Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

Alternating,all-trans polyenynes: Model compounds for poly(diacetylene)s with defined conjugation length

The syntheses of polyenynes as model compounds for poly(diacetylene)s (PDAs) are described. Variation of properties (UV–VIS, Raman, NMR and bond geometries) as a function of the chain length was investigated. After extrapolation to infinite chain length these data were compared to those for PDAs. From UV–VIS spectra a value of λ = 551 nm (2.25 eV) was calculated corresponding to the electronic transition of a single polyenyne chain. This energy is located at the low energy end of a yellow PDA solution spectrum. From Raman scattering v_(C?C) = 2108–2128 cm^?1 and v_(C?C) = 1505–1532 cm^?1 were calculated after extrapolation. Similarly sp-C¹³C NMR data yielded a shift of δ = 100 ppm. These data are almost identical to data known for yellow PDA solutions. Bond geometries are almost identical to those of poly(diacetylene)s and theoretical data.     

Propagation of the Chain Explosive‐Decomposition Reaction in Silver Azide Crystals

The velocity of the chain explosivedecomposition reaction in silver azide whiskes has been measured (1500 m/sec). The measured velocity is identified as the propagation velocity of the diffusion front of holes generated in the course of explosive decomposition.     

Conditional probability Markov chain simulation based reliability analysis method for nonnormal variables

Based on fast Markov chain simulation for generating the samples distributed in failure region and saddlepoint approximation(SA) technique,an efficient reliability analysis method is presented to evaluate the small failure probability of non-linear limit state function(LSF) with non-normal variables.In the presented method,the failure probability of the non-linear LSF is transformed into a product of the failure probability of the introduced linear LSF and a feature ratio factor.The introduced linear LSF wh...     

资源生产供应链履行社会责任的内在动力研究

运用博弈论的基本思想,通过构造一个资源生产供应链,从资源保护这个角度出发,以资源生产供应链中的节点企业——资源开采商开采自然资源为例,对资源生产供应链履行社会责任的内在动力进行了研究。结果表明：对资源开采商合作开采带来的收益增量进行合理分配,既能使资源开采商进行合作,又能实现供应链系统与各资源开采商的帕累托最优。     

RAZUMIKHIN-TYPE THEOREMS OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS

The stability of stochastic functional differential equation with Markovian switching was studied by several authors,but there was almost no work on the stability of the neutral stochastic functional differential equations with Markovian switching.The aim of this article is to close this gap.The authors establish Razumikhin-type theorem of the neutral stochastic functional differential equations with Markovian switching,and those without Markovian switching.     

A mildly exponential approximation algorithm for the permanent

A new approximation algorithm for the permanent of ann ×n 0,1-matrix is presented. The algorithm is shown to have worst-case time complexity exp(O(n ^1/2 log² n)). Asymptotically, this represents a considerable improvement over the best existing algorithm, which has worst-case time complexity exp((n)).Supported by SERC Grant GR/F 90363; work done in part while visiting DIMACS (Center for Discrete Mathematics and Computer Science).Supported by an NSF PYI grant, with matching equipment grant from the AT&T Foundation; work done in part while visiting DIMACS.     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

Approximate steady-state analysis of large Markov models based on the structure of their decision diagram encoding

We propose a new approximate numerical algorithm for the steady-state solution of general structured ergodic Markov models. The approximation uses a state-space encoding based on multiway decision diagrams and a transition rate encoding based on a new class of edge-valued decision diagrams. The new method retains the favorable properties of a previously proposed Kronecker-based approximation, while eliminating the need for a Kronecker-consistent model decomposition. Removing this restriction allows for a greater utilization of event locality, which facilitates the generation of both the state-space and the transition rate matrix, thus extends the applicability of this algorithm to larger and more complex models.