7723209
1-Generic splittings of computably enumerable degrees
Say a set G⊆ω is 1-generic if for any e∈ω, there is a string σ⊂G such that {e}σ(e)↓ or ∀τ⊇σ({e}τ(e)↑). It is known that 0′ can be split into two 1-generic degrees. In this paper, we generalize this and prove that any nonzero computably enumerable degree can be split into two 1-generic degrees. As a corollary, no two computably enumerable degrees bound the same class of 1-generic degrees.
1638130
Bounding computably enumerable degrees in the Ershov hierarchy
Nanyang Technol Univ, Sch Math & Phys Sci, Div Math Sci, Singapore 639798, Singapore. Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China. Natl Univ Singapore, Dept Math, Singapore 119260, Singapore.
13757062
Faster solution to the maximum quartet consistency problem with constraint programming
Evolution is an important sub-area of study in biological science, whereby the evolutionary history, or phylogeny, would shed light on the genetic linkage and the functional correlation for the species under consideration. Many kinds of species data can be deployed for the task and many phylogeny reconstruction methods have been examined in the literature. A quartet approach is to build a local phylogeny for every 4 species, which is called a quartet for these 4 species, and then to assemble a phylogeny for the whole set of species satisfying the topological constraints imposed by these quartets built. In practice, those predicted quartets might not agree each other and the optimization problem, the well-known Maximum Quartet Consistency (MQC) problem, is to construct a phylogeny to satisfy a maximum number of the predicted quartets. An equivalent representation for the MQC problem through searching for a certain ultrametric matrix via Answer Set Programming has recently been proposed. This paper follows the approach and presents a number of optimization techniques to speed up the searching process. The experimental results on both the simulated and real datasets suggest that the new representation combined with Constraint Programming presents a unique perspective to the MQC problem.
2388375
Congestion control via online sampling
We consider the congestion control problem in a communication network with multiple traffic sources, each modeled as a fully-controllable stream of fluid traffic. The controlled traffic shares a common bottleneck node with high-priority cross traffic described by a Markov-modulated fluid (MMF). Each controlled source is assumed to have a unique round-trip delay. We wish to maximize a linear combination of the throughput, delay, traffic loss rate, and a fairness metric at the bottleneck node. We introduce an online sampling-based burst-level congestion control scheme capable of performing effectively under rapidly-varying cross traffic by making explicit use of the provided MMF model of that variation. The control problem is posed as a finite-horizon Markov decision process and is solved heuristically using a technique called hindsight optimization. We provide a detailed derivation of our congestion control algorithm based on this technique. The distinguishing feature of our scheme relative to conventional congestion control schemes is that we exploit a stochastic model of the cross traffic. Our empirical study shows that our control scheme significantly outperforms the conventional proportional-derivative (PD) controller, achieving higher utilization, lower delay, and lower loss under reasonable fairness. The performance advantage of our scheme over the PD scheme grows as the rate variance of cross traffic increases, underscoring the effectiveness of our control scheme under variable cross traffic.
858614
Formulating context-dependent similarity functions
Tasks of information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a context-dependent (also application-, data-, and user-dependent) way. In this paper, we present a novel method, which learns a distance function by capturing the nonlinear relationships among contextual information provided by the application, data, or user. We show that through a process called the "kernel trick," such nonlinear relationships can be learned efficiently in a projected space. In addition to using the kernel trick, we propose two algorithms to further enhance efficiency and effectiveness of function learning. For efficiency, we propose a SMO-like solver to achieve O(N2) learning performance. For effectiveness, we propose using unsupervised learning in an innovative way to address the challenge of lack of labeled data (contextual information). Theoretically, we substantiate that our method is both sound and optimal. Empirically, we demonstrate that our method is effective and useful.
4178545
Data placement and query processing based on RPE parallelisms
The basic idea behind parallel database systems is to perform operations in parallel to reduce the response time and improve the system throughput. Data placement is a key factor on the performance of parallel database systems. This paper proposes two data partition strategies to decluster XML documents with very large size, path schema based path instance balancing (PSPIB) strategy, in which all path instances with the same path schema in a data tree are declustered evenly over all sites, and node schema based node round-robin (NSNRR) strategy, in which all node objects with the same node schema in a data tree are declustered over all sites in a round-robin way. Accordingly, two query processing algorithms are proposed based on the two partition methods, parallel path merge (PPM) algorithm and parallel pipelining path join (PPPJ) algorithm. The performance analysis and evaluation on the two data placement strategies and corresponding query processing algorithms are given in this paper.
1677219
Application of Smodels in Quartet Based Phylogeny Construction
Evolution is an important sub-area of study in biological science, where given a set of taxa, the goal is to reconstruct their evolutionary history, or phylogeny. One very recent approach is to predict a local phylogeny for every subset of 4 taxa, called a quartet topology, and then to assemble a phylogeny for the whole set of taxa satisfying these predicted quartet topologies. In general, the predicted quartet topologies might not always agree with each other, and thus the objective function becomes to satisfy a maximum number of them. This is the well known Maximum Quartet Consistency (MQC) problem. In the past, the MQC problem has been solved by dynamic programming and the so-called fixed-parameter method. Recently, we have proposed to solve the MQC in answer set programming. In this note, we summarize the theoretical results of this approach and report new experimental results for the purpose of comparison, which show that our approach in answer set programming is favored over the existing approaches based on dynamic programming and fixed-parameter method. In particular, some of the hard instances (where the error ratio is high) that were not reported to be solved in other approaches can now be solved in our approach.