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When an awakened populace threatens, evil turns to tech. The devil is just following the book. Being true to yourself means working with others. More horrific evidence emerges. The team, the urgency, and twenty long years of coordination. Targeting corruption by pretending. The domino effect in a gory world. Bribes, favors, loyalty and quietly building cases. Compare and contrast while solving the impossible. The RFIA promises a responsible finance approach. Merging AI with humans. Elon's competition. Chips and inserts should be internal repaired circuits. Are they helping or mapping? The innovation and industry behind graphene oxide. The error correction codes in firmly rooted algos. Yes, your DNA is involved. Hey AI, get some morality. Tracing the philosophical perspectives. Yes indeed math can prove what they say. When machine learning falls off the rails. Modern info theory and Shannon's vision. An OS on top of an OS. Is it all about controlling us? We are God's best quantum computer. Humanity can recognize good, have true faith, and be courageous in the endless fight against evil.Learn more about your ad choices. Visit podcastchoices.com/adchoices

Introduction: Activity theory, developed in the USSR as a Marxist alternative to Western psychology, has been applied widely in educational studies and increasingly in human-computer interaction research. Argument: The key elements of activity theory, Motivation, Goal, Activity, Tools, Object, Outcome, Rules, Community and Division of labour are all directly applicable to the conduct of information behaviour research. An activity-theoretical approach to information behaviour research would provide a sound basis for the elaboration of contextual issues, for the discovering of organizational and other contradictions that affect information behaviour. It may be used to aid the design and analysis of investigations. Elaboration: The basic ideas of activity theory are outlined and an attempt is made to harmonize different perspectives. A contrast is made between an activity system perspective and an activity process perspective and a diagrammatic representation of the process perspective is offered. Conclusion: Activity theory is not a predictive theory but a conceptual framework within which different theoretical perspectives may be employed. Typically, it is suggested that several methods of data collection should be employed and that the time frame for investigation should be long enough for the full range of contextual issues to emerge. Activity theory offers not only a useful conceptual framework, but also a coherent terminology to be shared by researchers, and a rapidly developing body of literature in associated disciplines.

This is the third annual listing of graduate theses and dissertations in English as a Second Language (ESL). The studies cited here were completed during the academic year 1977-1978. The listing is intended to provide graduate students and others doing research in ESL and related fields with specific, comprehensive data on current graduate-level studies. Information was contributed by program directors, graduate advisers, and department chairmen from among the institutions in the United States and Canada that are known to have graduate programs in ESL. Each of the 90 entries provides the writer's name, thesis or dissertation title, degree earned, university, year degree was granted, name of thesis adviser, department or program, and writer's address. In addition, most entries include summaries of abstracts prepared by the writers. Entries are listed under the following headings: (1) Adult Education, (2) Bilingualism, (3) Contrastive Studies, (4) Culture, (5) Curriculum, (6) Grammar, (7) Literature, (8) Materials and Methods, (9) Phonology, (10) Profession, (11) Psycholinguistics, (12) Reading, (13) Research, (14) Second Dialect, (15) Second Language Learning, (16) Sociolinguistics, (17) Testing, (18) Visual Aids, and (19) Writing. (Author/AMH)

This study examined the effects of public service advertising from two theoretical backgrounds: social learning theory and social control theory. Traditional social learning theory assumes that learning occurs by subjects performing responses and experiencing their effects, with reinforcement as the main determinant. Social control theory, as defined by Donald Black (1984), emphasizes all practices by which people define and respond to deviant behavior. This study is a secondary analysis of data on the attitudinal and behavioral effects of the crime prevention campaign, "Take a Bite Out of Crime," initiated in 1979 and delivered by television, radio, newspapers, and magazines. Data were collected through 1,454 personal interviews conducted by the Roper Organization. Two-way analysis of variance produced 6 permutations comprised of 54 dependent variables. The study examined variance in: crime prevention behavior, self-estimation of likelihood of future crime prevention behavior, cognitive effects, recall of campaign, social control involving neighborhood integration, and additional social control agents. Results indicated that the effectiveness of public service advertising campaigns is closely tied to interpersonal communication activity within a community and that, on their own, these campaigns rarely, if ever, achieve the intended attitudinal or behavioral results. (Contains 6 tables of data and 31 references.) (CR)

