Meaning of the Density Matrix

Protective measurement, which was proposed as a method of observing the wavefunction of a single system, is extended to the observation of the density matrix of a single system. This provides a new meaning to the density matrix as having the same ontological status as the wave function describing a pure state. This also enables quantum entropy to be associated with a single system.     

Differential Entropy and Dynamics of Uncertainty

We analyze the functioning of Gibbs-type entropy functionals in the time domain, with emphasis on Shannon and Kullback-Leibler entropies of time-dependent continuous probability distributions. The Shannon entropy validity is extended to probability distributions inferred from L ²(R ⁿ ) quantum wave packets. In contrast to the von Neumann entropy which simply vanishes on pure states, the differential entropy quantifies the degree of probability (de)localization and its time development. The associated dynamics of the Fisher information functional quantifies nontrivial power transfer processes in the mean, both in dissipative and quantum mechanical cases. PACS NUMBERS: 05.45.+b, 02.50.-r, 03.65.Ta, 03.67.-a     

Quantum Information and Entropy Spueezing of a Nonlinear Multiquantum JC Model

We investigate the entropy squeezing of the nonlinear k-quantum JC model. A definition of squeezing is presented for this system based on the quantum information theory. The utility of the definition is illustrated by examining squeezing in the information entropy of a nonlinear k-quantum two-level atom. The influence of the atomic coherence and the detuning parameter on the properties of the information entropy and squeezing of the atomic variables is examined.     

Dynamics and Entropy Analysis of a Frictionally Loaded Pendulum

We use friction to simultaneously damp and excite a pendulum system. A Froude pendulum attached to a suspension shaft is subjected to a frictional load. We investigate two types of response of the system: regular and chaotic responses, depending on the excitation frequency. A transient chaotic solution was also obtained. We identify the motions using phase portraits, Poincaré maps, and Fourier spectra. Finally, the composite multiscaled entropy was estimated for the specified cases to confirm the preliminary classification.     

Subshifts on Infinite Alphabets and Their Entropy

We analyze symbolic dynamics to infinite alphabets by endowing the alphabet with the cofinite topology. The topological entropy is shown to be equal to the supremum of the growth rate of the complexity function with respect to finite subalphabets. For the case of topological Markov chains induced by countably infinite graphs, our approach yields the same entropy as the approach of Gurevich We give formulae for the entropy of countable topological Markov chains in terms of the spectral radius in l2.     

Using the T‐Matrix Method for Light Scattering Computations by Non‐axisymmetric Particles: Superellipsoids and Realistically Shaped Particles

Light scattering by non‐axisymmetric particles is commonly needed in particle characterization and other fields. After much work devoted to volume discretization methods to compute scattering by such particles, there is renewed interest in the T‐matrix method. We extended the null‐field method with discrete sources for T‐matrix computation and implemented the superellipsoid shape using an implicit equation. Additionally, a triangular surface patch model of a realistically shaped particle can be used for scattering computations. In this paper some exemplary results of scattering by non‐axisymmetric particles are presented.     

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.     

Algebraic and Geometric Properties of Matrix Solutions of Nonlinear Wave Equations

We improve the construction of exact matrix solutions for nonlinear wave equations by using unitary anti-Hermitian and anticommuting matrices. We prove the theorem that constructs the matrix functions u _n satisfying the nonlinear wave equation for a set of special potentials. In this case, the graph of complex solution u ₁ has a soliton-like form with a finite number of coils. Exponential representation of matrix solutions u _n is associated with continuous rotations that can be used for describing intrinsic rotations and state changes of elementary particles. We also prove the theorem on the decomposition of continuous rotation (described by solution u ₂) onto three simultaneous rotations about coordinate vectors. Each of the three constructed matrix solutions u ₃ is also decomposed into the triplet of elementary matrix solutions.     

Learning in Convolutional Neural Networks Accelerated by Transfer Entropy

Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. According to our experiments on CNN classifiers, to achieve a reasonable computational overhead–accuracy trade-off, it is efficient to consider only the inter-neural information transfer of the neuron pairs between the last two fully connected layers. The TE acts as a smoothing factor, generating stability and becoming active only periodically, not after processing each input sample. Therefore, we can consider the TE is in our model a slowly changing meta-parameter.     

含葡萄糖的混浊介质的Mueller矩阵模拟与分析

利用矢量Monte Carlo算法结合葡萄糖的旋光特性模拟了含糖混浊介质的背散射Mueller矩阵.数值模拟表明:含糖混浊介质的背散射Mueller矩阵元素中除Mll、M14、M41、M44外的其它元素花样均发生龙卷风状的不同程度的扭曲,其中M24、M34、M42、M43的扭曲程度更为显著.研究发现M24、M34、M4...     

快速数值差分递推改进算法在超宽连续谱模拟中的应用研究

对快速数值差分递推公式进行了改进,使之能够求解带有脉冲自陡峭项和脉冲内喇曼散射项的非线性薛定谔方程.通过与传统孤子解析结果及分步傅里叶方法数值结果的对比分析表明,快速数值差分递推改进算法是一种快速而准确的数值计算方法,它不仅能够同步考虑光学媒质中的群速色散作用和非线性克尔作用,而且将所有高阶非线性项对光脉冲传输的影响也...     

