Parallel Dynamics of Extremely Diluted Symmetric Q-Ising Neural Networks

The parallel dynamics of extremely diluted symmetric Q-Ising neural networks is studied for arbitrary Q using a probabilistic approach. In spite of the extremely diluted architecture the feedback correlations arising from the symmetry prevent a closed-form solution in contrast with the extremely diluted asymmetric model. A recursive scheme is found determining the complete time evolution of the order parameters taking into account all feedback. It is based upon the evolution of the distribution of the local field, as in the fully connected model. As an illustrative example an explicit analysis is carried out for the Q=2 and Q=3 model. These results agree with and extend the partial results existing for Q=2. For Q>2 the analysis is entirely new. Finally, equilibrium fixed-point equations are derived and a capacity-gain function diagram is obtained.     

Retrieval and chaos in layeredQ-Ising neural networks

Using a probabilistic approach, we study the parallel dynamics of theQ-Ising layered networks for arbitraryQ. By introducing auxiliary thermal fields, we express the stochastic dynamics within the gain function formulation of the deterministic dynamics. Evolution equations are derived for arbitraryQ at both zero and finite temperatures. An explicit analysis of the fixed-point equations is carried out for bothQ=3 andQ. The retrieval properties are discussed in terms of the gain parameter, the storage capacity, and the temperature. Using the time evolution of the distance between two network configurations, we investigate the possibility of microscopic chaos. Chaotic behavior is always present for arbitrary finiteQ. However, in the limitQ the existence of chaos depends on the parameters of the system.     

On the parallel dynamics of theQ-state Potts andQ-Ising neural networks

Using a probabilistic approach, the parallel dynamics of theQ-state Potts andQ-Ising neural networks are studied at zero and at nonzero temperatures. Evolution equations are derived for the first time step and arbitraryQ. These formulas constitute recursion relations for the exact parallel dynamics of the extremely diluted asymmetric versions of these networks. An explicit analysis, including dynamical capacity-temperature diagrams and the temperature dependence of the overlap, is carried out forQ=3. Both types of models are compared.On leave of absence from the Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna 141980, Russia.     

Parallel Subtraction using LCLV and Lenslet Array in Optical Neural Networks

Basing on the method of wavelength and beam deviation encoding for implementing a bipolar optical neural networks, the subtraction between the positive and negative stimulation of a neuron can be converted to the addition by the normal and in verse states of a liquid crystal light value (LCLV). The summation can be performed by using a lenslet to focus the two light spots which represent the two stimulations. The pre liminary experimental results demonstrate the feasibility of the presental method.     

基于邻域粗糙集和并行神经网络的故障诊断

针对目前使用神经网络诊断故障时出现的输入向量选择困难、网络结构复杂、对并发故障诊断效果不好等问题,提出了基于邻域粗糙集和并行神经网络的故障诊断方法。先利用邻域粗糙集对初始征兆进行约简,留下有价值的征兆作为神经网络的输入向量,然后针对每种故障类型设计一个神经网络。用多个训练好的神经网络来并行诊断故障,综合每个神经网络的结果给出最终的诊断结论。用转子实验台的实验数据对这种故障诊断方法进行验证,结果显示该方法能优化神经网络结构,且神经网络具有训练速度快、诊断正确率高的特点。     

Discontinuity and Complex Dynamics of Neural Networks

We focus on the discontinuity of a neural network model with diluted and clipped synaptic connections (±l only). The exact evolution rule of the average firing rate becomes a discontinuous piece-wise nonlinear map when very simple functions of dynamical threshold are introduced into the network. Complex dynamics is observed.     

Retrieval and chaos in extremely dilutedQ-Ising neural networks

Using a probabilistic approach, the deterministic and the stochastic parallel dynamics of aQ-Ising neural network are studied at finiteQ and in the limitQ. Exact evolution equations are presented for the first time-step. These formulas constitute recursion relations for the parallel dynamics of the extremely diluted asymmetric versions of these networks. An explicit analysis of the retrieval properties is carried out in terms of the gain parameter, the loading capacity, and the temperature. The results for theQ network are compared with those for theQ=3 andQ=4 models. Possible chaotic microscopic behavior is studied using the time evolution of the distance between two network configurations. For arbitrary finiteQ the retrieval regime is always chaotic. In the limitQ the network exhibits a dynamical transition toward chaos.     

