Showing papers by "Ming-Yang Kao published in 2008"

Encyclopedia of Algorithms

Abstract: This dynamic reference work provides solutions to vital algorithmic problems for scholars, researchers, practitioners, teachers and students in fields such as computer science, mathematics, statistics, biology, economics, financial software, and medical informatics. This second edition is broadly expanded, building upon the success of its former edition with more than 450 new and updated entries. These entries are designed to ensure algorithms are presented from growing areas of research such as bioinformatics, combinatorial group testing, differential privacy, enumeration algorithms, game theory, massive data algorithms, modern learning theory, social networks, and VLSI CAD algorithms. Over 630 entries are organized alphabetically by problem, with subentries allowing for distinct solutions. Each entry includes a description of the basic algorithmic problem; the input and output specifications; key results; examples of applications; citations to key literature, open problems, experimental results, links to data sets and downloadable code. All entries are peer-reviewed, written by leading experts in the fieldand each entry contains links to a summary of the authors research work. This defining reference is available in both print and onlinea dynamic living work with hyperlinks to related entries, cross references citations, and a myriad other valuable URLs. New and Updated entries include: Algorithmic Aspects of Distributed Sensor Networks, Algorithms for Modern Computers Bioinformatics Certified Reconstruction and Mesh Generation Combinatorial Group Testing Compression of Text and Data Structures Computational Counting Computational Economics Computational Geometry Differential Privacy Enumeration Algorithms Exact Exponential Algorithms Game Theory Graph Drawing Group Testing Internet Algorithms Kernels and Compressions Massive Data Algorithms Mathematical Optimization Modern Learning Theory Social Networks Stable Marriage Problems, k-SAT Algorithms Sublinear Algorithms Tile Self-Assembly VLSI CAD Algorithms

Randomized Self-assembly for Approximate Shapes

Abstract: In this paper we design tile self-assembly systems which assemble arbitrarily close approximations to target squares with arbitrarily high probability This is in contrast to previous work which has only considered deterministic assemblies of a single shape Our technique takes advantage of the ability to assign tile concentrations to each tile type of a self-assembly system Such an assignment yields a probability distribution over the set of possible assembled shapes We show that by considering the assembly of close approximations to target shapes with high probability, as opposed to exact deterministic assembly, we are able to achieve significant reductions in tile complexity In fact, we restrict ourselves to constant sized tile systems, encoding all information about the target shape into the tile concentration assignment In practice, this offers a potentially useful tradeoff, as large libraries of particles may be infeasible or require substantial effort to create, while the replication of existing particles to adjust relative concentration may be much easier To illustrate our technique we focus on the assembly of n×nsquares, a special case class of shapes whose study has proven fruitful in the development of new self-assembly systems

Reconstructing phylogenies from noisy quartets in polynomial time with a high success probability

Abstract: Background In recent years, quartet-based phylogeny reconstruction methods have received considerable attentions in the computational biology community. Traditionally, the accuracy of a phylogeny reconstruction method is measured by simulations on synthetic datasets with known "true" phylogenies, while little theoretical analysis has been done. In this paper, we present a new model-based approach to measuring the accuracy of a quartet-based phylogeny reconstruction method. Under this model, we propose three efficient algorithms to reconstruct the "true" phylogeny with a high success probability.

Efficient algorithms for model-based motif discovery from multiple sequences

Abstract: We study a natural probabilistic model for motif discovery that has been used to experimentally test the quality of motif discovery programs. In thismodel, there are k background sequences, and each character in a background sequence is a random character from an alphabet Σ. A motif G = g1g2...gm is a string of m characters. Each background sequence is implanted a randomly generated approximate copy of G. For a randomly generated approximate copy b1b2...bm of G, every character is randomly generated such that the probability for bi ≠ gi is at most α. In this paper, we give the first analytical proof that multiple background sequences do help for finding subtle and faint motifs.