5809396
Self-adapting numerical software (SANS) effort
The challenge for the development of next-generation software is the successful management of the complex computational environment while delivering to the scientist the full power of flexible compositions of the available algorithmic alternatives. Self-adapting numerical software (SANS) systems are intended to meet this significant challenge. The process of arriving at an efficient numerical solution of problems in computational science involves numerous decisions by a numerical expert. Attempts to automate such decisions distinguish three levels: algorithmic decision, management of the parallel environment, and processor-specific tuning of kernels. Additionally, at any of these levels we can decide to rearrange the user's data. In this paper we look at a number of efforts at the University of Tennessee to investigate these areas.
1853937
Numerically Stable Real Number Codes Based on Random Matrices
Error correction codes defined over real-number field have been studied and recognized as useful in many applications. However, most real-number codes in literature are quite suspect in their numerical stability. In this paper, we introduce a class of real-number codes based on random generator matrices over real-number fields. Codes over complex-number field are also discussed. Experiment results demonstrate our codes are numerically much more stable than existing codes in literature.
17550632
OCFS: optimal orthogonal centroid feature selection for text categorization
Text categorization is an important research area in many Information Retrieval (IR) applications. To save the storage space and computation time in text categorization, efficient and effective algorithms for reducing the data before analysis are highly desired. Traditional techniques for this purpose can generally be classified into feature extraction and feature selection. Because of efficiency, the latter is more suitable for text data such as web documents. However, many popular feature selection techniques such as Information Gain (IG) andχ2-test (CHI) are all greedy in nature and thus may not be optimal according to some criterion. Moreover, the performance of these greedy methods may be deteriorated when the reserved data dimension is extremely low. In this paper, we propose an efficient optimal feature selection algorithm by optimizing the objective function of Orthogonal Centroid (OC) subspace learning algorithm in a discrete solution space, called Orthogonal Centroid Feature Selection (OCFS). Experiments on 20 Newsgroups (20NG), Reuters Corpus Volume 1 (RCV1) and Open Directory Project (ODP) data show that OCFS is consistently better than IG and CHI with smaller computation time especially when the reduced dimension is extremely small.
13035041
Searching the Web with Queries
In this paper we study the problem of searching the Web with online learning algorithms. We consider that Web documents can be represented by vectors of n boolean attributes. A search engine is viewed as a learner, and a user is viewed as a teacher. We investigate the number of queries a search engine needs from the user to search for a collection of Web documents. We design several efficient learning algorithms to search for any collection of documents represented by a disjunction (or a conjunction) of relevant attributes with the help of membership queries or equivalence queries.
2801577
Scalable collaborative filtering using cluster-based smoothing
Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these problems, but this approach tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms.
3177556
A parallel architecture for probabilistic relaxation operations on images
Abstract The design of a parallel architecture for executing probabilistic relaxation operations on two dimensional images is addressed. First of all, the concerned relaxation process is divided into three different parallel operations, i.e. systolic, simultaneous, and pipelined. All these parallel operations are mapped onto a linear array architecture that runs smoothly without any bottleneck in the data flows. The proposed architecture and its components are described. An illustrative running of the relaxation process for an image thresholding application on the architecture is described in some details. The hardware implementation and CAD logic simulation are then given. Finally, performance comparison between the proposed architecture and some existing ones is reported.
8030523
Video management in peer-to-peer systems
Providing scalable video services in a peer-to-peer (P2P) environment is challenging. Since videos are typically large and require high communication bandwidth for delivery, many peers may be unwilling to cache them in whole to serve others. In this paper, the authors addressed two fundamental research problems in providing scalable P2P video services, namely (1) how a host can find enough video pieces, which may scatter among the whole system, to assemble a complete video, and (2) given a limited buffer size, what part of a video a host should cache. A new distributed video management technique was proposed. This scheme organizes hosts into a number of cells, each of which is a distinct set of hosts which together can supply a video in its entirety. A client looking for a video can stop its search as soon as it finds a host that caches any part of the video. Caching operations can be coordinated within each cell to balance data redundancy in the system. The extensive study on a Gnutella-like simulation network shows convincingly the performance advantage of the new scheme.