The statistical mechanics of Gibbs is a juxtaposition of subjective, probabilistic ideas on the one hand and objective, mechanical ideas on the other. In this paper, we follow the path set out by Jaynes, including elements added subsequently to that original work, to explore the consequences of the purely statistical point of view. We show how standard methods in the equilibrium theory could have been derived simply from a description of the available problem information. In addition, our presentation leads to novel insights into questions associated with symmetry and non-equilibrium statistical mechanics. Two surprising consequences to be explored in further work are that (in)distinguishability factors are automatically predicted from the problem formulation and that a quantity related to the thermodynamic entropy production is found by considering information loss in non-equilibrium processes. Using the problem of ion channel thermodynamics as an example, we illustrate the idea of building up complexity by successively adding information to create progressively more complex descriptions of a physical system. Our result is that such statistical mechanical descriptions can be used to create transparent, computable, experimentally-relevant models that may be informed by more detailed atomistic simulations. We also derive a theory for the kinetic behavior of this system, identifying the nonequilibrium `process' free energy functional. The Gibbs relation for this functional is a fluctuation-dissipation theorem applicable arbitrarily far from equilibrium, that captures the effect of non-local and time-dependent behavior from transient driving forces. Based on this work, it is clear that statistical mechanics is a general tool for constructing the relationships between constraints on system information.

The statistical mechanics of Gibbs is a juxtaposition of subjective, probabilistic ideas on the one hand and objective, mechanical ideas on the other. In this paper, we follow the path set out by Jaynes, including elements added subsequently to that original work, to explore the consequences of the purely statistical point of view. We show how standard methods in the equilibrium theory could have been derived simply from a description of the available problem information. In addition, our presentation leads to novel insights into questions associated with symmetry and non-equilibrium statistical mechanics. Two surprising consequences to be explored in further work are that (in)distinguishability factors are automatically predicted from the problem formulation and that a quantity related to the thermodynamic entropy production is found by considering information loss in non-equilibrium processes. Using the problem of ion channel thermodynamics as an example, we illustrate the idea of building up complexity by successively adding information to create progressively more complex descriptions of a physical system. Our result is that such statistical mechanical descriptions can be used to create transparent, computable, experimentally-relevant models that may be informed by more detailed atomistic simulations. We also derive a theory for the kinetic behavior of this system, identifying the nonequilibrium `process' free energy functional. The Gibbs relation for this functional is a fluctuation-dissipation theorem applicable arbitrarily far from equilibrium, that captures the effect of non-local and time-dependent behavior from transient driving forces. Based on this work, it is clear that statistical mechanics is a general tool for constructing the relationships between constraints on system information.

In 1686 in his Discours de Metaphysique, Leibniz points out that if an arbitrarily complex theory is permitted then the notion of "theory" becomes vacuous because there is always a theory. This idea is developed in the modern theory of algorithmic information, which deals with the size of computer programs and provides a new view of Godel's work on incompleteness and Turing's work on uncomputability. Of particular interest is the halting probability Omega, whose bits are irreducible, i.e., maximally unknowable mathematical facts. More generally, these ideas constitute a kind of "digital philosophy" related to recent attempts of Edward Fredkin, Stephen Wolfram and others to view the world as a giant computer. There are also connections with recent "digital physics" speculations that the universe might actually be discrete, not continuous. This systeme du monde is presented as a coherent whole in my book Meta Math!, which will be published this fall.

Molecular Communication (MC) is a communication strategy that uses molecules as carriers of information, and is widely used by biological cells. As an interdisciplinary topic, it has been studied by biologists, communication theorists and a growing number of information theorists. This paper aims to specifically bring MC to the attention of information theorists. To do this, we first highlight the unique mathematical challenges of studying the capacity of molecular channels. Addressing these problems require use of known, or development of new mathematical tools. Toward this goal, we review a subjective selection of the existing literature on information theoretic aspect of molecular communication. The emphasis here is on the mathematical techniques used, rather than on the setup or modeling of a specific paper. Finally, as an example, we propose a concrete information theoretic problem that was motivated by our study of molecular communication.

We provide some examples showing how game-theoretic arguments can be used in computability theory and algorithmic information theory: unique numbering theorem (Friedberg), the gap between conditional complexity and total conditional complexity, Epstein--Levin theorem and some (yet unpublished) result of Muchnik and Vyugin

These two modules cover aspects of the coding process and algebraic coding theory. The first unit defines coding as a branch of information and communication science, which draws extensively upon many diverse mathematical fields, primarily abstract and linear algebra, number theory, probability and statistics, and combinatorial theory. Aspects of the process, including linear codes and error correction, are discussed. The second unit focuses on the construction of a double-error correcting code. Both units contain problems and answers to these exercises. The second unit contains a model exam and concludes with an answer key to this test. (MP)

A class of problems in quantum information theory, having an elementary formulation but still resisting solution, concerns the additivity properties of various quantities characterizing quantum channels, notably the "classical capacity", and the "maximal output purity". All known results, including extensive numerical work, are consistent with the conjecture that these quantities are indeed additive (resp. multiplicative) with respect to tensor products of channels. A proof of this conjecture would have important consequences in quantum information theory. In particular, according to this conjecture, the classical capacity or the maximal purity of outputs cannot be increased by using entangled inputs of the channel. In this paper we state the additivity/multiplicativity problems, give some relations between them, and prove some new partial results, which also support the conjecture.

