Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture
The EM algorithm is generally considered as a linearly convergent algorithm. However, many empirical results show that it can converge significantly faster than those gradient based first-order iterative algorithms, especially when the overlap of densities in a mixture is small. This paper explores this issue theoretically on mixtures of densities from a class of exponential families. We have proved that as an average overlap measure of densities in the mixture tends to zero, the asymptotic convergence rate of the EM algorithm locally around the true solution is a higher order infinitesimal than a positive order power of this overlap measure. Thus, the large sample local convergence rate for the EM algorithm tends to be asymptotically superlinear when the overlap of densities in the mixture tends to zero. Moreover, this result has been detailed on Gaussian mixtures.

title: The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode
abstract: We present a study on the stability of the generalized Hopfield network in randomly asynchronous mode. First, the stability is investigated from the state space of the network. We introduce a concept of hole and define two kinds of stability in randomly asynchronous mode. By mathematical inductive method, we have proved that a generalized Hopfield network with non negative weights is strictly stable, that is, the network evolves to a simple hole-stable state with probability one when it starts at any initial state. For the general case, we made the simulation experiments on the networks with the number of neurons from 5 to 10. The empirical results have shown that almost any generalized Hopfield network with simple holes is strictly stable.

title: A Neural Network Approach to Real-Time Pattern Recognition
abstract: This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.

title: The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
abstract: The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.

title: Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
abstract: This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n− 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n− 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.

title: MFCC and SVM Based Recognition of Chinese Vowels
abstract: The recognition of vowels in Chinese speech is very important for Chinese speech recognition and understanding. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose a new approach to the recognition of Chinese vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) as the vowel’s features. It is shown by the experiments that this method can reach a high recognition accuracy on the given vowels database and outperform the SVM with the Linear Prediction Coding Cepstral (LPCC) coefficients as the vowel’s features.

title: A P2P Ubiquitous System for Testing Network Programs
abstract: This paper presents design and implementation of a peer-to-peer (P2P) ubiquitous system for assisting a programmer to efficiently test network programs in their development process by using multiple computers simultaneously. This system is implemented using the TOMSCOP (Technology Of Multi-user Synchronous COllaboration Platform). It is a synchronous P2P collaboration platform built atop the JXTA technology. JXTA is a set of protocols for developing P2P shared applications, and enables peers to find each other, form groups and exchange messages across firewalls and NATs. This system has the following three main features. Firstly, it enables the program source files being shared among peers. Secondly, a user is able to simultaneously compile, execute and stop network programs of all peers in a same group by simple operations on the system GUI. Lastly, due to using Java and JXTA technology, the system can run on any platform.

title: A genetic algorithm for optimal 3-connected telecommunication network designs
abstract: In this paper, a genetic algorithm for three connected telecommunication network designs is proposed. The genetic algorithm uses a new approach to solution representation, in which constraints such as diameter and connectivity constraints can be easily encoded and two-point crossover with the operation of swapping duplicated nodes ensures solutions generated through genetic evolution are all feasible so that both checking of the constraints and repair mechanism can be avoided. Both efficiency and effectiveness are increased due to no checking of the constraints and no repair mechanism conducted. In fact, our design algorithm for three-connected network design can he extended to a high-connectivity network design.

title: A method for repairing the inconsistency of fuzzy preference relations
abstract: This paper investigates the inconsistency problems of preference information about alternatives expressed as a fuzzy preference relation by a decision maker. An analysis method is presented to identify the inconsistency and weak transitivity of a fuzzy preference relation and to repair its inconsistency to reach weak transitivity. First, several definitions are given on additive consistency, inconsistency and weak transitivity of a fuzzy preference relation. Next, two methods derived from graph theory are presented to judge whether a fuzzy preference relation has weak transitivity or not. Then, an algorithm is developed to repair an inconsistent fuzzy preference relation and to make it become one with weak transitivity, via a synthesis matrix which reflects the relationship between the fuzzy preference relation with additive consistency and the initial one given by a decision maker. A convergence theorem is also given for the algorithm. Lastly, two numerical examples are shown to illustrate the proposed method.

title: Ubisafe Computing: Vision and Challenges (I)
abstract: In recent years, a variety of new computing paradigms have been proposed for various purposes. It is true that many of them intend to and really can gratify some of the people sometime, somewhere; a few of them can even gratify some of the people anytime, anywhere. However, at present, none of the computing paradigms intend to gratify all the people anytime, anywhere. With the rapid advance of information technology and the spread of information services, the IT disparity in age, social standing, and race of the people has been expanding and has become a critical social problem of the 21st century. Thus, we have a fundamental question: Can we construct, in a unified methodology, a computing environment that can gratify all the people in all situations, all places and all the time? We propose a novel and inclusive computing paradigm, named ubisafe computing, for studying and providing possible solutions to the above problem. The ultimate goal of ubisafe computing is to build a computing environment in which all people and organizations can benefit from ubiquitous services anytime anywhere with assured and desired satisfaction without worrying or thinking about safety. That is, the ubisafe computing vision emphasizes two basic aspects: ubiquitous safety and ubiquitous satisfaction to all people in all situations. This paper presents the motivations for the ubisafe computing vision but focuses on one basic aspect of ubiquitous safety that covers reliability, security, privacy, persistency, trust, risk, out of control, and other watchfulness while considering novel, essential ubicomp or percomp features of unobtrusive computers, diverse users/people and life-like systems.

title: Fast interval-valued statistical modeling of interconnect and effective capacitance
abstract: Correlated interval representations of range uncertainty offer an attractive solution to approximating computations on statistical quantities. The key idea is to use finite intervals to approximate the essential mass of a probability density function (pdf) as it moves through numerical operators; the resulting compact interval-valued solution can be easily interpreted as a statistical distribution and efficiently sampled. This paper first describes improved interval-valued algorithms for asymptotic wave evaluation (AWE)/passive reduced-order interconnect macromodeling algorithm (PRIMA) model order reduction for tree-structured interconnect circuits with correlated resistance, inductance, and capacitance (RLC) parameter variations. By moving to a much faster interval-valued linear solver based on path-tracing ideas, and making more optimal tradeoffs between interval- and scalar-valued computations, the delay statistics roughly 10/spl times/ faster than classical Monte Carlo (MC) simulation, with accuracy to within 5% can be extracted. This improved interval analysis strategy is further applied in order to build statistical effective capacitance (C/sub eff/) models for variational interconnect, and show how to extract statistics of C/sub eff/ over 100/spl times/ faster than classical MC simulation, with errors less than 4%.