Modeling the neurodynamic complexity of submarine navigation teams

Our objective was to apply ideas from complexity theory to derive neurophysiologic models of Submarine Piloting and Navigation showing how teams cognitively organize around changes in the task and how this organization is altered with experience. The cognitive metric highlighted was an electroencephalography (EEG)-derived measure of engagement (termed NS_E) which was modeled into a collective team variable showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that during a navigation task the NS_E data stream contains historical information about the cognitive organization of the team and that this organization can be quantified by fluctuations in the Shannon entropy of the data stream. The fluctuations in the NS_E entropy were complex, showing both rapid changes over a period of seconds and longer fluctuations that occurred over periods of minutes. The periods of low NS_E entropy represented moments when the team’s cognition had undergone significant re-organization, i.e. when fewer NS_E symbols were being expressed. Decreases in NS_E entropy were associated with periods of poorer team performance as indicated by delays/omissions in the regular determination of the submarine’s position; parallel communication data suggested that these were also periods of increased stress. Experienced submarine navigation teams performed better than Junior Officer teams, had higher overall levels of NS_E entropy and appeared more cognitively flexible as indicated by the use of a larger repertoire of available NS_E patterns. The quantitative information in the NS_E entropy may provide a framework for designing future adaptive team training systems as it can be modeled and reported in near real time.     

Density of first Poincaré returns,periodic orbits,and Kolmogorov–Sinai entropy

It is known that unstable periodic orbits of a given map give information about the natural measure of a chaotic attractor. In this work we show how these orbits can be used to calculate the density function of the first Poincaré returns. The close relation between periodic orbits and the Poincaré returns allows for estimates of relevant quantities in dynamical systems, as the Kolmogorov–Sinai entropy, in terms of this density function. Since return times can be trivially observed and measured, our approach to calculate this entropy is highly oriented to the treatment of experimental systems. We also develop a method for the numerical computation of unstable periodic orbits.     

Universal finitary codes with exponential tails

In 1977, Keane and Smorodinsky showed that there exists a finitaryhomomorphism from any finite-alphabet Bernoulli process to anyother finite-alphabet Bernoulli process of strictly lower entropy.In 1996, Serafin proved the existence of a finitary homomorphismwith finite expected coding length. In this paper, we constructsuch a homomorphism in which the coding length has exponentialtails. Our construction is source-universal, in the sense thatit does not use any information on the source distribution otherthan the alphabet size and a bound on the entropy gap betweenthe source and target distributions. We also indicate how ourmethods can be extended to prove a source-specific version ofthe result for Markov chains.     

Path integral for the probability of the trajectories generated by fractional dynamics subject to Gaussian white noise

One considers a fractional stochastic process defined as the dynamics of a non-random fractional system subject to a Gaussian white noise. One shows how the probability distribution of the random paths so generated can be obtained by combining path integrals and the maximum entropy principle.     

Entropies and Equilibria of Many-Particle Systems: An Essay on Recent Research

This essay is intended to present a fruitful collaboration which has developed among a group of people whose names are listed above: entropy methods have proved over the last years to be an efficient tool for the understanding of the qualitative properties of physically sound models, for accurate numerics and for a more mathematical understanding of nonlinear PDEs. The goal of this essay is to sketch the historical development of the concept of entropy in connection with PDEs of continuum mechanics, to present recent results which have been obtained by the members of the group and to emphasize the most striking achievements of this research. The presentation is by no way an exhaustive review of the methods and results involving the entropy, not even in the field of PDEs. Many other researchers in and outside Europe have contributed to the development of this field, including – but not only – in collaboration with some of the people of the group. However, it can be claimed that this group had a leading role over the recent years and this essay is intended to explain how this occurred.     

