7378848
Adaptive filters for continuous queries over distributed data streams
We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Significant communication overhead is incurred in the presence of rapid update streams, and we propose a new technique for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive the updates necessary to provide answers of adequate precision at all times. Our approach enables applications to trade precision for communication overhead at a fine granularity by individually adjusting the precision constraints of continuous queries over streams in a multi-query workload. Through experiments performed on synthetic data simulations and a real network monitoring implementation, we demonstrate the effectiveness of our approach in achieving low communication overhead compared with alternate approaches.
1349350
Extraction of coherent relevant passages using hidden Markov models
In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage is presumably highly sensitive to both the query and document.In this article, we present a new method for accurately detecting coherent relevant passages of variable lengths using hidden Markov models (HMMs). The HMM-based method naturally captures the topical boundaries between passages relevant and nonrelevant to the query. Pseudo-feedback mechanisms can be naturally incorporated into such an HMM-based framework to improve parameter estimation. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two datasets. We further show how the HMM method can be applied on top of any basic passage extraction method to improve passage boundaries.
8322277
Accurately extracting coherent relevant passages using hidden Markov models
In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets.
2507744
Exploiting Domain Structure for Named Entity Recognition
Named Entity Recognition (NER) is a fundamental task in text mining and natural language understanding. Current approaches to NER (mostly based on supervised learning) perform well on domains similar to the training domain, but they tend to adapt poorly to slightly different domains. We present several strategies for exploiting the domain structure in the training data to learn a more robust named entity recognizer that can perform well on a new domain. First, we propose a simple yet effective way to automatically rank features based on their generalizabilities across domains. We then train a classifier with strong emphasis on the most generalizable features. This emphasis is imposed by putting a rank-based prior on a logistic regression model. We further propose a domain-aware cross validation strategy to help choose an appropriate parameter for the rank-based prior. We evaluated the proposed method with a task of recognizing named entities (genes) in biology text involving three species. The experiment results show that the new domain-aware approach outperforms a state-of-the-art baseline method in adapting to new domains, especially when there is a great difference between the new domain and the training domain.
1711864
Automatically Generating Gene Summaries from Biomedical Literature
Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature. We present a two-stage summarization method, which involves first retrieving relevant articles and then extracting the most informative sentences from the retrieved articles to generate a structured gene summary. The generated summary explicitly covers multiple aspects of a gene, such as the sequence information, mutant phenotypes, and molecular interaction with other genes. We propose several heuristic approaches to improve the accuracy in both stages. The proposed methods are evaluated using 10 randomly chosen genes from FlyBase and a subset of Medline abstracts about Drosophila. The results show that the precision of the top selected sentences in the 6 aspects is typically about 50-70%, and the generated summaries are quite informative, indicating that our approaches are effective in automatically summarizing literature information about genes. The generated summaries not only are directly useful to biologists but also serve as useful entry points to enable them to quickly digest the retrieved literature articles.
2667365
Self-stabilizing Asynchronous Phase Synchronization in General Graphs
The phase synchronization problem requires each node to infinitely transfer from one phase to the next one under the restriction that at most two consecutive phases can appear among all nodes. In this paper, we propose a self-stabilizing algorithm under the parallel execution model to solve this problem for semi-uniform systems of general graph topologies. The proposed algorithm is memory-efficient; its space complexity per node is O(logΔ + logK) bits, where Δ is the maximum degree of the system and K > 1 is the number of phases.
9154823
An efficient tree search algorithm for optimal detection of MIMO signals with channel estimation errors
In this paper, we investigate the optimum detection of MIMO signals in the presence of channel estimation errors. An efficient tree search based detector is proposed for optimal MIMO signal detection, based on the modified ML criterion, which takes channel estimation errors into account. It is shown that this tree search ML detector is almost as computationally efficient as the corresponding sphere decoder, ignoring channel estimation errors. When applied to the MIMO system, concatenated with an outer channel code, the proposed ML detector, taking into account channel estimation errors, achieves significant gain over sphere detectors ignoring channel estimation errors.
2581986
Iterative Soft-Input Soft-Output Decoding of Reed&#8211;Solomon Codes by Adapting the Parity-Check Matrix
An iterative algorithm is presented for soft-input soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum-product algorithm (SPA) in conjunction with a binary parity-check matrix of the RS code. The novelty is in reducing a submatrix of the binary parity-check matrix that corresponds to less reliable bits to a sparse nature before the SPA is applied at each iteration. The proposed algorithm can be geometrically interpreted as a two-stage gradient descent with an adaptive potential function. This adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and, therefore, significantly improves the performance. Simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard-decision decoding (HDD) and compare favorably with other popular soft-decision decoding methods
119111
Analysis of Backward Congestion Notification (BCN) for Ethernet In Datacenter Applications
IEEE 802.1 standards committee is working on a new specification for congestion notification in Ethernet networks. The goal of this work is to enable application of Ethernet in backend datacenter applications. Such applications typically use fiber channel and infiniband due to their loss-free characteristics. A backward congestion notification (BCN) scheme is one of the candidate schemes proposed to avoid long delays and minimize loss in Ethernet networks. This paper presents an analysis of this scheme. We develop an analytical model to analyze the stability and the rate of convergence of the scheme. It is shown that BCN achieves proportional fairness and not max-min fairness under certain circumstances. Simulation results are presented that validate the analytical results.
6293007
A revisit to block and recursive least squares for parameter estimation
In this paper, the classical least squares (LS) and recursive least squares (RLS) for parameter estimation have been re-examined in the light of the present day computing capabilities. It has been demonstrated that for linear time-invariant systems, the performance of blockwise least squares (BLS) is always superior to that of RLS. In the context of parameter estimation for dynamic systems, the current computational capability of personal computers are more than adequate for BLS. However, for time-varying systems with abrupt parameter changes, standard blockwise LS may no longer be suitable due to its inefficiency in discarding ''old'' data. To deal with this limitation, a novel sliding window blockwise least squares approach with automatically adjustable window length triggered by a change detection scheme is proposed. Two types of sliding windows, rectangular and exponential, have been investigated. The performance of the proposed algorithm has been illustrated by comparing with the standard RLS and an exponentially weighted RLS (EWRLS) using two examples. The simulation results have conclusively shown that: (1) BLS has better performance than RLS; (2) the proposed variable-length sliding window blockwise least squares (VLSWBLS) algorithm can outperform RLS with forgetting factors; (3) the scheme has both good tracking ability for abrupt parameter changes and can ensure the high accuracy of parameter estimate at the steady-state; and (4) the computational burden of VLSWBLS is completely manageable with the current computer technology. Even though the idea presented here is straightforward, it has significant implications to virtually all areas of application where RLS schemes are used.