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This paper investigates the finite time ruin probability in non-homogeneous Poisson risk model, conditional Poisson risk models and renewal risk model with stochastic returns. Under the assumption that the claimsize is subexponentially distributed, a simple asymptotic relation is established when the initial capital tends to infinity. The results obtained extend the corresponding results of constant interest force. Key Words: [ABSTRACT FROM AUTHOR]

In this paper, under the assumption that the claimsize is Negatively dependent subexponentially distributed and the constant interest force is considered, a simple asymptotics of ruin probability for renewal risk model within finite horizon is obtained. The results obtained extended the corresponding results of related papers. [ABSTRACT FROM AUTHOR]

In this paper, we establish a simple asymptotic formula for the finite-time ruin probability of the renewal model with risky investment in the case that the claimsize is subexponentially distributed and the initial capital is large. The result is consistent with known results for the ultimate and finite-time ruin probability and, particularly, is inconsistent with the corresponding Poisson risk model when the time-arrivals are exponentially distributed. [ABSTRACT FROM AUTHOR]

The study included 26 patients with glioma involving motor cortical areas to assess the value of preoperative blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) in 3T high magnetic field for brain cortical mapping operation. With the guidance of preoperative mapping, all patients received microsurgery under awake anaesthesia to identify motor area by direct electrical stimulation. The tumor was resected at maximum with preservation of eloquent areas. Preoperative mapping was succeeded in twenty-three patients; Primary motor cortex, premoter area, supplementary motor area and cerebellar motor related area were localized. There was good correlation between preoperative fMRI and intraoperative cortical stimulation results. BOLD-fMRI in 3T high magnetic field could non-invasively localize the brain motor cortex and show its relationship to lesion before operation, which may help neurosurgeons to optimize the surgical planning, guide the intraoperative motor areas mapping and resect the tumor with brain eloquent areas preservation. [ABSTRACT FROM AUTHOR]

The diverse and distributed nature of the information published on the World Wide Web has made it difficult to collate and track information related to specific topics. Whereas most existing work on web information fusion has focused on multiple document summarization, this paper presents a novel approach for discovering associations between images and text segments, which subsequently can be used to support cross-media web content summarization. Specifically, we employ a similarity-based multilingual retrieval model and adopt a vague transformation technique for measuring the information similarity between visual features and textual features. The experimental results on a terrorist domain document set suggest that combining visual and textual features provides a promising approach to image and text fusion. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel frequent generalized pattern mining algorithm, called GP-Close, for mining generalized associations from RDF metadata. To solve the over-generalization problem encountered by existing methods, GP-Close employs the notion of generalization closure for systematic over-generalization reduction. Empirical experiments conducted on real world RDF data sets show that our method can substantially reduce pattern redundancy and perform much better than the original generalized association rule mining algorithm Cumulate in term of time efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a new scheme, called Adaptive Cross Layer Unequal Protection (ACLUEP), has been proposed for video transmission over wireless channels. The proposed scheme performs joint optimization across application layer, link layer and physical layer to provide unequal protection ability for video bit-streams with different priority levels. Analysis and simulation results show an extraordinary improvement in Peak Signal-to-Noise Ratio (PSNR) of the proposed method over a variety of channel conditions. [ABSTRACT FROM AUTHOR]

Approximate string matching is a fundamental and challenging problem in computer science, for which a fast algorithm is highly demanded in many applications including text processing and DNA sequence analysis. In this paper, we present a fast algorithm for approximate string matching, called FAAST. It aims at solving a popular variant of the approximate string matching problem, the k-mismatch problem, whose objective is to find all occurrences of a short pattern in a long text string with at most k mismatches. FAAST generalizes the well-known Tarhio-Ukkonen algorithm by requiring two or more matches when calculating shift distances, which makes the approximate string matching process significantly faster than the Tarhio-Ukkonen algorithm. Theoretically, we prove that FAAST on average skips more characters than the Tarhio-Ukkonen algorithm in a single shift, and makes fewer character comparisons in an entire matching process. Experiments on both simulated data sets and real gene sequences also demonstrate that FAAST runs several times faster than the Tarhio-Ukkonen algorithm in all the cases that we tested. [ABSTRACT FROM AUTHOR]

Secure routing in ad hoc networks has been extensively studied in recent years. The vast majority of this work, however, has only focused on providing authenticity of the route. Availability of the network in a malicious environment has largely been ignored. In this paper, we divide the secure routing problem into two layers. The first layer provides authenticated routing and the second layer provides a route selection algorithm that selects a route with the highest probability of successful delivery rather than the shortest route. We provide a metric for evaluating this probability. We provide simulation results that demonstrate that our approach increases the throughput by at least ten percent in a network where fifty percent of the nodes are malicious when compared to an approach that selects the shortest route. Furthermore, our approach incurs only a small delay when compared to the delay along the shortest route. [ABSTRACT FROM AUTHOR]

With the appearance of a number of e-mail worms in recent years, we urgently need a solution to detect unknown e-mail worms rather than using the traditional solution: signature-based scanning which does not deal with the new e-mail worms well. Our collected data shows that the quantitative trend of e-mail worms is really exploding. In this paper, we propose an e-mail worm Detection System that is based on analysis on human and worm behavior for detecting unknown e-mail worms. Message data such as e-mail or short messages are the result of human behavior. The proposed system detects unknown worms by assessment of behavior in communication because human behavior and worm behavior have different projection on data. [ABSTRACT FROM AUTHOR]

