13903489
Whole genome phylogeny based on clustered signature string composition
Peptide compositions constructed out of whole sets of protein sequences can be used as species signatures for phylogenetic analysis. To account for point mutations, an amino acid substitution model is integrated into the complete composition vectors through a novel peptide clustering algorithm. Such a refined signature is expected to highlight deeper evolutionary relationships among the species and employed into the whole genome phylogenetic analysis to define a new evolutionary distance measure. Computational experiments have been set up to validate the effectiveness of this new measure and a vertebrate evolutionary tree using a dataset of 832 proteins for 64 vertebrates is reported.
18740255
Selected String Representation for Whole Genomes
The increase in the amount of available genomic data has made phylogenetic analysis possible at the whole genome scale. However, such a huge amount of data imposes computational challenges in both memory consumption and CPU usage. One novel proposal in this paper is to extract sequence patterns that are biologically meaningful. Using these patterns, whole genomes can be mapped into a significantly lower dimensional space and subsequent studies using these representations become computationally feasible. Experiments on two datasets of 64 vertebrate mitochondrial genomes and 99 prokaryote whole genomes demonstrate that the selected sequence patterns result in good quality evolutionary distances in terms of the final phylogeny.
7000665
Multiple Agents Moving Target Search
Traditional single-agent search algorithms usually make simplifying assumptions (single search agent, stationary target, complete knowledge of the state, and sufficient time). There are algorithms for relaxing one or two of these constraints; in this paper we want to relax all four. The application domain is to have multiple search agents cooperate to pursue and capture a moving target. Agents are allowed to communicate with each other. For solving Multiple Agents Moving Target (MAMT) applications, we present a framework for specifying a family of suitable search algorithms. This paper investigates several effective approaches for solving problem instances in this domain.
45029055
Image Segmentation with Asteroidality/Tubularity and Smoothness Constraints
Image segmentation with specific constraints has found applications in several areas such as biomedical image analysis and data mining. In this paper, we study the problem of segmenting star-shaped and smooth objects in 2-D and tubular objects in 3-D images. Image objects of these shapes are often studied in medical applications. For the 2-D case of the problem, we present an O(IJ log J) time algorithm, improving the previously best known O(IJ2M) time algorithm by a factor of time, where the size of the input 2-D image is I × J and M is the smoothness parameter with 1 ≤ M ≤ J. Our 2-D algorithm is based on a combination of dynamic programming and divide-and-conquer strategy, and on computing an optimal path in an implicitly represented graph. We also prove that a generalized version of the 3-D case of the problem is NP-hard.
17164296
Optimal context quantization in lossless compression of image data sequences
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C/sub t/=(X/sub t-t1/,X/sub t-t2/,...,X/sub t-tK/) of a source symbol X/sub t/ into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and /spl alpha/-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
20120387
Optimal Net Surface Problems with Applications
In this paper, we study an interesting geometric graph called multi-column graph in the d-D space (d ? 3), and formulate two combinatorial optimization problems called the optimal net surface problems on such graphs. Our formulations capture a number of important problems such as surface reconstruction with a given topology, medical image segmentation, and metric labeling. We prove that the optimal net surface problems on general d-D multicolumn graphs (d ? 3) are NP-hard. For two useful special cases of these d-D (d ? 3) optimal net surface problems (on the so-called proper ordered multi-column graphs) that often arise in applications, we present polynomial time algorithms. We further apply our algorithms to some surface reconstruction problems in 3-D and 4-D, and some medical image segmentation problems in 3-D and 4-D, obtaining polynomial time solutions for these problems. The previously best known algorithms for some of these applied problems, even for relatively simple cases, take at least exponential time. Our approaches for these applied problems can be extended to higher dimensions.
29396765
Detection of LSB Steganography via Sample Pair Analysis
This paper introduces a new, principled approach to detecting LSB steganography in digital signals such as images and audio. It is shown that the length of hidden message embedded in the least significant bits of signal samples can be estimated with relatively high precision. The new steganalytic approach is based on some statistical measures of sample pairs that are highly sensitive to LSB embedding operations. The resulting detection algorithm is simple and fast. To evaluate the robustness of the proposed steganalytic approach, bounds on estimation errors are developed. Furthermore, the vulnerability of the new approach to possible attacks is also assessed, and counter measures are suggested.
43142618
Efficient Algorithms for k-Terminal Cuts on Planar Graphs
The minimum k-terminal cut problem is of considerable theoretical interest and arises in several applied areas such as parallel and distributed computing, VLSI circuit design, and networking. In this paper, we present two new approximation and exact algorithms for this problem on an n-vertex undirected weighted planar graph G. For the case when the k terminals are covered by the boundaries of m > 1 faces of G, we give a min{O(n2 log n log m), O(m2n1.5 log2 n+kn)} time algorithm with a (2 - 2/k)-approximation ratio (clearly, m ≤ k). For the case when all k terminals are covered by the boundary of one face of G, we give an O(nk3 +(n log n)k2) time exact algorithm, or a linear time exact algorithm if k = 3, for computing an optimal k-terminal cut. Our algorithms are based on interesting observations and improve the previous algorithms when they are applied to planar graphs.