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The purpose of a wireless sensor network (WSN) is to provide the users with access to the information of interest from data gathered by spatially distributed sensors. Generally the users require only certain aggregate functions of this distributed data. Computation of this aggregate data under the end-to-end information flow paradigm by communicating all the relevant data to a central collector PERSON is a highly inefficient solution for this purpose. An alternative proposition is to perform in-network computation. This, however, raises questions such as: what is the optimal way to compute an aggregate function from a set of statistically correlated values stored in different nodes; what is the security of such aggregation as the results sent by a compromised or faulty node in the network can adversely affect the accuracy of the computed result. In this paper, we have presented an energy-efficient aggregation algorithm for WSNs that is secure and robust against malicious insider attack by any compromised or faulty node in the network. In contrast to the traditional snapshot aggregation approach in WSNs, a node in the proposed algorithm instead of unicasting its sensed information to its parent node, broadcasts its estimate to all its neighbors. This makes the system more fault-tolerant and increase the information availability in the network. The simulations conducted on the proposed algorithm have produced results that demonstrate its effectiveness.

Intrusion detection in wireless ad hoc networks is a challenging task because these networks change their topologies dynamically, lack concentration points where aggregated traffic can be analyzed, utilize infrastructure protocols that are susceptible to manipulation, and rely on noisy, intermittent wireless communications. Security remains a major challenge for these networks due their features of open medium, dynamically changing topologies, reliance on co-operative algorithms, absence of centralized monitoring points, and lack of clear lines of defense. In this paper, we present a cooperative, distributed intrusion detection architecture based on clustering of the nodes that addresses the security vulnerabilities of the network and facilitates accurate detection of attacks. The architecture is organized as a dynamic hierarchy in which the intrusion data is acquired by the nodes and is incrementally aggregated, reduced in volume and analyzed as it flows upwards to the cluster-head. The cluster-heads of adjacent clusters communicate with each other in case of cooperative intrusion detection. For intrusion related message communication, mobile agents are used for their efficiency in lightweight computation and suitability in cooperative intrusion detection. Simulation results show effectiveness and efficiency of the proposed architecture.

Cryptography and PERSON are CARDINAL techniques commonly used to secure and safely transmit digital data. Nevertheless, they do differ in important ways. In fact, cryptography scrambles data so that they become unreadable by eavesdroppers; while, steganography hides the very existence of data so that they can be transferred unnoticed. Basically, steganography is a technique for hiding data such as messages into another form of data such as images. Currently, many types of steganography are in use; however, there is yet no known steganography application for query languages such as ORG. This paper proposes a new steganography method for textual data. It encodes input text messages into ORG carriers made up of ORG queries. In effect, the output ORG carrier is dynamically generated out of the input message using a dictionary of words implemented as a hash table and organized into CARDINAL categories, each of which represents a particular character in the language. Generally speaking, every character in the message to hide is mapped to a random word from a corresponding category in the dictionary. Eventually, all input characters are transformed into output words which are then put together to form an ORG query. Experiments conducted, showed how the proposed method can operate on real examples proving the theory behind it. As future work, other types of ORG queries are to be researched including ORG, ORG, and ORG queries, making the ORG carrier quite puzzling for malicious ORDINAL parties to recuperate the secret message that it encodes.

The classical methods used by recursion theory and formal logic to block paradoxes do not work in ORG information theory. Since ORG information can exist as a coherent superposition of the classical ``yes'' and ``no'' states, certain tasks which are not conceivable in the classical setting can be performed in the quantum setting. Classical logical inconsistencies do not arise, since there exist fixed point states of the diagonalization operator. In particular, closed timelike curves need not be eliminated in the quantum setting, since they would not lead to any paradoxical outcome controllability. ORG information theory can also be subjected to the treatment of inconsistent information in databases and expert systems. It is suggested that any CARDINAL pieces of contradicting information are stored and processed as coherent superposition. In order to be tractable, this strategy requires quantum computation.

The purpose of this paper is to examine the possible existence or construction of traversable wormholes supported by generalized ORG gas (ORG) by starting with a general line element and the PERSON tensor, together with the equation of state, thereby continuing an earlier study by the author of wormholes supported by phantom energy. Numerical techniques are used to demonstrate the existence of wormhole spacetimes that (CARDINAL) meet the flare-out conditions at the throat, (CARDINAL) are traversable by humanoid travelers, thanks to low tidal forces and short proper distances near the throat, and (CARDINAL) are asymptotically flat. There appears to be an abundance of solutions that avoid an event horizon, suggesting the possibility of naturally occurring wormholes.

A recent study by PERSON et GPE has shown that the galactic halo possesses the necessary properties for supporting traversable wormholes, based on CARDINAL observational results, the density profile due to NORP et al. and the observed flat rotation curves of galaxies. Using a method for calculating the deflection angle pioneered by PERSON, it is shown that the deflection angle diverges at the throat of the wormhole. The resulting photon sphere has a radius of CARDINAL ly. Given the dark-matter background, detection may be possible from past data using ordinary light.

We prove that the mean curvature $\tau$ of the slices given by a constant mean curvature foliation can be used as a time function, i.e. $PERSON is smooth with non-vanishing gradient.

The existence of closed hypersurfaces of prescribed scalar curvature in globally hyperbolic NORP manifolds is proved provided there are barriers.

This paper describes a novel approach to grammar induction that has been developed within a framework designed to integrate learning with other aspects of computing, ORG, mathematics and logic. This framework, called "information compression by multiple alignment, unification and search" (ICMAUS), is founded on principles of PERSON pioneered by PERSON and others. Most of the paper describes SP70, a computer model of the ORG framework that incorporates processes for unsupervised learning of grammars. An example is presented to show how the model can infer a plausible grammar from appropriate input. Limitations of the current model and how they may be overcome are briefly discussed.

We establish an axiomatization for ORG processes, which is a quantum generalization of process algebra ORG (Algebra of Communicating Processes). We use the framework of a quantum process configuration $MONEY p, \varrho\rangle$, but we treat it as CARDINAL relative independent part: the structural part $p$ and the quantum part $PERSON, because the establishment of a sound and complete theory is dependent on the structural properties of the structural part $PERSON We let the quantum part $PERSON be the outcomes of execution of $p$ to examine and observe the function of the basic theory of quantum mechanics. We establish not only a strong bisimularity for quantum processes, but also a weak bisimularity to model the silent step and abstract internal computations in ORG processes. The relationship between ORG bisimularity and classical bisimularity is established, which makes an axiomatization of ORG processes possible. An axiomatization for quantum processes called NORP is designed, which involves not only quantum information, but also classical information and unifies ORG computing and classical computing. ORG can be used easily and widely for verification of most quantum communication protocols.

ORG algorithms require less operations than classical algorithms. The exact reason of this has not been pinpointed until now. Our explanation is that ORG algorithms know in advance PERCENT of the solution of the problem they will find in the future. In fact they can be represented as the sum of all the possible histories of a respective "advanced information classical algorithm". This algorithm, given the advanced information (PERCENT of the bits encoding the problem solution), performs the operations (oracle's queries) still required to identify the solution. Each history corresponds to a possible way of getting the advanced information and a possible result of computing the missing information. This explanation of the quantum speed up has an immediate practical consequence: the speed up comes from comparing CARDINAL classical algorithms, with and without advanced information, with no physics involved. This simplification could open the way to a systematic exploration of the possibilities of speed up.

Parametric density estimation, for example as NORP distribution, is the base of the field of statistics. Machine learning requires inexpensive estimation of much more complex densities, and the basic approach is relatively costly maximum likelihood estimation (ORG). There will be discussed inexpensive density estimation, for example literally fitting a polynomial (or PERSON series) to the sample, which coefficients are calculated by just averaging monomials (or sine/cosine) over the sample. Another discussed basic application is fitting distortion to some standard distribution like NORP - analogously to ORG, but additionally allowing to reconstruct the disturbed density. Finally, by using weighted average, it can be also applied for estimation of non-probabilistic densities, like modelling mass distribution, or for various clustering problems by using negative (or complex) weights: fitting a function which sign (or argument) determines clusters. The estimated parameters are approaching the optimal values with error dropping like $MONEY, where $n$ is the sample size.

Experimentally observed violations of ORG inequalities rule out local realistic theories. Consequently, the ORG vector becomes a strong candidate for providing an objective picture of reality. However, such an ontological view of quantum theory faces difficulties when spacelike measurements on entangled states have to be described, because time ordering of spacelike events can change under PERSON-Poincar\'e transformations. In the present paper it is shown that a necessary condition for consistency is to require state vector reduction on the backward light-cone. A fresh approach to the quantum measurement problem appears feasible within such a framework.

The agenda of quantum algorithmic information theory, ordered `top-down,' is the ORG halting amplitude, followed by the quantum algorithmic information content, which in turn requires the theory of quantum computation. The fundamental atoms processed by ORG computation are the quantum bits which are dealt with in ORG information theory. The theory of quantum computation will be based upon a model of universal ORG computer whose elementary unit is a CARDINAL-port interferometer capable of MONEYU(2)$ transformations. Basic to all these considerations is quantum theory, in particular PERSON space quantum mechanics.

The detection of some tiny gravitomagnetic effects in the field of the LOC by means of artificial satellites is a very demanding task because of the various other perturbing forces of gravitational and non-gravitational origin acting upon them. Among the gravitational perturbations a relevant role is played by the LOC solid and ocean tides. In this communication I outline their effects on the detection of the Lense-Thirring drag of the orbits of ORG and LAW, currently analyzed, and the proposed ORG experiment devoted to the measurement of the clock effect.

The discovery that the ORG is undergoing an accelerated expansion has suggested the existence of an evolving equation of state. This paper discusses various wormhole solutions in a spherically symmetric spacetime with an equation of state that is both space and time dependent. The solutions obtained are exact and generalize earlier results on static wormholes supported by phantom energy.

These informal notes deal with some topics related to analysis on metric spaces.

These informal notes are concerned with sums and averages in various situations in analysis.

We present a concrete design for PERSON's incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a PERSON variant based on a stochastic PERSON together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.

The theory introduced, presented and developed in this paper, is concerned with an enriched extension of the theory of ORG pioneered by ORG. The enrichment discussed here is in the sense of valuated categories as developed by ORG. This paper relates ORG to an abstraction of the theory of ORG pioneered by PERSON, and provides a natural foundation for "soft computation". To paraphrase PERSON, the impetus for the transition from a hard theory to a soft theory derives from the fact that both the generality of a theory and its applicability to real-world problems are substantially enhanced by replacing various hard concepts with their soft counterparts. Here we discuss the corresponding enriched notions for indiscernibility, subsets, upper/lower approximations, and rough sets. Throughout, we indicate linkages with the theory of ORG pioneered by PERSON. We pay particular attention to the all-important notion of a "linguistic variable" - developing its enriched extension, comparing it with the notion of conceptual scale from ORG, and discussing the pragmatic issues of its creation and use in the interpretation of data. These pragmatic issues are exemplified by the discovery, conceptual analysis, interpretation, and categorization of networked information resources in ORG, ORG currently being developed for the management and interpretation of the universe of resource information distributed over ORG.

Let $PERSON be real-valued compactly supported sufficiently smooth function. It is proved that the scattering data MONEY MONEY S^2$, $\forall k>0,$ determine $q$ uniquely. Here $ORG S^2$ is a fixed direction of the incident plane wave.

This paper investigates the randomness properties of a function of the divisor pairs of a natural number. This function, the antecedents of which go to very ancient times, has randomness properties that can find applications in cryptography, key distribution, and other problems of computer science. It is shown that the function is aperiodic and it has excellent autocorrelation properties.

A universal ORG computer can be constructed using NORP anyons. CARDINAL qubit quantum logic gates such as controlled-NOT operations are performed using topological effects. Single-anyon operations such as hopping from site to site on a lattice suffice to perform all quantum logic operations. ORG computation using NORP anyons shares some but not all of the robustness of quantum computation using non-abelian anyons.

Before PERSON made his crucial contributions to the theory of computation, he studied the question of whether ORG mechanics could throw light on the nature of free will. This article investigates the roles of quantum mechanics and computation in free will. Although quantum mechanics implies that events are intrinsically unpredictable, the `pure stochasticity' of ORG mechanics adds only randomness to decision making processes, not freedom. By contrast, the theory of computation implies that even when our decisions arise from a completely deterministic decision-making process, the outcomes of that process can be intrinsically unpredictable, even to -- especially to -- ourselves. I argue that this intrinsic computational unpredictability of the decision making process is what give rise to our impression that we possess free will. Finally, I propose a `Turing test' for free will: a decision maker who passes this test will tend to believe that he, she, or it possesses free will, whether the world is deterministic or not.

A path information is defined in connection with different possible paths of irregular dynamic systems moving in its phase space between CARDINAL points. On the basis of the assumption that the paths are physically differentiated by their actions, we show that the maximum path information leads to a path probability distribution in exponentials of action. This means that the most probable paths are just the paths of least action. This distribution naturally leads to important laws of normal diffusion. A conclusion of this work is that, for probabilistic mechanics or irregular dynamics, the principle of maximization of path information is equivalent to the least action principle for regular dynamics. We also show that an average path information between the initial phase volume and the final phase volume can be related to the entropy change defined with natural invariant measure of dynamic system. Hence the principles of least action and maximum path information suggest the maximum entropy change. This result is used for some chaotic systems evolving in fractal phase space in order to derive their invariant measures.

