8251892
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning
We show how a conceptually simple search operator called Optimal Reinsertion can be applied to learning Bayesian Network structure from data. On each step we pick a node called the target. We delete all arcs entering or exiting the target. We then find, subject to some constraints, the globally optimal combination of in-arcs and out-arcs with which to reinsert it. The heart of the paper is a new algorithm called ORSearch which allows each optimal reinsertion step to be computed efficiently on large datasets. Our empirical results compare Optimal Reinsertion against a highly tuned implementation of multirestart hill climbing. The results typically show one to two orders of magnitude speed-up on a variety of datasets. They usually show better final results, both in terms of BDEU score and in modeling of future data drawn from the same distribution.
5661913
Distributed Value Functions
Many interesting problems, such as power grids, network switches, and tra c ow, that are candidates for solving with reinforcement learning (RL), also have properties that make distributed solutions desirable. We propose an algorithm for distributed reinforcement learning based on distributing the representation of the value function across nodes. Each node in the system only has the ability to sense state locally, choose actions locally, and receive reward locally (the goal of the system is to maximize the sum of the rewards over all nodes and over all time). However each node is allowed to give its neighbors the current estimate of its value function for the states it passes through. We present a value function learning rule, using that information, that allows each node to learn a value function that is an estimate of a weighted sum of future rewards for all the nodes in the network. With this representation, each node can choose actions to improve the performance of the overall system. We demonstrate our algorithm on the distributed control of a simulated power grid. We compare it against other methods including: use of a global reward signal, nodes that act locally with no communication, and nodes that share rewards (but not value function) information with each other. Our results show that the distributed value function algorithm outperforms the others, and we conclude with an analysis of what problems are best suited for distributed value functions and the new research directions opened up by this work.
2986922
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and weather. Creating the baseline distribution without taking these trends into account can lead to unacceptably high false positive counts and slow detection times. This paper replaces the baseline method of (Wong et al., 2002) with a Bayesian network which produces the baseline distribution by taking the joint distribution of the data and conditioning on attributes that are responsible for the trends. We show that our algorithm, called WSARE 3.0, is able to detect outbreaks in simulated data with almost the earliest possible detection time while keeping a low false positive count. We also include the results of running WSARE 3.0 on real Emergency Department data
6423442
Rule-based anomaly pattern detection for detecting disease outbreaks
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.
18768398
Coverage testing of Java programs and components
Although software testing is a crucial activity in the software development process, only recently have more sound and consistent testing tools become available for testing Java programs and their components. These tools support mostly functional and control-flow based structural criteria. In this paper we explore control-flow and data-flow based coverage criteria to support the testing of Java programs and/or components. We also describe a testing tool, named JaBUTi, which can be used by both the component developer and the component user to test Java-based components and/or systems. To achieve this goal, the tool works at the bytecode level such that no source code is required during the testing activity. We illustrate these ideas and concepts with an example extracted from the literature.
21945290
Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections
Segmentation of three-dimensional rotational angiography (3D-RA) can provide quantitative 3D morphological information of vasculature. The expectation maximization-(EM-) based segmentation techniques have been widely used in the medical image processing community, because of the implementation simplicity, and computational efficiency of the approach. In a brain 3D-RA, vascular regions usually occupy a very small proportion (around 1%) inside an entire image volume. This severe imbalance between the intensity distributions of vessels and background can lead to inaccurate statistical modeling in the EM-based segmentation methods, and thus adversely affect the segmentation quality for 3D-RA. In this paper we present a new method for the extraction of vasculature in 3D-RA images. The new method is fully automatic and computationally efficient. As compared with the original 3D-RA volume, there is a larger proportion (around 20%) of vessels in its corresponding maximum intensity projection (MIP) image. The proposed method exploits this property to increase the accuracy of statistical modeling with the EM algorithm. The algorithm takes an iterative approach to compiling the 3D vascular segmentation progressively with the segmentation of MIP images along the three principal axes, and use a winner-takes-all strategy to combine the results obtained along individual axes. Experimental results on 12 3D-RA clinical datasets indicate that the segmentations obtained by the new method exhibit a high degree of agreement to the ground truth segmentations and are comparable to those produced by the manual optimal global thresholding method.
206519361
An Empirical Comparison of Data Flow and Mutation-Based Test Adequacy Criteria
Evaluation of the adequacy of a test set consisting of one or more test cases is a problem oftes encountered in software testing environments. Two test adequacy criiteria are considered, namely the data flow based all-uses criterion and a mutation based criterion. An empirical study was conducted to compare the ‘difficulty’ of satisfying the two criteria and their costs. Similar studies conducted in the past are discussed in the light of this study. A discussion is also presented of how and why the results of this study, when viewed in conjunction with the results of earlier comparisons of testing methods, are useful to a software test team.
16317533
Multi Criteria Selection of Components Using the Analytic Hierarchy Process
The Analytic Hierarchy Process (AHP) has been successfully used in the past for the selection of components, as presented in case studies in the literature. In this paper, an empirical study using AHP to rank components is presented. The components used in the study are for data compression; each implements one of the Arithmetic Encoding (AREC), Huffman coding (HUFF), Burrows-Wheeler Transform (BWT), Fractal Image Encoding (FRAC), and Embedded Zero-Tree Wavelet Encoder (EZW) algorithms. The ranking is a semi-automated approach that is based on using rigorously collected data for the components' behavior; selection criteria include maximum memory usage, total response time, and security properties (e.g., data integrity). The results provide a clear indication that AHP is appropriate for the task of selecting components when several criteria must be considered. Though the study is limited to select components based on multiple non-functional criteria, the approach can be expanded to include multiple functional criteria.
5375170
Fast evaluation of the elementary functions in double precision
One of the most spectacular development in computer technology is the growth in memory density and speed. It is with this development in mind that we intend to tackle the old problem of computing the elementary functions. Since the dawn of computing, the fast and accurate computation of the elementary functions has been a constant concern of numerical computing. It now seems possible to use tables of sizes in the range of megabits to aid in such computation. To this end, in this paper, we propose a method called ATA-M (Add-Table Lookup-Add with Multiplication) for evaluating polynomials with the aid of tables. When applied to the elementary functions, we obtained a set of algorithms which computes the reciprocal, square root, exponential, sine, cosine, logarithm, are tangent and the hyperbolic functions in about 3 to 4 double precision floating point multiplication time and utilizing about 2 Mbyte of tables.<<ETX>>