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HyPhy

HyPhy stands for hypothesis testing using phylogenies. It is an open-source software package for the analysis of genetic sequences using techniques in phylogenetics, molecular evolution, and machine learning. It also features a complete graphical user interface (GUI) and a rich scripting language for limitless customization of analyses. Additionally, HyPhy features support for parallel computing environments (via message passing interface ) and it can be compiled as a shared library and called from other programming environments such as Python or R.

MEMS Proportional Pneumatic Valve

Abstract: 

MEMS Proportional Pneumatic Valve

This project involves designing a new type of generic pneumatic valve based on micro-electrical-mechanical-systems (MEMS) technology. The valve utilizes an array of micro-actuators positioned over a matching array of micro-orifices. Several benefits are realized by using this scheme instead of a single large actuator acting on a single large orifice. The three most notable are very low actuation power requirements, very fast response and potentially very low cost. The potential cost benefits are realized by exploiting MEMS batch fabrication technologies. MSI resources are utilized to do computational mechanics flow modeling for the valve.

A bibliography of this group’s publications is attached.

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Group name: 
chasetr
Attachment: 

This tutorial provides an introduction on how to write a parallel program using OpenMP, and will help researchers write better and more portable parallel codes for shared memory Linux nodes.

R

R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

netcdf

NetCDF (network Common Data Form) is an interface for array-oriented data access and a library that provides an implementation of the interface. The netCDF library also defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data.

mpiBLAST

mpiBLAST is a freely available, open-source, parallel implementation of NCBI BLAST . mpiBLAST takes advantage of distributed computational resources, i.e., a cluster, through explicit MPI communication and thereby utilizes all available resources unlike standard NCBI BLAST which can only take advantage of shared-memory multi-processor computers. The primary advantage to using mpiBLAST versus traditional NCBI BLAST is performance. mpiBLAST can increase performance by several orders of magnitude while still retaining identical results as output from NCBI BLAST.

Gaussian Graph Estimation and Recommendation System

Abstract: 

Gaussian Graph Estimation and Recommendation System

Multiple graphical models have been widely used to describe structural changes of a network responding to certain experimental conditions, which are expressed in terms of conditional dependence between interacting units. For instance, time-varying functional connectivity in brain image analysis is described by node connectivity over a dynamic network, with each node corresponding to one region of interest. Motivated from network analysis under different experimental conditions, such as gene networks for disparate cancer subtypes, these researchers model structural changes over multiple networks with possible heterogeneities. In particular, they estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest are homogeneous groups of similar entries across and zero-entries of these matrices, referred to as clustering and sparseness structures, respectively. A non-convex method is proposed to seek a sparse representation for each matrix and identify clusters of the entries across the matrices. An efficient method is developed on the basis of difference convex programming, the augmented Lagrangian method, and the blockwise coordinate descent method, which is scalable to hundreds of graphs of thousands nodes through a simple necessary and sufficient partition rule, which divides nodes into smaller disjoint subproblems excluding zero-coefficients nodes for arbitrary graphs with convex relaxation. Theoretically, a finite-sample error bound is derived for the proposed method to reconstruct the clustering and sparseness structures. This leads to consistent reconstruction of these two structures simultaneously, permitting the number of unknown parameters to be exponential in the sample size, and yielding the optimal performance of the oracle estimator as if the true structures were given a priori. Simulation studies suggest that the method enjoys the benefit of pursuing these two disparate kinds of structures, and compares favorably against its convex counterpart in the accuracy of structure pursuit and parameter estimation.

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Group name: 
shenx

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