Schema theory may be significant in determining if and how news audiences process information. For any given news topic, people have from none to many schemata (cognitive structures that represent organized knowledge about a given concept or type of stimulus abstracted from prior experience) upon which to draw. Models of how schemata are used should illustrate a process, and in the case of news media and information processing, the process may be associated with effective information transmission and retention between the news media and the individuals. A "data-pool" model offered by D. A. Norman and D. G. Bobrow traces the process by which schemata are activated, but fails to explain adequately the precise channels through which specific types of information must pass during information processing. It also cannot be tested adequately for its merit. An 11-stage process model for schema theory developed by R. Axelrod provides for processing of information based on a series of "yes" or "no" questions. This model has an "escape valve" or a means for departure of information without interpretation. The R. Hastie information processing model suggests that people must react to an event in one of two ways: either find an appropriate schema or begin the construction of a new schema. Since research in the area of mass media information processing is relatively new, it seems best to work with a model that can be easily operationalized, moving later to more complex plans. In view of this, the Hastie model offers the greatest utility for mass communications researchers. A five-page list of references concludes the document. (SRT)

The first step in quantum information theory is the identification of entanglement as a valuable resource. The next step is learning how to exploit this resource efficiently. We learn how to exploit entanglement efficiently by applying analogues of thermodynamical concepts. These concepts include reversibility, entropy, and the distinction between intensive and extensive quantities. We discuss some of these analogues and show how they lead to a measure of entanglement for pure states. We also ask whether these analogues are more than analogues, and note that, l o c a l l y, entropy of entanglement is thermodynamical entropy.

We apply Renormalization Group (RG) techniques to Classical Information Theory, in the limit of large codeword size $n$. In particular, we apply RG techniques to (1) noiseless coding (i.e., a coding used for compression) and (2) noisy coding (i.e., a coding used for channel transmission). Shannon's "first" and "second" theorems refer to (1) and (2), respectively. Our RG technique uses composition class (CC) ideas, so we call our technique Composition Class Renormalization Group (CCRG). Often, CC's are called "types" instead of CC's, and their theory is referred to as the "Method of Types". For (1) and (2), we find that the probability of error can be expressed as an Error Function whose argument contains variables that obey renormalization group equations. We describe a computer program called WimpyRG-C1.0 that implements the ideas of this paper. C++ source code for WimpyRG-C1.0 is publicly available.

We apply Renormalization Group (RG) techniques to Classical Information Theory, in the limit of large codeword size $n$. In particular, we apply RG techniques to (1) noiseless coding (i.e., a coding used for compression) and (2) noisy coding (i.e., a coding used for channel transmission). Shannon's "first" and "second" theorems refer to (1) and (2), respectively. Our RG technique uses composition class (CC) ideas, so we call our technique Composition Class Renormalization Group (CCRG). Often, CC's are called "types" instead of CC's, and their theory is referred to as the "Method of Types". For (1) and (2), we find that the probability of error can be expressed as an Error Function whose argument contains variables that obey renormalization group equations. We describe a computer program called WimpyRG-C1.0 that implements the ideas of this paper. C++ source code for WimpyRG-C1.0 is publicly available.

A comparison of structural features of quantum and classical physical theories, such as the information capacity of systems subject to these theories, requires a common formal framework for the presentation of corresponding concepts (such as states, observables, probability, entropy). Such a framework is provided by the notion of statistical model developed in the convexity approach to statistical physical theories. Here we use statistical models to classify and survey all possible types of embedding and extension of quantum probabilistic theories subject to certain reasonable constraints. It will be shown that the so-called canonical classical extension of quantum mechanics is essentially the only `good' representation of the quantum statistical model in a classical framework. All quantum observables are thus identified as fuzzy classical random variables.

While a positive operator valued measure gives the probabilities in a quantum measurement, an instrument gives both the probabilities and the a posteriori states. By interpreting the instrument as a quantum channel and by using the typical inequalities for the quantum and classical relative entropies, many bounds on the classical information extracted in a quantum measurement, of the type of Holevo's bound, are obtained in a unified manner.

