSON, PERSON, PERSON, PERSON, PERSON and many others under a common umbrella of the efficiency theory.

We investigate the information provided about a specified distributed apparatus of n units in the measurement of a quantum state. It is shown that, in contrast to such measurement of a classical state, which is bounded by PERSON(CARDINAL) bits, the information in a quantum measurement is bounded by CARDINAL x n^(1/2) bits. This means that the use of ORG apparatus offers an exponential advantage over classical apparatus.

The notion of ORG information related to the CARDINAL different perspectives of the global and local states is examined. There is circularity in the definition of quantum information because we can speak only of the information of systems that have been specifically prepared. In particular, we examine the final state obtained by applying unitary transformations on a single qubit that belongs to an entangled pair.

This paper is concerned with an inverse obstacle problem which employs the dynamical scattering data of NORP wave over a finite time interval. The unknown obstacle is assumed to be sound-soft one. The governing equation of the wave is given by the classical wave equation. The wave is generated by the initial data localized outside the obstacle and observed over a finite time interval at a place which is not necessary the same as the support of the initial data. The observed data are the so-called bistatic data. In this paper, an enclosure method which employs the bistatic data and is based on CARDINAL main analytical formulae, is developed. The ORDINAL CARDINAL enables us to extract the maximum spheroid with focal points at the center of the support of the initial data and that of the observation points whose exterior encloses the unknown obstacle of general shape. The ORDINAL one, under some technical assumption for the obstacle including convexity as an example, indicates the deviation of the geometry of the boundary of the obstacle and the maximum spheroid at the contact points. Several implications of those CARDINAL formulae are also given. In particular, a constructive proof of a uniqueness of a spherical obstacle using the bistatic data is given.

This paper discusses an axiomatic approach for the integration of ontologies, an approach that extends to ORDINAL order logic a previous approach (Kent 2000) based on information flow. This axiomatic approach is represented in the ORG (ORG), a metalevel framework for organizing the information that appears in digital libraries, distributed databases and ontologies (Kent 2001). The paper argues that the integration of ontologies is the CARDINAL-step process of alignment and unification. Ontological alignment consists of the sharing of common terminology and semantics through a mediating ontology. Ontological unification, concentrated in a virtual ontology of community connections, is fusion of the alignment diagram of participant community ontologies - the quotient of the sum of the participant portals modulo the ontological alignment structure.

This study of the CARDINAL dimensional PERSON model in a weak coupling perturbative regime points out the effective mass behavior as a function of the adiabatic parameter $MONEY, $PERSON is the zone boundary phonon energy and $MONEY is the electron band hopping integral. Computation of low order diagrams shows that CARDINAL phonons scattering processes become appreciable in the intermediate regime in which zone boundary phonons energetically compete with band electrons. Consistently, in the intermediate (and also moderately antiadiabatic) range the relevant mass renormalization signals the onset of a polaronic crossover whereas the electrons are essentially undressed in the fully adiabatic and antiadiabatic systems. The effective mass is roughly twice as much the bare band value in the intermediate regime while an abrupt increase (mainly related to the peculiar 1D dispersion relations) is obtained at $MONEY \sqrt{2}J$.

Online estimation and modelling of i.i.d. data for short sequences over large or complex "alphabets" is a ubiquitous (GPE in machine learning, information theory, data compression, statistical language processing, and document analysis. WORK_OF_ART distribution (also called Polya urn scheme) and extensions thereof are widely applied for online i.i.d. estimation. Good a-priori choices for the parameters in this regime are difficult to obtain though. I derive an optimal adaptive choice for the main parameter via tight, data-dependent redundancy bounds for a related model. The CARDINAL-line recommendation is to set the 'total mass' = 'precision' = 'concentration' parameter to m/2ln[(n+1)/m], where n is the (past) sample size and m the number of different symbols observed (so far). The resulting estimator (i) is simple, (ii) online, (iii) fast, (iv) performs well for all m, small, middle and large, (v) is independent of the base alphabet size, (vi) non-occurring symbols induce no redundancy, (vii) the constant sequence has constant redundancy, (viii) symbols that appear only finitely often have bounded/constant contribution to the redundancy, (ix) is competitive with (slow) NORP mixing over all sub-alphabets.

To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and ORG (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem. We introduce in this paper an algorithm named ORG Sampling (ORG) that manages the recommendation of fresh document according to the user's risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the exploration/exploitation (exr/exp) behaviour.

Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to bring a solution to some of the problems concerning the acceptance of the system by users. To achieve this, we propose a fundamental shift in terms of how we model the learning of recommender system: inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-base reasoning to define a recommendation process that uses these CARDINAL approaches for generating recommendations on different context dimensions (social, temporal, geographic). We describe an implementation of the recommender system based on this framework. We also present preliminary results from experiments with the system and show how our approach increases the recommendation quality.

Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and the receiver would realize that a secret communicating is taking place. Unlike cryptography which only scrambles secret data keeping them overt, steganography covers secret data into medium files such as image files and transmits them in total secrecy avoiding drawing eavesdroppers suspicions. However, considering that the public channel is monitored by eavesdroppers, it is vulnerable to stego-attacks which refer to randomly trying to break the medium file and recover the secret data out of it. That is often true because steganalysts assume that the secret data are encoded into a single medium file and not into multiple ones that complement each other. This paper proposes a text steganography method for hiding secret textual data using CARDINAL mediums; a ORG sentence containing all the characters of the alphabet, and an uncompressed image file. The algorithm tries to search for every character of the secret message into the ORG text. The search starts from a random index called seed and ends up on the index of the ORDINAL occurrence of the character being searched for. As a result, CARDINAL indexes are obtained, the seed and the offset indexes. Together they are embedded into the CARDINAL LSBs of the color channels of the image medium. Ultimately, both mediums mainly the ORG and the image are sent to the receiver. The advantage of the proposed method is that it makes the covert data hard to be recovered by unauthorized parties as it uses CARDINAL mediums, instead of one, to deliver the secret data. Experiments conducted, illustrated an example that explained how to encode and decode a secret text message using the ORG and the image mediums.

The evolution of the Internet and computer applications have generated colossal amount of data. They are referred to as ORG and they consist of huge volume, high velocity, and variable datasets that need to be managed at the right speed and within the right time frame to allow real-time data processing and analysis. Several ORG solutions were developed, however they are all based on distributed computing which can be sometimes expensive to build, manage, troubleshoot, and secure. This paper proposes a novel method for processing ORG using memory-based, multi-processing, and CARDINAL-server architecture. It is memory-based because data are loaded into memory prior to start processing. It is multi-processing because it leverages the power of parallel programming using shared memory and multiple threads running over several CPUs in a concurrent fashion. It is CARDINAL-server because it only requires a single server that operates in a non-distributed computing environment. The foremost advantages of the proposed method are high performance, low cost, and ease of management. The experiments conducted showed outstanding results as the proposed method outperformed other conventional methods that currently exist on the market. Further research can improve upon the proposed method so that it supports message passing between its different processes using remote procedure calls among other techniques.

Based on cumulative distribution functions, PERSON series expansion and PERSON tests, we present a simple method to display probability densities for data drawn from a continuous distribution. It is often more efficient than using histograms.

This article is a tutorial on PERSON chain PERSON simulations and their statistical analysis. The theoretical concepts are illustrated through many numerical assignments from the author's book on the subject. Computer code (in GPE) is available for all subjects covered and can be downloaded from the web.

We explore the possible connections between the dynamic behaviour of a system and Turing universality in terms of the system's ability to (effectively) transmit and manipulate information. Some arguments will be provided using a defined compression-based transition coefficient which quantifies the sensitivity of a system to being programmed. In the same spirit, a list of conjectures concerning the ability of FAC machines to perform universal computation will be formulated. The main working hypothesis is that universality is deeply connected to the qualitative behaviour of a system, particularly to its ability to react to external stimulus--as it needs to be programmed--and to its capacity for transmitting this information.

The paper attempts to describe the space of possible mind designs by ORDINAL equating all minds to software. Next it proves some interesting properties of the mind design space such as infinitude of minds, size and representation complexity of minds. A survey of mind design taxonomies is followed by a proposal for a new field of investigation devoted to study of minds, intellectology, a list of open problems for this new field is presented.

