Pathway model,superstatistics, Tsallis statistics,and a generalized measure of entropy

The pathway model of Mathai [A pathway to matrix-variate gamma and normal densities, Linear Algebra Appl. 396 (2005) 317–328] is shown to be inferable from the maximization of a certain generalized entropy measure. This entropy is a variant of the generalized entropy of order α$\alpha$, considered in Mathai and Rathie [Basic Concepts in Information Theory and Statistics: Axiomatic Foundations and Applications, Wiley Halsted, New York and Wiley Eastern, New Delhi, 1975], and it is also associated with Shannon, Boltzmann–Gibbs, Rényi, Tsallis, and Havrda–Charvát entropies. The generalized entropy measure introduced here is also shown to have interesting statistical properties and it can be given probabilistic interpretations in terms of inaccuracy measure, expected value, and information content in a scheme. Particular cases of the pathway model are shown to be Tsallis statistics [C. Tsallis, Possible generalization of Boltzmann-Gibbs statistics, J. Stat. Phys. 52 (1988) 479–487] and superstatistics introduced by Beck and Cohen [Superstatistics, Physica A 322 (2003) 267–275]. The pathway model's connection to fractional calculus is illustrated by considering a fractional reaction equation.     

The Tsallis entropy of natural information

Estimating the information contained in natural data, such as electroencephalography data, is unusually difficult because the relationship between the physical data and the information that it encodes is unknown. This unknown relationship is often called the encoding problem. The present work provides a solution to this problem by deriving a method to estimate the Tsallis entropy in natural data. The method is based on two findings. The first finding is that the physical instantiation of any information event, that is, the physical occurrence of a symbol of information, must begin and end at a discontinuity or critical point (maximum, minimum, or saddle point) in the data. The second finding is that, in certain data types such as the encephalogram (EEG), the variance within of an EEG waveform event is directly proportional to its probability of occurrence.These two outcomes yield two results. The first is the easy binning of data into separate information events. The second is the ability to estimate probabilities in two ways: frequency counting and computing the variance within of an EEG waveform. These results are used to derive a linear estimator of the Tsallis entropy functional, allowing it to be estimated without deducing the encoding.This method for estimating the Tsallis entropy is first used to estimate the information in simple signals. The amount of information estimated is highly accurate. The method is then applied to two problems in electroencephalography. The first is distinguishing normal aging from very early Alzheimer's disease (mild cognitive impairment), and the second is medication monitoring of Alzheimer's disease treatment. The former is done with an accuracy of 92% and the latter with an accuracy of 91%. This detection accuracy is the highest published accuracy in the literature, which suggests that this method for Tsallis entropy estimation is both accurate and useful.     

The Fisher information measure and Shannon entropy for particulate matter measurements

We investigated the dynamics of particulate matter data, recorded in Tito, a small industrial area of southern Italy. The analysis of these signals was performed using the Fisher information measure (FIM), which is a powerful tool for investigating complex and nonstationary signals, and the Shannon entropy, which is a well-known tool for investigating the degree of disorder in dynamical systems. Our results point to an increase of disorder and complexity from fine to coarse particulates.     

Suprathreshold stochastic resonance induced by ion channel fluctuation

In this study, we examine the signal detection ability of an array of neurons with intrinsic channel fluctuation. Numerical simulations show that estimation of the input signal from the output spiking activity of the neuronal array is most accurate if a proper amount of channel noise exists. Theoretical calculation of the mutual and Fisher information confirms the effect of the noise-aided information transfer in the array, or the presence of suprathreshold stochastic resonance. These results indicate that the channel noise, which induces response variability, may play an essential role in population coding.     

Experimental investigation of noise-assisted information transmission and storage via stochastic resonance

We present experimental results on the information transmission and storage via stochastic resonance in circuits designed and built around Schmitt triggers (STs). First, we investigate the performance of a transmission line comprised of five STs and show it to exhibit stochastic resonance. Each ST in the line is fed with white Gaussian noise, and the first ST is driven by a non-return-to-zero pseudo-random bit sequence with sub-threshold amplitude. Parameters such as bit error rate (Q-factor) are measured (calculated) and shown to exhibit a minimum (maximum) for an optimum amount of noise. Interestingly, we find that system performance degrades with the number of STs as if the system were linear and impaired only by additive Gaussian noise. We then propose and build a 1-bit storage device based on two STs in a loop configuration. We demonstrate that such a system is capable of storing one bit of information only in the presence of noise, and that there is a regime where the efficiency of such a device increases with increasing noise.Our results point to the feasibility of building ‘blocks’ that can transmit, store and eventually process information, whose performance is not only robust against noise, but can actually benefit from it.     

