52800785
Association-Based Multiple Imputation in Multivariate Datasets: A Summary
There are two general approaches to handling missing data. The rst one ignores missing data, by discarding the records with missing values from a dataset or taking unknown attribute values as special, null values and processing them during the process of answering queries. The second approach is called imputations, which attempts to ll in missing data with plausible values. There are two major existing methods of this approach. The rst uses statistical relationships among the variables under consideration, and builds statistical models for the variables, namely linear regression models for numerical values and statistical classi cation rules for categorical values. A possible value of an unspeci ed item is then estimated using the statistical models. The second method is multiple imputations that replaces each missing value by multiple simulated values. After the multiple imputations are obtained, each plausible value is analyzed, using EM algorithm and Gibbs sampling, to produce an inferential conclusion that include uncertainty caused by missing data. Note that the multiple imputationmethod can only apply to numerical attributes.
2426741
An analysis and application of distributed constraint satisfaction and optimization algorithms in sensor networks
This research is motivated by a distributed scheduling problem in distributed sensor networks, in which computational resources are scarce. To cope with limited computational resources and restricted real-time requirement, it is imperative to apply distributed algorithms that have low overhead on resource requirement and high anytime performance. In this paper, We study distributed stochastic algorithm (DSA) and distributed breakout algorithm (DBA), two distributed algorithms developed earlier for distributed constraint satisfaction problems. We experimentally investigate their properties and compare their performance using our distributed scheduling problem as a benchmark. We first formulate the scheduling problem as a distributed multi-coloring problem. We then experimentally show that the solution quality and communication cost of DSA exhibit phase-transition or threshold behavior, in that the performance degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. The results show that when controlled properly, DSA is superior to DBA, having better or competitive solution quality and significantly smaller communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scan scheduling problem.
205692410
Average-case analysis of best-first search in two representative directed acyclic graphs
Many problems that arise in the real world have search spaces that are graphs rather than trees. Understanding the properties of search algorithms and analyzing their performance have been major objectives of research in AI. But most published work on the analysis of search algorithms has been focused on tree search, and comparatively little has been reported on graph search. One of the major obstacles in analyzing average-case complexity of graph search is that no single graph can serve as a suitable representative of graph search problems. In this paper we propose one possible approach to analyzing graph search. We take two problem domains for which the search graphs are directed acyclic graphs of similar structure, and determine the average case performance of the best-first search algorithm A* on these graphs. The first domain relates to one-machine job sequencing problems in which a set of jobs must be sequenced on a machine in such a way that a penalty function is minimized. The second domain concerns the Traveling Salesman Problem. Our mathematical formulation extends a technique that has been used previously for analyzing tree search. We demonstrate the existence of a gap in computational cost between two classes of problem instances. One class has exponential complexity and the other has polynomial complexity. For the job sequencing domain we provide supporting experimental evidence showing that problems exhibit a huge difference in computational cost under different conditions. For the Traveling Salesman Problem, our theoretical results reflect on the long-standing debate on the expected complexity of branch-and-bound algorithms for solving the problem, indicating that the complexity can be polynomial or exponential, depending on the accuracy of the heuristic function used.
3517573
A template-based and pattern-driven approach to situation awareness and assessment in virtual humans
Situation awareness and assessment are fundamental components of rational virtual humans. They are central parts of models of virtual humans and critical elements of efficient reasoning and planning systems. In this paper, we describe our efforts of developing the capability of situation awareness in autonomous, synthetic virtual pilots in a military domain. After briefly describing the motivations of this research, we present an agent architecture that integrates perception, reasoning with situation awareness, and actions. We describe a representation of situations, methods for situation assessment, and applications of situation awareness in information collection, and creating focused attention. The quantitative measure from our experiment shows that by using focused attention based on the ability of situation awareness, the perceptional load of virtual pilots is significantly reduced, reducing pilots’ response time by more than 50 percent.
10248617
Towards flexible teamwork in persistent teams
Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams mast be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique to enable persistent teams to overcome such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.
12824361
Data Mining Methods for Modeling Gene Expression Regulation and Their Applications
This paper demonstrates machine learning and data mining methods that can be developed and applied to analyzing large quantities of genomic information and gene expression data for characterizing and modeling gene expression regulation. In particular, there will be a discussion on some of the methods that have been developed for modeling gene expression regulation underlying abiotic stress (e.g., drought, low temperature and salinity) tolerance, for identifying gene responsive to particular environmental stress conditions, and for characterizing the functions of microRNA genes for stress regulation in model plant Arabidopsis thaliana.
7781593
Kernel based 2D symmetrical principal component analysis for face classification
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.
9323907
Orientation refinement of virus structures with unknown symmetry
Structural biology, in particular the structure determination of viruses and other large macromolecular complexes lead to data- and compute-intensive problems that require resources well beyond those available on a single system. Thus, there is an imperative need to develop parallel algorithms and programs for clusters and computational grids. We present one of the most challenging computational problems posed by the three-dimensional structure determination of viruses, the orientation refinement.
1905684
An induced additive-noise model for memoryless Rayleigh-fading channels
This correspondence investigates a noncoherent discrete-time memoryless Rayleigh-fading channel. A logarithmic transform converts it into an induced channel with additive noise that is independent of the channel input. From this perspective, it is natural and convenient for us to revisit several known results and gain new insights. In particular, we specify a class of simple, log-scale uniform channel input distributions that performs well for moderate to high signal-to-noise ratio (SNR). Furthermore, a continuous-amplitude log-scale uniform distribution is asymptotically capacity-achieving in the sense that the difference between the resulting mutual information and the channel capacity approaches zero as SNR becomes large. We also extend the induced additive-noise channel approach to memoryless multiple-antenna channels possibly with transmit, but without receive, spatial correlation.
2749219
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
We present a technique for speeding up the convergence of value iteration for partially observable Markov decisions processes (POMDPs). The underlying idea is similar to that behind modified policy iteration for fully observable Markov decision processes (MDPs). The technique can be easily incorporated into any existing POMDP value iteration algorithms. Experiments have been conducted on several test problems with one POMDP value iteration algorithm called incremental pruning. We find that the technique can make incremental pruning run several orders of magnitude faster.
28687598
Some Basic Data Structures and Algorithms for Chemical Generic Programming
Here, we report a template library used for molecular operation, the Molecular Handling Template Library (MHTL). The library includes some generic data structures and generic algorithms, and the two parts are associated with each other by two concepts: Properties and Molecule. The concept Properties describes the interface to access objects' properties, and the concept Molecule describes the minimum requirement for a molecular class. Data structures include seven models of Properties, each using a different method to access properties, and two models of molecular classes. Algorithms include molecular file manipulation subroutines, SMARTS language interpreter and matcher functions, and molecular OpenGL rendering functions.