While a positive operator valued measure gives the probabilities in a quantum measurement, an instrument gives both the probabilities and the a posteriori states. By interpreting the instrument as a quantum channel and by using the typical inequalities for the quantum and classical relative entropies, many bounds on the classical information extracted in a quantum measurement, of the type of Holevo's bound, are obtained in a unified manner.

While a positive operator valued measure gives the probabilities in a quantum measurement, an instrument gives both the probabilities and the a posteriori states. By interpreting the instrument as a quantum channel and by using the typical inequalities for the quantum and classical relative entropies, many bounds on the classical information extracted in a quantum measurement, of the type of Holevo's bound, are obtained in a unified manner.

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

One of the most frequently used techniques for removing background noise from electroencephalogram (EEG) data is adaptive noise cancellation (ANC). Nonetheless, there exist two primary disadvantages associated with the adaptive noise reduction of EEG signals: the adaptive filter, which is supposed to be an approximation of contaminated noise, lacks the reference signal. The mean squared error (MSE) criterion is frequently employed to achieve this goal in adaptive filters. The MSE criterion, which only considers second-order errors, cannot be used since neither the EEG signal nor the EOG artifact are Gaussian. In this work, we employ an ANC system, deriving an estimate of EOG noise with a discrete wavelet transform (DWT) and input this signal into the reference of the ANC system. The entropy-based error metric is used to reduce the error signal instead of the MSE. Results from computer simulations demonstrate that the suggested system outperforms competing methods with respect to root-mean-square-error, signal-to-noise ratio, and coherence measurements.

Shannon's information theory was a towering achievement of 20th century science and laid a theoretical foundation for understanding how to design communication systems, and to judge their optimality. However, the bedrock assumptions in the Shannon framework of unbounded delay and reliability are not appropriate for mobile ad hoc networks, which has rapidly changing topologies and end-to-end performance constraints. The lack of a descriptive theory for ad hoc networks has greatly impeded their design and commercialization. In response to this roadblock, DARPA has recently initiated a $13.5m Grand Challenge to develop a new information theory capable of describing the performance limits of mobile wireless networks. UT Austin is leading this effort, with one of the two winning proposals, entitled 'Non-equilibrium information theory'. In this talk, I will describe our vision for progressing towards this daunting goal, will present some preliminary results, and attempt to explain how this might affect the future design of wireless networks. (Project page: http://wncg.ece.utexas.edu/itmanet/index.php) ©2007 Microsoft Corporation. All rights reserved.

Shannon's information theory was a towering achievement of 20th century science and laid a theoretical foundation for understanding how to design communication systems, and to judge their optimality. However, the bedrock assumptions in the Shannon framework of unbounded delay and reliability are not appropriate for mobile ad hoc networks, which has rapidly changing topologies and end-to-end performance constraints. The lack of a descriptive theory for ad hoc networks has greatly impeded their design and commercialization. In response to this roadblock, DARPA has recently initiated a $13.5m Grand Challenge to develop a new information theory capable of describing the performance limits of mobile wireless networks. UT Austin is leading this effort, with one of the two winning proposals, entitled 'Non-equilibrium information theory'. In this talk, I will describe our vision for progressing towards this daunting goal, will present some preliminary results, and attempt to explain how this might affect the future design of wireless networks. (Project page: http://wncg.ece.utexas.edu/itmanet/index.php) ©2007 Microsoft Corporation. All rights reserved.

Shannon's information theory was a towering achievement of 20th century science and laid a theoretical foundation for understanding how to design communication systems, and to judge their optimality. However, the bedrock assumptions in the Shannon framework of unbounded delay and reliability are not appropriate for mobile ad hoc networks, which has rapidly changing topologies and end-to-end performance constraints. The lack of a descriptive theory for ad hoc networks has greatly impeded their design and commercialization. In response to this roadblock, DARPA has recently initiated a $13.5m Grand Challenge to develop a new information theory capable of describing the performance limits of mobile wireless networks. UT Austin is leading this effort, with one of the two winning proposals, entitled 'Non-equilibrium information theory'. In this talk, I will describe our vision for progressing towards this daunting goal, will present some preliminary results, and attempt to explain how this might affect the future design of wireless networks. (Project page: http://wncg.ece.utexas.edu/itmanet/index.php) ©2007 Microsoft Corporation. All rights reserved.

