Results 91 to 100 of about 24,126 (306)
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Feature Selection Guided by Structural Information [PDF]
, 2009 In generalized linear regression problems with an abundant number of features, lasso-type regularization which imposes an l1-constraint on the regression coefficients has become a widely established technique.
Wolfgang zu Castell +9 more
core +1 more source
Semi-supervised learning by constructing query-document heterogeneous information network
Tongxin xuebao, 2014 Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored ...
Yu-feng LIU, Ren-fa LI
doaj +2 more sources
Deep Domain Adaptation Based on Adversarial Network With Graph Regularization
IEEE Access, 2020 Although most transfer learning methods can reduce the difference of the feature distributions between the source and target domains effectively, some classes in the two domains may still be misaligned after domain adaptation, especially for the classes ...
Xu Jia, Na Ma, Fuming Sun
doaj +1 more source
Edge-regular graphs with regular cliques [PDF]
European Journal of Combinatorics, 2018 We exhibit infinitely many examples of edge-regular graphs that have regular cliques and that are not strongly regular. This answers a question of Neumaier from 1981.
Gary R. W. Greaves, Jack H. Koolen
openaire +3 more sources
CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning
Advanced Science, EarlyView.CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang +11 more
wiley +1 more source
Manifold Regularization Graph Structure Auto-Encoder to Detect Loop Closure for Visual SLAM
IEEE Access, 2019 Loop closure detection plays a vital role in the visual simultaneous localization and mapping (SLAM) systems. In order to overcome the shortcomings of the artificial design algorithm to extract insufficient features, this paper proposes a graph ...
Zhonghua Wang +3 more
doaj +1 more source
Distance regular graphs of diameter 3 and strongly regular graphs
Discrete Mathematics, 1984 In a paper by \textit{N. L. Biggs} [Ann. Discrete Math. 15, 69-80 (1982; Zbl 0506.05057)], two parameter sets for distance regular graphs that are antipodal covers of a complete graph were mentioned for which the existence of a corresponding graph was unknown.
openaire +3 more sources
Advanced Electronic Materials, EarlyView.A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos +4 more
wiley +1 more source
Entire Regularization Paths for Graph Data
, 2007 Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary ...
Koji Tsuda, Tsuda, K.
core +1 more source