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.     

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.     

Entropy and irreversibility in a dilute gas of hard disks

The partition of the canonical entropy (invariant of motion) into a thermodynamic part 5th and a nonthermodynamic oneS _nonth, respectively increasing and decreasing functions of time for a system approaching equilibrium, was proposed by Prigogine and co-workers. This viewpoint is critically examined in the special case of an initially uncorrelated gas of hard disks. BothSth and the leading term ofS _nonth are evaluated for finite assemblies of 400,1600, and 6400 disks, by the method of molecular dynamics. There is good evidence that, in the limit of an infinite system, the Prigogine scheme is verified.On leave of absence from the University of Brussels, Belgium.     

Information Shift Dynamics Described by Tsallis q = 3 Entropy on a Compact Phase Space

Recent mathematical investigations have shown that under very general conditions, exponential mixing implies the Bernoulli property. As a concrete example of statistical mechanics that are exponentially mixing we consider the Bernoulli shift dynamics by Chebyshev maps of arbitrary order N≥2, which maximizes Tsallis q=3 entropy rather than the ordinary q=1 Boltzmann-Gibbs entropy. Such an information shift dynamics may be relevant in a pre-universe before ordinary space-time is created. We discuss symmetry properties of the coupled Chebyshev systems, which are different for even and odd N. We show that the value of the fine structure constant αel=1/137 is distinguished as a coupling constant in this context, leading to uncorrelated behaviour in the spatial direction of the corresponding coupled map lattice for N=3.     

基于光学传输矩阵的太赫兹时域光谱分析

提出了基于光学传输矩阵的提取太赫兹时域光谱光学参数的理论方法。相比于传统的提取主脉冲方法,该方法在未来可以推广用以分析主脉冲与次脉冲无法分辨的太赫兹时域光谱。利用该方法得到了蓝宝石(α-Al2O3)样品在0.3~1.0太赫兹频段的复折射率。通过与利用提取主脉冲方法得到的复折射率的结果比较,验证了基于光学传输矩阵的理论分析方法的可靠性和精确性。所提出的方法为今后主次脉冲无法分辨的太赫兹时域光谱的准确分析提供了坚实的理论基础。     

用光线矩阵元描述X射线激光输出特性

采用矩阵光学方法，研究了串接多靶，弯曲靶加反射镜“双程放大”Ｘ射线激光输出特性，给出了发散角普遍公式，分析了电子密度梯度分布，反射镜角失调对Ｘ射线激光输出场分布的影响，所得结果与已有实验符合较好，最后，对如何从靶，Ｘ光学元件空间排布来实现衍射极限的输出发散角提出了新的设想，作了半定量的探讨。     

Potassium Sensing by Using a Newly Synthesized Squaraine Dye in Sol-Gel Matrix

Squaraines are a group of fluorescent dyes and pigments derived from squaric acid and dialkylanilines well known in applications such as photoreceptors, organic solar cells, optical recording media, and non-linear optics. Their very promising spectral properties, long wavelength absorption and emission, and high absorptivity and quantum yields have not been exploited so far in relation to optical sensor design. They exhibit excellent solubility in sol-gel matrices, and the ligand is an integral part of the fluorophore system, which makes the molecule a fluoroionophore. In this work, potassium-sensing agent, bis[4-N-(1-aza-4,7,10,13,16-pentaox acyclooctadecyl)-3,5-dihydroxyphenyl] squaraine has been used for potassium sensing in a sol-gel matrix. The spectrofluorimetric response of dye-doped tetraethyl ortosilicate (TEOS) film after exposure to certain concentrations of K⁺ has been investigated, and 62% of relative signal change was achieved. The dynamic working range of the sensor membrane has been found between 10^–9 and 10^–6 M K⁺, in other terms from nanomolar to micromolar levels, which is an advantage over flame emission spectroscopy, in view of detection limit. The sensor is fully reversible within the dynamic range and the response time (₉₀) is found to be 2 min under batch conditions. The cross-sensitivity of the molecule to Na⁺, Ba²⁺, Ca²⁺, and NH⁺ ₄ was also tested in separate solutions.     

Stochastic modeling of a billiard in a gravitational field: Power law behavior of Lyapunov exponents

We consider the motion of a point particle (billiard) in a uniform gravitational field constrained to move in a symmetric wedge-shaped region. The billiard is reflected at the wedge boundary. The phase space of the system naturally divides itself into two regions in which the tangent maps are respectively parabolic and hyperbolic. It is known that the system is integrable for two values of the wedge half-angle ₁ and ₂ and chaotic for ₁<< ₂. We study the system at three levels of approximation: first, where the deterministic dynamics is replaced by a random evolution; second, where, in addition, the tangent map in each region is, replaced by its average; and third, where the tangent map is replaced by a single global average. We show that at all three levels the Lyapunov exponent exhibits power law behavior near ₁ and ₂ with exponents 1/2 and 1, respectively. We indicate the origin of the exponent 1, which has not been observed in unaccelerated billiards.