The notions of formal contexts and concept lattices, although introduced by ORG DATE, already have proven to be of great utility in various applications such as data analysis and knowledge representation. In this paper we give arguments that ORG's original notion of formal context, although quite appealing in its simplicity, now should be replaced by a more semantic notion. This new notion of formal context entails a modified approach to concept construction. We base our arguments for these new versions of formal context and concept construction upon ORG's philosophical attitude with reference to the intensional aspect of concepts. We give a brief development of the relational theory of formal contexts and concept construction, demonstrating the equivalence of "concept-lattice construction" of ORG with the well-known "completion by cuts" of ORG. Generalization and abstraction of these formal contexts offers a powerful approach to knowledge representation.

Dialectical logic is the logic of dialectical processes. The goal of dialectical logic is to introduce dynamic notions into logical computational systems. The fundamental notions of proposition and truth-value in standard logic are subsumed by the notions of process and flow in dialectical logic. PRODUCT logic has a standard aspect, which can be defined in terms of the "local cartesian closure" of subtypes. The standard aspect of dialectical logic provides a natural program semantics which incorporates ORG's precondition/postcondition semantics and extends the standard Kripke semantics of dynamic logic. The goal of the standard aspect of dialectical logic is to unify the logic of small-scale and large-scale programming.

Image information content is known to be a complicated and controvercial problem. This paper posits a new image information content definition. Following the theory of ORG complexity, we define image information content as a set of descriptions of imafe data structures. CARDINAL levels of such description can be generally distinguished: 1)the global level, where the coarse structure of the entire scene is initially outlined; CARDINAL) the intermediate level, where structures of separate, non-overlapping image regions usually associated with individual scene objects are deliniated; and CARDINAL) the low-level description, where local image structures observed in a limited and restricted field of view are resolved. A technique for creating such image information content descriptors is developed. Its algorithm is presented and elucidated with some examples, which demonstrate the effectiveness of the proposed approach.

We study electron transport through a quantum interferometer with side-coupled quantum dots. The interferometer, threaded by a magnetic flux CARDINAL\phi$, is attached symmetrically to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes. The calculations are based on the tight-binding model and the PERSON's function method, which numerically compute the conductance-energy and current-voltage characteristics. Our results predict that under certain conditions this particular geometry exhibits anti-resonant states. These states are specific to the interferometric nature of the scattering and do not occur in conventional one-dimensional scattering problems of potential barriers. Most importantly we show that, such a simple geometric model can also be used as a classical ORG gate, where the CARDINAL gate voltages, viz, $V_a$ and $PERSON, are applied, respectively, in the CARDINAL dots those are treated as the CARDINAL inputs of the ORG gate. For MONEY ($\phi_0=ch/e$, the elementary flux-quantum), a high output current (CARDINAL) (in the logical sense) appears if one, and CARDINAL, of the inputs to the gate is high (1), while if both inputs are low (0) or both are high (1), a low output current (0) appears. It clearly demonstrates the ORG gate behavior and this aspect may be utilized in designing the electronic logic gate.

ORG information is radically different from classical information in that the quantum formalism (PERSON space) makes necessary the introduction of irreducible ``nits,'' n being an arbitrary natural number (bigger than one), not just bits.

It has been argued that analogy is the core of cognition. In ORG research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present ORG, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual data. A proportional analogy is an analogy of the form A:B::C:D, meaning "A is to B as C is to D". A lexical proportional analogy is a proportional analogy with words, such as carpenter:wood::mason:stone. PairClass represents the semantic relations between CARDINAL words using a high-dimensional feature vector, in which the elements are based on frequencies of patterns in the corpus. PairClass recognizes analogies by applying standard supervised machine learning techniques to the feature vectors. We show how CARDINAL different tests of word comprehension can be framed as problems of analogy perception and we then apply PRODUCT to the CARDINAL resulting sets of analogy perception problems. We achieve competitive results on all CARDINAL tests. This is the ORDINAL time a uniform approach has handled such a range of tests of word comprehension.

The general pupose of the scholarly communication process is to support the creation and dissemination of ideas within the scientific community. At a finer granularity, there exists multiple stages which, when confronted by a member of the community, have different requirements and therefore different solutions. In order to take a researcher's idea from an initial inspiration to a community resource, the scholarly communication infrastructure may be required to CARDINAL) provide a scientist initial seed ideas; CARDINAL) form a team of well suited collaborators; CARDINAL) located the most appropriate venue to publish the formalized idea; CARDINAL) determine the most appropriate peers to review the manuscript; and CARDINAL) disseminate the end product to the most interested members of the community. Through the various delinieations of this process, the requirements of each stage are tied soley to the multi-functional resources of the community: its researchers, its journals, and its manuscritps. It is within the collection of these resources and their inherent relationships that the solutions to scholarly communication are to be found. This paper describes an associative network composed of multiple scholarly artifacts that can be used as a medium for supporting the scholarly communication process.

Semantic networks qualify the meaning of an edge relating any CARDINAL vertices. Determining which vertices are most "central" in a semantic network is difficult because CARDINAL relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank ORG (i.e. directed paths between CARDINAL vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and ORG in semantic networks using a modified version of the random walker model of PERSON chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of ORG (ORG) of the NORP Web initiative.

We use the system of p-adic numbers for the description of information processes. Basic objects of our models are so called transformers of information, basic processes are information processes, the statistics are information statistics (thus we present a model of information reality). The classical and quantum mechanical formalisms on information p-adic spaces are developed. It seems that classical and quantum mechanical models on p-adic information spaces can be applied for the investigation of flows of information in cognitive and social systems, since a p-adic metric gives quite natural description of the ability to form associations.

We present comparative analysis of ORG arguments directed against PERSON anti-Bell arguments. In general we support PERSON viewpoint to the sequence of measurements in the ORG experiments as stochastic time-like process. On the other hand, we support ORG arguments against the use of time-like correlations as the factor blocking the derivation of ORG-type inequalities. We presented our own time-analysis of measurements in the ORG experiments based on the frequency approach to probability. Our analysis gives strong arguments in favour of local realism. Moreover, our frequency analysis supports the original EPR-idea that ORG mechnaics is not complete.

An important theorem in classical complexity theory is that NORP=ORG, i.e. that languages decidable with double-logarithmic space bound are regular. We consider a transfinite analogue of this theorem. To this end, we introduce deterministic ordinal automata (DOAs), show that they satisfy many of the basic statements of the theory of deterministic finite automata and regular languages. We then consider languages decidable by an ordinal Turing machine (ORG), introduced by PERSON in DATE and show that if the working space of an ORG is of strictly smaller cardinality than the input length for all sufficiently long inputs, the language so decided is also decidable by a DOA.

ORG ($ITRM$'s) are a well-established machine model for infinitary computations. Their computational strength relative to oracles is understood, see e.g. PERSON (DATE), PERSON and PERSON (DATE) and PERSON and PERSON (DATE). We consider the notion of recognizability, which was ORDINAL formulated for ORG in ORG and PERSON (CARDINAL) and applied to $ITRM$'s in GPE (DATE). A real $x$ is $ITRM$-recognizable iff there is an MONEYMONEY such that $P^{y}$ stops with output CARDINAL iff $y=x$, and otherwise stops with output CARDINAL. In GPE (DATE), it is shown that the recognizable reals are not contained in the computable reals. Here, we investigate in detail how the $MONEY reals are distributed along the canonical well-ordering $<_{L}$ of G\"odel's constructible hierarchy $MONEY In particular, we prove that the recognizable reals have gaps in $<_{PERSON, that there is MONEY in terms of recognizability and consider a relativized notion of recognizability.

The possible distinction between inanimate and living matter has been of interest to humanity since DATE. Clearly, such a rich question can not be answered in a single manner, and a plethora of approaches naturally do exist. However, during DATE, a new standpoint, of thermostatistical nature, has emerged. It is related to the proposal of nonadditive entropies in DATE, in order to generalise the celebrated ORG additive functional, basis of standard statistical mechanics. Such entropies have found deep fundamental interest and uncountable applications in natural, artificial and social systems. In some sense, this perspective represents an epistemological paradigm shift. These entropies crucially concern complex systems, in particular those whose microscopic dynamics violate ergodicity. Among those, living matter and other living-like systems play a central role. We briefly review here this approach, and present some of its predictions, verifications and applications.