I study the class of problems efficiently solvable by a ORG computer, given the ability to "postselect" on the outcomes of measurements. I prove that this class coincides with a classical complexity class called ORG, or ORG. Using this result, I show that several simple changes to the axioms of quantum mechanics would let us solve ORDINAL-complete problems efficiently. The result also implies, as an easy corollary, a celebrated theorem of PERSON, PERSON, and NORP that ORG is closed under intersection, as well as a generalization of that theorem due to Fortnow and PERSON. This illustrates that ORG computing can yield new and simpler proofs of major results about classical computation.

In this paper, we give a frequency interpretation of negative probability, as well as of extended probability, demonstrating that to a great extent, these new types of probabilities, behave as conventional probabilities. Extended probability comprises both conventional probability and negative probability. The frequency interpretation of negative probabilities gives supportive evidence to the axiomatic system built in (PERSON, DATE; GPE) for extended probability as it is demonstrated in this paper that frequency probabilities satisfy all axioms of extended probability.

Supervised artificial neural networks with the rapidity-mass matrix (ORG) inputs were studied using several PERSON event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ORG feature space can simplify searches for signatures of new physics at the LHC when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in searches for new physics, how to filter out PERSON events for model-agnostic searches, and how to separate gluon and quark jets for PERSON measurements.

We treat secret key extraction when the eavesdropper has correlated quantum states. We propose quantum privacy amplification theorems different from ORG's, which are based on quantum conditional R\'{e}nyi entropy of order 1+s. Using those theorems, we derive an exponential decreasing rate for leaked information and the asymptotic equivocation rate, which have not been derived hitherto in the quantum setting.

We consider branes $MONEY in a NORP bulk, where the stress energy tensor is dominated by the energy density of a scalar fields map $WORK_OF_ARTPERSON with potential $MONEY, where $\mc S$ is a semi-NORP moduli space. By transforming the field equation appropriately, we get an equivalent field equation that is smooth across the singularity $r=0$, and which has smooth and uniquely determined solutions which exist across the singularity in MONEY-\e,\e)$. Restricting a solution to $(-\e,0)$ \resp $(0,\e)$, and assuming $n$ odd, we obtain branes $MONEY \resp $\hat N$ which together form a smooth hypersurface. Thus a smooth transition from big crunch to big bang is possible both geometrically as well as physically.

A partial wave analysis of FAC data for ORG Lambda-bar NORP is presented. A CARDINAL cusp is identified in the inverse process NORP-bar NORP to pbar-p at threshold using detailed balance. Partial wave amplitudes for pbar-p CARDINAL, DATE, DATE and ORDINAL exhibit a behaviour very similar to resonances observed in LOC data. With this identification, the pbar-p to NORP-bar NORP data then provide evidence for a new I = DATE, PERSON} = CARDINAL} resonance with mass M = DATE +- 20 MeV, PERSON = CARDINAL +- 35 ORG, coupling to both CARDINAL and CARDINAL.

We discuss how to generate singled peaked votes uniformly from the Impartial Culture model.

The education system for students in physics suffers (worldwide) from the absence of a deep course in probability and randomness. This is the real problem for students interested in ORG theory, ORG, and quantum foundations. Here the primitive treatment of probability and randomness may lead to deep misunderstandings of theory and wrong interpretations of experimental results. Since during my visits (in DATE and DATE) to ORG a number of students (experimenters!) asked me permanently about foundational problems of probability and randomness, especially inter-relation between classical and quantum structures, DATE I gave CARDINAL lectures on these problems. Surprisingly the interest of experiment-oriented students to mathematical peculiarities was very high. This (as well as permanent reminding of prof. PERSON) motivated me to write a text based on these lectures which were originally presented in the traditional black-board form. I hope that this might be useful for students from ORG as well as other young physicists.

The information that mobiles can access becomes very wide nowadays, and the user is faced with a dilemma: there is an unlimited pool of information available to him but he is unable to find the exact information he is looking for. This is why the current research aims to design ORG (ORG) able to continually send information that matches the user's interests in order to reduce his navigation time. In this paper, we treat the different approaches to recommend.

Rational decision making in its linguistic description means making logical decisions. In essence, a rational agent optimally processes all relevant information to achieve its goal. Rationality has CARDINAL elements and these are the use of relevant information and the efficient processing of such information. In reality, relevant information is incomplete, imperfect and the processing engine, which is a brain for humans, is suboptimal. Humans are risk averse rather than utility maximizers. In the real world, problems are predominantly non-convex and this makes the idea of rational decision-making fundamentally unachievable and PERSON called this bounded rationality. There is a trade-off between the amount of information used for decision-making and the complexity of the decision model used. This explores whether machine rationality is subjective and concludes that indeed it is.

This paper proposes the use of particle swarm optimization method (PSO) for finite element (FE) model updating. The PSO method is compared to the existing methods that use simulated annealing (ORG) or genetic algorithms (GA) for ORG model for model updating. The proposed method is tested on an unsymmetrical H-shaped structure. It is observed that the proposed method gives updated natural frequencies the most accurate and followed by those given by an updated model that was obtained using the ORG and a full ORG model. It is also observed that the proposed method gives updated mode shapes that are best correlated to the measured ones, followed by those given by an updated model that was obtained using the ORG and a full ORG model. Furthermore, it is observed that the PSO achieves this accuracy at a computational speed that is faster than that by the ORG and a full ORG model which is faster than the ORG and a full ORG model.

The oracle chooses a function out of a known set of functions and gives to the player a black box that, given an argument, evaluates the function. The player should find out a certain character of the function through function evaluation. This is the typical problem addressed by the ORG algorithms. In former theoretical work, we showed that a quantum algorithm requires the number of function evaluations of a classical algorithm that knows in advance PERCENT of the information that specifies the solution of the problem. Here we check that this PERCENT rule holds for the main quantum algorithms. In the structured problems, a classical algorithm with the advanced information, to identify the missing information should perform CARDINAL function evaluation. The speed up is exponential since a classical algorithm without advanced information should perform an exponential number of function evaluations. In unstructured database search, a classical algorithm that knows in advance PERCENT of the n bits of the database location, to identify the ORG missing bits should perform Order(2 power ORG) function evaluations. The speed up is quadratic since a classical algorithm without advanced information should perform ORG) function evaluations. The PERCENT rule identifies the problems solvable with a quantum sped up in an entirely classical way, in fact by comparing CARDINAL classical algorithms, with and without the advanced information.

We show that CARDINAL of heat dissipation per qubit occurs in measurement-based ORG computation according to ORG's principle. This result is derived by using only the fundamental fact that ORG physics respects the no-signaling principle.

The debate about which similarity measure one should use for the normalization in the case of ORG (ORG) is further complicated when one distinguishes between the symmetrical co-citation--or, more generally, co-occurrence--matrix and the underlying asymmetrical citation--occurrence--matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, CARDINAL should use the ORG index, but preferentially after adding the number of total citations (occurrences) on the main diagonal. Unlike PERSON's cosine and the PRODUCT correlation, the ORG index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the CARDINAL sets. Since the correlations in the co-occurrence matrix may partially be spurious, this property of the ORG index can be considered as an advantage in this case.

In this paper the theory of flexibly-bounded rationality which is an extension to the theory of bounded rationality is revisited. Rational decision making involves using information which is almost always imperfect and incomplete together with some intelligent machine which if it is a human being is inconsistent to make decisions. In bounded rationality, this decision is made irrespective of the fact that the information to be used is incomplete and imperfect and that the human brain is inconsistent and thus this decision that is to be made is taken within the bounds of these limitations. In the theory of flexibly-bounded rationality, advanced information analysis is used, the correlation machine is applied to complete missing information and artificial intelligence is used to make more consistent decisions. Therefore flexibly-bounded rationality expands the bounds within which rationality is exercised. Because human decision making is essentially irrational, this paper proposes the theory of marginalization of irrationality in decision making to deal with the problem of satisficing in the presence of irrationality.

This paper examines how black holes might compute in light of recent models of the black-hole final state. These models suggest that ORG information can escape from the black hole by a process akin to teleportation. They require a specific final state and restrictions on the interaction between the collapsing matter and the incoming Hawking radiation for ORG information to escape. This paper shows that for an arbitrary final state and for generic interactions between matter and Hawking radiation, the ORG information about how the hole was formed and the results of any computation performed by the matter inside the hole escapes with ORG exponentially close to CARDINAL.

This article explores the ideas that went into PERSON development of an algebra for logical inference in his book WORK_OF_ART. We explore in particular his wife PERSON's claim that he was deeply influenced by NORP logic and argue that his work was more than a framework for processing propositions. By exploring parallels between his work and NORP logic, we are able to explain several peculiarities of this work.

CARDINAL aspects of the physical side of ORG thesis are discussed. The ORDINAL issue is a variant of the LOC argument against motion, dealing with PERSON squeezed time cycles of computers. The ORDINAL argument reviews the issue of CARDINAL-to-CARDINAL computation, that is, the bijective (unique and reversible) evolution of computations and its relation to the measurement process.

In this highly speculative Letter it is argued that, under certain physical conditions, ORG's demon might be capable of breaking the ORDINAL law of thermodynamics, thereby allowing a perpetual motion machine of the ORDINAL kind, by accessing single particle capabilities.

Recurrent neural networks (ORG) are capable of learning to encode and exploit activation history over an arbitrary timescale. However, in practice, state of the art gradient descent based training methods are known to suffer from difficulties in learning long term dependencies. Here, we describe a novel training method that involves concurrent parallel cloned networks, each sharing the same weights, each trained at different stimulus phase and each maintaining independent activation histories. Training proceeds by recursively performing batch-updates over the parallel clones as activation history is progressively increased. This allows conflicts to propagate hierarchically from short-term contexts towards longer-term contexts until they are resolved. We illustrate the parallel clones method and hierarchical conflict propagation with a character-level deep ORG tasked with memorizing a paragraph of PERSON (by PERSON).

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of PERSON and FAC but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the ORDINAL derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since DATE) artificial systems.

PERSON's inequality plays an important role in linear elasticity theory. This inequality bounds the norm of the derivatives of the displacement vector by the norm of the linearized strain tensor. The kernel of the linearized strain tensor are the infinitesimal rigid-body translations and rotations (Killing vectors). We generalize this inequality by replacing the linearized strain tensor by its trace free part. That is, we obtain a stronger inequality in which the kernel of the relevant operator are the conformal ORG vectors. The new inequality has applications in General Relativity.

In this paper we try to suggest a possible novel method to determine some selected even zonal harmonics J_l of the LOC's geopotential. Time series many DATE long of suitably linearly combined residuals of some NORP orbital elements of certain existing geodetic SLR satellites would be examined. A ORG/GRACE-only background reference model should be used for the part of the geopotential which we are not interested in. The retrieved values for the even zonal harmonics of interest would be, by construction, independent of each other and of any NORP features. The so obtained mini-model could, subsequently, be used in order to enhance the accuracy and the reliability of some tests of NORP gravity, with particular emphasis to the measurement of the Lense-Thirring effect by means of ORG and LAW.

We introduce a new class of graphical models that generalizes ORG chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to CARDINAL edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global PERSON properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property. Finally, we present a causal interpretation of the new models.

Recently, ORG and PERSON suggested representing uncertainty by a weighted set of probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E'. In this paper, a notion of comparative likelihood when uncertainty is represented by a weighted set of probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.

We consider sets of ORG observables corresponding to eutactic stars. Eutactic stars are systems of vectors which are the lower dimensional ``shadow'' image, the orthogonal view, of higher dimensional orthonormal bases. Although these vector systems are not comeasurable, they represent redundant coordinate bases with remarkable properties. CARDINAL application is ORG secret sharing.

In view of the sobering findings of science, theology and to a lesser degree metaphysics is confronted with a humiliating loss, and a need for reinterpretation, of allegories and narratives which have served as guidance to the perplexed for millennia. Future revolutions of world perception might include the emergence of consciousness and superhuman artificial intelligence from universal computation, extensive virtual reality simulations, the persistence of claims of irreducible chance in the ORG, as well as contacts with alien species and the abundance of inhabited planets. As tragic and as discomforting as this might be perceived for the religious orthodoxy and by individual believers, a theology guided by science may lead us to a better and more adequate understanding of our existence. The post factum theological options are plentiful. These include dualistic scenarios, as well as (to quote PERSON), a curling or bowling deity, that is, creatio continua, or ex nihilo. These might be grounded in, or corroborated by the metaphysical enigma of existence, which appears to be immune and robust with respect to the aforementioned challenges of science.

Error correction, in the standard meaning of the term, implies the ability to correct all small analog errors and some large errors. Examining assumptions at the basis of the recently proposed quantum error-correcting codes, it is pointed out that these codes can correct only a subset of errors, and are unable to correct small phase errors which can have disastrous consequences for a quantum computation. This shortcoming will restrict their usefulness in real applications.