We present a possible approach to the study of the renormalization group (RG) flow based entirely on the information theory. The average information loss under a single step of Wilsonian RG transformation is evaluated as a conditional entropy of the fast variables, which are integrated out, when the slow ones are held fixed. Its positivity results in the monotonic decrease of the informational entropy under renormalization. This, however, does not necessarily imply the irreversibility of the RG flow, because the entropy explicitly depends on the total number of degrees of freedom, which is reduced. Only some size-independent additive part of the entropy could possibly provide the required Lyapunov function. We also introduce a mutual information of fast and slow variables as probably a more adequate quantity to represent the changes in the system under renormalization and evaluate it for some simple systems. It is shown that for certain real space decimation transformations the positivity of the mutual information directly leads to the monotonic growth of the entropy per lattice site along the RG flow and hence to its irreversibility.

We investigate cooperative radar and communications signaling. While each system typically considers the other system a source of interference, by considering the radar and communications operations to be a single joint system, the performance of both systems can, under certain conditions, be improved by the existence of the other. As an initial demonstration, we focus on the radar as relay scenario and present an approach denoted multiuser detection radar (MUDR). A novel joint estimation and information theoretic bound formulation is constructed for a receiver that observes communications and radar return in the same frequency allocation. The joint performance bound is presented in terms of the communication rate and the estimation rate of the system.

The ``Gibbs Paradox'' refers to several related questions concerning entropy in thermodynamics and statistical mechanics: whether it is an extensive quantity or not, how it changes when identical particles are mixed, and the proper way to count states in systems of identical particles. Several authors have recognized that the paradox is resolved once it is realized that there is no such thing as the entropy of a system, that there are many entropies, and that the choice between treating particles as being distinguishable or not depends on the resolution of the experiment. The purpose of this note is essentially pedagogical; we add to their analysis by examining the paradox from the point of view of information theory. Our argument is based on that `grouping' property of entropy that Shannon recognized, by including it among his axioms, as an essential requirement on any measure of information. Not only does it provide the right connection between different entropies but, in addition, it draws our attention to the obvious fact that addressing issues of distinguishability and of counting states requires a clear idea about what precisely do we mean by a state.

A crucial challenge in network theory is the study of the robustness of a network after facing a sequence of failures. In this work, we propose a dynamical definition of network's robustness based on Information Theory, that considers measurements of the structural changes caused by failures of the network's components. Failures are defined here, as a temporal process defined in a sequence. The robustness of the network is then evaluated by measuring dissimilarities between topologies after each time step of the sequence, providing a dynamical information about the topological damage. We thoroughly analyze the efficiency of the method in capturing small perturbations by considering both, the degree and distance distributions. We found the network's distance distribution more consistent in capturing network structural deviations, as better reflects the consequences of the failures. Theoretical examples and real networks are used to study the performance of this methodology.

The Conservation of Energy plays a pivotal part in the development of the physical sciences. With the growth of computation and the study of other discrete token based systems such as the genome, it is useful to ask if there are conservation principles which apply to such systems and what kind of functional behaviour they imply for such systems. Here I propose that the Conservation of Hartley-Shannon Information plays the same over-arching role in discrete token based systems as the Conservation of Energy does in physical systems. I will go on to prove that this implies power-law behaviour in component sizes in software systems no matter what they do or how they were built, and also implies the constancy of average gene length in biological systems as reported for example by Lin Xu et al (10.1093/molbev/msk019). These propositions are supported by very large amounts of experimental data extending the first presentation of these ideas in Hatton (2011, IFIP / SIAM / NIST Working Conference on Uncertainty Quantification in Scientific Computing, Boulder, August 2011).

Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.

Recent theoretical work on solid-state proposals for the implementation of quantum computation and quantum information processing is reviewed. The differences and similarities between microscopic and macroscopic qubits are highlighted and exemplified by the spin qubit proposal on one side and the superconducting qubits on the other. Before explaining the spin and supercondcuting qubits in detail, some general concepts that are relevant for both types of solid-state qubits are reviewed. The controlled production of entanglement in solid-state devices, the transport of carriers of entanglement, and entanglement detection will be discussed in the final part of this review.

We propose a quantum version of a gedanken experiment which supports the generalized second law of black hole thermodynamics. A quantum measurement of particles in the region outside of the event horizon decreases the entropy of the outside matter due to the entanglement of the inside and outside particle states. This decrease is compensated, however, by the increase in the detector entropy. If the detector is conditionally dropped into the black hole depending on the experimental outcome, the decrease of the matter entropy is more than compensated by the increase of the black hole entropy via the increase of the black hole mass which is ultimately attributed to the work done by the measurement.

