13348734
Growing genetic regulatory networks from seed genes
MOTIVATION
A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism.


RESULTS
Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset.


AVAILABILITY
Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm
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
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.
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.
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.
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.
60809080
Phase Transitions of the Asymmetric Traveling Salesman
We empirically study phase transitions of the asymmetric Traveling Salesman. Using random instances of up to 1,500 cities, we show that many properties of the problem, including the backbone and optimal tour cost, experience sharp transitions as the precision of intercity distances increases across a critical value. We also show that the average computational cost of the well-known branch-and-bound subtour elimination algorithm for the problem also exhibits a threshold behavior, transitioning from easy to difficult as the distance precision increases. These results provide strong positive evidences to a decade-long open question regarding the existence of phase transitions of the Traveling Salesman.
45074176
Knowledge Adoption in Online Communities of Practice
Although researchers and practitioners have recognized that online Communities of Practice (COPs) can be valuable tools for knowledge management, there is little quantitative research about this relatively new phenomenon. This dissertation helps fill the gap by investigating how individual members of text-based, asynchronous online COPs adopt knowledge contributed by others for their own problem solving. 
For this purpose, this dissertation methodologically utilizes dual-process theories of information processing. It first verifies the appropriateness of the theories by replicating previous research in the new domain. Then, the behaviors and effects of factors that are not previously researched in this way, yet especially pertain to online COPs, are investigated. It is hypothesized that genre conformity and information consistency operate as two heuristic cues in the knowledge adoption in online COPs. It is also hypothesized that focused search and the presence of disconfirming information increase elaboration likelihood during dual-process knowledge adoption. These hypotheses were tested and partially supported with survey data collected from members of two online COPs with different characteristics. 
This dissertation contributes to both IS research and the dual-process theories. Contrary to the cue-filtering paradigm of previous media research, it expands the computer-mediated communication (CMC) literature by identifying additional cues that are utilized for CMC-based knowledge adoption. It invokes the notion that the CMC context plays a dynamic role in ongoing knowledge adoption processes, both through elaboration likelihood and through interactions between the dual processes. Moreover, it advances the situated view of knowledge adoption by demonstrating that how members search for information and how current information compares to prior knowledge matter in online COPs. Focusing on the pull technology of online COPS, it offers another theoretical link between CMC technologies and knowledge management. 
Practically, this dissertation provides us with new insights into the utilization of online COPs for improving organizational knowledge management. It also suggests new directions for designing software systems to better support knowledge sharing in online COPs.
16889617
Forward Estimation for Game-Tree Search
It is known that bounds on the mmnnax values of nodes in a game tree can be used to reduce the computational complexity of minimax search for two-player games. We describe a very simple method to estimate bounds on the minimax values of interior nodes of a game tree, and use the bounds to improve minimax search. The new algorithm, called forward estimation, does not require additional domain knowledge other than a static node evaluation function, and has small constant overhead per node expansion. We also propose a variation of forward estimation, which provides a tradeoff between computational complexity and decision quality. Our experimental results show that forward estimation outperforms alpha-beta pruning on random game trees and the game of Othello.
3023214
Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering
Cluster analysis is a fundamental problem and technique in many areas related to machine learning. In this paper, we consider rearrangement clustering, which is the problem of finding sets of objects that share common or similar features by arranging the rows (objects) of a matrix (specifying object features) in such a way that adjacent objects are similar to each other (based on a similarity measure of the features) so as to maximize the overall similarity. Based on formulating this problem as the Traveling Salesman Problem (TSP), we develop a new TSP-based optimal clustering algorithm called TSPCluster. We overcome a flaw that is inherent in previous approaches by relaxing restrictions on dissimilarities between clusters. Our new algorithm has three important features: finding the optimal k clusters for a given k, automatically detecting cluster borders, and ascertaining a set of most viable clustering results that make good balances among maximizing the overall similarity within clusters and dissimilarity between clusters. We apply TSPCluster to cluster and display ~500 genes of flowering plant Arabidopsis which are regulated under various abiotic stress conditions. We compare TSPCluster to the bond energy algorithm and two existing clustering algorithms. Our TSPCluster code is available at (Climer & Zhang, 2004).
9748208
CAGER: classification analysis of gene expression regulation using multiple information sources
BackgroundMany classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene's expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features.ResultsIn this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChlP-chip data, or their combinations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Classification Analysis of Gene Expression Regulation) that integrates several software components for automated analysis of transcriptional regulation using decision trees. Finally, we use CAGER to study the transcriptional regulation of Arabidopsis genes in response to abscisic acid, and report some interesting new results.ConclusionModels built with ChlP-chip data suffer from low accuracies when the condition under which gene expressions are measured is significantly different from the condition under which the ChIP experiment is conducted. Models built with automatically identified motifs can sometimes discover new features, but their modeling accuracies may have been over-estimated in previous studies. Furthermore, models built with automatically identified motifs are not stable with respect to noises. A combination of ChlP-chip data and predefined motifs can substantially improve modeling accuracies, and is effective in identifying true regulons. The CAGER web server, which is freely available at http://cic.cs.wustl.edu/CAGER/, allows the user to select combinations of different feature types for building decision trees, and interact with the models graphically. We believe that it will be a useful tool to facilitate the discovery of gene transcriptional regulatory networks.