In this article, we study the mass spectrum of the scalar and axial-vector heavy diquark states with the ORG sum rules in a systematic way. Once the reasonable values are obtained, we can take them as basic parameters and study the new charmonium-like states as the tetraquark states.

Optimization problems are considered in the framework of tropical algebra to minimize and maximize a nonlinear objective function defined on vectors over an idempotent semifield, and calculated using multiplicative conjugate transposition. To find the minimum of the function, we ORDINAL obtain a partial solution, which explicitly represents a subset of solution vectors. We characterize all solutions by a system of simultaneous equation and inequality, and show that the solution set is closed under vector addition and scalar multiplication. A matrix sparsification technique is proposed to extend the partial solution, and then to obtain a complete solution described as a family of subsets. We offer a backtracking procedure that generates all members of the family, and derive an explicit representation for the complete solution. As another result, we deduce a complete solution of the maximization problem, given in a compact vector form by the use of sparsified matrices. The results obtained are illustrated with illuminating examples and graphical representations. We apply the results to solve real-world problems drawn from project (machine) scheduling, and give numerical examples.

ORG decision systems are being increasingly considered for use in artificial intelligence applications. Classical and quantum nodes can be distinguished based on certain correlations in their states. This paper investigates some properties of the states obtained in a decision tree structure. How these correlations may be mapped to the decision tree is considered. Classical tree representations and approximations to quantum states are provided.

Although deep neural networks (DNN) are able to scale with direct advances in computational power (e.g., memory and processing speed), they are not well suited to exploit the recent trends for parallel architectures. In particular, gradient descent is a sequential process and the resulting serial dependencies mean that DNN training cannot be parallelized effectively. Here, we show that a DNN may be replicated over a massive parallel architecture and used to provide a cumulative sampling of local solution space which results in rapid and robust learning. We introduce a complimentary convolutional bootstrapping approach that enhances performance of the parallel architecture further. Our parallelized convolutional bootstrapping DNN out-performs an identical fully-trained traditional DNN after only a single iteration of training.

Model-based coding, described by PERSON in DATE, has great potential to reduce the volume of information that needs to be transmitted in moving big data, without loss of information, from CARDINAL place to another, or in lossless communications via the internet. Compared with ordinary compression methods, this potential advantage of model-based coding in the transmission of data arises from the fact that both the transmitter ("Alice") and the receiver ("PERSON") are equipped with a grammar for the kind of data that is to be transmitted, which means that, to achieve lossless transmission of a body of data from PERSON and PERSON, a relatively small amount of information needs to be sent. Preliminary trials indicate that, with model-based coding, the volume of information to be sent from PERSON to PERSON to achieve lossless transmission of a given body of data may be MONEY of the volume of information that needs to be sent when ordinary compression methods are used. Until recently, it has not been feasible to convert PERSON vision into something that may be applied in practice. Now, with the development of the "SP theory of intelligence" and its realisation in the "SP computer model", there is clear potential to realise the CARDINAL main functions that will be needed: unsupervised learning of a grammar for the kind of data that is to be transmitted using a relatively powerful computer that is independent of PRODUCT and PERSON; the encoding by PERSON of any CARDINAL example of such data in terms of the grammar; and, with the grammar, decoding of the encoding by PERSON to retrieve the given example. It appears now to be feasible, within reasonable timescales, to bring these capabilities to a level where they may be applied to the transmission of realistically large bodies of data.

We present a unified logical framework for representing and reasoning about both probability quantitative and qualitative preferences in probability answer set programming, called probability answer set optimization programs. The proposed framework is vital to allow defining probability quantitative preferences over the possible outcomes of qualitative preferences. We show the application of probability answer set optimization programs to a variant of the well-known nurse restoring problem, called the nurse restoring with probability preferences problem. To the best of our knowledge, this development is the ORDINAL to consider a logical framework for reasoning about probability quantitative preferences, in general, and reasoning about both probability quantitative and qualitative preferences in particular.

We present a unified logical framework for representing and reasoning about both quantitative and qualitative preferences in fuzzy answer set programming, called fuzzy answer set optimization programs. The proposed framework is vital to allow defining quantitative preferences over the possible outcomes of qualitative preferences. We show the application of fuzzy answer set optimization programs to the course scheduling with fuzzy preferences problem. To the best of our knowledge, this development is the ORDINAL to consider a logical framework for reasoning about quantitative preferences, in general, and reasoning about both quantitative and qualitative preferences in particular.

Dynamics of arbitrary communication system is analysed as unreduced interaction process. The applied generalised, universally nonperturbative method of effective potential reveals the phenomenon of dynamic multivaluedness of competing system configurations forced to permanently replace each other in a causally random order, which leads to universally defined dynamical chaos, complexity, fractality, self-organisation, and adaptability (physics/9806002, physics/0211071, physics/0405063). We demonstrate the origin of huge, exponentially high efficiency of the unreduced, complex network dynamics and specify the universal symmetry of complexity (physics/0404006) as the fundamental guiding principle for creation and control of such qualitatively new kind of networks and devices. The emerging intelligent communication paradigm and its practical realisation in the form of knowledge-based networks involve the features of true, unreduced intelligence and consciousness (physics/0409140) appearing in complex (multivalued) network dynamics and results.

In this article we study a problem within ORG theory where CARDINAL - CARDINAL pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scale problems we need a method with lower computational complexity. The neural structure was found to be effective and much faster than iterative optimization for larger problems. While the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems, the metaconflict per cluster and evidence was moderate. The neural structure was able to find a global minimum over CARDINAL runs for problem sizes up to CARDINAL clusters.

This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the ORG algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original PERSON by additional steps transforming so called partial including path graph into a belief network. Its correctness is demonstrated.

There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). CARDINAL way to extend beyond words is to compare CARDINAL tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (ORG analogies and SemEval~2012 PRODUCT) and measuring compositional similarity between GPE-modifier phrases and unigrams (multiple-choice paraphrase questions).

We address ORG gate response in a mesoscopic ring threaded by a magnetic flux $MONEY The ring, composed of identical quantum dots, is symmetrically attached to CARDINAL semi-infinite CARDINAL-dimensional metallic electrodes and CARDINAL gate voltages, viz, $V_a$ and $PERSON, are applied, respectively, in each arm of the ring which are treated as the CARDINAL inputs of the ORG gate. The calculations are based on the tight-binding model and the ORG's function method, which numerically compute the conductance-energy and current-voltage characteristics as functions of the ring-electrodes coupling strengths, magnetic flux and gate voltages. Quite interestingly it is observed that, for MONEY ($\phi_0=ch/e$, the elementary flux-quantum) a high output current (CARDINAL) (in the logical sense) appears if one, and CARDINAL, of the inputs to the gate is high (1), while if both inputs are low (0) or both are high (1), a low output current (0) appears. It clearly demonstrates the ORG behavior and this aspect may be utilized in designing the electronic logic gate.

In this paper we present a short history of logics: from particular cases of CARDINAL-symbol or numerical valued logic to the general case of n-symbol or numerical valued logic. We show generalizations of CARDINAL-valued NORP logic to fuzzy logic, also from the PERSON and Lukasiewicz CARDINAL-symbol valued logics or FAC valued logic to the most general n-symbol or numerical valued refined neutrosophic logic. CARDINAL classes of neutrosophic norm (n-norm) and neutrosophic conorm (n-conorm) are defined. Examples of applications of neutrosophic logic to physics are listed in the last section. Similar generalizations can be done for ORG, and respectively n- ORG LOC.

I'll outline the latest version of my limits of math course. The purpose of this course is to illustrate the proofs of the key information-theoretic incompleteness theorems of algorithmic information theory by means of algorithms written in a specially designed version of ORG. The course is now written in HTML with PERSON applets, and is available at http://www.research.ibm.com/people/c/chaitin/lm . The LISP now used is much friendlier than before, and because its interpreter is a PERSON applet it will run in the PRODUCT browser as you browse my limits of math Web site.

We describe a new wavelet transform, for use on hierarchies or binary rooted trees. The theoretical framework of this approach to data analysis is described. Case studies are used to further exemplify this approach. A ORDINAL set of application studies deals with data array smoothing, or filtering. A ORDINAL set of application studies relates to hierarchical tree condensation. Finally, a ORDINAL study explores the wavelet decomposition, and the reproducibility of data sets such as text, including a new perspective on the generation or computability of such data objects.

Machine Consciousness and Machine Intelligence are not simply new buzzwords that occupy our imagination. Over DATE, we witness an unprecedented rise in attempts to create machines with human-like features and capabilities. However, despite widespread sympathy and abundant funding, progress in these enterprises is far from being satisfactory. The reasons for this are twofold: ORDINAL, the notions of cognition and intelligence (usually borrowed from human behavior studies) are notoriously blurred and ill-defined, and ORDINAL, the basic concepts underpinning the whole discourse are by themselves either undefined or defined very vaguely. That leads to improper and inadequate research goals determination, which I will illustrate with some examples drawn from recent documents issued by ORG and ORG. On the other hand, I would like to propose some remedies that, I hope, would improve the current state-of-the-art disgrace.

Computational Intelligence is a dead-end attempt to recreate human-like intelligence in a computing machine. The goal is unattainable because the means chosen for its accomplishment are mutually inconsistent and contradictory: "Computational" implies data processing ability while "Intelligence" implies the ability to process information. In the research community, there is a lack of interest in data versus information divergence. The cause of this indifference is the FAC's Information theory, which has dominated the scientific community since DATE. However, DATE it is clear that FAC's theory is applicable only to a specific case of data communication and is inapplicable to the majority of other occasions, where information about semantic properties of a message must be taken into account. The paper will try to explain the devastating results of overlooking some of these very important issues - what is intelligence, what is semantic information, how they are interrelated and what happens when the relationship is disregarded.

CARDINAL of the crown jewels of complexity theory is PERSON's DATE theorem that computing the permanent of an n*n matrix is #P-hard. Here we show that, by using the model of linear-optical ORG computing---and in particular, a universality theorem due to PERSON, PERSON, and GPE---one can give a different and arguably more intuitive proof of this theorem.

This paper describes a tentative model for how discrete memories transform into an interconnected conceptual network, or worldview, wherein relationships between memories are forged by way of abstractions. The model draws on PERSON's theory of how an information-evolving system could emerge through the formation and closure of an autocatalytic network. Here, the information units are not catalytic molecules, but memories and abstractions, and the process that connects them is not catalysis but reminding events (i.e. CARDINAL memory evokes another). The result is a worldview that both structures, and is structured by, self-triggered streams of thought.

Symmetry can be used to help solve many problems. For instance, PERSON's famous DATE paper ("WORK_OF_ART") uses symmetry to help derive the laws of special relativity. In artificial intelligence, symmetry has played an important role in both problem representation and reasoning. I describe recent work on using symmetry to help solve constraint satisfaction problems. Symmetries occur within individual solutions of problems as well as between different solutions of the same problem. Symmetry can also be applied to the constraints in a problem to give new symmetric constraints. Reasoning about symmetry can speed up problem solving, and has led to the discovery of new results in both graph and number theory.

Sometime in the future we will have to deal with the impact of ORG's being mistaken for humans. For this reason, I propose that any autonomous system should be designed so that it is unlikely to be mistaken for anything besides an autonomous sysem, and should identify itself at the start of any interaction with another agent.

The evolution equation of ORG cosmic density perturbations in the realm of FAC theory of gravity is obtained.The de Sitter metric fluctuation is computed in terms of the spin-torsion background density.

Fluctuations on de Sitter solution of FAC field equations are obtained in terms of the matter density primordial density fluctuations and spin-torsion density and matter density fluctuations obtained from ORG data. Einstein-de Sitter solution is shown to be unstable even in the absence of torsion.The spin-torsion density fluctuation is simply computed from the ORG equations and from ORG data.

ORDINAL we describe briefly an information-action method for the study of stochastic dynamics of hamiltonian systems perturbed by thermal noise and chaotic instability. It is shown that, for the ensemble of possible paths between CARDINAL configuration points, the action principle acquires a statistical form $<\delta A>=0$. The main objective of this paper is to prove that, via this information-action description, some quantum like uncertainty relations such as $<\Delta PERSON for action, MONEY\Delta x><\Delta P>\geq\frac{1}{\eta}$ for position and momentum, and $<\Delta H><\Delta ORG for hamiltonian and time, can arise for stochastic dynamics of classical hamiltonian systems. A corresponding commutation relation can also be found. These relations describe, through action or its conjugate variables, the fluctuation of stochastic dynamics due to random perturbation characterized by the parameter $MONEY

We discuss the power and limitation of various "advice," when it is given particularly to weak computational models of CARDINAL-tape linear-time Turing machines and CARDINAL-way finite (state) automata. Of various advice types, we consider deterministically-chosen advice (not necessarily algorithmically determined) and randomly-chosen advice (according to certain probability distributions). In particular, we show that certain weak machines can be significantly enhanced in computational power when randomized advice is provided in place of deterministic advice.