Entropies and Equilibria of Many-Particle Systems: An Essay on Recent Research

This essay is intended to present a fruitful collaboration which has developed among a group of people whose names are listed above: entropy methods have proved over the last years to be an efficient tool for the understanding of the qualitative properties of physically sound models, for accurate numerics and for a more mathematical understanding of nonlinear PDEs. The goal of this essay is to sketch the historical development of the concept of entropy in connection with PDEs of continuum mechanics, to present recent results which have been obtained by the members of the group and to emphasize the most striking achievements of this research. The presentation is by no way an exhaustive review of the methods and results involving the entropy, not even in the field of PDEs. Many other researchers in and outside Europe have contributed to the development of this field, including – but not only – in collaboration with some of the people of the group. However, it can be claimed that this group had a leading role over the recent years and this essay is intended to explain how this occurred.     

Forecasting financial time series with Boltzmann entropy through neural networks

Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex.

     

Selective responses by entropy minimization

In this paper, we propose a method of entropy minimization for increasing selectivity and obtaining simple network architectures. An entropy function is defined with respect to the state of hidden units. By minimizing this entropy, the selectivity of hidden units can significantly be increased. Since a unit tends to respond to specific input patterns, the meaning or the function of the hidden units can easily be understood. In addition, we have observed that by minimizing the entropy, some units are forced to be inactive, responding to no input patterns. Thus, these inactive units can be deleted, and we can construct smaller network architectures. We applied the entropy method to standard and recurrent back-propagation. Experimental results confirmed that the number of units selectively responding to a specific pattern increased gradually, while units with low selectivity responding to multiple patterns decreased as entropy decreased. In addition, the number of units responding to no input patterns increased in proportion to the decrease of the entropy. These results show that the entropy minimization method can be used to improve the selectivity, and therefore, the interpretability of the network's behaviors. Then, the method can be used to suppress unnecessary units and to produce simple internal representation or simple network architectures.     

Measuring complexity in a business cycle model of the Kaldor type

The purpose of this paper is to study the dynamical behavior of a family of two-dimensional nonlinear maps associated to an economic model. Our objective is to measure the complexity of the system using techniques of symbolic dynamics in order to compute the topological entropy. The analysis of the variation of this important topological invariant with the parameters of the system, allows us to distinguish different chaotic scenarios. Finally, we use a another topological invariant to distinguish isentropic dynamics and we exhibit numerical results about maps with the same topological entropy. This work provides an illustration of how our understanding of higher dimensional economic models can be enhanced by the theory of dynamical systems.     

双曲型守恒律方程的熵稳定格式的一些讨论

本文讨论双曲型守恒律方程的熵稳定格式.对于给定的熵对,格式所满足的熵条件中的数值熵通量是不唯一的.Tadmor的充分条件可以唯一地确定标量方程的熵守恒通量,但不能唯一确定方程组的熵守恒通量,却可以给出方程组的空间一阶精度的熵守恒格式.也讨论了在熵守恒通量上添加数值粘性得到的显式熵稳定格式需要满足的条件及常见的时间离散对熵守恒和熵稳定的影响.     

Pre-image entropy for free semigroup actions

The purpose of this study is to indicate fundamental propositions of the pre-image entropy related to a system proposed by Bufetov [Bufetov A. Topological entropy of free semigroup actions and skew-product transformations. J Dyn Control Syst 1999;5:137–143. 1.] for generating free semigroup actions. This study reveals the formula for the pre-image entropy of skew-product transformation with respect to the one-sided shift space. Finally, one example is presented to show how to obtain the pre-image entropy value for the skew-product transformation.     

不等式组与变分不等式的极大熵函数方法

利用极大熵函数方法将不等式组及变分不等式的求解问题转化为近似可微优化问题，给出了不等式组及变分不等式问题近似解的可微优化方法，得到了不等式组和变分不等式问题的解集合的示性函数．     

变分不等式的极大熵函数方法

利用极大熵函数方法将不等式组及变分不等式的求解问题转化为近似可微优化问题,给出了不等式组及变分不等式问题近似解的可微优化方法,得到了不等式组和变分不等式问题的解集合的示性函数.     

