Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Simple Regularized Multiple Criteria Linear Programs for Binary Classification

AbstractOptimization is an important tool in computational finance and business intelligence. Multiple criteria mathematical pro- gram(MCMP), which is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously, is one of the ways of utilizing optimization techniques. Due to the existence of multiple objec- tives, MCMPs are usually difficult to be optimized. In fact, for a nontrivial MCMP, there does not exist a single solution that optimizes all the objectives at the same time. In practice, many methods convert the original MCMP into a single-objective program and solve the obtained scalarized optimization problem. If the values of scalarization parameters, which measure the trade-offs between the conflicting objectives, are not chosen carefully, the converted single-objective optimization problem may be not solvable. Therefore, to make sure MCMP always can be solved successfully, heuristic search and expert knowledge for deciding the value of scalarization parameters are always necessary, which is not an easy task and limits the applications of MCMP to some extend. In this paper, we take the multiple criteria linear program(MCLP) for binary classification as the example and discuss how to modified the formulation of MCLP directly to guarantee the solvability. In details, we propose adding a quadratic regularization term into the converted single-objective linear program. The new regularized formulation does not only overcomes some defects of the original scalarized problem in modeling, it also can be shown in theory that the finite optimal solutions always exist. To test the performance of the proposed method, we compare our algorithm with sever- al state-of-the-art algorithms for binary classification on several different kinds of datasets. Preliminary experimental results demonstrate the effectiveness of our regularization method

Free-Form Shape Optimization of Advanced High-Strength Steel Members

The high yielding strength of advanced high-strength steel (AHSS) provides great opportunities for cold-formed steel (CFS) members with much higher load-carrying capability. However, if manufactured into the traditional cross-section shapes, such as C and Z, the material advantage cannot be fully exploited due to the cross-section instabilities. The purpose of this study was to establish a shape optimization method for cold-formed sections with AHSS and explore the potentially material efficiency that AHSS could provide to these sections in terms of their axial strength. In this study, the insights provided from the elastic buckling analysis and nonlinear finite element (FE) simulations of a set of traditional CFS sections were employed to determine the appropriate section size and length for optimization. Then, the optimization method was established using the particle swarm optimization (PSO) algorithm with the integration of computational analysis through CUFSM and the design approach (i.e., the direct strength method, DSM). The objective function is the maximum axial strength of the CFS sections manufactured with AHSS using the same amount of material (i.e., the same cross-section area). Finally, the optimal sections were simulated and verified by FE analysis, and the characteristics of the optimal cross-sections were analyzed. Overall, the optimization method in this paper achieved good optimization results with greatly improved axial strength capacity from the optimal sections

Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines*

Bound-constrained Support Vector Machine(SVM) is one of the stateof- art model for binary classification. The decomposition method is currently one of the major methods for training SVMs, especially when the nonlinear kernel is used. In this paper, we proposed two new decomposition algorithms for training bound-constrained SVMs. Projected gradient algorithm and interior point method are combined together to solve the quadratic subproblem effciently. The main difference between the two algorithms is the way of choosing working set. The first one only uses first order derivative information of the model for simplicity. The second one incorporate part of second order information into the process of working set selection, besides the gradient. Both algorithms are proved to be global convergent in theory. New algorithms is compared with the famous package BSVM. Numerical experiments on several public data sets validate the effciency of the proposed methods