36676803
A Hypergraph Separator Based Variable Ordering Heuristic for Solving Real World SAT
Problem structure has been used to guide the variable ordering heuristic in backtracking search since [1]. Recent experimental results show that guiding the variable ordering heuristic using tree decompositions can improve the performance of SAT and CSP solvers. Huang and Darwiche's [2] variable ordering heuristic uses a Dtree, a static binary tree decomposition, to compute the variable group ordering. Since the Dtree has to be constructed before search, the pre-established variable groupings never change during the execution of the solving. Boolean Constraint Propagation (BCP) is the look-ahead strategy for all of the cutting-edge SAT solvers. Because long implication chains exist in real world instances and a large number of variables on the implication chains can be instantiated after making a relatively small number of decisions, the internal structure of real world instances often changes dramatically in different parts of the search tree.
9445959
Guiding real-world SAT solving with dynamic hypergraph separator decomposition
The general solution of satisfiability problems is NP-complete. Although state-of-the-art SAT solvers can efficiently obtain the solutions of many real-world instances, there are still a large number of real-world SAT families which cannot be solved in reasonable time. Much effort has been spent to take advantage of the internal structure of SAT instances. Existing decomposition techniques are based on preprocessing the static structure of the original problem. We present a dynamic decomposition method based on hypergraph separators. Integrating the separator decomposition into the variable ordering of a modern SAT solver leads to speedups on large real-world satisfiability problems. Compared with a static decomposition based variable ordering, such as Dtree (Huang and Darwiche, 2003), our approach does not need time to construct the full tree decomposition, which sometimes needs more time than the solving process itself. Our primary focus is to achieve speedups on large real-world satisfiability problems. Our results show that the new solver often outperforms both regular zChaff and zChaff integrated with Dtree decomposition. The dynamic separator decomposition shows promise in that it significantly decreases the number of decisions for some real-world problems.
6649365
Performing Incremental Bayesian Inference by Dynamic Model Counting
The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 2005) demonstrates that the well-known Davis-Putnam procedure combined with a dynamic decomposition and caching technique is an effective method for exact inference in Bayesian networks with high density and width. In this paper, we define dynamic model counting and extend the dynamic decomposition and caching technique to multiple runs on a series of problems with similar structure. This allows us to perform Bayesian inference incrementally as the structure of the network changes. Experimental results show that our approach yields significant improvements over the previous model counting approaches on multiple challenging Bayesian network instances.
13400769
Simulation of Human Motion for Learning and Recognition
Acquisition of good quality training samples is becoming a major issue in machine learning based human motion analysis. This paper presents a method to simulate human body motion with the intention to establish a human motion corpus for learning and recognition. The simulation is achieved by a unique temporal-spatial-temporal decomposition of human body motion into actions, joint actions and actionlets based on the human kinematic model. The actionlet models the primitive moving phase of a joint and can be simulated based on the kinesiological study. A joint action is formed by proper concatenation of actionlets and an action is a group of synchronized joint actions. Methods for concatenation and synchronization are proposed in this paper for realistic simulation of human motion. Results on simulating ”running” verifies the feasibility of the proposed method.
1320878
Saturated BLAST: an automated multiple intermediate sequence search used to detect distant homology
MOTIVATION
Two proteins can have a similar 3-dimensional structure and biological function, but have sequences sufficiently different that traditional protein sequence comparison algorithms do not identify their relationship. The desire to identify such relations has led to the development of more sensitive sequence alignment strategies. One such strategy is the Intermediate Sequence Search (ISS), which connects two proteins through one or more intermediate sequences. In its brute-force implementation, ISS is a strategy that repetitively uses the results of the previous query as new search seeds, making it time-consuming and difficult to analyze.


RESULTS
Saturated BLAST is a package that performs ISS in an efficient and automated manner. It was developed using Perl and Perl/Tk and implemented on the LINUX operating system. Starting with a protein sequence, Saturated BLAST runs a BLAST search and identifies representative sequences for the next generation of searches. The procedure is run until convergence or until some predefined criteria are met. Saturated BLAST has a friendly graphic user interface, a built-in BLAST result parser, several multiple alignment tools, clustering algorithms and various filters for the elimination of false positives, thereby providing an easy way to edit, visualize, analyze, monitor and control the search. Besides detecting remote homologies, Saturated BLAST can be used to maintain protein family databases and to search for new genes in genomic databases.
6001623
A segment alignment approach to protein comparison
MOTIVATION
Local structure segments (LSSs) are small structural units shared by unrelated proteins. They are extensively used in protein structure comparison, and predicted LSSs (PLSSs) are used very successfully in ab initio folding simulations. However, predicted or real LSSs are rarely exploited by protein sequence comparison programs that are based on position-by-position alignments.


RESULTS
We developed a SEgment Alignment algorithm (SEA) to compare proteins described as a collection of predicted local structure segments (PLSSs), which is equivalent to an unweighted graph (network). Any specific structure, real or predicted corresponds to a specific path in this network. SEA then uses a network matching approach to find two most similar paths in networks representing two proteins. SEA explores the uncertainty and diversity of predicted local structure information to search for a globally optimal solution. It simultaneously solves two related problems: the alignment of two proteins and the local structure prediction for each of them. On a benchmark of protein pairs with low sequence similarity, we show that application of the SEA algorithm improves alignment quality as compared to FFAS profile-profile alignment, and in some cases SEA alignments can match the structural alignments, a feat previously impossible for any sequence based alignment methods.
8155707
A Question Answering System Supported by Information Extraction
This paper discusses an information extraction (IE) system, Textract, in natural language (NL) question answering (QA) and examines the role of IE in QA application. It shows: (i) Named Entity tagging is an important component for QA, (ii) an NL shallow parser provides a structural basis for questions, and (iii) high-level domain independent IE can result in a QA breakthrough.
10927916
Integration of Image Matching and Classification for Multimedia Navigation
With the recent explosive growth in the volume of images on the World-Wide Web, it has become increasingly difficult to search for images of interests. The classification of images helps users to access a large image collection efficiently. Classification reduces search space by filtering out unrelated images. Classification also allows for more user-friendly interfaces: users can better visualize easily result space by browsing the representative images of the candidates. In this paper, we present a technique for image classification based on color, shape and composition using the primary objects. We apply this classification technique in image matching for image retrieval on the Web. Our experimental results show that this approach can maintain 73% of recall by searching only 24% of the whole data set. We also show how we apply such technique to assist users in navigation.