Critically growing problems of fundamental science organisation and content are analysed with examples from physics and emerging interdisciplinary fields. Their origin is specified and new science structure (organisation and content) is proposed as a unified solution.

We compute ORG) correction to the stability critical exponent, omega, in the Landau-Ginzburg-Wilson model with O(N) x O(m) symmetry at the stable chiral fixed point and the stable direction at the unstable antichiral fixed point. Several constraints on ORG) coefficients of the CARDINAL loop perturbative beta-functions are computed.

By considering the scaling behaviour of various ORG graphs at leading order in large $\Nf$ at the non-trivial fixed point of the MONEY $\beta$-function of ORG we deduce the critical exponents corresponding to the quark, gluon and ghost anomalous dimensions as well as the anomalous dimensions of the quark-quark-gluon and ghost-ghost-gluon vertices in the PERSON gauge. As the exponents encode all orders information on the perturbation series of the corresponding renormalization group functions we find agreement with the known CARDINAL loop structure and, moreover, we provide new information at all subsequent orders.

We consider the estimation of hidden NORP process by using information geometry with respect to transition matrices. We consider the case when we use only the histogram of $k$-memory data. ORDINAL, we focus on a partial observation model with NORP process and we show that the asymptotic estimation error of this model is given as the inverse of projective ORG information of transition matrices. Next, we apply this result to the estimation of hidden NORP process. We carefully discuss the equivalence problem for hidden PERSON process on the tangent space. Then, we propose a novel method to estimate hidden NORP process.

The path integral formalism is applied to derive the full partition function of a generalized PERSON describing a particle motion in a bath of oscillators. The electronic correlations are computed versus temperature for some choices of oscillators energies. We study the perturbing effect of a time averaged particle path on the phonon subsystem deriving the relevant temperature dependent cumulant corrections to the harmonic partition function and free energy. The method has been applied to compute the total heat capacity up to room temeperature: a low temperature upturn in the heat capacity over temperature ratio points to a glassy like behavior ascribable to a time dependent electronic hopping with variable range in the linear chain.

Purpose - To test major Web search engines on their performance on navigational queries, i.e. searches for homepages. Design/methodology/approach - CARDINAL real user queries are posed to CARDINAL search engines (ORG, ORG, ORG, Ask, ORG, and PERSON). Users described the desired pages, and the results position of these is recorded. Measured success N and mean reciprocal rank are calculated. ORG of the major search engines ORG, ORG, and ORG is best, with PERCENT of queries answered correctly. Ask and PERSON perform worse but receive good scores as well. Research limitations/implications - All queries were in NORP, and the NORP-language interfaces of the search engines were used. Therefore, the results are only valid for NORP queries. Practical implications - When designing a search engine to compete with the major search engines, care should be taken on the performance on navigational queries. Users can be influenced easily in their quality ratings of search engines based on this performance. Originality/value - This study systematically compares the major search engines on navigational queries and compares the findings with studies on the retrieval effectiveness of the engines on informational queries. Paper type - research paper

We carried out a retrieval effectiveness test on the CARDINAL major web search engines (i.e., ORG, ORG and ORG). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. ORG provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments.

Over DATE, ORG has made a remarkable progress. It is agreed that this is due to the recently revived ORG technology. PERSON enables to process large amounts of data using simplified neuron networks that simulate the way in which the brain works. However, there is a different point of view, which posits that the brain is processing information, not data. This unresolved duality hampered ORG progress for DATE. In this paper, I propose a notion of NORP information that hopefully will resolve the problem. I consider integrated information as a coupling between CARDINAL separate entities - physical information (that implies data processing) and semantic information (that provides physical information interpretation). In this regard, intelligence becomes a product of information processing. Extending further this line of thinking, it can be said that information processing does not require more a human brain for its implementation. Indeed, bacteria and amoebas exhibit intelligent behavior without any sign of a brain. That dramatically removes the need for ORG systems to emulate the human brain complexity! The paper tries to explore this shift in ORG systems design philosophy.

This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like 'shape' of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.

This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In DATE, our large ORG recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe ORG-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its ORG-based world model. Unlike our previous model-building ORG-based ORG machines dating back to DATE, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially "learning to think." The basic ideas of this report can be applied to many other cases where CARDINAL ORG-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in DATE, and also explain concepts such as "mirror neurons." Experimental results will be described in separate papers.

Self-delimiting (ORG) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce ORG NNs. Neurons of a typical ORG have threshold activation functions. During a computational episode, activations are spreading from input neurons through ORG until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. The reset of the initial NN state does not cost more than the latest program execution. Since prefixes of ORG programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), ORG learning algorithms (LAs) should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing ORG NNs. To efficiently teach a ORG to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a ORG GPE needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Future SLIM NNs will be implemented on CARDINAL-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of ORG LAs is currently being probed in ongoing experiments to be reported in separate papers.

An upper limit is given to the amount of ORG information that can be transmitted reliably down a noisy, decoherent ORG channel. A class of quantum error-correcting codes is presented that allow the information transmitted to attain this limit. The result is the quantum analog of FAC's bound and code for the noisy classical channel.

This paper investigates a variety of unconventional quantum computation devices, including fermionic quantum computers and computers that exploit nonlinear ORG mechanics. It is shown that unconventional ORG computing devices can in principle compute some quantities more rapidly than `conventional' quantum computers.

The possibility of measuring the NORP gravitoelectric correction to the orbital period of a test particle freely orbiting a spherically symmetric mass in ORG is analyzed. It should be possible, in principle, to detect it for ORG at a precision level of CARDINAL^-4. This level is mainly set by the unavoidable systematic errors due to the mismodelling in the NORP period which could not be reduced by accumulating a large number of orbital revolutions. Future missions like PERSON and ORG should allow to improve it by increasing our knowledge of the ORG's orbital parameters. The observational accuracy is estimated to be CARDINAL^-4 from the knowledge of ORG (ICRF) axes. It could be improved by observing as many planetary transits as possible. It is not possible to measure such an effect in the gravitational field of the LOC by analyzing the motion of artificial satellites or the PERSON because of the unavoidable systematic errors related to the uncertainties in the NORP periods. In the case of some recently discovered exoplanets the problems come from the observational errors which are larger than the relativistic effect.

In this paper we calculate explicitly the secular classical precessions of the node \Omega and the perigee \omega of an LOC artificial satellite induced by the static, even zonal harmonics of the geopotential up to PERSON. ORG, their systematic errors induced by the mismodelling in the even zonal geopotential coefficients J_l are compared to the general relativistic secular gravitomagnetic and gravitoelectric precessions of the node and the perigee of the existing laser-ranged geodetic satellites and of the proposed PRODUCT.

The history of data analysis that is addressed here is underpinned by CARDINAL themes, -- those of tabular data analysis, and the analysis of collected heterogeneous data. "Exploratory data analysis" is taken as the heuristic approach that begins with data and information and seeks underlying explanation for what is observed or measured. I also cover some of the evolving context of research and applications, including scholarly publishing, technology transfer and the economic relationship of the university to society.

The new interface of ORG (of ORG) enables users to retrieve sets CARDINAL documents in a single search. This makes it possible to compare publication trends for GPE, the GPE, PRODUCT, and a number of smaller countries. GPE no longer grew exponentially during DATE, but linearly. Contrary to previous predictions on the basis of exponential growth or Scopus data, the cross-over of the lines for GPE and the GPE is postponed to DATE (after DATE) according to this data. These long extrapolations, however, should be used only as indicators and not as predictions. Along with the dynamics in the publication trends, one also has to take into account the dynamics of the databases used for the measurement.

Results about the redundancy of circumscriptive and default theories are presented. In particular, the complexity of establishing whether a given theory is redundant is establihsed.

These are some informal notes concerning topological vector spaces, with a brief overview of background material and basic notions, and emphasis on examples related to classical analysis.

