"A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structu ..."
"This book provides a basic, in-depth look at techniques for the design and analysis of parallel algorithms and for programming them on commercially available parallel platforms. Principles of parallel algorithms design and different parallel programming models are both discussed, with extensive coverage of MPI, POSIX threads, and Open MP. This second edition includes two new chapters on the principles of parallel programming and progr ..."
"J. Wang, H. Lee, and S. Ahmad. Prediction and evolutionary information analysis
of protein solvent accessibility using multiple linear regression. Proteins, 61:481–
491, 2005. 208. Z. Xu, C. Zhang, S. Liu, and Y. Zhou. QBES: Predicting real
values of solvent accessibility from sequences by efficient, constrained energy
optimization. Proteins, 63:961–966, 2006. 209. H. Naderi-Manesh, M. Sadeghi, S
. Arab, and A.A.M. Movahedi. Predict ..."
"This is a OFFICE OF NAVAL RESEARCH ARLINGTON VA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A987124. The abstract provided by the Pentagon follows: Database Tomography (DT) is a textual database analysis system consist ..."
"This Multi Pack is made up of the following components; Grama/ Introduction to Parallel Computing 0201648652 Waldron/ Introduction to RISC Assembly Language Programming 0201398281"
"Take an in-depth look at techniques for the design and analysis of parallel algorithms with this new text. The broad, balanced coverage of important core topics includes sorting and graph algorithms, discrete optimization techniques, and scientific computing applications. The authors focus on parallel algorithms for realistic machine models while avoiding architectures that are unrealizable in practice. They provide numerous examples an ..."
Advanced Data Mining and Applications 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Shuigeng Zhou, Songmao Zhang, GeorgeKarypis Paperback, 816 Pages, Published 2012 by Springer ISBN-13: 978-3-642-35526-4, ISBN: 3-642-35526-9
"This book constitutes the refereed proceedings of the 8th International Conference on Advanced Data Mining and Applications, ADMA 2012, held in Nanjing, China, in December 2012. The 32 regular papers and 32 short papers presented in this volume were carefully reviewed and selected from 168 submissions. They are organized in topical sections named: social media mining; clustering; machine learning: algorithms and applications; classifica ..."
"Introducation to Parallel Computing is a complete end-to-end source of information on almost all aspects of parallel computing from introduction to architectures to programming paradigms to algorithms to programming standards. It is the only book to have complete coverage of traditional Computer Science algorithms (sorting, graph and matrix algorithms), scientific computing algorithms (FFT, sparse matrix computations, N-body methods), a ..."
Lecture Notes in Computer Science Advanced Data Mining and Applications : 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012, Proceedings 7713 by Shuigeng Zhou, Songmao Zhang, GeorgeKarypis 795 Pages, Published 2012 by Springer Science & Business Media ISBN-13: 978-3-642-35527-1, ISBN: 3-642-35527-7
"Therefore, in each learning iteration of feedback, the confidence of unlabeled
data point xu can be evaluated using a criterion as: Exu = ∑ ( (yi−M(xi))2− (yi−M(
xi))2 ) (18) xi∈XL here, M is the original semi-supervised regressor trained by the
labeled dataset (X L ,yL) and unlabeled dataset XU, while M is the one re-trained
by the new labeled dataset {(XL,yL) ∪ (xu,ˆyu} and unlabeled dataset {XU − xu}.
Here xu is an unlabeled data ..."