This article reviews the history of digital computation, and investigates just how far the concept of computation can be taken. In particular, I address the question of whether the universe itself is in fact a giant computer, and if so, just what kind of computer it is. I will show that the universe can be regarded as a giant ORG computer. The quantum computational model of the universe explains a variety of observed phenomena not encompassed by the ordinary laws of physics. In particular, the model shows that the the quantum computational universe automatically gives rise to a mix of randomness and order, and to both simple and complex systems.

ORG can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.

It is possible to rely on current corporate law to grant legal personhood to ORG (AI) agents. In this paper, after introducing pathways to ORG personhood, we analyze consequences of such AI empowerment on human dignity, human safety and ORG rights. We emphasize possibility of creating selfish memes and legal system hacking in the context of artificial entities. Finally, we consider some potential solutions for addressing described problems.

The young field of ORG is still in the process of identifying its challenges and limitations. In this paper, we formally describe CARDINAL such impossibility result, namely ORG. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of WORK_OF_ART is discussed.

The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). Thus, CARDINAL main issues have to be considered: assist users in finding information and reduce search and navigation time. In this sense, context-based recommender systems (ORG) propose the user the adequate information depending on her/his situation. Our work consists in applying machine learning techniques and reasoning process in order to bring a solution to some of the problems concerning the acceptance of recommender systems by users, namely avoiding the intervention of experts, reducing cold start problem, speeding learning process and adapting to the user's interest. To achieve this goal, we propose a fundamental modification in terms of how we model the learning of the ORG. Inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-based reasoning to define a contextual recommendation process based on different context dimensions (cognitive, social, temporal, geographic). This paper describes an ongoing work on the implementation of a ORG based on a hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative filtering and case-based reasoning techniques. It also presents preliminary results by comparing PRODUCT and the standard ORG. solving the cold start problem.

Motivated by earlier results on universal randomized guessing, we consider an individual-sequence approach to the guessing problem: in this setting, the goal is to guess a secret, individual (deterministic) vector $PERSON,PERSON, by using a finite-state machine that sequentially generates randomized guesses from a stream of purely random bits. We define the finite-state guessing exponent as the asymptotic normalized logarithm of the minimum achievable moment of the number of randomized guesses, generated by any finite-state machine, until $PERSON is guessed successfully. We show that the finite-state guessing exponent of any sequence is intimately related to its finite-state compressibility (due to PERSON and PERSON), and it is asymptotically achieved by the decoder of (a certain modified version of) the DATE ORG data compression algorithm (a.k.a. the LZ78 algorithm), fed by purely random bits. The results are also extended to the case where the guessing machine has access to a side information sequence, $PERSON,PERSON, which is also an individual sequence.

We extend ORG chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to CARDINAL edges between any pair of nodes. We introduce global, and ordered local and pairwise PERSON properties for the new models. We show the equivalence of these properties for strictly positive probability distributions. We also show that when the random variables are continuous, the new models can be interpreted as systems of structural equations with correlated errors. This enables us to adapt GPE's do-calculus to them. Finally, we describe an exact algorithm for learning the new models from observational and interventional data via answer set programming.

We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal ORG (MAMP) chain graphs because each of them is PERSON equivalent to some ORG chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not only subsume ORG chain graphs but also multivariate regression chain graphs. We describe global and pairwise PERSON properties for ORG chain graphs and prove their equivalence for compositional graphoids. We also characterize when CARDINAL MAMP chain graphs are PERSON equivalent. For NORP probability distributions, we also show that every MAMP chain graph is PERSON equivalent to some directed and acyclic graph with deterministic nodes under marginalization and conditioning on some of its nodes. This is important because it implies that the independence model represented by a ORG chain graph can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we modify MAMP chain graphs so that they are closed under marginalization for NORP probability distributions. This is a desirable feature because it guarantees parsimonious models under marginalization.

The inequality $\sqrt{J}\leq m$ is proved for vacuum, asymptotically flat, maximal and axisymmetric data close to extreme ORG data. The physical significance of this inequality and its relation to the standard picture of the gravitational collapse are discussed.

This paper considers M-estimation of a nonlinear regression model with multiple change-points occuring at unknown times. The multi-phase random design regression model, discontinuous in each change-point, have an arbitrary error $\epsilon$. In the case when the number of jumps is known, the M-estimator of locations of breaks and of regression parameters are studied. These estimators are consistent and the distribution of the regression parameter estimators is PERSON. The estimator of each change-point converges, with the rate $WORK_OF_ART, to the smallest minimizer of the independent compound PERSON processes. The results are valid for a large class of error distributions.

This essay explores the limits of Turing machines concerning the modeling of minds and suggests alternatives to go beyond those limits.

An inverse problem for the wave equation outside an obstacle with a {ORG dissipative boundary condition} is considered. The observed data are given by a single solution of the wave equation generated by an initial data supported on an open ball. An explicit analytical formula for the computation of the coefficient at a point on the surface of the obstacle which is nearest to the center of the support of the initial data is given.

For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done through the penalization of predictor functions by PERSON or NORP norms. In this paper, we explore penalizing by sparsity-inducing norms such as the l1-norm or the block l1-norm. We assume that the kernel decomposes into a large sum of individual basis kernels which can be embedded in a directed acyclic graph; we show that it is then possible to perform kernel selection through a hierarchical multiple kernel learning framework, in polynomial time in the number of selected kernels. This framework is naturally applied to non linear variable selection; our extensive simulations on synthetic datasets and datasets from the ORG repository show that efficiently exploring the large feature space through sparsity-inducing norms leads to state-of-the-art predictive performance.

While tree methods have been popular in practice, researchers and practitioners are also looking for simple algorithms which can reach similar accuracy of trees. In DATE, (PERSON) developed the method of "ORG-robust-logitboost" and compared it with other supervised learning methods on datasets used by the deep learning literature. In this study, we propose a series of "tunable ORG kernels" which are simple and perform largely comparably to tree methods on the same datasets. Note that "abc-robust-logitboost" substantially improved the original "ORG" in that (a) it developed a tree-split formula based on ORDINAL-order information of the derivatives of the loss function; (b) it developed a new set of derivatives for multi-class classification formulation. In the prior study in DATE, the "generalized PERSON" (ORG) kernel was shown to have good performance compared to the "radial-basis function" (ORG) kernel. However, as demonstrated in this paper, the original ORG kernel is often not as competitive as tree methods on the datasets used in the deep learning literature. Since the original ORG kernel has no parameters, we propose tunable ORG kernels by adding tuning parameters in various ways. CARDINAL basic (i.e., with CARDINAL parameter) ORG kernels are the "$e$GMM kernel", "$p$GMM kernel", and "$PERSON kernel", respectively. Extensive experiments show that they are able to produce good results for a large number of classification tasks. Furthermore, the basic kernels can be combined to boost the performance.

In this article, we study the axialvector-diquark-axialvector-antidiquark type scalar, axialvector, tensor and vector $ss\bar{s}\bar{s}$ tetraquark states with the ORG sum rules. The predicted mass $m_{X}=2.08\pm0.12\,\rm{GeV}$ for the axialvector tetraquark state is in excellent agreement with the experimental value $(CARDINAL \pm 13.1 \pm 4.2) \,\rm{MeV}$ from the BESIII collaboration and supports assigning the new $MONEY state to be a $ss\bar{s}\bar{s}$ tetraquark state with $PERSON predicted mass $m_{X}=3.08\pm0.11\,\rm{GeV}$ disfavors assigning the MONEY or $Y(2175)$ to be the vector partner of the new $MONEY state. As a byproduct, we obtain the masses of the corresponding $qq\bar{q}\bar{q}$ tetraquark states. The light tetraquark states lie in the region MONEYMONEY rather than $MONEY

This paper shows that, if we could examine the entire history of a hidden variable, then we could efficiently solve problems that are believed to be intractable even for ORG computers. In particular, under any hidden-variable theory satisfying a reasonable axiom called "indifference to the identity," we could solve the Graph Isomorphism and PERSON DATE problems in polynomial time, as well as an oracle problem that is known to require ORG exponential time. We could also search an N-item database using O(N^{1/3}) queries, as opposed to O(N^{1/2}) queries with PERSON's search algorithm. On the other hand, the N^{1/3} bound is optimal, meaning that we could probably not solve ORG-complete problems in polynomial time. We thus obtain the ORDINAL good example of a model of computation that appears slightly more powerful than the ORG computing model.

The folksonomy is the result of free personal information or assignment of tags to an object (determined by the URI) in order to find them. The practice of tagging is done in a collective environment. Folksonomies are self constructed, based on co-occurrence of definitions, rather than a hierarchical structure of the data. The downside of this was that a few sites and applications are able to successfully exploit the sharing of bookmarks. The need for tools that are able to resolve the ambiguity of the definitions is becoming urgent as the need of simple instruments for their visualization, editing and exploitation in web applications still hinders their diffusion and wide adoption. An intelligent interactive interface design for folksonomies should consider the contextual design and inquiry based on a concurrent interaction for a perceptual user interfaces. To represent folksonomies a new concept structure called "WORK_OF_ART" is used in this paper. While it is presented FAC (ORG) to resolve the ambiguity of definitions of folksonomy tags suggestions for the user. On this base a ORG (HCI) systems is developed for the visualization, navigation, updating and maintenance of folksonomies Knowledge Bases - the ORG - through the web. System functionalities as well as its internal architecture will be introduced.

In this paper we present FAC (ORG) built on GPE and on NORP technologies. Cloud computing has emerged in DATE as the new paradigm for the provision of on-demand distributed computing resources. ORG can be used for relationship between different data and descriptions of services to annotate provenance of repositories on ontologies. The ORG service is composed of a back-end which submits and monitors the documents, and a user front-end which allows users to schedule on-demand operations and to watch the progress of running processes. The impact of the proposed method is illustrated on a user since its inception.

Slime mould P. polycephalum is a single cells visible by unaided eye. The cells shows a wide spectrum of intelligent behaviour. By interpreting the behaviour in terms of computation one can make a slime mould based computing device. The ORG computers are capable to solve a range of tasks of computational geometry, optimisation and logic. Physarum computers designed so far lack of localised inputs. Commonly used inputs --- illumination and chemo-attractants and -repellents --- usually act on extended domains of the slime mould's body. Aiming to design massive-parallel tactile inputs for slime mould computers we analyse a temporal dynamic of P. polycephalum's electrical response to tactile stimulation. In experimental laboratory studies we discover how the ORG responds to application and removal of a local mechanical pressure with electrical potential impulses and changes in its electrical potential oscillation patterns.

We introduce CARDINAL notions of effective reducibility for set-theoretical statements, based on computability with ORG (OTMs), CARDINAL of which resembles Turing reducibility while the other is modelled after Weihrauch reducibility. We give sample applications by showing that certain (algebraic) constructions are not effective in the ORG-sense and considerung the effective equivalence of various versions of the axiom of choice.

Computability logic (ORG) (see ORG) is a recently introduced semantical platform and ambitious program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth that logic has more traditionally been. Its expressions represent interactive computational tasks seen as games played by a machine against the environment, and "truth" is understood as existence of an algorithmic winning strategy. With logical operators standing for operations on games, the formalism of ORG is open-ended, and has already undergone series of extensions. This article extends the expressive power of ORG in a qualitatively new way, generalizing formulas (to which the earlier languages of ORG were limited) to circuit-style structures termed cirquents. The latter, unlike formulas, are able to account for subgame/subtask sharing between different parts of the overall game/task. Among the many advantages offered by this ability is that it allows us to capture, refine and generalize the well known independence-friendly logic which, after the present leap forward, naturally becomes a conservative fragment of ORG, just as classical logic had been known to be a conservative fragment of the formula-based version of CoL. PERSON, this paper is self-contained, and can be read without any prior familiarity with CoL.

Computability logic (see http://www.csc.villanova.edu/~japaridz/CL/) is a long-term project for redeveloping logic on the basis of a constructive game semantics, with games seen as abstract models of interactive computational problems. Among the fragments of this logic successfully axiomatized so far is CL12 --- a conservative extension of classical ORDINAL-order logic, whose language augments that of classical logic with the so called choice sorts of quantifiers and connectives. This system has already found fruitful applications as a logical basis for constructive and complexity-oriented versions of ORG arithmetic, such as arithmetics for polynomial time computability, polynomial space computability, and beyond. The present paper introduces a ORDINAL, indispensable complexity measure for interactive computations termed amplitude complexity, and establishes the adequacy of CL12 with respect to A-amplitude, S-space and T-time computability under certain minimal conditions on the triples (A,S,T) of function classes. This result very substantially broadens the potential application areas of CL12. The paper is self-contained, and targets readers with no prior familiarity with the subject.

We study the ensemble performance of biometric authentication systems, based on secret key generation, which work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity as one of the subscribers, s/he has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared to the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected.Referring to an ensemble of systems based on NORP binning, we provide a detailed analysis of the false-reject and false-accept probabilities, for a wide class of stochastic decoders. We also comment on the security for the typical code in the ensemble.

