2464851
Fast Memory-Efficient Generalized Belief Propagation
Generalized Belief Propagation (gbp) has proven to be a promising technique for performing inference on Markov random fields (mrfs). However, its heavy computational cost and large memory requirements have restricted its application to problems with small state spaces. We present methods for reducing both run time and storage needed by gbp for a large class of pairwise potentials of the mrf. Further, we show how the problem of subgraph matching can be formulated using this class of mrfs and thus, solved efficiently using our approach. Our results significantly outperform the state-of-the-art method. We also obtain excellent results for the related problem of matching pictorial structures for object recognition.
14151624
Solving Markov Random Fields using Second Order Cone Programming Relaxations
This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
7847540
Electronic Multidimensional Auctions and the Role of Information Feedback
* Part of this work was done while the first author was visiting the IBM T.J. Watson Research Center in Hawthorne, NY, whose hospitality is gratefully acknowledged. Anant Jhingran and Sunil Noronha and especially Mike Rothkopf provided valuable feedback on earlier versions. However, any remaining errors are solely the responsibility of the authors. AbstractTraditionally, companies aiming to achieve competition among suppliers have used sealed bidding procedures in their sourcing processes. The advances in information technology and in particular the Internet now allow these companies to use different and more complex auction mechanisms. In particular multidimensional auctions are a natural extension of the standard sealed-bid auctions, but these auctions raise a whole host of issues that have been little investigated. In this article we focus on one of these issues, namely the role of information feedback given during the auction process. We describe various feedback policies and analyze the expected impact on the performance of the auction mechanism using the criteria of speed of convergence, allocative efficiency and Pareto optimality. This can help both researchers and practitioners in a more detailed and thorough analysis of electronic auctions.
5533650
The hierarchical cliques interconnection network
The fully connected network possesses extremely good topological, fault-tolerant, and embedding properties. However, due to its high degree, the fully connected network has not been an attractive candidate for building parallel computers. On the other hand, tree-based networks are popular as parallel computer networks, even though they suffer from poor fault-tolerance and embedding properties. The hierarchical cliques interconnection network described in this paper incorporates positive features of the fully connected network and the tree network. In other words, the hierarchical cliques possess such desirable properties as low diameter, low degree, self routing, versatile embedding, good fault-tolerance and strong resilience. Hierarchical cliques can efficiently embed most important networks and possess a scalable, modular structure. Further, by combining hierarchical cliques with fat trees congestion in the upper levels can be alleviated.
10875247
Measuring Parallelism in Computation-Intensive Scientific/Engineering Applications
Describes COMET, (concurrency measurement tool), a software tool for measuring parallelism in large scientific/engineering applications. The proposed tool measures the total parallelism present in programs, filtering out the effects of communication/synchronization delays, finite storage, limited number of processors, the policies for management of processors and storage, etc. Although an ideal machine that can exploit the total parallelism is not realizable, such measures would aid the calibration and design of various architectures/compilers. The proposed software tool accepts ordinary Fortran programs as input. Therefore, parallelism can be measured easily on many fairly big programs. Some measurements for parallelism obtained with the help of this tool are also reported. It is observed that the average parallelism in the chosen programs is in the range of 500-3500 Fortran statements executing concurrently in each clock cycle in an idealized environment. >