400858
Toward good elimination orders for symbolic SAT solving
Fundamentally different from DPLL, a new approach to SAT has recently emerged that abandons search and enlists BDDs to symbolically represent clauses of the CNF. These BDDs are conjoined according to a schedule where some variables may be eliminated by quantification at each step to reduce the size of the intermediate BDDs. SAT solving then reduces to checking whether the final BDD is the zero constant. For this approach to be practical, finding a good quantification schedule is critical. We study the use of a variable elimination algorithm for this purpose, as well as two specific methods for the generation of good elimination orders based on CNF structure. While neither method appears to dominate, we show how one can heuristically select the better using the notion of width. We implement a symbolic SAT solver based on these techniques and evaluate its efficiency and robustness on a set of benchmarks against five other solvers, each having unique characteristics, including winners of the most recent SAT competition.
8956273
Hierarchical Diagnosis of Multiple Faults
Due to large search spaces, diagnosis of combinational circuits is often practical for finding only single and double faults. In principle, system models can be compiled into a tractable representation (such as DNNF) on which faults of arbitrary cardinality can be found efficiently. For large circuits, however, compilation can become a bottleneck due to the large number of variables necessary to model the health of individual gates. We propose a novel method that greatly reduces this number, allowing the compilation, as well as the diagnosis, to scale to larger circuits. The basic idea is to identify regions of a circuit, called cones, that are dominated by single gates, and model the health of each cone with a single health variable. When a cone is found to be possibly faulty, we diagnose it by again identifying the cones inside it, and so on, until we reach a base case. We show that results combined from these hierarchical sessions are sound and complete with respect to minimum-cardinality diagnoses. We implement this method on top of the diagnoser developed by Huang and Darwiche in 2005, and present evidence that it significantly improves the efficiency and scalability of diagnosis on the ISCAS-85 circuits.
392551
Finding Target Language Correspondence for Lexicalized EBMT System
This paper presents a three-phase approach to find the correspondence in Target Language (TL) sentence for a fragment of Source Language (SL) sentence in a lexicalized EBMT system. To be practical, it exploits surface information as much as possible instead of using parsers. Experiments show that, although not so perfect, it is very robust and effective. The three phases are: First, align the sentence pair at word level in order to provide anchors for phrase alignment. Second, based on the aligned anchors, find all the TL fragments which may possibly correspond to a SL fragment. And finally, using a score function, select the best TL fragment as the correspondence of a SL fragment.
2763612
Information Marginalization on Subgraphs
Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers' and ‘authors' exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers'), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems.
9984897
Structure Alignment Using Bilingual Chunking
A new statistical method called "bilingual chunking" for structure alignment is proposed. Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks. The alignment is finished through a simultaneous bilingual chunking algorithm. Using the constrains of chunk correspondence between source language (SL) and target language (TL), our algorithm can dramatically reduce search space, support time synchronous DP algorithm, and lead to highly consistent chunking. Furthermore, by unifying the POS tagging and chunking in the search process, our algorithm alleviates effectively the influence of POS tagging deficiency to the chunking result.The experimental results with English-Chinese structure alignment show that our model can produce 90% in precision for chunking, and 87% in precision for chunk alignment.
16728359
TRAC: toward recency and consistency reporting in a database with distributed data sources
Distributed computing environments, including workflows in computational grids, present challenges for monitoring, as the state of the system may be captured only in logs distributed throughout the system. One approach to monitoring such systems is to "sniff" these distributed logs and to store their transformed content in a DBMS. This centralizes the state and exposes it for querying; unfortunately, it also creates uncertainty with respect to the recency and consistency of the data. Previous related work has focused on allowing queries to express currency and consistency constraints, which are then enforced by "pulling" data from the distributed sources on demand, or by requiring synchronous updates of a centralized data store. In some instances this is impossible due to legacy system issues or inefficient as the system scales to large numbers of processors. Accordingly, we propose that instead of enforcing consistency and recency, such monitoring systems should report these properties along with query results, with the hope that this will allow the data to be appropriately interpreted. We present techniques for reporting consistency and recency for queries and evaluate them with respect to efficiency and precision. Finally, we describe our prototype implementation and present experimental results of our techniques.
6888787
Distributed data management for large volume visualization
We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.