It is suggested that quantum mechanics is not fundamental but emerges from classical information theory applied to causal horizons. The path integral quantization and quantum randomness can be derived by considering information loss of fields or particles crossing Rindler horizons for accelerating observers. This implies that information is one of the fundamental roots of all physical phenomena. The connection between this theory and Verlinde's entropic gravity theory is also investigated.

This paper is devoted to finding the general solutions of the functional equation $\sumin \sumjm h(p_iq_j)=\sumin h(p_i)+\sumjm k_j(q_j)+\lambda\sumin h(p_i)\sumjm k_j(q_j)$ valid for all complete probability distributions $(p_1,\ldots,p_n)$, $(q_1,\ldots,q_m)$, $0\le p_i\le 1$, $0\le q_j\le 1$, $i=1,\ldots,n$; $j=1,\ldots,m$, $\sumin p_i=1$, $\sumjm q_j=1$; $n\ge 3$, $m\ge 3$ fixed integers; $\lambda\in\RR$, $\lambda\neq 0$ and the mappings $h:I\to\RR$, $k_j:I\to\RR$, $j=1,\ldots,m$; $I=[0,1]$, $\RR$ denoting the set of all real numbers. A special case of the above functional equation was treated earlier by L. Losonczi and Gy. Maksa.

We consider a network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity (STDP), which is representative of a cortical column or hypercolumn. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to another neurons. In this work we use a permutation entropy approach to characterize the dynamic of the spiking neural activity of a given population of neurons. This allow us to investigate the fine temporal structure of complex neuronal signals not just by using the probability distributions associated with inter-spike intervals, but instead considering much more subtle measures accounting for its causal structure.

Consider a photon that has just emerged from a linear polarizing filter. If the photon is then subjected to an orthogonal polarization measurement-e.g., horizontal vs vertical-the photon's preparation cannot be fully expressed in the outcome: a binary outcome cannot reveal the value of a continuous variable. However, a stream of identically prepared photons can do much better. To quantify this effect, one can compute the mutual information between the angle of polarization and the observed frequencies of occurrence of "horizontal" and "vertical." Remarkably, one finds that the quantum-mechanical rule for computing probabilities--Born's rule--maximizes this mutual information relative to other conceivable probability rules. However, the maximization is achieved only because linear polarization can be modeled with a real state space; the argument fails when one considers the full set of complex states. This result generalizes to higher dimensional Hilbert spaces: in every case, one finds that information is transferred optimally from preparation to measurement in the real-vector-space theory but not in the complex theory. Attempts to modify the statement of the problem so as to see a similar optimization in the standard complex theory are not successful (with one limited exception). So it seems that this optimization should be regarded as a special feature of real-vector-space quantum theory.

In this article, we prove Shannon-MacMillan-Breiman Theorem for Wireless Sensor Networks modelled as coloured geometric random graphs. For large $n,$ we show that a Wireless Sensor Network consisting of $n$ sensors in $[0,1]^d$ connected by an average number of links of order $n\log n $ can be coded by about $[n(\log n )^2\pi^{d/2}/(d/2)!]\,\mathcal{H}$ bits, where $\mathcal{H}$ is an explicitly defined entropy. In the process, we derive a joint large deviation principle (LDP) for the \emph{empirical sensor measure} and \emph{the empirical link measure} of coloured random geometric graph models.

An exact reduced-density-operator for the output quantum states in time-convolutionless form was derived by solving the quantum Liouville equation which governs the dynamics of a noisy quantum channel by using a projection operator method and both advanced and retarded propagators in time. The formalism developed in this work is general enough to model a noisy quantum channel provided specific forms of the Hamiltonians for the system, reservoir, and the mutual interaction between the system and the reservoir are given. Then, we apply the formulation to model a two-bit quantum gate composed of coupled spin systems in which the Heisenberg coupling is controlled by the tunneling barrier between neighboring quantum dots. Gate Characteristics including the entropy, fidelity, and purity are calculated numerically for both mixed and entangled initial states.

One of the most fundamental problems in science is to define {\it quantitatively} the complexity of organized matters, i.e., {\it organized complexity}. Although many measures have been proposed toward this aim in previous decades, there is no agreed upon definition. This paper presents a new quantitative definition of organized complexity. This definition {\it simultaneously} captures the three major features of complexity: computational (similar to logical depth), descriptional (similar to the Kolmogorov complexity and effective complexity) and distributional (similar to statistical complexity). In addition, the proposed definition is computable and can measure both probabilistic and deterministic forms of objects in a unified manner. The proposed definition is based on circuits rather than Turing machines and $\epsilon$-machines. We give several criteria required for organized complexity measures and show that the proposed definition satisfies all of them for the first time. We then apply this quantitative definition to formulate a {\it semantic information theory}. We present the first formal definition of a {\it semantic information amount}, which is the core concept of the semantic information theory, that is based only on concretely defined notions. Previous semantic information theories defined this amount under some a priori information which is not concretely specified. We then unveil several fundamental properties in the semantic information theory, e.g., a semantic source coding theorem, semantic channel coding theorem, and effectiveness coding theorem. Although the semantic information theory has a long history of research going back more than six decades, there has been no study on its relation to organized complexity. This paper offers the first unified paradigm of organized complexity and semantic information theory.

