We bring you a report from the CSHL Genome Sequencing and Biology Meeting, which has a long and prestigious history. This year there were sessions on large-scale sequencing and analysis, polymorphisms (covering discovery and technologies and mapping and analysis), comparative genomics of mammalian and model organism genomes, functional genomics and bioinformatics.

there are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. league championship algorithm is such algorithm which may be used in the mentioned problems. in the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. the proposed algori...

runoff simulation is a vital issue in water resource planning and management. various models with different levels of accuracy and precision are developed for this purpose considering various prediction time scales. in this paper, two models of ihacres (identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data) and ann (artificial neural network) mode...

in this paper, an observer based fuzzy adaptive controller (fac) is designed fora class of large scale systems with non-canonical non-affine nonlinear subsystems. it isassumed that functions of the subsystems and the interactions among subsystems areunknown. by constructing a new class of state observer for each follower, the proposedconsensus control method solves the problem of unmeasured sta...

Given a vector space V and a non-singular quadratic form Q on V , the orthogonal group O(Q) is the subgroup of GL(V ) that preserves Q. The special orthogonal group SO(Q) is given by O(Q) ∩ SL(V ). If Q is the standard inner product on R then SO(Q) is denoted SO(n). This is a connected, compact Lie group. For n > 2, the group is semi-simple. In the case that n is odd, SO(n) corresponds to the D...

This paper considers a general class of iterative optimization algorithms, referred to as linear-optimizationbased convex programming (LCP) methods, for solving large-scale convex programming (CP) problems. The LCP methods, covering the classic conditional gradient (CG) method (a.k.a., Frank-Wolfe method) as a special case, can only solve a linear optimization subproblem at each iteration. In t...