We study the complexity and approximation of the problem of reconstructing haplotypes from genotypes on pedigrees under the Mendelian Law of Inheritance and the minimum recombinant principle (MRHC). First, we show that MRHC for simple pedigrees where each member has at most one mate and at most one child (i.e. binary-tree pedigrees) is NP-hard. Second, we present some approximation results for the MRHC problem, which are the first approximation results in the literature to the best of our knowledge. We prove that MRHC on two-locus pedigrees or binary-tree pedigrees with missing data cannot be approximated (the formal definition is given in section 1.2) unless P=NP. Next we show that MRHC on two-locus pedigrees without missing data cannot be approximated within any constant ratio under the Unique Games Conjecture and can be approximated within ratio O( ). Our L-reduction for the approximation hardness gives a simple alternative proof that MRHC on two-locus pedigrees is NP-hard, which is much easier to understand than the original proof. We also show that MRHC for tree pedigrees without missing data cannot be approximated within any constant ratio under the Unique Games Conjecture, too. Finally, we explore the hardness and approximation of MRHC on pedigrees where each member has a bounded number of children and mates mirroring real pedigrees. Keywords: Haplotyping, pedigree, recombinant, SNP, complexity, approximation, L-reduction, positive result, negative result, bounded number, children, mates. [ABSTRACT FROM AUTHOR]

The Correlation Clustering problem has been introduced recently [5] as a model for clustering data when a binary relationship between data points is known. More precisely, for each pair of points we have two scores measuring respectively the similarity and dissimilarity of the two points, and we would like to compute an optimal partition where the value of a partition is obtained by summing up scores of pairs involving points from a same cluster and scores of pairs involving points from different clusters. A closely related problem is Consensus Clustering, where we are given a set of partitions and we would like to obtain a partition that best summarizes the input partitions. The latter problem is a restricted case of Correlation Clustering. In this paper we prove that Min Consensus Clustering is APX-hard even for three input partitions, answering an open question, while Max Consensus Clustering admits a PTAS on instances with a bounded number of input partitions. We exhibit a combinatorial and practical -approximation algorithm based on a greedy technique for Max Consensus Clustering on three partitions. Moreover, we prove that a PTAS exists for Max Correlation Clustering when the maximum ratio between two scores is at most a constant. [ABSTRACT FROM AUTHOR]

EST databases have grown exponentially in recent years and now represent the largest collection of genetic sequences. An important application of these databases is that they contain information useful for the design of gene-specific oligonucleotides (or simply, oligos) that can be used in PCR primer design, microarray experiments, and genomic library screening. In this paper, we study two complementary problems concerning the selection of short oligos, e.g., 20-50 bases, from a large database of tens of thousands of EST sequences: (i) selection of oligos each of which appears (exactly) in one EST sequence but does not appear (exactly or approximately) in any other EST sequence and (ii) selection of oligos that appear (exactly or approximately) in many ESTs. The first problem is called the unique oligo problem and has applications in PCR primer and microarray probe designs. The second is called the popular oligo problem and is useful in screening genomic libraries (such as BAC libraries) for gene-rich regions. We present an efficient algorithm to identify all unique oligos in the ESTs and an efficient heuristic algorithm to enumerate the most popular oligos. By taking into account the distribution of the frequencies of the words in the EST database, the algorithms have been carefully engineered to achieve remarkable running times on regular PCs. Each of the algorithms takes only a couple of hours (on a 1.2 GHz CPU, 1 GB RAM machine) to run on a dataset 28 Mbases of barley ESTs from the HarvEST database. We present simulation results on synthetic data and a preliminary analysis of the barley EST database. [ABSTRACT FROM AUTHOR]

We study two fundamental problems concerning the search for interesting regions in sequences: (i) given a sequence of real numbers of length n and an upper bound U, find a consecutive subsequence of length at most U with the maximum sum and (ii) given a sequence of real numbers of length n and a lower bound L, find a consecutive subsequence of length at least L with the maximum average. We present an O(n)-time algorithm for the first problem and an O(n log L)-time algorithm for the second. The algorithms have potential applications in several areas of biomolecular sequence analysis including locating GC-rich regions in a genomic DNA sequence, post-processing sequence alignments, annotating multiple sequence alignments, and computing length-constrained ungapped local alignment. Our preliminary tests on both simulated and real data demonstrate that the algorithms are very efficient and able to locate useful (such as GC-rich) regions. [ABSTRACT FROM AUTHOR]

The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement and gene duplication. It is then formulated as a problem of computing the signed reversal distance with duplicates between two genomes of interest, for which an efficient heuristic algorithm was constructed based on solutions to two new optimization problems, minimum common partition and maximum cycle decomposition. Following this approach, we have implemented a high-throughput system for assigning orthologs on a genome scale, called MSOAR, and tested it on both simulated data and real genome sequence data. Our predicted orthologs between the human and mouse genomes are strongly supported by ortholog and protein function information in authoritative databases, and predictions made by other key ortholog assignment methods such as Ensembl, Homologene, INPARANOID, and HGNC. The simulation results demonstrate that MSOAR in general performs better than the iterated exemplar algorithm of D. Sankoff's in terms of identifying true exemplar genes. This is joint work with X. Chen (Nanyang Tech. Univ., Singapore), Z. Fu (UCR), J. Zheng (NCBI), V. Vacic (UCR), P. Nan (SCBIT), Y. Zhong (SCBIT), and S. Lonardi (UCR). [ABSTRACT FROM AUTHOR]