One of the most fundamental problems in science is to define {\it quantitatively} the complexity of organized matters, i.e., {\it organized complexity}. Although many measures have been proposed toward this aim in previous decades, there is no agreed upon definition. This paper presents a new quantitative definition of organized complexity. This definition {\it simultaneously} captures the three major features of complexity: computational (similar to logical depth), descriptional (similar to the Kolmogorov complexity and effective complexity) and distributional (similar to statistical complexity). In addition, the proposed definition is computable and can measure both probabilistic and deterministic forms of objects in a unified manner. The proposed definition is based on circuits rather than Turing machines and $\epsilon$-machines. We give several criteria required for organized complexity measures and show that the proposed definition satisfies all of them for the first time. We then apply this quantitative definition to formulate a {\it semantic information theory}. We present the first formal definition of a {\it semantic information amount}, which is the core concept of the semantic information theory, that is based only on concretely defined notions. Previous semantic information theories defined this amount under some a priori information which is not concretely specified. We then unveil several fundamental properties in the semantic information theory, e.g., a semantic source coding theorem, semantic channel coding theorem, and effectiveness coding theorem. Although the semantic information theory has a long history of research going back more than six decades, there has been no study on its relation to organized complexity. This paper offers the first unified paradigm of organized complexity and semantic information theory.

One of the most fundamental problems in science is to define {\it quantitatively} the complexity of organized matters, i.e., {\it organized complexity}. Although many measures have been proposed toward this aim in previous decades, there is no agreed upon definition. This paper presents a new quantitative definition of organized complexity. This definition {\it simultaneously} captures the three major features of complexity: computational (similar to logical depth), descriptional (similar to the Kolmogorov complexity and effective complexity) and distributional (similar to statistical complexity). In addition, the proposed definition is computable and can measure both probabilistic and deterministic forms of objects in a unified manner. The proposed definition is based on circuits rather than Turing machines and $\epsilon$-machines. We give several criteria required for organized complexity measures and show that the proposed definition satisfies all of them for the first time. We then apply this quantitative definition to formulate a {\it semantic information theory}. We present the first formal definition of a {\it semantic information amount}, which is the core concept of the semantic information theory, that is based only on concretely defined notions. Previous semantic information theories defined this amount under some a priori information which is not concretely specified. We then unveil several fundamental properties in the semantic information theory, e.g., a semantic source coding theorem, semantic channel coding theorem, and effectiveness coding theorem. Although the semantic information theory has a long history of research going back more than six decades, there has been no study on its relation to organized complexity. This paper offers the first unified paradigm of organized complexity and semantic information theory.

One of the most fundamental problems in science is to define {\it quantitatively} the complexity of organized matters, i.e., {\it organized complexity}. Although many measures have been proposed toward this aim in previous decades, there is no agreed upon definition. This paper presents a new quantitative definition of organized complexity. This definition {\it simultaneously} captures the three major features of complexity: computational (similar to logical depth), descriptional (similar to the Kolmogorov complexity and effective complexity) and distributional (similar to statistical complexity). In addition, the proposed definition is computable and can measure both probabilistic and deterministic forms of objects in a unified manner. The proposed definition is based on circuits rather than Turing machines and $\epsilon$-machines. We give several criteria required for organized complexity measures and show that the proposed definition satisfies all of them for the first time. We then apply this quantitative definition to formulate a {\it semantic information theory}. We present the first formal definition of a {\it semantic information amount}, which is the core concept of the semantic information theory, that is based only on concretely defined notions. Previous semantic information theories defined this amount under some a priori information which is not concretely specified. We then unveil several fundamental properties in the semantic information theory, e.g., a semantic source coding theorem, semantic channel coding theorem, and effectiveness coding theorem. Although the semantic information theory has a long history of research going back more than six decades, there has been no study on its relation to organized complexity. This paper offers the first unified paradigm of organized complexity and semantic information theory.

