14452513
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of these search methods to temporal Bayesian networks [Tucker01]. A characteristic of many datasets from medical to geographical data is the spatial arrangement of variables. In this paper we investigate a set of operators that have been designed to exploit the spatial nature of such data in order to learn dynamic Bayesian networks more efficiently. We test these operators on synthetic data generated from a Gaussian network where the architecture is based upon a Cartesian coordinate system, and real-world medical data taken from visual field tests of patients suffering from ocular hypertension.
39184835
Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering
Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data. This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using these models to cluster the data. Similarity measures for polynomials are given that comply with commonly used standard measures. The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It shows significantly better results as noise and missing data are increased.
36065326
Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on Natural Computation and sparse matrices that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the associated parameters. Extensive results are presented, where the proposed method is compared with both traditional statistical and heuristic search techniques and evaluated on a number of criteria. The results have implications for a wide range of applications involving the learning of short MTS models.
6104018
A framework for modelling virus gene expression data
Short, high-dimensional, Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this truer than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a “global” view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a three-step modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, producing promising results.
36513177
A Bayesian network approach to explaining time series with changing structure
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
14510898
Evolutionary learning of dynamic probabilistic models with large time lags
In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.
9356286
A design of overlay anonymous multicast protocol
Multicast services are demanded by a variety of applications. Many applications require anonymity during their communication. However, there has been very little work on anonymous multicasting and such services are not available yet. Since there are fundamental differences between multicast and unicast, the solutions proposed for anonymity in unicast communications cannot be directly applied to multicast applications. In this paper, we define the anonymous multicast system, and propose a mutual anonymous multicast (MAM) protocol including the design of a unicast mutual anonymity protocol and construction and optimization of an anonymous multicast tree. MAM is self organizing and completely distributed. We define the attack model in an anonymous multicast system and analyze the anonymity degree. We also evaluate the performance of MAM by simulations
1087779
A note on the robust stability of uncertain stochastic fuzzy systems with time-delays
Takagi-Sugeno (T-S) fuzzy models are now often used to describe complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear submodels. In this note, the T-S fuzzy model approach is exploited to establish stability criteria for a class of nonlinear stochastic systems with time delay. Sufficient conditions are derived in the format of linear matrix inequalities (LMIs), such that for all admissible parameter uncertainties, the overall fuzzy system is stochastically exponentially stable in the mean square, independent of the time delay. Therefore, with the numerically attractive Matlab LMI toolbox, the robust stability of the uncertain stochastic fuzzy systems with time delays can be easily checked.
11705555
RIP: an efficient hybrid repeater insertion scheme for low power
The paper presents a novel repeater insertion algorithm for interconnect power minimization. The novelty of our approach is in the judicious integration of an analytical solver and a dynamic programming based method. Specifically, the analytical solver chooses a concise repeater library and a small set of repeater location candidates such that the dynamic programming algorithm can be performed fast with little degradation of the solution quality. In comparison with previously reported repeater insertion schemes, within comparable runtimes, our approach achieves up to 37% higher power savings. Moreover, for the same design quality, our scheme attains a speedup of two orders of magnitude.
460263
Heuristic algorithms for multi-constrained quality of service routing
Multi-constrained quality of service (QoS) routing finds a route in the network that satisfies multiple independent quality of service constraints. This problem is NP-hard and a number of heuristic algorithms have been proposed to solve the problem. This paper studies two heuristics, the limited granularity heuristic and the limited path heuristic, for solving general k-constrained problems. Analytical and simulation studies are conducted to compare the time/space requirements of the heuristics and the effectiveness of the heuristics in finding the paths that satisfy the QoS constraints. We prove analytically that for an N nodes and E edges network with k (a small constant) independent QoS constraints, the limited granularity heuristic must maintain a table of size O(|N|/sup k-1/) in each node to be effective, which results in a time complexity of O(|N|/sup k/|E|). We also prove that the limited path heuristic can achieve very high performance by maintaining O(|N|/sup 2/lg(|N|)) entries in each node, which indicates that the performance of the limited path heuristic is not sensitive to the number of constraints. We conclude that although both the limited granularity heuristic and the limited path heuristic can efficiently solve 2-constrained QoS routing problems, the limited path heuristic is superior to the limited granularity heuristic in solving k-constrained QoS routing problems when k>3. Our simulation study further confirms this conclusion.
18361624
A new approach to retrieve video by example video clip
The similarity measure between video clips is a key issue in video retrieval. In the developing of our video retrieval system, we propose a new video similarity model. In contrast to existing algorithms, it proposes many influencing factors, such as order factor, speed factor, disturbance factor, etc, based on the subjective visual judgement of human. So this algorithm embodies the degree of similarity completely and systematically. On the other hand, it has resolution adaptation because it can be applied to every level of video structure. In the retrieval system, it can be used to process video query by example clip. This paper introduces it in detail and presents experiment results at the end of the paper.