16097405
A Biodiversity Semantic Associative Annotation Tool
MorphBank, an on-line museum quality collection of biological images, is an NSF funded project designed to facilitate the on-line collaboration of biologists from around the world [3]. This paper presents a methodology for the creation of an annotation tool for on-line collaboration and sharing of heterogeneous data through a common medium. The annotation tool combines the advantages of highly organized relational database, extensible XML schemas, Life Science Identifiers, and accepted industry ontologies using DataGrid technologies to facilitate collection and sharing information on biological specimens. Schematized annotation provides biologists with a flexible framework to perform annotations using their own data models. Structured XML documents enable structure-based semantic retrieval to improve the query accuracy. Retrieval performance can also be improved by combining the relational database and
43025995
Processing Probabilistic Range Query over Imprecise Data Based on Quality of Result
Sensors are employed in many applications to monitor entities such as environment temperature or illumination. Because of limited resources (e.g., battery power and wireless bandwidth), it is impractical for the database to track continuous changing readings from sensors at all times. Consequently, database may produce incorrect query results based on recorded data. However, if the error between the recorded value and the actual value is bounded, probabilistic approach can be applied to process queries over the imprecise data in database. The paper proposes a quality of result based probabilistic range query processing method. Two measures are defined to evaluate the quality of result. A probability model is proposed to estimate the quality of returned results. Based on the model, an algorithm is developed to choose the probability threshold according to user’s requirement on the quality of result.
17633305
The Impact of Cache Organization in Optimizing Microprocessor Power Consumption
In the recent years, power consumption has become increasingly an important design concern as silicon area and performance in modern computer systems design. Several factors have contributed to this trend. Perhaps the most visible have been the remarkable success and growth of Personal Digital Assistants (PDA’s), Cellular phones and pagers, etc .This work is an attempt to explore the impact of feature size shrinkage and cache configuration on optimizing power consumption in modern processors. The work studies two commercially known RISC microprocessors. The StrongARM and the Alpha-21064 microprocessors. The latter is the ancestor of the former and is therefore used here as the baseline in our analysis. Our analysis has illustrated quantitatively great power saving when technology is downsized and cache is well organized
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
14422830
Compiler-guided register reliability improvement against soft errors
With the scaling of technology, transient errors caused by external particle strikes have become a critical challenge for microprocessor design. As embedded processors are widely used in reliability-sensitive environments, it becomes increasingly important to develop cost-effective techniques to improve the processor reliability against soft errors. This paper focuses on studying the register file immunity against soft errors since modern processors typically employ a large number of registers, which are accessed very frequently. As a result, soft errors occurred in registers can easily propagate to functional units or the memory system, leading to silent data error (SDC) or system crash.To develop cost-effective techniques to fight soft errors for embedded processors, the first step is to understand the register file susceptibility to soft errors and its impact on the system reliability accurately. Toward this goal, this paper proposes the concept of register vulnerability factor (RVF) to characterize the probability that register transient errors can escape the register file and thus potentially impact the system reliability. Built upon the RVF concept, we then propose two cost-effective compiler-guided techniques to improve the register file reliability by lowering the RVF value. Our experiments indicate that on average, the RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly while protecting register files against transient errors.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
1499660
A bi-dimensional regression tree approach to the modeling of gene expression regulation
MOTIVATION
The transcriptional regulation of a gene depends on the binding of cis-regulatory elements on its promoter to some transcription factors and the expression levels of the transcription factors. Most existing approaches to studying transcriptional regulation model these dependencies separately, i.e. either from promoters to gene expression or from the expression levels of transcription factors to the expression levels of genes. Little effort has been devoted to a single model for integrating both dependencies.


