Error-resilient decoding of context-based adaptive binary arithmetic codes

This paper addresses the problem of error-resilient decoding of bitstreams produced by the CABAC (context-based adaptive binary arithmetic coding) algorithm used in the H.264 video coding standard. The paper describes a maximum a posteriori (MAP) estimation algorithm improving the CABAC decoding performances in the presence of transmission errors. Methods improving the re-synchronization and error detection capabilities of the decoder are then described. A variant of the CABAC algorithm supporting error detection based on a forbidden interval is presented. The performances of the decoding algorithm are first assessed with theoretical sources and by considering different binarization codes. They are compared against those obtained with Exp-Golomb codes and with a transmission chain making use of an error-correcting code. The approach has been integrated in an H.264/MPEG-4 AVC video coder and decoder. The PSNR gains obtained are discussed.     

Vacuum Radiation,Entropy, and Molecular Chaos

Vacuum radiation causes a particle to make a random walk about its dynamical trajectory. In this random walk the root mean square change in spatial coordinate is proportional to t ^1/2, and the fractional changes in momentum and energy are proportional to t ^−1/2, where t is time. Thus the exchange of energy and momentum between a particle and the vacuum tends to zero over time. At the end of a mean free path the fractional change in momentum of a particle in a gas is very small. However, at the end of the mean free path each particle undergoes an interaction that magnifies the preceding change, and the net result is that the momentum distribution of the particles in a gas is randomized in a few collision times. In this way the random action of vacuum radiation and its subsequent magnification by molecular interaction produces entropy increase. This process justifies the assumption of molecular chaos used in the Boltzmann transport equation.     

On the Validity of Entropy Production Principles for Linear Electrical Circuits

We explain the (non-)validity of close-to-equilibrium entropy production principles in the context of linear electrical circuits. Both the minimum and the maximum entropy production principles are understood within dynamical fluctuation theory. The starting point are Langevin equations obtained by combining Kirchoff’s laws with a Johnson-Nyquist noise at each dissipative element in the circuit. The main observation is that the fluctuation functional for time averages, that can be read off from the path-space action, is in first order around equilibrium given by an entropy production rate. That allows to understand beyond the schemes of irreversible thermodynamics (1) the validity of the least dissipation, the minimum entropy production, and the maximum entropy production principles close to equilibrium; (2) the role of the observables’ parity under time-reversal and, in particular, the origin of Landauer’s counterexample (1975) from the fact that the fluctuating observable there is odd under time-reversal; (3) the critical remark of Jaynes (1980) concerning the apparent inappropriateness of entropy production principles in temperature-inhomogeneous circuits.     

Generalized entropy optimization problems and the existence of their solutions

In the present study we have formulated a generalization of entropy optimization problems (GEOP), proposed sufficient conditions for the existence of solution. We have suggested also a new method based on a priori evaluations and Newton's methods for calculation of Langrange multipliers. Mentioned method allows calculating Langrange multipliers by starting from arbitrary initial point for Newton's approximations of constructed auxiliary equation. The solution of auxiliary equation is chosen as initial point for second constructed auxiliary equation. The recurring mentioned process for finite time leads to achieve an initial point for Newton's approximations of given equation and allows to find its unknown solution.     

熵损失函数下两参数指数威布尔分布尺度参数的Bayes估计

本文给定一截尾样本，在熵损失函数下，研究了两参数指数威布尔分布尺度参数在先验伽玛分布下的Bayes估计，并给出了该参数的Bayes区间估计。     

A geometrical measure for entropy changes

The geometrical approach to statistical mechanics is used to discuss changes in entropy upon sequential displacements of the state of the system. An interpretation of the angle between two states in terms of entropy differences is thereby provided. A particular result of note is that any state can be resolved into a state of maximal entropy (both states having the same expectation values for the constraints) and an orthogonal component. A cosine law for the general case is also derived.     

Possibilistic networks for information retrieval

This paper proposes an information retrieval (IR) model based on possibilistic directed networks. The relevance of a document w.r.t a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. This new interpretation of relevance led us to revisit the term weighting scheme by explicitly distinguishing between informative and non-informative terms in a document. Experiments carried out on three standard TREC collections show the effectiveness of the model.     

The boundary value problem and the Jensen inequality for an entropy functional of a Markov diffusion process

The paper introduces the recent results related to an entropy functional on trajectories of a controlled diffusion process, expressed through an additive functional of the diffusion process, with a Lagrangian, determined by the parameters of a controlled stochastic equation. These results include a minimum condition for the entropy functional and the functional's Jensen inequality, which both are useful for the solution of important mathematical and applied problems.     

Transport of biomolecules in asymmetric nanofilter arrays

We propose a theoretical model for describing the electric-field-driven migration of rod-like biomolecules in nanofilters comprising a periodic array of shallow passages connecting deep wells. The electrophoretic migration of the biomolecules is modeled as transport of point-sized Brownian particles, with the orientational degree of freedom captured by an entropy term. Using appropriate projections, the formulation dimensionality is reduced to one physical dimension, requiring minimal computation and making it ideal for device design and optimization. Our formulation is used to assess the effect of slanted well walls on the energy landscape and resulting molecule mobility. Using this approach, we show that asymmetry in the well shape, such as a well with one slanted and one vertical wall, may be used for separation using low-frequency alternating-current fields because the mobility of a biomolecule is different in the two directions of travel. Our results show that, compared to methods using direct-current fields, the proposed method remains effective at higher field strengths and can achieve comparable separation using a significantly shorter device.     

Methods and instruments for designing S-boxes

The design of S-boxes with minimal mutual information between input and output subvectors (considered as random variables) is investigated. First, the conditional entropy of the value of a boolean function conditioned on its random arguments is expressed as a function of the Walsh transform of the function. The entropy profile of a function is then defined; it allows the comparison of functions with regard to their (conditional) entropies. An algorithm to construct functions with good entropy profiles is then presented. It consists of a stepwise improvement of randomly chosen functions and uses the relation between the Walsh transform and the (conditional) entropies of a function. The statistical independency of boolean functions is investigated in the final section.