One of the most fundamental problems in science is to define {\it quantitatively} the complexity of organized matters, i.e., {\it organized complexity}. Although many measures have been proposed toward this aim in previous decades, there is no agreed upon definition. This paper presents a new quantitative definition of organized complexity. This definition {\it simultaneously} captures the three major features of complexity: computational (similar to logical depth), descriptional (similar to the Kolmogorov complexity and effective complexity) and distributional (similar to statistical complexity). In addition, the proposed definition is computable and can measure both probabilistic and deterministic forms of objects in a unified manner. The proposed definition is based on circuits rather than Turing machines and $\epsilon$-machines. We give several criteria required for organized complexity measures and show that the proposed definition satisfies all of them for the first time. We then apply this quantitative definition to formulate a {\it semantic information theory}. We present the first formal definition of a {\it semantic information amount}, which is the core concept of the semantic information theory, that is based only on concretely defined notions. Previous semantic information theories defined this amount under some a priori information which is not concretely specified. We then unveil several fundamental properties in the semantic information theory, e.g., a semantic source coding theorem, semantic channel coding theorem, and effectiveness coding theorem. Although the semantic information theory has a long history of research going back more than six decades, there has been no study on its relation to organized complexity. This paper offers the first unified paradigm of organized complexity and semantic information theory.

This article is a snap-shot of a web site, which has been collecting open problems in quantum information for several years, and documenting the progress made on these problems. By posting it we make the complete collection available in one printout. We also hope to draw more attention to this project, inviting every researcher in the field to raise to the challenges, but also to suggest new problems.

When additional information sources are available, an important question for an agent solving a certain problem is how to optimally use the information the sources are capable of providing. A framework that relates information accuracy on the source side to information relevance on the problem side is proposed. An optimal information acquisition problem is formulated as that of question selection to maximize the loss reduction for the problem solved by the agent. A duality relationship between pseudoenergy (accuracy related) quantities on the source side and loss (relevance related) quantities on the problem side is observed.

When additional information sources are available in decision making problems that allow stochastic optimization formulations, an important question is how to optimally use the information the sources are capable of providing. A framework that relates information accuracy determined by the source's knowledge structure to its relevance determined by the problem being solved was proposed in a companion paper. There, the problem of optimal information acquisition was formulated as that of minimization of the expected loss of the solution subject to constraints dictated by the information source knowledge structure and depth. Approximate solution methods for this problem are developed making use of probability metrics method and its application for scenario reduction in stochastic optimization.

This document consists of lecture notes for a graduate course, which focuses on the relations between Information Theory and Statistical Physics. The course is aimed at EE graduate students in the area of Communications and Information Theory, as well as to graduate students in Physics who have basic background in Information Theory. Strong emphasis is given to the analogy and parallelism between Information Theory and Statistical Physics, as well as to the insights, the analysis tools and techniques that can be borrowed from Statistical Physics and `imported' to certain problem areas in Information Theory. This is a research trend that has been very active in the last few decades, and the hope is that by exposing the student to the meeting points between these two disciplines, we will enhance his/her background and perspective to carry out research in the field. A short outline of the course is as follows: Introduction; Elementary Statistical Physics and its Relation to Information Theory; Analysis Tools in Statistical Physics; Systems of Interacting Particles and Phase Transitions; The Random Energy Model (REM) and Random Channel Coding; Additional Topics (optional).

Consider the problem of finding high dimensional approximate nearest neighbors, where the data is generated by some known probabilistic model. We will investigate a large natural class of algorithms which we call bucketing codes. We will define bucketing information, prove that it bounds the performance of all bucketing codes, and that the bucketing information bound can be asymptotically attained by randomly constructed bucketing codes. For example suppose we have n Bernoulli(1/2) very long (length d-->infinity) sequences of bits. Let n-2m sequences be completely independent, while the remaining 2m sequences are composed of m independent pairs. The interdependence within each pair is that their bits agree with probability 1/2 0. Moreover if one sequence out of each pair belongs to a a known set of n^{(2p-1)^{2}-\epsilon} sequences, than pairing can be done using order n comparisons!

The classical statistics indication for the impossibility to derive quantum mechanics from classical mechanics is proved. The formalism of the statistical Fisher information is used. Next the Fisher information as a tool of the construction of a self-consistent field theory, which joins the quantum theory and classical field theory, is proposed.

The classical statistics indication for the impossibility to derive quantum mechanics from classical mechanics is proved. The formalism of the statistical Fisher information is used. Next the Fisher information as a tool of the construction of a self-consistent field theory, which joins the quantum theory and classical field theory, is proposed.