The service discovery methods based on structure matching has high computational cost and can not distinguish the Web services with similar semantics but in different structure definitions, due to the flexibility of XML Schema. To solve this problem, a so-called fine-grained data semantics method is proposed, which can be obtained by a transformation algorithm that decomposes parameters at mess...

Materials with ultrafine-grained structure and unique physical and mechanical properties can be obtained by methods of severe plastic deformation, which include processes of asymmetric rolling (AR). Asymmetric rolling is a very effective way to create ultrafine-grained structures of metals and alloys. Since the asymmetric rolling is a continuous process, it has great potential for industrial pr...

Protein structure prediction from sequence remains a premiere computational problem for modern molecular biology. Just as protein structure prediction may be divided into sub-problems of main-chain and side-chain placement, so the protein structure prediction track this year has been divided into sub-tracks of protein threading (organized by Daniel Fischer and Adam Godzik) and side-chain packin...

Coarse-grained simulation is a powerful and well-established suite of computational methods for studying structure and dynamics in nanoscale biophysical systems. As our understanding of the plant photosynthetic apparatus has become increasingly nuanced, opportunities have arisen for coarse-grained simulation to complement experiment by testing hypotheses and making predictions. Here, we give an...

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein foldin...

Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in the next generation DNN accelerators such as TPU[1]. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning brings more reg...

Patterns of phylogenetic structure of assemblages are increasingly used to gain insight into the ecological and evolutionary processes involved in the assembly of co-occurring species. Metrics of phylogenetic structure can be sensitive to scaling issues and data availability. Here we empirically assess the sensitivity of four metrics of phylogenetic structure of assemblages to changes in (i) th...

Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task due to two main issues: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e. ...