17059774
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (hidden Markov model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian mixture model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (expectation-maximum) algorithm is a general method to improve the descent algorithm for finding the maximum likelihood estimation. The EM of HMM and the EM of GMM have similar formulae. Two points are proposed in this paper. One is that the EM of GMM can be regarded as a special EM of HMM. The other is that the EM algorithm of GMM based on symbols is faster in implementation than the EM algorithm of GMM based on samples (or on observation) traditionally.
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.
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.
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.
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
60947848
Backbone Guided Local Search for Maximum Satisfiability
Maximum satisfiability (Max-SAT) is more general and more difficult to solve than satisfiability (SAT). In this paper, we first investigate the effectiveness of Walksat, one of the best local search algorithms designed for SAT, on Max-SAT. We show that Walksat is also effective on Max-SAT, while its effectiveness degrades as the problem is more constrained. We then develop a novel method that exploits the backbone information in the local minima from Walksat and applies the backbone information in different ways to improve the performance of the Walksat algorithm. We call our new algorithm backbone guided Walksat (BGWalksat). On large random SAT and Max-SAT problems as well as instances from the SATLIB, BGWalksat significantly improves Walksat's performance.
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.
15388288
Performance of Linear-Space Search Algorithms
Abstract Search algorithms that use space linear in the search depth are widely employed in practice to solve difficult problems optimally, such as planning and scheduling. In this paper, we study the average-case performance of linear-space search algorithms, including depth-first branch-and-bound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS). To facilitate our analyses, we use a random tree T(b,d) that has mean branching factor b , depth d , and node costs that are the sum of the costs of the edges from the root to the nodes. We prove that the expected number of nodes expanded by DFBnB on a random tree is no more than bd times the expected number of nodes expanded by best-first search (BFS) on the same tree, which usually requires space that is exponential in depth d . We also show that DFBnB is asymptotically optimal when BFS runs in exponential time, and ID and RBFS are asymptotically optimal when the edge costs of T ( b , d ) are integers. If bp 0 is the expected number of children of a node whose costs are the same as that of their parent, then the expected number of nodes expanded by these three linear-space algorithms is exponential when bp o d 4 ) when bp 0 = 1, and at most quadratic when bp 0 > 1. In addition, we study the heuristic branching factor of T ( b , d ) and the effective branching factor of BFS, DFBnB, ID, and RBFS on T ( b , d ). Furthermore, we use our analytic results to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.
52800785
Association-Based Multiple Imputation in Multivariate Datasets: A Summary
There are two general approaches to handling missing data. The rst one ignores missing data, by discarding the records with missing values from a dataset or taking unknown attribute values as special, null values and processing them during the process of answering queries. The second approach is called imputations, which attempts to ll in missing data with plausible values. There are two major existing methods of this approach. The rst uses statistical relationships among the variables under consideration, and builds statistical models for the variables, namely linear regression models for numerical values and statistical classi cation rules for categorical values. A possible value of an unspeci ed item is then estimated using the statistical models. The second method is multiple imputations that replaces each missing value by multiple simulated values. After the multiple imputations are obtained, each plausible value is analyzed, using EM algorithm and Gibbs sampling, to produce an inferential conclusion that include uncertainty caused by missing data. Note that the multiple imputationmethod can only apply to numerical attributes.
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).