Boltzmann introduced in the DATE's a logarithmic measure for the connection between the thermodynamical entropy and the probabilities of the microscopic configurations of the system. His entropic functional for classical systems was extended by GPE to the entire phase space of a many-body system, and by PERSON in order to cover ORG systems as well. Finally, it was used by FAC within the theory of information. The simplest expression of this functional corresponds to a discrete set of $MONEY microscopic possibilities, and is given by $S_{BG}= PERSON p_i \ln p_i$ ($PERSON is a positive universal constant; {ORG BG} stands for ORG}). This relation enables the construction of GPE statistical mechanics. The GPE theory has provided uncountable important applications. Its application in physical systems is legitimate whenever the hypothesis of {ORG ergodicity} is satisfied. However, {\it what can we do when ergodicity and similar simple hypotheses are violated?}, which indeed happens in very many natural, artificial and social complex systems. It was advanced in DATE the possibility of generalizing GPE statistical mechanics through a family of nonadditive entropies, namely $S_q=PERSON, which recovers the additive $PERSON entropy in the $q \to1$ limit. The index $PERSON is to be determined from mechanical ORDINAL principles. Along DATE, this idea intensively evolved world-wide (see Bibliography in \url{http://tsallis.cat.cbpf.br/biblio.htm}), and led to a plethora of predictions, verifications, and applications in physical systems and elsewhere. As expected whenever a {ORG paradigm shift} is explored, some controversy naturally emerged as well in the community. The present status of the general picture is here described, starting from its dynamical and thermodynamical foundations, and ending with its most recent physical applications.

The black hole information paradox is CARDINAL of the most important issues in theoretical physics. We review some recent progress using string theory in understanding the nature of black hole microstates. For all cases where these microstates have been constructed, one finds that they are horizon sized `fuzzballs'. Most computations are for extremal states, but recently one has been able to study a special family of non-extremal microstates, and see `information carrying radiation' emerge from these gravity solutions. We discuss how the fuzzball picture can resolve the information paradox. We use the nature of fuzzball states to make some conjectures on the dynamical aspects of black holes, observing that the large phase space of fuzzball solutions can make the black hole more `quantum' than assumed in traditional treatments.

Steganography is an information hiding technique in which secret data are secured by covering them into a computer carrier file without damaging the file or changing its size. The difference between steganography and cryptography is that steganography is a stealthy method of communication that only the communicating parties are aware of; while, cryptography is an overt method of communication that anyone is aware of, despite its payload is scribbled. Typically, an irrecoverable steganography algorithm is the algorithm that makes it hard for malicious ORDINAL parties to discover how it works and how to recover the secret data out of the carrier file. CARDINAL popular way to achieve irrecoverability is to digitally process the carrier file after hiding the secret data into it. However, such process is irreversible as it would destroy the concealed data. This paper proposes a new image steganography method for textual data, as well as for any form of digital data, based on adjusting the brightness of the carrier image after covering the secret data into it. The algorithm used is parameterized as it can be configured using CARDINAL different parameters defined by the communicating parties. They include the amount of brightness to apply on the carrier image after the completion of the covering process, the color channels whose brightness should be adjusted, and the bytes that should carry in the secret data. The novelty of the proposed method is that it embeds bits of the secret data into the CARDINAL LSBs of the bytes that compose the carrier image in such a way that does not destroy the secret data when restoring back the original brightness of the carrier image. The simulation conducted proved that the proposed algorithm is valid and correct.

Permutation is the different arrangements that can be made with a given number of things taking some or all of them at a time. The notation P(n,r) is used to denote the number of permutations of n things taken r at a time. Permutation is used in various fields such as mathematics, group theory, statistics, and computing, to solve several combinatorial problems such as the job assignment problem and the traveling salesman problem. In effect, permutation algorithms have been studied and experimented for DATE now. Bottom-Up, PERSON, and PERSON are CARDINAL of the most popular permutation algorithms that emerged during DATE. In this paper, we are implementing CARDINAL of the most eminent permutation algorithms, they are respectively: Bottom-Up, PERSON, and PERSON algorithms. The implementation of each algorithm will be carried out using CARDINAL different approaches: brute-force and divide and conquer. The algorithms codes will be tested using a computer simulation tool to measure and evaluate the execution time between the different implementations.

We survey the prospects for an ORG which can serve as the basis for a fundamental theory of information, incorporating qualitative and structural as well as quantitative aspects. We motivate our discussion with some basic conceptual puzzles: how can information increase in computation, and what is it that we are actually computing in general? Then we survey a number of the theories which have been developed within ORG, as partial exemplifications of the kind of fundamental theory which we seek: including WORK_OF_ART, and PERSON. We look at recent work showing new ways of combining quantitative and qualitative theories of information, as embodied respectively by ORG and ORG. Then we look at ORG and ORG, as examples of dynamic models of logic and computation in which information flow and interaction are made central and explicit. We conclude by looking briefly at some key issues for future progress.

We look at intensionality from the perspective of computation. In particular, we review how game semantics has been used to characterize the sequential functional processes, leading to powerful and flexible methods for constructing fully abstract models of programming languages, with applications in program analysis and verification. In a broader context, we can regard game semantics as a ORDINAL step towards developing a positive theory of intensional structures with a robust mathematical structure, and finding the right notions of invariance for these structures.

The internal structure of a measuring device, which depends on what its components are and how they are organized, determines how it categorizes its inputs. This paper presents a geometric approach to studying the internal structure of measurements performed by distributed systems such as probabilistic cellular automata. It constructs the quale, a family of sections of a suitably defined presheaf, whose elements correspond to the measurements performed by all subsystems of a distributed system. Using the quale we quantify (i) the information generated by a measurement; (ii) the extent to which a measurement is context-dependent; and (iii) whether a measurement is decomposable into independent submeasurements, which turns out to be equivalent to context-dependence. Finally, we show that only indecomposable measurements are more informative than the sum of their submeasurements.

The paper demonstrates that falsifiability is fundamental to learning. We prove the following theorem for statistical learning and sequential prediction: If a theory is falsifiable then it is learnable -- i.e. admits a strategy that predicts optimally. An analogous result is shown for universal induction.

An inverse source problem for the heat equation is considered. Extraction formulae for information about the time and location when and where the unknown source of the equation ORDINAL appeared are given from a single lateral boundary measurement. New roles of the plane progressive wave solutions or their complex versions for the backward heat equation are given.

In this paper a wave is generated by an initial data whose support is localized at the outside of unknown obstacles and observed in a limited time on a known closed surface or the same position as the support of the initial data. The observed data in the latter process are nothing but the back-scattering data. CARDINAL types of obstacles are considered. One is obstacles with a dissipative boundary condition which is a generalization of the sound-hard obstacles; another is obstacles with a finite refractive index, so-called, transparent obstacles. For each type of obstacles CARDINAL formulae which yield explicitly the distance from the support of the initial data to unknown obstacles are given.

"Information Processing" is a recently launched buzzword whose meaning is vague and obscure even for the majority of its users. The reason for this is the lack of a suitable definition for the term "information". In my attempt to amend this bizarre situation, I have realized that, following the insights of ORG theory, information can be defined as a description of structures observable in a given data set. CARDINAL types of structures could be easily distinguished in every data set - in this regard, CARDINAL types of information (information descriptions) should be designated: physical information and semantic information. ORG's theory also posits that the information descriptions should be provided as a linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as a kind of text processing. The idea is not new - inspired by the observation that human information processing is deeply rooted in natural language handling customs, PERSON and his followers have introduced the so-called "WORK_OF_ART" paradigm. Despite of promotional efforts, the idea is not taking off yet. The reason - a lack of a coherent understanding of what should be called "information", and, as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues would be helpful in avoiding common traps and pitfalls.

Over DATE, ORG has made a remarkable progress due to recently revived PERSON technology. ORG enables to process large amounts of data using simplified neuron networks that simulate the way in which the brain works. At the same time, there is another point of view that posits that brain is processing information, not data. This duality hampered ORG progress for DATE. To provide a remedy for this situation, I propose a new definition of information that considers it as a coupling between CARDINAL separate entities - physical information (that implies data processing) and semantic information (that provides physical information interpretation). In such a case, intelligence arises as a result of information processing. The paper points on the consequences of this turn for the ORG design philosophy.