Information and complexity measures in the interface of a metal and a superconductor

Fisher information, Shannon information entropy and Statistical Complexity are calculated for the interface of a normal metal and a superconductor, as a function of the temperature for several materials. The order parameter $\mathrm{\Psi }(\mathbf{r})$ derived from the Ginzburg–Landau theory is used as an input together with experimental values of critical transition temperature $T_c$ and the superconducting coherence length ${\xi }_0$. Analytical expressions are obtained for information and complexity measures. Thus $T_c$ is directly related in a simple way with disorder and complexity. An analytical relation is found of the Fisher Information with the energy profile of superconductivity i.e. the ratio of surface free energy and the bulk free energy. We verify that a simple relation holds between Shannon and Fisher information i.e. a decomposition of a global information quantity (Shannon) in terms of two local ones (Fisher information), previously derived and verified for atoms and molecules by Liu et al. Finally, we find analytical expressions for generalized information measures like the Tsallis entropy and Fisher information. We conclude that the proper value of the non-extensivity parameter $q?1$, in agreement with previous work using a different model, where $q?1.005$.     

Scaled Bregman divergences in a Tsallis scenario

There exist two different versions of the Kullback-Leibler divergence (K-Ld) in Tsallis statistics, namely the usual generalized K-Ld and the generalized Bregman K-Ld. Problems have been encountered in trying to reconcile them. A condition for consistency between these two generalized K-Ld forms is derived by recourse to the additive duality of Tsallis statistics. It is also shown that the usual generalized K-Ld subjected to this additive duality, known as the dual generalized K-Ld, is a scaled Bregman divergence. This leads to an interesting conclusion: the dual generalized mutual information is a scaled Bregman information. The utility and implications of these results are discussed.     

Pseudo information entropy of a three-level atom interaction with two-laser fields in Λ-configuration

In this report we investigate some aspects of the pseudo entropy of multi-level system in the language of quantum information theory. The influence of the non-linear interaction and detuning parameter on the properties of the pseudo information entropy is examined.     

Quantum uncertainty relations of Tsallis relative α entropy coherence based on MUBs

In this paper,we discuss quantum uncertainty relations of Tsallis relative α entropy coherence for a single qubit system based on three mutually unbiased bases.For α∈[1/2,1)U(1,2],the upper and lower bounds of sums of coherence are obtained.However,the above results cannot be verified directly for any α∈(0,1/2).Hence,we only consider the special case of α=1/n+1,where n is a positive integer,and we obtain the upper and lower bounds.By comparing the upper and lower bounds,we find that the upper bound is equal to the lower bound for the special α=1/2,and the differences between the upper and the lower bounds will increase as α increases.Furthermore,we discuss the tendency of the sum of coherence,and find that it has the same tendency with respect to the different θ or φ,which is opposite to the uncertainty relations based on the Rényi entropy and Tsallis entropy.     

The maximization of Tsallis entropy with complete deformed functions and the problem of constraints

By only requiring the q deformed logarithms (q exponentials) to possess arguments chosen from the entire set of positive real numbers (all real numbers), we show that the q-logarithm (q exponential) can be written in such a way that its argument varies between 0 and 1 (among negative real numbers) for 1?q<2, while the interval 0<q?1 corresponds to any real argument greater than 1 (positive real numbers). These two distinct intervals of the nonextensivity index q, also the expressions of the deformed functions associated with them, are related to one another through the relation (2−q), which is so far used to obtain the ordinary stationary distributions from the corresponding escort distributions, and vice versa in an almost ad hoc manner. This shows that the escort distributions are only a means of extending the interval of validity of the deformed functions to the one of ordinary, undeformed ones. Moreover, we show that, since the Tsallis entropy is written in terms of the q-logarithm and its argument, being the inverse of microstate probabilities, takes values equal to or greater than 1, the resulting stationary solution is uniquely described by the one obtained from the ordinary constraint. Finally, we observe that even the escort stationary distributions can be obtained through the use of the ordinary averaging procedure if the argument of the q-exponential lies in (−∞,0]. However, this case corresponds to, although related, a different entropy expression than the Tsallis entropy.     