Dynamics of a Boundary Layer in Nonlinear Total Internal Reflection Inside a Laser Resonator

Computer simulation of the processes occurring in a thin layer of absorbing liquid adjoining a transparent dielectric with internal reflection from the interface is carried out for the case where the reflector is incorporated into the resonator of a solid-state laser. It is shown that the process of heating the liquid layer near the wall is essentially nonadiabatic. The sharp increase in the pressure of the liquid in the period of maximal heating leads to a situation where the temperatures of switching on and switching off of the total internal reflection at the interface become substantially different. The difference becomes smaller with decrease in the liquid-layer thickness due to a more efficient heat removal, which displays itself as a change in the character of single-pulse laser generation. Periodic emission of nanosecond pulses in the liquid layer in a quasistationary lasing regime is found, whose characteristic feature is stabilization of the liquid-layer temperature.     

Chaotic Dynamics in Weight Space of Neural Networks

When a special nonlinear self-feedback term is introduced into the dynamical equation of the backpropagation training algorithm for networks, the dynamics in weight space of networks can become chaotic. Chaotic dynamics of the system can help it escape from the most commonplace local minima of the energy. Simulation on the XOR problem and the prediction of chaotic time series have shown that the proposed chaotic training algorithm can converge to the global minimum or its approximate solutions efficiently and dramatically faster than the original backpropagation training algorithm.     

Permeability Coefficients from NMR q-Space Data: Models with Unevenly Spaced Semi-permeable Parallel Membranes

The NMR "q-space" experiment conducted on water provides information on the sizes of repeated structures on the micrometer-length scale in heterogeneous samples, including cell suspensions or tissues. Under some circumstances these plots display coherence peaks, and it has been implied theoretically that the position of the peaks will vary with the rate of molecular exchange across the membranes. This has been demonstrated (qualitatively) with human erythrocytes in suspension. Thus, in the quest for a quantitative approach to the interpretation of such data, we address here the "inverse problem," namely the estimate of the permeability coefficient of membranes from q-space experiments. The present work describes theoretical predictions of q-space plots from molecules diffusing in a simple system of parallel semi-permeable membranes arranged with separations that alternate between two different values; this was designed to (loosely) mimic the intra- and extracellular compartments in a suspension of cells or a tissue. The development of the theory was facilitated by symbolic computation, and the analysis of synthetic data was shown to be achievable by the use of a three-layer back-propagation artificial neural network.     

Significance of Updating Schemes in Computational Models: Dynamics of Neutral Networks

A Hopfield neural network was constructed with relevance to protein dynamics. The dynamics of this network was analyzed by determining the distribution of first passage times between memories and its dependence on temperature. The distribution depended on the updating scheme. This illustrates the importance of choosing an updating scheme that leads to physically meaningful results in computational models of dynamic processes, such as in neural networks or molecular dynamics.     

重频Yb:YAG片状激光器电光调$lt;i$gt;Q$lt;/i$gt;时空演化模拟计算和实验研究

基于Yb³⁺抽运动力学,结合光线追迹的方法,建立了抽运过程中的放大自发辐射模型,得到激光介质中三维含时储能分布.将速率方程理论和角谱传播理论结合,对谐振腔内调Q脉冲的形成、传播过程进行建模计算,得到激光脉冲的时间-空间分布和光束质量因子变化规律.同时进行了激光二极管抽运重频Yb：YAG片状激光器电光调Q实验,并与模拟计算的结果进行了对比校核,印证了计算模型的正确性.这为主动调Q固体激光器的设计提供了参考. 关键词： Yb：YAG激光器 Q')" href="#">调Q 时空演化     

微尺度串列双圆柱绕流的分子动力学并行模拟

本文采用基于空间分解算法的分子动力学并行模拟方法,研究了微尺度、低雷诺数（Re=40）下串列等大的双圆柱绕流现象。结果表明：随着间距比L＊／D＊的增加,流动存在3种特征状态：当L＊／D＊〈1．1时,同单一物体的绕钝体流动相似;当1．1〈L＊／0＊〈3．5时,涡脱落现象只在下游圆柱出现,在两圆柱之间有交替附着于下游圆柱的...     