As far as algorithmic thinking is bound by symbolic paper-and-pencil operations, the Church-Turing thesis appears to hold. But is physics, and even more so, is the human mind, bound by symbolic paper-and-pencil operations? What about the powers of the continuum, the quantum, and what about human intuition, human thought? These questions still remain unanswered. With the strong ORG assumption, human consciousness is just a function of the organs (maybe in a very wide sense and not only restricted to neuronal brain activity), and thus the question is relegated to physics. In dualistic models of the mind, human thought transcends symbolic paper-and-pencil operations.

ORG bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a CARDINAL-hot encoder by CARDINAL-nearest-neighbor optimization. Geometrically, the nonparametric density estimator at each layer projects the input data space to a uniformly-distributed discrete feature space, where the similarity of CARDINAL data points in the discrete feature space is measured by the number of the nearest centroids they share in common. The multilayer network gradually reduces the nonlinear variations of data from bottom up by building a vast number of hierarchical trees implicitly on the original data space. Theoretically, the estimation error caused by the nonparametric density estimator is proportional to the correlation between the clusterings, both of which are reduced by the randomization steps.

Since no fusion theory neither rule fully satisfy all needed applications, the author proposes ORG and a combination of fusion rules in solving problems/applications. For each particular application, CARDINAL selects the most appropriate model, rule(s), and algorithm of implementation. We are working in the unification of the fusion theories and rules, which looks like a cooking recipe, better we'd say like a logical chart for a computer programmer, but we don't see another method to comprise/unify all things. The unification scenario presented herein, which is now in an incipient form, should periodically be updated incorporating new discoveries from the fusion and engineering research.

The present work includes some of the author's original researches on integer solutions of GPE liner equations and systems. The notion of "general integer solution" of a GPE linear equation with CARDINAL unknowns is extended to GPE linear equations with $n$ unknowns and then to GPE linear systems. The proprieties of the general integer solution are determined (both for a GPE linear equation and for a GPE linear system). CARDINAL original integer algorithms (CARDINAL for GPE linear equations, and CARDINAL for GPE linear systems) are exposed. The algorithms are strictly proved and an example for each of them is given. These algorithms can be easily implemented on the computer.

Can ORG-complete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, ORG computing, quantum advice, quantum adiabatic algorithms, quantum-mechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and "anthropic computing." The section on soap bubbles even includes some "experimental" results. While I do not believe that any of the proposals will let us solve ORG-complete problems efficiently, I argue that by studying them, we can learn something not only about computation but also about physics.

We show that any quantum algorithm to decide whether a function f:[n]->[n] is a permutation or far from a permutation must make GPE) queries to f, even if the algorithm is given a w-qubit quantum witness in support of f being a permutation. This implies that there exists an oracle A such that ORG is not contained in GPE, answering an DATE open question of the author. Indeed, we show that relative to some oracle, ORG is not in the counting class A0PP defined by PERSON. The proof is a fairly simple extension of the quantum lower bound for the collision problem.

CARDINAL of the most important aims of the fields of robotics, artificial intelligence and artificial life is the design and construction of systems and machines as versatile and as reliable as living organisms at performing high level human-like tasks. But how are we to evaluate artificial systems if we are not certain how to measure these capacities in living systems, let alone how to define life or intelligence? Here I survey a concrete metric towards measuring abstract properties of natural and artificial systems, such as the ability to react to the environment and to control one's own behaviour.

I will survey some matters of relevance to a philosophical discussion of information, taking into account developments in algorithmic information theory (ORG). I will propose that meaning is deep in the sense of PERSON's logical depth, and that algorithmic probability may provide the stability needed for a robust algorithmic definition of meaning, one that takes into consideration the interpretation and the recipient's own knowledge encoded in the story attached to a message.

PERSON of the Internet in the early 90's increased dramatically the number of images being distributed and shared over the web. As a result, image information retrieval systems were developed to index and retrieve image files spread over the Internet. Most of these systems are keyword-based which search for images based on their textual metadata; and thus, they are imprecise as it is vague to describe an image with a human language. Besides, there exist the content-based image retrieval systems which search for images based on their visual information. However, content-based type systems are still immature and not that effective as they suffer from low retrieval recall/precision rate. This paper proposes a new hybrid image information retrieval model for indexing and retrieving web images published in HTML documents. The distinguishing mark of the proposed model is that it is based on both graphical content and textual metadata. The graphical content is denoted by color features and color histogram of the image; while PERSON are denoted by the terms that surround the image in the HTML document, more particularly, the terms that appear in the tags p, h1, and h2, in addition to the terms that appear in the image's alt attribute, filename, and class-label. Moreover, this paper presents a new term weighting scheme called VTF-IDF short for WORK_OF_ART which unlike traditional schemes, it exploits the ORG tag structure and assigns an extra bonus weight for terms that appear within certain particular HTML tags that are correlated to the semantics of the image. Experiments conducted to evaluate the proposed ORG model showed a high retrieval precision rate that outpaced other current models.

This paper describes a process for clustering concepts into chains from data presented randomly to an evaluating system. There are a number of rules or guidelines that help the system to determine more accurately what concepts belong to a particular chain and what ones do not, but it should be possible to write these in a generic way. This mechanism also uses a flat structure without any hierarchical path information, where the link between CARDINAL concepts is made at the level of the concept itself. It does not require related metadata, but instead, a simple counting mechanism is used. Key to this is a count for both the concept itself and also the group or chain that it belongs to. To test the possible success of the mechanism, concept chain parts taken randomly from a larger ontology were presented to the system, but only at a depth of CARDINAL concepts each time. That is - root concept plus a concept that it is linked to. The results show that this can still lead to very variable structures being formed and can also accommodate some level of randomness.

Many researches proposed the use of the TIME state as the input state for phase estimation, which is CARDINAL topic of quantum metrology. This is because the input TIME state provides the maximum ORG information at the specific point. However, the ORG information does not necessarily give the attainable bound for estimation error. In this paper, we adopt the local asymptotic mini-PERSON criterion as well as the mini-max criterion, and show that the maximum ORG information does not give the attainable bound for estimation error under these criteria in the phase estimation. We also propose the optimal input state under the constraints for photon number of the input state instead of the TIME state.

In the setting of a complete metric space that is equipped with a doubling measure and supports a Poincar\'e inequality, we prove the fine ORG property, the quasi-Lindel\"of principle, and the ORG property for the fine topology in the case $PERSON

PERSON-cognitive action reproduces and changes both social and cognitive structures. The analytical distinction between these dimensions of structure provides us with richer models of scientific development. In this study, I assume that (i) social structures organize expectations into belief structures that can be attributed to individuals and communities; (ii) expectations are specified in scholarly literature; and (iii) intellectually the sciences (disciplines, specialties) tend to self-organize as systems of rationalized expectations. Whereas social organizations remain localized, academic writings can circulate, and expectations can be stabilized and globalized using symbolically generalized codes of communication. The intellectual restructuring, however, remains latent as a ORDINAL-order dynamics that can be accessed by participants only reflexively. Yet, the emerging "horizons of meaning" provide feedback to the historically developing organizations by constraining the possible future states as boundary conditions. I propose to model these possible future states using incursive and hyper-incursive equations from the computation of anticipatory systems. Simulations of these equations enable us to visualize the couplings among the historical--i.e., recursive--progression of social structures along trajectories, the evolutionary--i.e., hyper-incursive--development of systems of expectations at the regime level, and the incursive instantiations of expectations in actions, organizations, and texts.

The tension between qualitative theorizing and quantitative methods is pervasive in the social sciences, and poses a constant challenge to empirical research. But in science studies as an interdisciplinary specialty, there are additional reasons why a more reflexive consciousness of the differences among the relevant disciplines is necessary. How can qualitative insights from the history of ideas and the sociology of science be combined with the quantitative perspective? By using the example of the lexical and semantic value of word occurrences, the issue of qualitatively different meanings of the same phenomena is discussed as a methodological problem. CARDINAL criteria for methods which are needed for the development of science studies as an integrated enterprise can then be specified. Information calculus is suggested as a method which can comply with these criteria.