负值量子条件熵与双量子系统一类混合态纠缠量度

运用负值量子条件熵研究了双量子系统一类混合态的纠缠量度.给出了负值量子条件作为条件熵纠缠度的定义，证明了条件熵纠缠满足作为2×2系统一类混合纠缠态量度的四个基本条件.当双量子系统处于纯态时，条件熵纠缠度即为部分熵纠缠度.应用条件熵纠缠度研究了真空腔场中两全同二能级原子之间纯态和一类混合态纠缠的时间演化，比较了相同条件下两全同原子系统concurrence纠缠度的时间演化.结果表明，两纠缠度演化规律完全一致，验证了负值量子条件熵可以作为双量子系统纯态和一类混合态的纠缠量度. 关键词： 双量子系统 负值量子条件熵 条件熵纠缠度 混合态纠缠度     

Onicescu information energy in terms of Shannon entropy and Fisher information densities

Following recent studies concerning the use of information theory in electronic structure theory of atomic and molecular systems, an analytical relationship between Onicescu information energy and densities of Shannon entropy and the two forms of the Fisher information has been presented. The established proof must be viewed in the light of the exponentially decaying nature of the asymptotic density of atoms and molecules.     

DC-DC变换器的信息熵分析

为确定不同反馈系数k下DC-DC变换器系统的行为,结合系统处于周期状态时的稳定性和系统处于混沌时不会重复经过每一点的特点,提出了一种采用极限思想和信息熵来估计DC-DC变换器非线性行为的方法.该方法能准确分析系统处于周期状态和混沌状态的熵值,量化了DC-DC变换器倍周期分叉和混沌行为.以一阶电压反馈DCM Boost变换器和DCM Buck变换器为例进行仿真.研究结果表明,所提出的信息熵可以准确反映分叉点、周期数及产生混沌的位置,完善了该类变换器非线性动力学分析的理论和方法.     

Stochastic resonance in Hopfield neural networks for transmitting binary signals

We investigate the stochastic resonance phenomenon in a discrete Hopfield neural network for transmitting binary amplitude modulated signals, wherein the binary information is represented by two stored patterns. Based on the potential energy function and the input binary signal amplitude, the observed stochastic resonance phenomena involve two general noise-improvement mechanisms. A suitable amount of added noise assists or accelerates the switch of the network state vectors to follow input binary signals more correctly, yielding a lower probability of error. Moreover, at a given added noise level, the probability of error can be further reduced by the increase of the number of neurons. When the binary signals are corrupted by external heavy-tailed noise, it is found that the Hopfield neural network with a large number of neurons can outperform the matched filter in the region of low input signal-to-noise ratios per bit.     

一种研究二元对称离散信道平均互信息的新方法

在有干扰(噪声)无记忆信道和有记忆信道两种情况下，运用协同学的方法研究如何分配输入概率获取二元对称离散信道最大的平均互信息. 研究结果表明：对于无记忆二元对称离散信道，最大平均互信息与信息论中相同输入概率使平均互信息最大化的结果是一致的；对于干扰(噪声)有记忆二元对称离散信道，考虑输入、输出符号满足不同程度的记忆度和干扰(噪声)因子情况下，得到符号最佳输入概率. 拓展了一种研究信息论的方法. 关键词： 协同学 二元对称离散信道 互信息 转移概率     

准单色噪声驱动的耗散动力系统的信息熵演化

准单色噪声是一类所谓真正有色的噪声.本文对准单色噪声驱动的耗散动力系统的信息熵演化进行研究,文中首先运用线性变换的方法给出了所研究系统的Fokker-Planck方程,然后结合Shannon信息熵定义推导了该系统随时间演化信息熵的精确表达式, 最后分析了系统耗散参数和准单色噪声对系统信息熵的显著影响.     

准单色噪声驱动的耗散动力系统的信息熵演化

准单色噪声是一类所谓真正有色的噪声.本文对准单色噪声驱动的耗散动力系统的信息熵演化进行研究,文中首先运用线性变换的方法给出了所研究系统的Fokker-Planck方程,然后结合Shannon信息熵定义推导了该系统随时间演化信息熵的精确表达式,最后分析了系统耗散参数和准单色噪声对系统信息熵的显著影响.     

Scaling properties of the measure of constant topological entropy

The topological entropy for some families of one-dimensional unimodal maps is studied. By arranging the windows of constant topological entropy in a binary tree, we have obtained the total measure of these windows. The scaling properties of this measure are studied.     

Quantum tunneling and information entropy in a double square well potential: The ammonia molecule

We study the implications of quantum tunneling on information entropy measures (Shannon and Fisher), disequilibrium and LMC complexity in a Double Square Well Potential (DSWP), using the ammonia molecule as a test bed. We also apply a similar analysis to the Infinite Square Well Potential (ISWP) in order to compare the corresponding results with a system where tunneling is absent. In particular, we show that contrary to the Heisenberg uncertainty product, information-theoretic tools provide a more sensitive analysis and manage to differentiate DSWP from ISWP case, formulating an empirical criterion whether the tunneling effect is present or not.