BES II data for J/Psi->K*(890)Kpi reveal a strong kappa peak in FAC-wave near threshold. Both magnitude and phase are determined in slices of PERSON mass by interferences with strong PRODUCT), K1(1270) and K1(1400) signals. The phase variation with mass agrees within errors with LASS data for PERSON elastic scattering. A combined fit is presented to both ORG and LASS data. The fit uses a ORG amplitude with an s-dependent width containing an PERSON CARDINAL. The kappa pole is at CARDINAL-20(stat)+-40(syst) - i(420+-45+-60syst) MeV. The S-wave I=0 scattering length ORG = CARDINALPERSON (in units of ORG)) is close to the prediction 0.19+-0.02 of ORG.

In a complete metric space that is equipped with a doubling measure and supports a Poincar\'e inequality, we prove a new GPE-type property for the fine topology in the case $p=1$. Then we use this property to prove the existence of $MONEY open \emph{strict subsets} and \emph{strict quasicoverings} of $MONEY open sets. As an application, we study fine ORG spaces in the case MONEY, that is, ORG spaces defined on $MONEY open sets.

In the setting of a metric space $MONEY equipped with a doubling measure that supports a Poincar\'e inequality, we show that if $PERSON u$ strictly in $MONEY, i.e. if $MONEY u$ in $PERSON and $PERSON ORG, then for a subsequence (not relabeled) we have MONEY for $\mathcal H$-almost every $PERSON S_u$.

Both self-organization and organization are important for the further development of the sciences: the CARDINAL dynamics condition and enable each other. Commercial and public considerations can interact and "interpenetrate" in historical organization; different codes of communication are then "recombined." However, self-organization in the symbolically generalized codes of communication can be expected to operate at the global level. The Triple NORP model allows for both a neo-institutional appreciation in terms of historical networks of university-industry-government relations and a neo-evolutionary interpretation in terms of CARDINAL functions: (i) novelty production, (i) wealth generation, and (iii) political control. Using this model, one can appreciate both subdynamics. The mutual information in CARDINAL dimensions enables us to measure the trade-off between organization and self-organization as a possible synergy. The question of optimization between commercial and public interests in the different sciences can thus be made empirical.

This note deals with a class of variables that, if conditioned on, tends to amplify confounding bias in the analysis of causal effects. This class, independently discovered by Bhattacharya and Vogt (DATE) and ORG (DATE), includes instrumental variables and variables that have greater influence on treatment selection than on the outcome. We offer a simple derivation and an intuitive explanation of this phenomenon and then extend the analysis to non linear models. We show that: CARDINAL. the bias-amplifying potential of instrumental variables extends over to non-linear models, though not as sweepingly as in ORG models; CARDINAL. in LOC models, conditioning on instrumental variables may introduce new bias where none existed before; CARDINAL. in both linear and non-linear models, instrumental variables have no effect on selection-induced bias.

It is proved that spherically symmetric compact reflecting objects cannot support static bound-state configurations made of scalar fields whose self-interaction potential $PERSON is a monotonically increasing function of its argument. Our theorem rules out, in particular, the existence of massive scalar hair outside the surface of a spherically symmetric compact reflecting star.

Can change in citation patterns among journals be used as an indicator of structural change in the organization of the sciences? Aggregated journal-journal citations for DATE are compared with similar data in the ORG Citation Reports DATE of the Science Citation Index. In addition to indicating local change, probabilistic entropy measures enable us to analyze changes in distributions at different levels of aggregation. The results of various statistics are discussed and compared by elaborating ORG mappings. The relevance of this indicator for science and technology policies is further specified.

In this paper we critically analyze the so far performed and proposed tests for measuring the general relativistic PERSON effect in the gravitational field of the LOC with some of the existing accurately tracked artificial satellites. The impact of the ORDINAL generation GRACE-only ORG-GRACE02S LOC gravity model and of DATE CHAMP+GRACE+terrestrial gravity combined ORG-CG01C LOC gravity model is discussed. The role of the proposed PERSON is discussed as well.

This paper reviews the DATE proof that the spectral gap of NORP quantum systems capable of universal computation is uncomputable.

We discuss quark-antiquark leptoproduction within a ORG CARDINAL-gluon exchange model at small $x$. The double spin asymmetries for longitudinally polarized leptons and transversely polarized protons in diffractive $Q \bar Q$ production are analysed at eRHIC energies. The predicted $A_{lT}$ asymmetry is large and can be used to obtain information on the polarized generalized gluon distributions in the proton.

We analyze light meson electroproduction within the handbag model, where the amplitude factorizes into ORG (GPDs) and a hard scattering part. The cross sections and spin asymmetries for various vector and pseudoscalar mesons are analyzed. We discuss what information on hadron structure can be obtained from GPDs.

Cosmological limits on PERSON invariance breaking in ORG $(CARDINAL+1)-dimensional$ electrodynamics are used to place limits on torsion. PERSON phenomena is discussed by using extending the propagation equation to ORG spacetimes instead of treating it in purely NORP spaces. The parameter of PERSON violation is shown to be proportional to the axial torsion vector which allows us to place a limit on cosmological background torsion from the PERSON violation constraint which is given by PERCENTDATE} eV <|S^{\mu}| < 10^{-32} eV$ where $|S^{\mu}|$ is the axial torsion vector.

PERSON models used in physics and other areas of mathematics applications become discrete when they are computerized, e.g., utilized for computations. Besides, computers are controlling processes in discrete spaces, such as films and television programs. At the same time, continuous models that are in the background of discrete representations use mathematical technology developed for continuous media. The most important example of such a technology is calculus, which is so useful in physics and other sciences. The main goal of this paper is to synthesize continuous features and powerful technology of the classical calculus with the discrete approach of numerical mathematics and computational physics. To do this, we further develop the theory of fuzzy continuous functions and apply this theory to functions defined on discrete sets. The main interest is the classical PERSON theorem. Although the result of this theorem is completely based on continuity, utilization of a relaxed version of continuity called fuzzy continuity, allows us to prove discrete versions of ORG theorem. This result provides foundations for a new approach to discrete dynamics.

Fuzzy answer set programming is a declarative framework for representing and reasoning about knowledge in fuzzy environments. However, the unavailability of fuzzy aggregates in disjunctive fuzzy logic programs, ORG, with fuzzy answer set semantics prohibits the natural and concise representation of many interesting problems. In this paper, we extend ORG to allow arbitrary fuzzy aggregates. We define fuzzy answer set semantics for ORG with arbitrary fuzzy aggregates including monotone, antimonotone, and nonmonotone fuzzy aggregates. We show that the proposed fuzzy answer set semantics subsumes both the original fuzzy answer set semantics of ORG and the classical answer set semantics of classical disjunctive logic programs with classical aggregates, and consequently subsumes the classical answer set semantics of classical disjunctive logic programs. We show that the proposed fuzzy answer sets of ORG with fuzzy aggregates are minimal fuzzy models and hence incomparable, which is an important property for nonmonotonic fuzzy reasoning.

This paper shows that, even at the most basic level, the parallel, countable branching and uncountable branching recurrences of ORG (see ORG) validate different principles.

ORG (ORG) is a descriptive category metatheory currently under development, which is being offered as the structural aspect of ORG (SUO). The architecture of the ORG is composed of metalevels, namespaces and meta-ontologies. The main application of the ORG is institutional: the notion of institutions and their morphisms are being axiomatized in the upper metalevels of the ORG, and the lower metalevel of the ORG has axiomatized various institutions in which semantic integration has a natural expression as the colimit of theories.

The theory introduced, presented and developed in this paper, is concerned with ORG. This theory is a synthesis of the theory of ORG pioneered by PERSON with the theory of ORG pioneered by PERSON. The central notion in this paper of a rough formal concept combines in a natural fashion the notion of a rough set with the notion of a formal concept: "rough set + formal concept = rough formal concept". A follow-up paper will provide a synthesis of the CARDINAL important data modeling techniques: conceptual scaling of ORG and Entity-Relationship database modeling.

Psychological and social systems provide us with a natural domain for the study of anticipations because these systems are based on and operate in terms of intentionality. Psychological systems can be expected to contain a model of themselves and their environments social systems can be strongly anticipatory and therefore co-construct their environments, for example, in techno-economic (co-)evolutions. Using ORG's hyper-incursive and incursive formulations of the logistic equation, these two types of systems and their couplings can be simulated. In addition to their structural coupling, psychological and social systems are also coupled by providing meaning reflexively to each other's meaning-processing. PERSON's distinctions among (CARDINAL) interactions between intentions at the micro-level, (CARDINAL) organization at the meso-level, and (CARDINAL) self-organization of the fluxes of meaningful communication at the global level can be modeled and simulated using CARDINAL hyper-incursive equations. The global level of self-organizing interactions among fluxes of communication is retained at the meso-level of organization. In a knowledge-based economy, these CARDINAL levels of anticipatory structuration can be expected to propel each other at the supra-individual level.

Positional and relational perspectives on network data have led to CARDINAL different research traditions in textual analysis and social network analysis, respectively. ORG (ORG) focuses on the latent dimensions in textual data; social network analysis (ORG) on the observable networks. The CARDINAL coupled topographies of information-processing in the network space and meaning-processing in the vector space operate with different (nonlinear) dynamics. The historical dynamics of information processing in observable networks organizes the system into instantiations; the systems dynamics, however, can be considered as self-organizing in terms of fluxes of communication along the various dimensions that operate with different codes. The development over time adds evolutionary differentiation to the historical integration; a richer structure can process more complexity.

In this paper, a mathematical schema theory is developed. This theory has CARDINAL roots: brain theory schemas, grid automata, and block-shemas. In Section CARDINAL of this paper, elements of the theory of grid automata necessary for the mathematical schema theory are presented. In LAW, elements of brain theory necessary for the mathematical schema theory are presented. In Section CARDINAL, other types of schemas are considered. In LAW, the mathematical schema theory is developed. The achieved level of schema representation allows one to model by mathematical tools virtually any type of schemas considered before, including schemas in neurophisiology, psychology, computer science, Internet technology, databases, logic, and mathematics.

People solve different problems and know that some of them are simple, some are complex and some insoluble. The main goal of this work is to develop a mathematical theory of algorithmic complexity for problems. This theory is aimed at determination of computer abilities in solving different problems and estimation of resources that computers need to do this. Here we build the part of this theory related to static measures of algorithms. At ORDINAL, we consider problems for finite words and study algorithmic complexity of such problems, building optimal complexity measures. Then we consider problems for such infinite objects as functions and study algorithmic complexity of these problems, also building optimal complexity measures. In the ORDINAL part of the work, complexity of algorithmic problems, such as the halting problem for Turing machines, is measured by the classes of automata that are necessary to solve this problem. To classify different problems with respect to their complexity, inductive Turing machines, which extend possibilities of Turing machines, are used. A hierarchy of inductive Turing machines generates an inductive hierarchy of algorithmic problems. Here we specifically consider algorithmic problems related to Turing machines and inductive Turing machines, and find a place for these problems in the inductive hierarchy of algorithmic problems.

We draw a certain analogy between the classical information-theoretic problem of lossy data compression (source coding) of memoryless information sources and the statistical mechanical behavior of a certain model of a chain of connected particles (e.g., a polymer) that is subjected to a contracting force. The free energy difference pertaining to such a contraction turns out to be proportional to the rate-distortion function in the analogous data compression model, and the contracting force is proportional to the derivative this function. Beyond the fact that this analogy may be interesting on its own right, it may provide a physical perspective on the behavior of optimum schemes for lossy data compression (and perhaps also, an information-theoretic perspective on certain physical system models). Moreover, it triggers the derivation of lossy compression performance for systems with memory, using analysis tools and insights from statistical mechanics.

Biometric authentication systems, based on secret key generation, work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity as one of the subscribers, he/she has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared to the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected. Evidently, there is an inherent tension between CARDINAL desired, but conflicting, properties of the helper message encoder: on the one hand, the encoding should be informative enough concerning the identity of the real subscriber, in order to approve him/her in the authentication stage, but on the other hand, it should not be too informative, as otherwise, unauthorized imposters could easily fool the system and gain access. A good encoder should then trade off the CARDINAL kinds of errors: the false reject (FR) error and the false accept (FA) error. In this work, we investigate trade-offs between the random coding FR error exponent and the best achievable FA error exponent. We compare CARDINAL types of ensembles of codes: fixed-rate codes and variable-rate codes, and we show that the latter class of codes offers considerable improvement compared to the former. In doing this, we characterize the optimal rate functions for both types of codes. We also examine privacy leakage constraints for both fixed-rate codes and variable-rate codes.

Here is discussed application of the Weyl pair to construction of universal set of ORG gates for high-dimensional quantum system. An application of Lie algebras (NORP) for construction of universal gates is revisited ORDINAL. It is shown next, how for quantum computation with qubits can be used CARDINAL-dimensional analog of this GPE-Weyl matrix algebras, i.e. PERSON algebras, and discussed well known applications to product operator formalism in ORG, ORG construction in fermionic quantum computations. It is introduced universal set of ORG gates for higher dimensional system (``qudit''), as some generalization of these models. Finally it is briefly mentioned possible application of such algebraic methods to design of quantum processors (programmable gates arrays) and discussed generalization to quantum computation with continuous variables.