We show that a highly-mixed state in terms of a large min-entropy is useless as a resource state for measurement-based ORG computation in the sense that if a classically efficiently verifiable problem is efficiently solved with such a highly-mixed measurement-based quantum computation then such a problem can also be classically efficiently solved. We derive a similar result also for the DQC1$_k$ model, which is a generalized version of the DQC1 model where $k$ output qubits are measured. We also show that the measurement-based ORG computing on a highly-mixed resource state in terms of the von ORG entropy, and PERSON model are useless in another sense that the mutual information between the computation results and inputs is very small.

The string-net condensate is a new class of materials which exhibits the quantum topological order. In order to answer the important question, "how useful is the string-net condensate in quantum information processing?", we consider the most basic example of the string-net condensate, namely the MONEY gauge string-net condensate on the CARDINAL-dimensional hexagonal lattice, and show that the universal measurement-based quantum computation (in the sense of the quantum computational webs) is possible on it by using the framework of the quantum computational tensor network. This result implies that even the most basic example of the string-net condensate is equipped with the correlation space that has the capacity for the universal quantum computation.

We develop a classification method for incoming pieces of evidence in NORP theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of evidence by comparing them with prototypes representing the clusters, instead of making a full clustering of all evidence. This method has a computational complexity of O(M * N) for each new piece of evidence, where M is the maximum number of subsets and ORG is the number of prototypes chosen for each subset. That is, a computational complexity independent of the total number of previously arrived pieces of evidence. The parameters M and ORG are typically fixed and domain dependent in any application.

With the emergence of the ORG gradient flow technique there is renewed interest in the issue of scale setting in lattice gauge theory. Here I compare for the SU(3) Wilson gauge action non-perturbative scale functions of ORG, PERSON and PERSON (ORG), ORG and PERSON (NS), both relying on PERSON's method using the quark potential, and the scale function derived by PERSON, PERSON and PERSON (ORG) from a deconfining phase transition investigation by the PERSON group. It turns out that the scale functions are based on mutually inconsistent data, though the ORG scale function is consistent with the ORG data when their low $MONEY (MONEY) data point is removed. Besides, only the ORG scale function is consistent with CARDINAL data points calculated from the gradient flow by L\"uscher. In the range for which data exist the discrepancies between the scale functions are only up to $\pm CARDINAL of their values, but clearly visible within the statistical accuracy.

In this abstract paper, we introduce a new kernel learning method by a nonparametric density estimator. The estimator consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a CARDINAL-hot encoder by CARDINAL-nearest-neighbor optimization. The estimator generates a sparse representation for each data point. Then, we construct a nonlinear kernel matrix from the sparse representation of data. CARDINAL major advantage of the proposed kernel method is that it is relatively insensitive to its free parameters, and therefore, it can produce reasonable results without parameter tuning. Another advantage is that it is simple. We conjecture that the proposed method can find its applications in many learning tasks or methods where sparse representation or kernel matrix is explored. In this preliminary study, we have applied the kernel matrix to spectral clustering. Our experimental results demonstrate that the kernel generated by the proposed method outperforms the well-tuned NORP RBF kernel. This abstract paper is used to protect the idea, full versions will be updated later.

We consider a simplified version of a solvable model by Mandal and PERSON, which constructively demonstrates the interplay between work extraction and the increase of the FAC entropy of an information reservoir which is in contact with the physical system. We extend ORG and PERSON's main findings in several directions: ORDINAL, we allow sequences of correlated bits rather than just independent bits. ORDINAL, at least for the case of binary information, we show that, in fact, the FAC entropy is CARDINAL measure of complexity of the information that must increase in order for work to be extracted. The extracted work can also be upper bounded in terms of the increase in other quantities that measure complexity, like the predictability of future bits from past ones. ORDINAL, we provide an extension to the case of non-binary information (i.e., a larger alphabet), and finally, we extend the scope to the case where the incoming bits (before the interaction) form an individual sequence, rather than a random one. In this case, the entropy before the interaction can be replaced by ORG (LZ) complexity of the incoming sequence, a fact that gives rise to an entropic meaning of the LZ complexity, not only in information theory, but also in physics.

The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took DATE for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. PERSON (PERSON 2010) article is the ORDINAL comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment PERSON' and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity.

Purpose - To test the ability of major search engines, ORG, ORG, ORG, and Ask, to distinguish between NORP and LANGUAGE-language documents Design/methodology/approach - 50 queries, using words common in NORP and in LANGUAGE, were posed to the engines. The advanced search option of language restriction was used, once in NORP and once in LANGUAGE. The ORDINAL CARDINAL results per engine in each language were investigated. Findings - While none of the search engines faces problems in providing results in the language of the interface that is used, both ORG and ORG face problems when the results are restricted to a foreign language. Research limitations/implications - Search engines were only tested in NORP and in LANGUAGE. We have only anecdotal evidence that the problems are the same with other languages. Practical implications - Searchers should not use the language restriction in ORG and ORG when searching for foreign-language documents. Instead, searchers should use ORG or Ask. If searching for foreign language documents in ORG or ORG, the interface in the target language/country should be used. Value of paper - Demonstrates a problem with search engines that has not been previously investigated.

There are (at least) CARDINAL approaches to quantifying information. The ORDINAL, algorithmic information or NORP complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it. The ORDINAL, FAC information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out. The ORDINAL, statistical learning theory, has introduced measures of capacity that control (in part) the expected risk of classifiers. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. This note describes a new method of quantifying information, effective information, that links algorithmic information to FAC information, and also links both to capacities arising in statistical learning theory. After introducing the measure, we show that it provides a non-universal analog of NORP complexity. We then apply it to derive basic capacities in statistical learning theory: empirical PERSON-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies, counted in CARDINAL different ways for the CARDINAL capacities. We also discuss how effective information relates to information gain, FAC and mutual information.

In the setting of a metric space equipped with a doubling measure and supporting a Poincar\'e inequality, and based on results by Bj\"orn and Shanmugalingam (DATE), we show that functions of bounded variation can be extended from any bounded uniform domain to the whole space. Closely related to extensions is the concept of boundary traces, which have previously been studied by PERSON (DATE). On spaces that satisfy a suitable locality condition for sets of finite perimeter, we establish some basic results for the traces of functions of bounded variation. Our analysis of traces also produces novel results on the behavior of functions of bounded variation in their jump sets.

The notion of profile appeared in DATE, which was mainly due to the need to create custom applications that could be adapted to the user. In this paper, we treat the different aspects of the user's profile, defining it, profile, its features and its indicators of interest, and then we describe the different approaches of modelling and acquiring the user's interests.

We present PERSON LINUCB, an algorithm for con-textual multi-armed bandits. This algorithm uses ORG to find the optimal exploration of the ORG. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

Physical entities are ultimately (re)constructed from elementary yes/no events, in particular clicks in detectors or measurement devices recording quanta. Recently, the interpretation of certain such clicks has given rise to unfounded claims which are neither necessary nor sufficient, although they are presented in that way. In particular, clicks can neither inductively support nor "(WORK_OF_ART" the Kochen-Specker theorem, which is a formal result that has a deductive proof by contradiction. More importantly, the alleged empirical evidence of quantum contextuality, which is "inferred" from violations of bounds of classical probabilities by quantum correlations, is based on highly nontrivial assumptions, in particular on physical omniscience.

Suspicions that the world might be some sort of a machine or algorithm existing ``in the mind'' of some symbolic number cruncher have lingered from antiquity. Although popular at times, the most radical forms of this idea never reached mainstream. Modern developments in physics and computer science have lent support to the thesis, but empirical evidence is needed before it can begin to replace our contemporary world view.

This paper proposes a theory of creativity, referred to as honing theory, which posits that creativity fuels the process by which culture evolves through communal exchange amongst minds that are self-organizing, self-maintaining, and self-reproducing. According to honing theory, minds, like other selforganizing systems, modify their contents and adapt to their environments to minimize entropy. Creativity begins with detection of high psychological entropy material, which provokes uncertainty and is arousalinducing. The creative process involves recursively considering this material from new contexts until it is sufficiently restructured that arousal dissipates. Restructuring involves neural synchrony and dynamic binding, and may be facilitated by temporarily shifting to a more associative mode of thought. A creative work may similarly induce restructuring in others, and thereby contribute to the cultural evolution of more nuanced worldviews. Since lines of cultural descent connecting creative outputs may exhibit little continuity, it is proposed that cultural evolution occurs at the level of self-organizing minds, outputs reflect their evolutionary state. Honing theory addresses challenges not addressed by other theories of creativity, such as the factors that guide restructuring, and in what sense creative works evolve. Evidence comes from empirical studies, an agent-based computational model of cultural evolution, and a model of concept combination.