Some examples of application of the metric entropy method

We show how the metric entropy method can be substituted for the dyadic chaining, to prove in a unified setting several classical results. Among them are Stechkin's theorem, Gál--Koksma theorems and quantitative Borel--Cantelli lemmas. We give simpler proofs and improve some of these results. Two classes of examples are given: firstly stationary Gaussian sequences with applications to upper and lower classes and the law of the iterated logarithm for subsequences, and secondly in Diophantine approximation relatively to Gál and Schmidt's theorems.     

Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets

A multicriteria fuzzy decision-making method based on weighted correlation coefficients using entropy weights is proposed under interval-valued intuitionistic fuzzy environment for the some situations where the information about criteria weights for alternatives is completely unknown. To determine the entropy weights with respect to a decision matrix provided as interval-valued intuitionistic fuzzy sets (IVIFSs), we propose two entropy measures for IVIFSs and establish an entropy weight model, which can be used to determine the criteria weights on alternatives, and then propose an evaluation formula of weighted correlation coefficient between an alternative and the ideal alternative. The alternatives can be ranked and the most desirable one(s) can be selected according to the values of the weighted correlation coefficients. Finally, two applied examples demonstrate the applicability and benefit of the proposed method: it is capable for handling the multicriteria fuzzy decision-making problems with completely unknown weights for criteria.     

信息熵增量最小化准则在供应链中的应用

本文研究了在考虑利润最大化准则和信息熵增量最小准则下，既能满足了利润最大化，又要增加利润可得性，销售商如何确定订购量的问题。数字实验表明：考虑双重准则得到的策略比仅考虑利润最大化准则得到的策略更好，可以使供应链、销售商及制造商都受益。     

The Bernstein–Orlicz norm and deviation inequalities

We introduce two new concepts designed for the study of empirical processes. First, we introduce a new Orlicz norm which we call the Bernstein–Orlicz norm. This new norm interpolates sub-Gaussian and sub-exponential tail behavior. In particular, we show how this norm can be used to simplify the derivation of deviation inequalities for suprema of collections of random variables. Secondly, we introduce chaining and generic chaining along a tree. These simplify the well-known concepts of chaining and generic chaining. The supremum of the empirical process is then studied as a special case. We show that chaining along a tree can be done using entropy with bracketing. Finally, we establish a deviation inequality for the empirical process for the unbounded case.     

Mean dimension, small entropy factors and an embedding theorem

In this paper we show how the notion of mean dimension is connected in a natural way to the following two questions: what points in a dynamical system (X, T) can be distinguished by factors with arbitrarily small topological entropy, and when can a system (X, T) be embedded in (([0, 1] ^d ) ^Z , shift). Our results apply to extensions of minimalZ-actions, and for this case we also show that there is a very satisfying dimension theory for mean dimension.     

Retrieving the Bingham model from a bi-viscous model: Some explanatory remarks

In this note we prove some analytical results on the Bingham model. In particular we show how to derive some constitutive and kinematical properties through a limit procedure in which the visco-plastic model is retrieved from a linear bi-viscous model. We also prove that, assuming a no-slip condition at the interface separating the two viscous fluids, no source of entropy can be present on such interface.     

Entropy, longevity and the cost of annuities

This paper presents an extension of the application of the concept of entropy to annuity costs. Keyfitz (1985) introduced the concept of entropy, and analysed this in the context of continuous changes in life expectancy. He showed that a higher level of entropy indicates that the life expectancy has a greater propensity to respond to a change in the force of mortality than a lower level of entropy. In other words, a high level of entropy means that further reductions in mortality rates would have an impact on measures like life expectancy. In this paper, we apply this to the cost of annuities and show how it allows the sensitivity of the cost of a life annuity contract to changes in longevity to be summarized in a single figure index.