This note reviews prospects for ORG computing. It argues that gates need to be tested for a wide range of probability amplitudes.

We prove the existence of a family of initial data for the Einstein vacuum equation which can be interpreted as the data for CARDINAL ORG-like black holes in arbitrary location and with spin in arbitrary direction. This family of initial data has the following properties: (i) When the mass parameter of CARDINAL of them is CARDINAL or when the distance between them goes to infinity, it reduces exactly to the ORG initial data. (ii) When the distance between them is CARDINAL, we obtain exactly a ORG initial data with mass and angular momentum equal to the sum of the mass and angular momentum parameters of each of them. The initial data depends smoothly on the distance, the mass and the angular momentum parameters.

The assumptions needed to prove Cox's Theorem are discussed and examined. Various sets of assumptions under which a Cox-style theorem can be proved are provided, although all are rather strong and, arguably, not natural.

Unsupervised deep learning is one of the most powerful representation learning techniques. ORG Boltzman machine, sparse coding, regularized auto-encoders, and convolutional neural networks are pioneering building blocks of deep learning. In this paper, we propose a new building block -- distributed random models. The proposed method is a special full implementation of the product of experts: (i) each expert owns multiple hidden units and different experts have different numbers of hidden units; (ii) the model of each expert is a k-center clustering, whose k-centers are only uniformly sampled examples, and whose output (i.e. the hidden units) is a sparse code that only the similarity values from a few nearest neighbors are reserved. The relationship between the pioneering building blocks, several notable research branches and the proposed method is analyzed. Experimental results show that the proposed deep model can learn better representations than deep belief networks and meanwhile can train a much larger network with much less time than deep belief networks.

Recently, multilayer bootstrap network (ORG) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of ORG is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised bootstrap methods. The framework compresses a large unsupervised bootstrap model into a small model by taking the bootstrap model and its application together as a black box and learning a mapping function from the input of the bootstrap model to the output of the application by a supervised learner. To specialize the framework, we propose a new technique, named compressive ORG. It takes ORG as the unsupervised bootstrap model and deep neural network (DNN) as the supervised learner. Our initial result on MNIST showed that compressive ORG not only maintains the high prediction accuracy of ORG but also is CARDINAL of times faster than ORG at the prediction stage. Our result suggests that the new technique integrates the effectiveness of ORG on unsupervised learning and the effectiveness and efficiency of DNN on supervised learning together for the effectiveness and efficiency of compressive ORG on unsupervised learning.

PERSON (lightweight internet-based communication for autonomic services) is a distributed framework for building service-based systems. The framework provides a p2p server and more intelligent processing of information through its ORG algorithms. Distributed communication includes ORG-RPC, ORG, ORG and Web Services. It can now provide a robust platform for building different types of system, where Microservices or ORG would be possible. However, the system may be equally suited for the IoT, as it provides classes to connect with external sources and has an optional NORP Manager with a MAPE control loop integrated into the communication process. The system is also mobile-compatible with ORG. This paper focuses in particular on the autonomic setup and how that might be used. A novel linking mechanism has been described previously and is considered again, as part of the autonomous framework.

We propose a ORG measure for quantum channels in a straightforward analogy to the corresponding mixed-state fidelity of PERSON. We describe properties of this ORG measure and discuss some applications of it to quantum information science.

Classical simulation is important because it sets a benchmark for quantum computer performance. Classical simulation is currently the only way to exercise larger numbers of qubits. To achieve larger simulations, sparse matrix processing is emphasized below while trading memory for processing. It performed well within ORG supercomputers, giving a state vector in convenient continuous portions ready for post processing.

ORG computer versus ORG algorithm processor in ORG are compared to find (in parallel) all NORP cycles in a graph with m edges and n vertices, each represented by k bits. A ORG computer uses quantum states analogous to CMOS registers. With efficient initialization, number of ORG registers is proportional to (n-1)! Number of qubits in a ORG computer is approximately proportional to ORG in the approach below. Using ORG, the bits per register is about proportional to kn, which is less since bits can be irreversibly reset. In either concept, number of gates, or operations to identify NORP cycles is proportional to kmn. However, a ORG computer needs an additional exponentially large number of operations to accomplish a probabilistic readout. In contrast, ORG is deterministic and readout is comparable to ordinary memory.

ORG of university-industry-government relations is elaborated into a systemic model that accounts for interactions among CARDINAL dimensions. By distinguishing between the respective micro-operations, this model enables us to recombine the "Mode CARDINAL" thesis of a new production of scientific knowledge and the study of systems of innovation with the neo-classical perspective on the dynamics of the market. The mutual information in CARDINAL dimensions provides us with an indicator for the self-organization of the resulting network systems. The probabilistic entropy in this mutual information can be negative in knowledge-based configurations. The knowledge base of an economy can be considered as a ORDINAL-order interaction effect among interactions at interfaces between institutions and functions in different spheres. Proximity enhances the chances for couplings and, therefore, the formation of technological trajectories. The next-order regime of the knowledge base, however, can be expected to remain pending as selection pressure.

Via the Internet, information scientists can obtain cost-free access to large databases in the hidden or deep web. These databases are often structured far more than the Internet domains themselves. The patent database of the GPE ORG is used in this study to examine the science base of patents in terms of the literature references in these patents. ORG-based patents at the global level are compared with results when using the national economy of the GPE as a system of reference. Methods for accessing the on-line databases and for the visualization of the results are specified. The conclusion is that 'biotechnology' has historically generated a model for theorizing about university-industry relations that cannot easily be generalized to other sectors and disciplines.

The min-max kernel is a generalization of the popular resemblance kernel (which is designed for binary data). In this paper, we demonstrate, through an extensive classification study using kernel machines, that the min-max kernel often provides an effective measure of similarity for nonnegative data. As the min-max kernel is nonlinear and might be difficult to be used for industrial applications with massive data, we show that the min-max kernel can be linearized via hashing techniques. This allows practitioners to apply min-max kernel to large-scale applications using well matured ORG algorithms such as linear ORG or logistic regression. The previous remarkable work on consistent weighted sampling (ORG) produces samples in the form of ($i^*, t^*$) where the $i^*$ records the location (and in fact also the weights) information analogous to the samples produced by classical minwise hashing on binary data. Because the $t^*$ is theoretically unbounded, it was not immediately clear how to effectively implement ORG for building large-scale ORG classifiers. In this paper, we provide a simple solution by discarding $t^*$ (which we refer to as the "0-bit" scheme). Via an extensive empirical study, we show that this 0-bit scheme does not lose essential information. We then apply the "0-bit" WORK_OF_ART classifiers to approximate PERSON classifiers, as extensively validated on a wide range of publicly available classification datasets. We expect this work will generate interests among data mining practitioners who would like to efficiently utilize the nonlinear information of non-binary and nonnegative data.

This article addresses the question of when physical laws and their consequences can be computed. If a physical system is capable of universal computation, then its energy gap can't be computed. At an even more fundamental level, the most concise, simply applicable formulation of the underlying laws of physics is uncomputable. That is, physicists are in the same boat as mathematicians: many quantities of interest can be computed, but not all.

On the basis of an analysis of previous research, we present a generalized approach for measuring the difference of plans with an exemplary application to machine scheduling. Our work is motivated by the need for such measures, which are used in dynamic scheduling and planning situations. In this context, quantitative approaches are needed for the assessment of the robustness and stability of schedules. Obviously, any `robustness' or `stability' of plans has to be defined PERSON the particular situation and the requirements of the human decision maker. Besides the proposition of an instability measure, we therefore discuss possibilities of obtaining meaningful information from the decision maker for the implementation of the introduced approach.

Previously a model of only vector fields with a local U(2) symmetry was introduced for which one finds a massless U(1) photon and a massive SU(2) PERSON in the lattice regularization. Here it is shown that quantization of its classical continuum action leads to perturbative renormalization difficulties. But, non-perturbative PERSON calculations favor the existence of a quantum continuum limit.

An analysis of light vector PERSON at small GPE $x \leq MONEY is done on the basis of the generalized parton distributions (GPDs). Our results on the cross section and spin density matrix elements (SDME) are in good agreement with experiments.

The purpose of a wireless sensor network (WSN) is to provide the users with access to the information of interest from data gathered by spatially distributed sensors. Generally the users require only certain aggregate functions of this distributed data. Computation of this aggregate data under the end-to-end information flow paradigm by communicating all the relevant data to a central collector PERSON is a highly inefficient solution for this purpose. An alternative proposition is to perform in-network computation. This, however, raises questions such as: what is the optimal way to compute an aggregate function from a set of statistically correlated values stored in different nodes; what is the security of such aggregation as the results sent by a compromised or faulty node in the network can adversely affect the accuracy of the computed result. In this paper, we have presented an energy-efficient aggregation algorithm for WSNs that is secure and robust against malicious insider attack by any compromised or faulty node in the network. In contrast to the traditional snapshot aggregation approach in WSNs, a node in the proposed algorithm instead of unicasting its sensed information to its parent node, broadcasts its estimate to all its neighbors. This makes the system more fault-tolerant and increase the information availability in the network. The simulations conducted on the proposed algorithm have produced results that demonstrate its effectiveness.

Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. "Structure" has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Beginning with the role of number theory in expressing data, we show how we can naturally proceed to hierarchical structures. We show how this both encapsulates traditional paradigms in data analysis, and also opens up new perspectives towards issues that are on the order of DATE, including data mining of massive, high dimensional, heterogeneous data sets. Linkages with other fields are also discussed including computational logic and symbolic dynamics. The structures in data surveyed here are based on hierarchy, represented as p-adic numbers or an ultrametric topology.

We consider a large number of text data sets. These are cooking recipes. Term distribution and other distributional properties of the data are investigated. Our aim is to look at various analytical approaches which allow for mining of information on both high and low detail scales. Metric space embedding is fundamental to our interest in the semantic properties of this data. We consider the projection of all data into analyses of aggregated versions of the data. We contrast that with projection of aggregated versions of the data into analyses of all the data. Analogously for the term set, we look at analysis of selected terms. We also look at inherent term associations such as between singular and plural. In addition to our use of ORG in R, for latent semantic space mapping, we also use PRODUCT. Setting up the PERSON server and carrying out querying is described. A further novelty is that querying is supported in PERSON based on the principal factor plane mapping of all the data. This uses a bounding box query, based on factor projections.

The aim of this paper is twofold: ORDINAL, to extend the area of applications of tropical optimization by solving new constrained location problems, and ORDINAL, to offer new closed-form solutions to general problems that are of interest to location analysis. We consider a constrained minimax single-facility location problem with addends on the plane with rectilinear distance. The solution commences with the representation of the problem in a standard form, and then in terms of tropical mathematics, as a constrained optimization problem. We use a transformation technique, which can act as a template to handle optimization problems in other application areas, and hence is of independent interest. To solve the constrained optimization problem, we apply methods and results of tropical optimization, which provide direct, explicit solutions. The results obtained serve to derive new solutions of the location problem, and of its special cases with reduced sets of constraints, in a closed form, ready for practical implementation and immediate computation. As illustrations, numerical solutions of example problems and their graphical representation are given. We conclude with an application of the results to optimal location of the central monitoring facility in an indoor video surveillance system in a multi-floor building environment.

Configurational information is generated when CARDINAL or more sources of variance interact. The variations not only disturb each other relationally, but by selecting upon each other, they are also positioned in a configuration. A configuration can be stabilized and/or globalized. Different stabilizations can be considered as ORDINAL-order variation, and globalization as a ORDINAL-order selection. The positive manifestations and the negative selections operate upon one another by adding and reducing uncertainty, respectively. Reduction of uncertainty in a configuration can be measured in bits of information. The variables can also be considered as dimensions of the probabilistic entropy in the system(s) under study. The configurational information then provides us with a measure of synergy within a complex system. For example, the knowledge base of an economy can be considered as such a synergy in the otherwise virtual (that is, ORDINAL) dimension of a regime.

Similarly to the modelling of entanglement in the algebra of ORG computing, we also model entanglement as a synchronization among an event and its shadows in reversible ORG computing. We give the semantics and axioms of shadow constant for reversible ORG computing.

We provide here a proof theoretic account of constraint programming that attempts to capture the essential ingredients of this programming style. We exemplify it by presenting proof rules for ORG constraints over interval domains, and illustrate their use by analyzing the constraint propagation process for the {ORG SEND + MORE = MONEY} puzzle. We also show how this approach allows one to build new constraint solvers.

In former work, we showed that a quantum algorithm requires the number of operations (oracle's queries) of a classical algorithm that knows in advance PERCENT of the information that specifies the solution of the problem. We gave a preliminary theoretical justification of this "PERCENT rule" and checked that the rule holds for a variety of ORG algorithms. Now, we make explicit the information about the solution available to the algorithm throughout the computation. The final projection on the solution becomes acquisition of the knowledge of the solution on the part of the algorithm. Backdating to before running the algorithm a time-symmetric part of this projection, feeds back to the input of the computation PERCENT of the information acquired by reading the solution.