An idea is not a replicator because it does not consist of coded self-assembly instructions. It may retain structure as it passes from CARDINAL individual to another, but does not replicate it. The cultural replicator is not an idea but an associatively-structured network of them that together form an internal model of the world, or worldview. A worldview is a primitive, uncoded replicator, like the autocatalytic sets of polymers widely believed to be the earliest form of life. Primitive replicators generate self-similar structure, but because the process happens in a piecemeal manner, through bottom-up interactions rather than a top-down code, they replicate with low fidelity, and acquired characteristics are inherited. Just as polymers catalyze reactions that generate other polymers, the retrieval of an item from memory can in turn trigger other items, thus cross-linking memories, ideas, and concepts into an integrated conceptual structure. Worldviews evolve idea by idea, largely through social exchange. An idea participates in the evolution of culture by revealing certain aspects of the worldview that generated it, thereby affecting the worldviews of those exposed to it. If an idea influences seemingly unrelated fields this does not mean that separate cultural lineages are contaminating one another, because it is worldviews, not ideas, that are the basic unit of cultural evolution.

Some PERSON centenary reflections on whether incompleteness is really serious, and whether mathematics should be done somewhat differently, based on using algorithmic complexity measured in bits of information. [Enriques lecture given DATE, at ORG.]

This is an alternative version of the course notes in PERSON. The previous version is based on measuring the size of lisp s-expressions. This version is based on measuring the size of what I call lisp m-expressions, which are lisp s-expressions with most parentheses omitted. This formulation of algorithmic information theory is harder to understand than the one that was presented in PERSON, but the constants obtained in all theorems are now CARDINAL the size that they were before. It is not clear to me which version of algorithmic information theory is to be preferred.

We allow representing and reasoning in the presence of nested multiple aggregates over multiple variables and nested multiple aggregates over functions involving multiple variables in answer sets, precisely, in answer set optimization programming and in answer set programming. We show the applicability of the answer set optimization programming with nested multiple aggregates and the answer set programming with nested multiple aggregates to ORG, a fundamental a priori optimization problem in ORG.

PERSON is important issue in reinforcement learning. In this paper, we bridge the gap between reinforcement learning and knowledge representation, by providing a rich knowledge representation framework, based on normal logic programs with answer set semantics, that is capable of solving model-free reinforcement learning problems for more complex do-mains and exploits the domain-specific knowledge. We prove the correctness of our approach. We show that the complexity of finding an offline and online policy for a model-free reinforcement learning problem in our approach is ORG-complete. Moreover, we show that any model-free reinforcement learning problem in ORG environment can be encoded as a ORG problem. The importance of that is model-free reinforcement

In this article, we calculate the contributions of the vacuum condensates up to dimension-6 including the $\mathcal{O}(\alpha_s)$ corrections to the quark condensates in the operator product expansion, then study the masses and decay constants of the pseudoscalar, scalar, vector and axial-vector heavy-light mesons with the ORG sum rules in a systematic way. The masses of the observed mesons $(D,ORG, $(D_s,D_s^*)$, $(D_0^*(2400),D_1(2430))$, $(D_{s0}^*(2317),D_{s1}(2460))$, $(B,ORG, $(B_s,GPE can be well reproduced, while the predictions for the masses of the $(PERSON}, PERSON and $(B^*_{s0}, B_{s1})$ can be confronted with the experimental data in the future. We obtain the decay constants of the pseudoscalar, scalar, vector and axial-vector heavy-light mesons, which have many phenomenological applications in studying the semi-leptonic and ORG decays of the heavy-light mesons.

In this article, we calculate the masses and residues of the heavy baryons MONEY with spin-parity ${MONEY with the ORG sum rules. The numerical values are compatible with experimental data and other theoretical estimations.

Well, hello there! I'm just fine, thanks! I had a very eventful night TIME at work. Can I just say, that the older I get, the more I hate teenagers. I'm serious. I've been working at the skate center since DATE, and they just get shittier and shittier every year. Now, there are some exceptions. I know this girl named PERSON who is DATE, and is wise beyond her years. PERSON has a great head on her shoulders, and she doesn't ACT 17. Most other teenagers blow, though. TIME I broke up a fight between a DATE boy and a DATE boy. My ORG training is paying off, because I snatched those boys up like they were a couple of puppies fighting over a cheeto. But they were fighting over garbage. CARDINAL of them called the other one of them a bitch in front of a girl or some nonsense like that. Silliness. ORG, myself and another employee of mine broke the fight up, only to realize no one else was out there with us, and that we had to wade throught the rabble that had encircled us. ('FIGHT FIGHT FIGHT!!') I find out that my adult coworker was just hanging out in the office, and the DJ-or 'the bitch' as I like to call her(see ORDINAL blog post for more info on the bitch)- was chilling out, eating her dinner away from the lights and music controls, which would have been helpful to bust up the crowd. I was irritated, because in breaking up said fight, I took a punch to the arm. I was also irritated because they made me run, and they made me sweat. SO I snatched the kid up and took him to the office, and went to find the DJbitch to tell her to get back to the ORG booth. I yelled at her. I did not mean to, I was flustered and frustrated, so I let her have it, but then I tried to apologize, and she wasn't having it. She just stomped out the door, and didn't bother to ask me (her boss) if it was okay to leave. This is why I hate teenagers. Because if they're not doing dumb shit like fighting over something stupid, they're being disrespectful assholes to their bosses and coworkers. I need a new job. ON a lighter note. I have a new passion* in this world. It is a band called urlLink Five Star Iris . For anyone who has ever been part of the GPE/Columbus local music scene, you may remember them as WORK_OF_ART. This band is truly awesome. It has been a while since I've been passionate about a band. The last one was ORG-until one too many extasy popping teenagers (damned teenagers!!) ruined my PERSON experiences. Anywho...the guys of 5SI are some hard working guys. They are constantly on the road, because they are trying to make it. I have made it my personal mission to get the word out about them in GPE, and I've even bugged the hell out of EVENT to get them on the air. (Which is looking futile, but radio in GPE is a whole other diatribe. I'll have to put that in a separate post.) They've opened for WORK_OF_ART, and Fuel. They have a Fuel-y-ish sound, except they rock just a bit harder. They play some originals, which are well written, and catchy. Its like they're the cover band in my brain. They played NORP Girl, (PERSON) CARDINAL, (U2) I Want You to Want Me, (PERSON) and PERSON in the Sun, (PERSON) during the ORDINAL set at the Loft DATE. I can't say enough about them really. For anyone who's curious, you can truck on over to the site, which has mp3's. GPE nite or good morning! *and this is not because I think PERSON, the bassist is cute .

Hey folks! There's not too much new in my world. Oh yeah, Joel and I decided not to take a break. Even though the marriage issue isn't resolved...we've decided we like being happy together way more then we'd like being lonely apart. I'm sure eventually he'll really need one, but until then, I guess he's stuck with me. The new mission I have in life is finding my roomate (who is also PERSON's brother, for those of you who don't know.) PERSON is a terribly nice guy who has trouble meeting girls you would want to stick around. I've put his picture on several personals websites, and sneakily diverted those girls here to this website, so that they can learn a little more about him. PERSON is handsome and driven. He works as a claims auditor at AFLAC. He's been in college, taking his time (like me) for DATE now. He's a business finance major. He's supernice, he just has issues with the wrong kind of girl being attracted to him. He's very responsible, but likes to have a good time. I have known PERSON for DATE now. I have been dating his younger brother, PERSON, for CARDINAL of those CARDINAL. Despite recent problems, PERSON and I are doing well, and think PERSON needs to go out on dates. He had his heart broken DATE by his ORDINAL love, and has been uncapable of finding decent female friends ever since. Help a brotha out! Give Eric a Holla! You can email PERSON at jledge@knology.net I hope this information is useful. ;) I'll keep you all posted on the search!