RESULTS
We propose a novel method to model gene expression using both promoter sequences and the expression levels of putative regulators. The proposed method, called bi-dimensional regression tree (BDTree), extends a multivariate regression tree approach by applying it simultaneously to both genes and conditions of an expression matrix. The method produces hypotheses about the condition-specific binding motifs and regulators for each gene. As a side-product, the method also partitions the expression matrix into small submatrices in a way similar to bi-clustering. We propose and compare several splitting functions for building the tree. When applied to two microarray datasets of the yeast Saccharomyces cerevisiae, BDTree successfully identifies most motifs and regulators that are known to regulate the biological processes underlying the datasets. Comparing with an existing algorithm, BDTree provides a higher prediction accuracy in cross-validations.
15005856
Security analysis for key generation systems using face images
In this paper, we analyze the security problem of user information associated key generation (UIAKG) systems. We consider three kinds of attacks from the hacker: the exhaustive search attack, the authentic key statistics attack and the device key statistics attack. Under each attack, we give the estimate of the number of guesses the hacker has to make in order to access the system. Such analysis provides useful guidelines in designing a UIAKG system. The analysis also suggests that a user-dependent UIAKG is more secure than a user-independent one. Two UIAKG systems using face images as inputs are designed and compared to support the above theoretical analysis.
3338701
Epsilon-Transformation: Exploiting Phase Transitions to Solve Combinatorial Optimization Problems
Abstract It has been shown that there exists a transition in the average-case complexity of tree search problems, from exponential to polynomial in the search depth. We develop a new method, called e-transformation, which makes use of this complexity transition, to find a suboptimal solution. With a random tree model, we show that, as the tree depth approaches infinity, the expected number of nodes expanded by branch-and-bound (BnB) using e-transformation is at most cubic in the search depth, and that the relative error of the solution cost found with respect to the optimal solution cost is bounded above by a small constant. We also present an iterative version of e-transformation that can be used to find both suboptimal and optimal goal nodes. Depth-first BnB (DFBnB) using iterative e-transformation significantly improves upon DFBnB on random trees with large branching factors and deep goal nodes, finding a better solution sooner. We then present experimental results for e-transformation and iterative e-transformation on the asymmetric traveling salesman problem (ATSP) and the maximum boolean satisfiability problem, and identify the conditions under which these two methods are effective. On the ATSP, DFBnB using e-transformation outperforms a well-known local search method, and DFBnB using iterative e-transformation improves upon DFBnB.
6016662
Generalized optimal thresholding for biometric key generation using face images
In this paper, we study biometric key generation using face images. Given a face image, a set of biometric features are extracted. Each feature is compared with a threshold to generate a key-bit. By cascading the key-bits from all the features, we obtain one bio-key that can be used for security applications. The performance of a biometric key generation system, determined by the chosen thresholds, can be evaluated according to reliability and security. A generalized optimal thresholding method is proposed in this paper to improve the reliability by minimizing the authentication error rate and the security by maximizing the entropy of the generated key.
3005507
Iterative state-space reduction for flexible computation
Abstract Flexible computation is a general framework for decision making under limited computational resources. It enables an agent to allocate limited computational resources to maximize its overall performance or utility. In this paper, we present a strategy for flexible computation, which we call iterative state-space reduction . The main ideas are to reduce a problem space that is difficult to search to one that is relatively easy to explore, to use the optimal solution from the reduced space as an approximate solution to the original problem, and to iteratively apply multiple reductions to progressively find better solutions. The basic operation for state-space reduction is heuristic node pruning, which excludes the nodes that do not seem to lead to high-quality solutions from further examination. Iterative state-space reduction can be combined with a state-space search, such as best-first search or depth-first branch-and-bound (DFBnB). The resulting algorithm can run as an anytime algorithm, which provides a tradeoff between solution quality and computation. Based on an analytical modal, we analyze the probability that one iteration of the iterative process finds a solution. Furthermore, by combining it within DFBnB we apply iterative state-space reduction to three combinatorial problems, the maximum Boolean satisfiability, the symmetric TSP and asymmetric TSP. Our experimental results show that iterative state-space reduction is effective and efficient, making DFBnB find better solutions with less computation.