On the Dynamics of Kac p-Spin Glasses

This paper discusses the dynamical properties of p-spin models with Kac interactions. For large but finite interaction range R one finds two different well separated time scales for relaxation. A first short time scale, roughly independent of R, on which the system remains confined to limited regions of the configuration space and an R dependent long time scale on which the system is able to escape from the confining regions. I will argue that the R independent time scales can be described through dynamical mean field theory, while non-perturbative new techniques have to be used to deal with the R dependent scales.     

结构材料辐照损伤的分子动力学程序GPU并行化及优化

基于NIVIDIA公司的CUDA架构对结构材料辐照损伤的分子动力学程序在单个GPU上进行并行化,并对影响程序运行效率的相关因素进行分析和测试.经过一系列优化,当粒子数为两百万时,对比单CPU的执行时间,优化后的GPU程序其双精度加速比可达112倍,单精度加速比达到了三百倍,为后续扩展多GPU结构材料辐照损伤的分子动力学程序奠定基础.     

Role of Time Scales in the Coupled Epidemic-Opinion Dynamics on Multiplex Networks

Modelling the epidemic’s spread on multiplex networks, considering complex human behaviours, has recently gained the attention of many scientists. In this work, we study the interplay between epidemic spreading and opinion dynamics on multiplex networks. An agent in the epidemic layer could remain in one of five distinct states, resulting in the SIRQD model. The agent’s attitude towards respecting the restrictions of the pandemic plays a crucial role in its prevalence. In our model, the agent’s point of view could be altered by either conformism mechanism, social pressure, or independent actions. As the underlying opinion model, we leverage the q-voter model. The entire system constitutes a coupled opinion–dynamic model where two distinct processes occur. The question arises of how to properly align these dynamics, i.e., whether they should possess equal or disparate timescales. This paper highlights the impact of different timescales of opinion dynamics on epidemic spreading, focusing on the time and the infection’s peak.     

Dynamics of a waveguide pulsed-periodic CO2 laser with loss modulation

The nonlinear dynamics of a waveguide CO₂ laser with combined periodic loss and pumping modulation is investigated theoretically and experimentally. The possibilities of controlling time and energy parameters of the output radiation using the corresponding selection of the detuning of the cavity's longitudinal mode from the center of the amplification line, the amplitude, and the frequency of loss modulation are demonstrated. The observed asymmetric dependence of the lasing-radiation density on the detuning of the cavity frequency is explained by the attendant modulation of the refractive index of the loss modulator. To whom correspondence should be addressed. B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, 68, F. Skorina Ave., Minsk, 220072, Belarus. Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 66, No. 4, pp. 471–478, July–August, 1999.     

Dynamics of Hopfield-Type Neural Networks with Modulo Periodic Unpredictable Synaptic Connections,Rates and Inputs

In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates and inputs are synchronized to obtain the convergence of outputs on the compact subsets of the real axis. The existence, uniqueness, and exponential stability of such motions are discussed. The method of included intervals and the contraction mapping principle are applied to attain the theoretical results. In addition to the analysis, we have provided strong simulation arguments, considering that all the assumed conditions are satisfied. It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network.     

氢催化的二硝酰胺铵热分解的从头算分子动力学研究

Thermal decomposition of a famous high oxidizer ammonium dinitramide (ADN) under high temperatures (2000 and 3000 K) was studied by using the ab initio molecular dynamics method.Two different temperature-dependent initial decomposition mechanisms were observed in the unimolecular decomposition of ADN,which were the intramolecular hydrogen transfer and N-NO₂ cleavage in N (NO₂)^-.They were competitive at 2000 K,whereas the former one was predominant at 3000 K.As for the multimolecular decomposition of ADN,four different initial decomposition reactions that were also temperature-dependent were observed.Apart from the aforementioned mechanisms,another two new reactions were the intermolecular hydrogen transfer and direct N-H cleavage in NH₄⁺.At the temperature of 2000 K,the N-NO₂ cleavage competed with the rest three hydrogen-related decomposition reactions,while the direct N-H cleavage in NH₄⁺ was predominant at 3000 K.After the initial decomposition,it was found that the temperature increase could facilitate the decomposition of ADN,and would not change the key decomposition events.ADN decomposed into small molecules by hydrogen-promoted simple,fast and direct chemical bonds cleavage without forming any large intermediates that may impede the decomposition.The main decomposition products at 2000 and 3000 K were the same,which were NH₃,NO₂,NO,N₂O,N₂,H₂O,and HNO₂.