Hello all. It's PERSON. How is everyone's book going for them? The Great Gatsby is pretty good. I thought it would be boring but I actually got into the story, although some parts of it were confusing. The people in our discussion group were having a hard time trying to figure out Dr. PERSON. Does he represent God? PERSON? Or just a random billboard, watching everything? Anyone have any ideas? Also, we had a group consensus that we dislike PERSON and Mrs. PERSON (well, mostly a consensus). I have to go, but I'll come back later! Bye

Hey everyone, this is a test to see if it works. If any of you have suggestions, i can change the templates and settings to suit everyone. Also, does anyone have any good AP LANGUAGE or literature links I can put up on the website?

Hello everyone. Sorry I haven't posted in ages, I've been sick, in school, and sleepy. Getting up at TIME sucks :( So anywho. I heard you guys were at the library DATE and I think PERSON said he saw me. Sadly I didn't see you guys, otherwise I would have stopped by. Hopefully your study group thingy went well and I'll get to meet you all some time soon. I was there picking up some stuff to read for school to take to the beach. Yah I'm going to the beach DATE :) Well I gotta run, I'll post more about myself later PERSON. Nice site by the way

My japanese name is 中村 PERSON (center of the village) 海斗 PERSON (big dipper of the ocean) .

urlLink Brains can hurt job applicants : This is just amazing. A teacher with a list of qualifications QUANTITY long tries to get a teaching gig in GPE. Here is the response. 'Recently, I interviewed with a school in one of the metro GPE counties, only to receive an e-mail from the principal stating, 'Though your qualifications are quite impressive, I regret to inform you that we have selected another candidate. It was felt that your demeanor and therefore presence in the classroom would serve as an unrealistic expectation as to what high school students could strive to achieve or become. However, it is highly recommended that you seek employment at the collegiate level; there your intellectual comportment would be greatly appreciated. Good luck.''

urlLink 13abc.com: GPE Priest-Nun Slaying Is it OK to post this, since it was on national news TIME also?

'To find the point where hypothesis and fact meet; the delicate equilibrium between dream and reality; the place where fantasy and earthly things are metamorphosed into a work of art; this is what mans journey is about, I think.' ORG

urlLink Crazymaggiemay I've converted my Mom. She's entitled her new blog, urlLink PERSON, PERSON, and I . Stop by and check it out. She's still getting started, and I'm going to help her tweak her template, get some comments, and some other fun stuff. So, stay tuned. For now, leave me comments here about it if you're interested.

Wanting Out Lyrics I turned around and there you were standing in front of me I can't ignore the fact that you were back for more It's kind of funny when you think about it It's kind of hard to agree to disagree and we're back to the step number no but like you want me back So take me away 'cause I don't want to be back I should learn to speak up when enough is enough and I'm wanting out I'm wanting out I saw you smiling in the photograph and I remember how you used to laugh But that was then forgive and forget It's just easiest So take me away 'cause I don't want to be back I should learn to speak up when enough is enough and I'm wanting out and I'm wanting out Da da da da da da da oooooooh And I don't know if I can get through this And I never knew that before Oh You make up your mind and suddenly find you're wanting more So take me away Cause I don't want to be back I should learn to speak up when enough is enough and I'm wanting out Oh I'm wanting out [repeat] And I'm wanting out And i'm wanting out And i'm wanting out Oh. And I'm wanting out

As an ORG, I develop in-house courses, among other things. And I have always wondered how I can do a better job structuring the content of my Financial Statements course i.e. what comes ORDINAL, ORDINAL, etc. I believe a good presentation draws people into the course, and urlLink this book does just that:- 1: The Five Rules For Successful Stock Investing Our CARDINAL rules of profitable stock investing. 2: Seven Mistakes to Avoid Costly errors committed by even the best investors. 3: Economic Moats An economic advantage you can't afford to overlook. 4: WORK_OF_ART The key to unlocking a kingdom of critical information. 5: Financial Statements Explained What information to find where and why it's important. 6: Analyzing a Company: The Basics Looking at growth, profitability, and financial health. 7: Analyzing a Company: Management Delving into compensation, character, and running the business. 8: Avoiding LOC Ignore these CARDINAL warning signs at your own peril. 9: Valuation: The LOC What price multiples and yields tell about a company's value. 10: Valuation: Intrinsic Value Learning from cash flow, present value, and discount rates. 11: Putting It All Together Applying the principles to real-world examples. CARDINAL: The TIME How to tell quickly if a company is worth in-depth investigation. Plus—individual chapters on all these industries! Chapters explain specific factors that drive industries • Health Care • Consumer Services • Business Services • Banks • Asset Management and Insurance • Hardware • Media • Telecom • Consumer Goods • Industrial Materials • Energy • Utilities I like the way the&nbsp;content is structured. &nbsp; [And at the moment, it sells at a PERCENT discount for&nbsp; urlLink $MONEY at you-know-where .]

Excuse me, is your refrigerator running? Because if it is, then it probably runs like you...very homosexually! The social rules in an office can be subtle and sometimes hard to grasp. It's not always apparent where social lines lie regarding acceptable conversation or behavior. An obvious example is that you can't tell the woman whose ass is a pair of medicine balls strapped together with sweat pants to stop going to the snack machine with a handful of quarters and an empty sack. That would get you fired. Telling that same woman to only THINK what she's doing, not say it, would at least get you a reprimand. Here is a list that might help figure out what to do in troublesome office situations. CARDINAL. ORG from your ORG director. This person's job is to manipulate people. Listen to how they speak and you will learn how to control people with the imperceptible tug of a string. CARDINAL. Always appear friendly or at least approachable. Relax your face and look at it in a mirror. Do you look like a bitch? A grumpy, Eskimo troll? Regardless of whether you FEEL friendly, people judge you by the way you look and move. CARDINAL. Pulling the corners of your mouth towards your ears is not a smile. It is an obviously forced attempt at civility and makes you look unpleasant. Raise your cheeks upwards when smiling. A sly grin can quickly substitute and only takes CARDINAL the muscles. CARDINAL. Modulate your voice to appear sincere when answering the most frequently asked rhetorical question in GPE, 'How ya doin'?' This is a greeting and not a genuine query; so don’t be concerned with telling them how you actually feel. The easiest, most amicable reply is, 'Great! How about yourself?' Also, listen to the person's tone when they ask you; sometimes a cheery acknowledgement of their presence is all that is necessary. CARDINAL. It is tiresome to exchange words every time you cross paths with someone, so it becomes necessary to greet people in a shorter manner. Try giving them a quick nod or, for the more socially challenged offices, looking straight ahead as if they didn't exist. Be conscious of which of these tactics your approaching target will employ. A friendly nod aimed at an oblivious recipient is awkward. CARDINAL. Become aware of your unconscious mannerisms. Do not fidget or cause undue attention towards yourself. It distracts others and causes them to hate you. CARDINAL. Do not look up gay sex stories on the Internet while at work. Your coworkers will find out and gossip about you being a disgusting old pervert.

Have you noticed the Shrek Postmarks from our friends at ORG? This makes me ill. CARDINAL: pop culture is pervasive enough as it is (which is what makes it pop culture, I guess), and I would rather not have its agents shoved in my face everywhere I go. CARDINAL: its CARDINAL more place you can't look without being marketed to. Shocked a Graphic Designer is so anti-marketing, or rather this type of marketing? urlLink I'm not the only one . If only I were as rich as I am idealistic.

Please any of you who read this and know of any good blogs, leave me a link. Because I have been having a hard time finding CARDINAL-way decent blogs.

Well DATE was an adventure in why I need meds. I woke up at TIME, rushed around to get out the door and took the kids to the mall- I never sleep in that late so I was running atround like a mad man and missed my morind and TIME dose- and low and behold by 4pm I was a nervous wreck. I got home and took my meds and now I am in the process of defragging. I think I am going to go play pool tonite and try to shake this nervous/anxious feeling. Ya I know- I am getting hopelessly boring, but My muse is on vacation. ORG Manically Yours, ~Tiffany