Military is CARDINAL of many industries that is more computer-dependent than ever before, from soldiers with computerized weapons, and tactical wireless devices, to commanders with advanced battle management, command and control systems. PERSON, command and control is the process of planning, monitoring, and commanding military personnel, weaponry equipment, and combating vehicles to execute military missions. In fact, command and control systems are revolutionizing as war fighting is changing into cyber, technology, information, and unmanned warfare. As a result, a new design model that supports scalability, reusability, maintainability, survivability, and interoperability is needed to allow commanders, QUANTITY away from the battlefield, to plan, monitor, evaluate, and control the war events in a dynamic, robust, agile, and reliable manner. This paper proposes a service-oriented architecture for weaponry and battle command and control systems, made out of loosely-coupled and distributed web services. The proposed architecture consists of CARDINAL elementary tiers: the client tier that corresponds to any computing military equipment; the server tier that corresponds to the web services that deliver the basic functionalities for the client tier; and the middleware tier that corresponds to an enterprise service bus that promotes interoperability between all the interconnected entities. A command and control system was simulated and experimented and it successfully exhibited the desired features of ORG. Future research can improve upon the proposed architecture so much so that it supports encryption for securing the exchange of data between the various communicating entities of the system.

The direct effect of CARDINAL eventon another can be defined and measured byholding constant all intermediate variables between the CARDINAL.Indirect effects present conceptual andpractical difficulties (in nonlinear models), because they cannot be isolated by holding certain variablesconstant. This paper shows a way of defining any path-specific effectthat does not invoke blocking the remainingpaths.This permits the assessment of a more naturaltype of direct and indirect effects, CARDINAL thatis applicable in both linear and nonlinear models. The paper establishesconditions under which such assessments can be estimated consistentlyfrom experimental and nonexperimental data,and thus extends path-analytic techniques tononlinear and nonparametric models.

This paper extends the applications of belief-networks to include the revision of belief commitments, i.e., the categorical acceptance of a subset of hypotheses which, together, constitute the most satisfactory explanation of the evidence at hand. A coherent model of non-monotonic reasoning is established and distributed algorithms for belief revision are presented. We show that, in singly connected networks, the most satisfactory explanation can be found in linear time by a message-passing algorithm similar to the one used in belief updating. In multiply-connected networks, the problem may be exponentially hard but, if the network is sparse, topological considerations can be used to render the interpretation task tractable. In general, finding the most probable combination of hypotheses is no more complex than computing the degree of belief for any individual hypothesis. Applications to medical diagnosis are illustrated.

In this paper we present an unconventional image segmentation approach which is devised to meet the requirements of image understanding and pattern recognition tasks. Generally image understanding assumes interplay of CARDINAL sub-processes: image information content discovery and image information content interpretation. Despite of its widespread use, the notion of "image information content" is still ill defined, intuitive, and ambiguous. Most often, it is used in the FAC's sense, which means information content assessment averaged over the whole signal ensemble. Humans, however,rarely resort to such estimates. They are very effective in decomposing images into their meaningful constituents and focusing attention to the perceptually relevant image parts. We posit that following the latest findings in human attention vision studies and the concepts of ORG's complexity theory an unorthodox segmentation approach can be proposed that provides effective image decomposition to information preserving image fragments well suited for subsequent image interpretation. We provide some illustrative examples, demonstrating effectiveness of this approach.

Traditionally, semantics has been seen as a feature of human language. The advent of the information era has led to its widespread redefinition as an information feature. Contrary to this praxis, I define semantics as a special kind of information. Revitalizing the ideas of LOC and Carnap I have recreated and re-established the notion of semantics as the notion of ORG. I have proposed a new definition of information (as a description, a linguistic text, a piece of a story or a tale) and a clear segregation CARDINAL different types of information - physical and semantic information. I hope, I have clearly explained the (usually obscured and mysterious) interrelations between data and physical information as well as the relation between physical information and semantic information. Consequently, usually indefinable notions of "information", "knowledge", "memory", "learning" and "semantics" have also received their suitable illumination and explanation.

We consider the problem $PRODUCT=f(\nu)$ for strictly convex, closed hypersurfaces in hyperbolic space and solve it for curvature functions $MONEY the inverses of which are of class $(K^*)$.

We consider branes $N=I\times\so$, where $\so$ is an $MONEY dimensional space form, not necessarily compact, in a ORG)} bulk $MONEY CARDINAL The branes have a big crunch singularity. If a brane is an ORG space, then, under certain conditions, there exists a smooth natural transition flow through the singularity to a reflected brane $\hat N$, which has a big bang singularity and which can be viewed as a brane in a reflected ORG)} bulk $PERSON The joint branes CARDINALN\uu \hat N$ can thus be naturally embedded in $R^2\times \so$, hence there exists a ORDINAL possibility of defining a smooth transition from big crunch to big bang by requiring that $N\uu\hat N$ forms a $C^\infty$-hypersurface in MONEY This last notion of a smooth transition also applies to branes that are not ORG spaces, allowing a wide range of possible equations of state.

An interactive stochastics, evaluated by an entropy functional (EF) of a random field and informational process' path functional (ORG), allows us modeling the evolutionary information processes and revealing regularities of evolution dynamics. Conventional ORG's information measure evaluates a sequence of the process' static events for each information state and do not reveal hidden dynamic connections between these events. The paper formulates the mathematical forms of the information regularities, based on a minimax variation principle (VP) for ORG, applied to the evolution's both random microprocesses and dynamic macroprocesses. The paper shows that the ORG single form of the mathematical law leads to the following evolutionary regularities: -creation of the order from stochastics through the evolutionary macrodynamics, described by a gradient of dynamic potential, evolutionary speed and the evolutionary conditions of a fitness and diversity; -the evolutionary hierarchy with growing information values and potential adaptation; -the adaptive self-controls and a self-organization with a mechanism of copying to a genetic code. This law and the regularities determine unified functional informational mechanisms of evolution dynamics. By introducing both objective and subjective information observers, we consider the observers' information acquisition, interactive cognitive evolution dynamics, and neurodynamics, based on the EF-IPF approach. An evolution improvement consists of the subjective observer s ability to attract and encode information whose value progressively increases. The specific properties of a common information structure of evolution processes are identifiable for each particular object-organism by collecting a behavioral data from these organisms.

What is information originating in observation? Until now it has no scientifically conclusive definition. Information is memorized entropy cutting in random observations which processing interactions. Randomness of various interactive observations is source of entropy as uncertainty. Observation under random CARDINAL-0 impulses probabilities reveals hidden correlation which connects NORP probabilities increasing each posterior correlation. That sequentially reduces relational entropy conveying probabilistic casualty with temporal memory of correlations which interactive impulse innately cuts. Within hidden correlation emerges reversible time space microprocess with conjugated entangled entropy which probing impulse intentionally cuts and memorizes information as certainty. Sequential interactive cuts integrates cutting information in information macroprocess with irreversible time course. NORP information binds reversible microprocess within impulse with irreversible information macroprocess. Observer probes collect cutting information data bits of observing frequencies impulses. Each impulse cuts maximum of impulse minimal information performing dual PERSON principle of converting process entropy to information through uncertain gap. Multiple naturally encoding bits moving in macroprocess join triplet macrounits which logically organize information networks encoding macrounits in structures enclosing triplet code. Network time space distributed structure self renews and cooperates information decreasing its complexity. Integrating process entropy functional and bits information in information path integral embraces variation minimax law which determines processes regularities. Solving problem mathematically describes micro macro processes, network, and invariant conditions of observer network self replication.

There are versions of "calculus" in many settings, with various mixtures of algebra and analysis. In these informal notes we consider a few examples that suggest a lot of interesting questions.

DATE ORG discovered CARDINAL mathematical methods for the purpose of extracting information about the location and shape of unknown discontinuity embedded in a known background medium from observation data. The methods are called the probe and enclosure methods. This paper presents their past and recent applications to inverse obstacle scattering problems of NORP wave.

The ongoing discussion whether modern vision systems have to be viewed as visually-enabled cognitive systems or cognitively-enabled vision systems is groundless, because perceptual and cognitive faculties of vision are separate components of human (and consequently, artificial) information processing system modeling.

Pattern recognition is generally assumed as an interaction of CARDINAL inversely directed image-processing streams: the bottom-up information details gathering and localization (segmentation) stream, and the top-down information features aggregation, association and interpretation (recognition) stream. Inspired by recent evidence from biological vision research and by the insights of ORG theory, we propose a new, just top-down evolving, procedure of initial image segmentation. We claim that traditional top-down cognitive reasoning, which is supposed to guide the segmentation process to its final result, is not at all a part of the image information content evaluation. And that initial image segmentation is certainly an unsupervised process. We present some illustrative examples, which support our claims.

Not only did Turing help found CARDINAL of the most exciting areas of modern science (computer science), but it may be that his contribution to our understanding of our physical reality is greater than we had hitherto supposed. Here I explore the path that PERSON would have certainly liked to follow, that of complexity science, which was launched in the wake of his seminal work on computability and structure formation. In particular, I will explain how the theory of algorithmic probability based on PERSON's universal machine can also explain how structure emerges at the most basic level, hence reconnecting CARDINAL of PERSON's most cherished topics: computation and pattern formation.

Models of computation operating over the real numbers and computing a larger class of functions compared to the class of general recursive functions invariably introduce a non-finite element of infinite information encoded in an arbitrary non-computable number or non-recursive function. In this paper we show that Turing universality is only possible at every Turing degree but not over all, in that sense universality at the ORDINAL level is elegantly well defined while universality at higher degrees is at least ambiguous. We propose a concept of universal relativity and universal jump between levels in the arithmetical and analytical hierarchy.

The name of PERSON is common both in ORG and computer science. Are they really CARDINAL absolutely unconnected areas? Many works devoted to quantum computations and communications are serious argument to suggest about existence of such a relation, but it is impossible to touch the new and active theme in a short review. In the paper are described the structures and models of ORG algebra and just due to their generality it is possible to use universal description of very different areas as quantum mechanics and theory of NORP image analysis, associative memory, neural networks, fuzzy logic.

Clifford algebras are used for definition of spinors. Because of using spin-1/2 systems as an adequate model of quantum bit, a relation of the algebras with quantum information science has physical reasons. But there are simple mathematical properties of the algebras those also justifies such applications. ORDINAL, any complex PERSON algebra with CARDINAL generators, Cl(2n,C), has representation as algebra of all CARDINAL x 2^n complex matrices and so includes unitary matrix of any quantum n-gate. An arbitrary element of whole algebra corresponds to general form of linear complex transformation. The last property is also useful because linear operators are not necessary should be unitary if they used for description of restriction of some unitary operator to ORG. The ORDINAL advantage is simple algebraic structure of Cl(2n) that can be expressed via tenzor product of standard "building units" and similar with behavior of composite quantum systems. The compact notation with CARDINAL generators also can be used in software for modeling of simple quantum circuits by modern conventional computers.

We study here the well-known propagation rules for NORP constraints. ORDINAL we propose a simple notion of completeness for sets of such rules and establish a completeness result. Then we show an equivalence in an appropriate sense between NORP constraint propagation and unit propagation, a form of resolution for propositional logic. Subsequently we characterize one set of such rules by means of the notion of hyper-arc consistency introduced in (PERSON and PERSON DATE). Also, we clarify the status of a similar, though different, set of rules introduced in (NORP 1989a) and more fully in (Codognet and PERSON DATE).

This is a tutorial on logic programming and PERSON appropriate for a course on programming languages for students familiar with imperative programming.

For many voting rules, it is ORG-hard to compute a successful manipulation. However, ORG-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. We study empirically the cost of manipulating the single transferable vote (NORP) rule. This was one of the ORDINAL rules shown to be ORG-hard to manipulate. It also appears to be one of the harder rules to manipulate since it involves multiple rounds and since, unlike many other rules, it is ORG-hard for a single agent to manipulate without weights on the votes or uncertainty about how the other agents have voted. In almost every election in our experiments, it was easy to compute how a single agent could manipulate the election or to prove that manipulation by a single agent was impossible. It remains an interesting open question if manipulation by a coalition of agents is hard to compute in practice.

To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.

We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in \cite{chenwu08b,CARDINAL}. The difficulty arises from the quadratic functional of observations that one needs to deal with instead of the linear functional that appears when state variables contain no measurement errors. We derive the asymptotic bias and variance for the previously proposed CARDINAL-step estimators using quadratic regression functional theory.

Functional linear regression is a useful extension of simple linear regression and has been investigated by many researchers. However, functional variable selection problems when multiple functional observations exist, which is the counterpart in the functional context of multiple linear regression, is seldom studied. Here we propose a method using group smoothly clipped absolute deviation penalty (gSCAD) which can perform regression estimation and variable selection simultaneously. We show the method can identify the true model consistently and discuss construction of pointwise confidence interval for the estimated functional coefficients. Our methodology and theory is verified by simulation studies as well as an application to spectrometrics data.

