Results 311 to 320 of about 816,628 (364)
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Interpenetrating Nets: Ordered, Periodic Entanglement
Angewandte Chemie - International Edition, 1998Stuart R Batten
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Neutrophil extracellular traps (NETs) in autoimmune diseases: A comprehensive review
Autoimmunity Reviews, 2017Keum Hwa Lee +2 more
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IEEE Transactions on Neural Networks and Learning Systems, 2022
Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks.
Junli Wang +5 more
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Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks.
Junli Wang +5 more
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IEEE Transactions on Visualization and Computer Graphics, 2009
Cosserat nets are networks of elastic rods that are linked by elastic joints. They allow to represent a large variety of objects such as elastic rings, coarse nets, or truss structures. In this paper, we propose a novel approach to model and dynamically simulate such Cosserat nets.
Spillmann, Jonas, Teschner, Matthias
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Cosserat nets are networks of elastic rods that are linked by elastic joints. They allow to represent a large variety of objects such as elastic rings, coarse nets, or truss structures. In this paper, we propose a novel approach to model and dynamically simulate such Cosserat nets.
Spillmann, Jonas, Teschner, Matthias
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
We consider the problem of representation learning for graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation ...
Hongyang Gao, Shuiwang Ji
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We consider the problem of representation learning for graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation ...
Hongyang Gao, Shuiwang Ji
semanticscholar +1 more source
Quarterly Reviews of Biophysics, 1988
The brain is one of the most highly organized structures in the known universe. It is a biological computer which has evolved over a billion years to program, monitor and control all bodily functions. It is also the organ of knowing, feeling and thinking. To understand how the brain works is perhaps the most difficult of all scientific problems.
J D, Cowan, D H, Sharp
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The brain is one of the most highly organized structures in the known universe. It is a biological computer which has evolved over a billion years to program, monitor and control all bodily functions. It is also the organ of knowing, feeling and thinking. To understand how the brain works is perhaps the most difficult of all scientific problems.
J D, Cowan, D H, Sharp
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LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
European Conference on Computer Vision, 2018Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction ...
Dongqing Zhang +3 more
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Replaceable Nets, Net Collineations, and Net Extensions
Canadian Journal of Mathematics, 1966A net of degree k and order n is a set of n2 points and nk designated sets of points, called lines, such that(1) The lines fall into k disjoint parallel classes, i.e. each line occurs in exactly one parallel class.(2) Lines in the same parallel class have no points in common; lines in different parallel classes have exactly one point in common.(3) Each
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Seismological Research Letters, 1998
INTRODUCTION After the Kobe (Hyogoken-nanbu) earthquake of 1995, the Japanese government decided as an action plan in 1995 to increase the density of strong-motion observation stations, to upgrade the observation network, and to release future strong-motion records as soon as possible.
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INTRODUCTION After the Kobe (Hyogoken-nanbu) earthquake of 1995, the Japanese government decided as an action plan in 1995 to increase the density of strong-motion observation stations, to upgrade the observation network, and to release future strong-motion records as soon as possible.
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The Journal of Physical Chemistry A, 2009
The concept of net electrophilicity (electroaccepting power relative to electrodonating power) is introduced. It provides expected trends in most cases. A net electrophilicity scale is presented. Various reactivity descriptors for 32 molecules are calculated at the B3LYP/6-311+G(d) level of theory.
Pratim Kumar, Chattaraj +2 more
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The concept of net electrophilicity (electroaccepting power relative to electrodonating power) is introduced. It provides expected trends in most cases. A net electrophilicity scale is presented. Various reactivity descriptors for 32 molecules are calculated at the B3LYP/6-311+G(d) level of theory.
Pratim Kumar, Chattaraj +2 more
openaire +2 more sources