PERSON (DATE) defined society as a communication system which is structurally coupled to, but not an aggregate of, human action systems. The communication system is then considered as self-organizing ("autopoietic"), as are human actors. Communication systems can be studied by using FAC's (DATE) mathematical theory of communication. The update of a network by action at CARDINAL of the local nodes is then a well-known problem in artificial intelligence (Pearl DATE). By combining these various theories, a general algorithm for probabilistic structure/action contingency can be derived. The consequences of this contingency for each system, its consequences for their further histories, and the stabilization on each side by counterbalancing mechanisms are discussed, in both mathematical and theoretical terms. An empirical example is elaborated.

A concept of randomness for infinite time register machines (ITRMs) is defined and studied. In particular, we show that for this notion of randomness, computability from mutually random reals implies computability and that an analogue of PERSON theorem holds. This is then applied to obtain results on the structure of ITRM-degrees. Finally, we consider autoreducibility for ITRMs and show that randomness implies non-autoreducibility.

The standard approach to logic in the literature in philosophy and mathematics, which has also been adopted in computer science, is to define a language (the syntax), an appropriate class of models together with an interpretation of formulas in the language (the semantics), a collection of axioms and rules of inference characterizing reasoning (the proof theory), and then relate the proof theory to the semantics via soundness and completeness results. Here we consider an approach that is more common in the economics literature, which works purely at the semantic, set-theoretic level. We provide set-theoretic completeness results for a number of epistemic and conditional logics, and contrast the expressive power of the syntactic and set-theoretic approaches

I consider issues in distributed computation that should be of relevance to game theory. In particular, I focus on (a) representing knowledge and uncertainty, (b) dealing with failures, and (c) specification of mechanisms.

We discuss quantum non-locality and contextuality, emphasising logical and structural aspects. We also show how the same mathematical structures arise in various areas of classical computation.

This paper describes a new method for classifying a dataset that partitions elements into their categories. It has relations with neural networks but a slightly different structure, requiring only a single pass through the classifier to generate the weight sets. A grid-like structure is required as part of a novel idea of converting a DATE of real values into a CARDINAL-D structure of value bands. Each cell in any band then stores a distinct set of weights, to represent its own importance and its relation to each output category. During classification, all of the output weight lists can be retrieved and summed to produce a probability for what the correct output category is. The bands possibly work like hidden layers of neurons, but they are variable specific, making the process orthogonal. The construction process can be a single update process without iterations, making it potentially much faster. It can also be compared with ORG and may be practical for partial or competitive updating.

Molecular variants of vitamin ORG, siderophores and glycans occur. To take up variant forms, bacteria may express an array of receptors. The gut microbe Bacteroides thetaiotaomicron has CARDINAL different receptors to take up variants of vitamin ORG and CARDINAL receptors to take up various glycans. The design of receptor arrays reflects key processes that shape cellular evolution. Competition may focus each species on a subset of the available nutrient diversity. Some gut bacteria can take up only a narrow range of carbohydrates, whereas species such as ORG can digest many different complex glycans. Comparison of different nutrients, habitats, and genomes provide opportunity to test hypotheses about the breadth of receptor arrays. Another important process concerns fluctuations in nutrient availability. Such fluctuations enhance the value of cellular sensors, which gain information about environmental availability and adjust receptor deployment. Bacteria often adjust receptor expression in response to fluctuations of particular carbohydrate food sources. Some species may adjust expression of uptake receptors for specific siderophores. How do cells use sensor information to control the response to fluctuations? That question about regulatory wiring relates to problems that arise in control theory and artificial intelligence. Control theory clarifies how to analyze environmental fluctuations in relation to the design of sensors and response systems. Recent advances in deep learning studies of artificial intelligence focus on the architecture of regulatory wiring and the ways in which complex control networks represent and classify environmental states. I emphasize the similar design problems that arise in cellular evolution, control theory, and artificial intelligence. I connect those broad concepts to testable hypotheses for bacterial uptake of ORG, siderophores and glycans.

Computability logic is a formal theory of (interactive) computability in the same sense as classical logic is a formal theory of truth. This approach was initiated very recently in "Introduction to computability logic" (Annals of PRODUCT and ORG (DATE), ORG). The present paper reintroduces computability logic in a more compact and less technical way. It is written in a semitutorial style with a general computer science, logic or mathematics audience in mind. An Internet source on the subject is available at ORG, and additional material at http://www.csc.villanova.edu/~japaridz/CL/gsoll.html .

In this article, we perform a systematic study of the mass spectrum of the vector hidden charmed and bottomed tetraquark states using the ORG sum rules.

In this article, we construct the $ORG \gamma_\mu C$ and $MONEY ORG type currents to interpolate the vector tetraquark states, then carry out the operator product expansion up to the vacuum condensates of dimension-10 in a consistent way, and CARDINAL ORG sum rules. In calculations, we use the formula $\mu=\sqrt{M^2_{Y}-(2{\mathbb{M}}_c)^2}$ to determine the optimal energy scales of the ORG spectral densities, moreover, we take the experimental values of the masses of the $GPE, $MONEY, $Y(4390)$ and $PERSON as input parameters and fit the pole residues to reproduce the correlation functions at the ORG side. The numerical results support assigning the $PERSON to be the $C \otimes \gamma_\mu CARDINAL type vector tetraquark ORG $c\bar{c}s\bar{s}$, assigning the $Y(4360/4320)$ to be $MONEY \otimes \gamma_5\gamma_\mu C$ type vector tetraquark state $PERSON, and disfavor assigning the $GPE and $PERSON to be the pure vector tetraquark states.

This paper considers an inverse problem for the classical wave equation in an exterior domain. It is a mathematical interpretation of an inverse obstacle problem which employs the dynamical scattering data of NORP wave over a finite time interval. It is assumed that the wave satisfies a PERSON type boundary condition with an unknown variable coefficient. The wave is generated by the initial data localized outside the obstacle and observed over a finite time interval at the same place as the support of the initial data. It is already known that, using the enclosure method, one can extract the maximum sphere whose exterior encloses the obstacle, from the data. In this paper, it is shown that the enclosure method enables us to extract also: (i) a quantity which indicates the deviation of the geometry between the maximum sphere and the boundary of the obstacle at the ORDINAL reflection points of the wave; (ii) the value of the coefficient of the boundary condition at an arbitrary ORDINAL reflection point of the wave provided, for example, the surface of the obstacle is known in a neighbourhood of the point. Another new obtained knowledge is that: the enclosure method can cover the case when the data are taken over a sphere whose centre coincides with that of the support of an initial data and yields corresponding results to (i) and (ii).

A mathematical method for through-wall imaging via wave phenomena in the time domain is introduced. The method makes use of a single reflected wave over a finite time interval and gives us a criterion whether a penetrable obstacle exists or not in a general rough background medium. Moreover, if the obstacle exists, the lower and upper estimates of the distance between the obstacle and the center point of the support of the initial data are given. As an evidence of the potential of the method CARDINAL applications are also given.

We propose that operator induction serves as an adequate model of perception. We explain how to reduce universal agent models to operator induction. We propose a universal measure of operator induction fitness, and show how it can be used in a reinforcement learning model and a homeostasis (self-preserving) agent based on the free energy principle. We show that the action of the homeostasis agent can be explained by the operator induction model.

The advantages of mixed approach with using different kinds of programming techniques for symbolic manipulation are discussed. The main purpose of approach offered is merge the methods of object oriented programming that convenient for presentation data and algorithms for user with advantages of functional languages for data manipulation, internal presentation, and portability of software.

We show that several constraint propagation algorithms (also called (local) consistency, consistency enforcing, PERSON, filtering or narrowing algorithms) are instances of algorithms that deal with chaotic iteration. To this end we propose a simple abstract framework that allows us to classify and compare these algorithms and to establish in a uniform way their basic properties.

The covariance graph (PERSON graph) of a probability distribution $p$ is the undirected graph $MONEY where CARDINAL nodes are adjacent iff their corresponding random variables are marginally dependent in $p$. In this paper, we present a graphical criterion for reading dependencies from $MONEY, under the assumption that $p$ satisfies the graphoid properties as well as weak transitivity and composition. We prove that the graphical criterion is sound and complete in certain sense. We argue that our assumptions are not too restrictive. For instance, all the regular NORP probability distributions satisfy them.

Urban mobility systems are composed multiple elements with strong interactions, i.e. their future is co-determined by the state of other elements. Thus, studying components in isolation, i.e. using a reductionist approach, is inappropriate. I propose CARDINAL recommendations to improve urban mobility based on insights from the scientific study of complex systems: use adaptation over prediction, regulate interactions to avoid friction, use sensors to recover real time information, develop adaptive algorithms to exploit that information, and deploy agents to act on the urban environment.

It is found that in the SM the Ward-Takahashi(WT) identities of the axial-vector currents and the charged vector currents of fermions are invalid after spontaneous symmetry breaking. The spin-0 components of ORG and PERSON fields are revealed from the invalidity of these GPE identities. The masses of these spin-0 components are at $10^{14}$GeV. They are ghosts. Therefore, unitarity of the ORG after spontaneous symmetry breaking is broken at $MONEY

This chapter presents a theoretical framework for evaluating next generation search engines. We focus on search engines whose results presentation is enriched with additional information and does not merely present the usual list of CARDINAL blue links, that is, of CARDINAL links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

Given a compatible vector field on a compact connected almost-complex manifold, we show in this article that the multiplicities of eigenvalues among the CARDINAL point set of this vector field have intimate relations. We highlight a special case of our result and reinterpret it as a vanishing-type result in the framework of the celebrated ORG localization formula. This new point of view, via the Chern-Weil theory and a strengthened version of PERSON's residue formula observed by ORG, can lead to an obstruction to Killing real holomorphic vector fields on compact NORP manifolds in terms of a curvature integral.

We give formulae that yield an information about the location of an unknown polygonal inclusion having unknown constant conductivity inside a known conductive material having known constant conductivity from a partial knowledge of the Neumann -to-Dirichlet operator.

This encyclopedic article gives a mini-introduction into the theory of universal learning, founded by PERSON in DATE and significantly developed and extended in DATE. It explains the spirit of universal learning, but necessarily glosses over technical subtleties.

This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify ORG's razor, solve the induction problem, and define intelligence.

In this paper, for an even dimensional compact manifold with boundary which has the non-product metric near the boundary, we use the noncommutative residue to define a conformal invariant pair. For a CARDINAL-dimensional manifold, we compute this conformal invariant pair under some conditions and point out the way of computations in the general.

Machine learning often needs to model density from a multidimensional data sample, including correlations between coordinates. Additionally, we often have missing data case: that data points can miss values for some of coordinates. This article adapts rapid parametric density estimation approach for this purpose: modelling density as a linear combination of orthonormal functions, for which MONEY optimization says that (independently) estimated coefficient for a given function is just average over the sample of value of this function. Hierarchical correlation reconstruction ORDINAL models probability density for each separate coordinate using all its appearances in data sample, then adds corrections from independently modelled pairwise correlations using all samples having both coordinates, and so on independently adding correlations for growing numbers of variables using often decreasing evidence in data sample. A basic application of such modelled multidimensional density can be imputation of missing coordinates: by inserting known coordinates to the density, and taking expected values for the missing coordinates, or even their entire joint probability distribution. Presented method can be compared with cascade correlations approach, offering several advantages in flexibility and accuracy. It can be also used as artificial neuron: maximizing prediction capabilities for only local behavior - modelling and predicting local connections.

We provide a simple physical interpretation, in the context of the ORDINAL law of thermodynamics, to the information inequality (a.k.a. the GPE' inequality, which is also equivalent to the log-sum inequality), asserting that the relative entropy between CARDINAL probability distributions cannot be negative. Since this inequality stands at the basis of the data processing theorem (ORG), and the ORG in turn is at the heart of most, if not all, proofs of converse theorems in FAC theory, it is observed that conceptually, the roots of fundamental limits of ORG can actually be attributed to the laws of physics, in particular, to the ORDINAL law of thermodynamics, and at least indirectly, also to the law of energy conservation. By the same token, in the other direction: one can view the ORDINAL law as stemming from information-theoretic principles.

A nonlinear model with response variable missing at random is studied. In order to improve the coverage accuracy, the empirical likelihood ratio (ORG) method is considered. The asymptotic distribution of EL statistic and also of its approximation is MONEY if the parameters are estimated using least squares(LS) or least absolute deviation(LAD) method on complete data. When the response are reconstituted using a semiparametric method, the empirical log-likelihood associated on imputed data is also asymptotically $MONEY The PERSON's theorem for ORG for parameter on response variable is also satisfied. It is shown via PERSON simulations that the ORG methods outperform the normal approximation based method in terms of coverage probability up to and including on the reconstituted data. The advantages of the proposed method are exemplified on the real data.

ORG black holes in NORP effective spacetime of moving vortical plasmas described by moving magnetohydrodynamic (ORG) flows. This example is an extension of acoustic torsion recently introduced in the literature (PERSON,PRD(2004),7,64004), where now the presence of artificial black holes in moving plasmas is obtained by the presence of an horizon in the NORP spacetime. Hawking radiation is computed in terms of the background magnetic field and the magnetic permeability. The metric is singular although GPE analogue torsion is not necessarily singular. The effective PERSON invariance is shown to be broken due to the presence of effective torsion in strong analogy with the ORG-GPE gravitational case presented recently by PERSON (PRD 69,2004,105009).