8,165 research outputs found
Structure fusion based on graph convolutional networks for semi-supervised classification
Suffering from the multi-view data diversity and complexity for
semi-supervised classification, most of existing graph convolutional networks
focus on the networks architecture construction or the salient graph structure
preservation, and ignore the the complete graph structure for semi-supervised
classification contribution. To mine the more complete distribution structure
from multi-view data with the consideration of the specificity and the
commonality, we propose structure fusion based on graph convolutional networks
(SF-GCN) for improving the performance of semi-supervised classification.
SF-GCN can not only retain the special characteristic of each view data by
spectral embedding, but also capture the common style of multi-view data by
distance metric between multi-graph structures. Suppose the linear relationship
between multi-graph structures, we can construct the optimization function of
structure fusion model by balancing the specificity loss and the commonality
loss. By solving this function, we can simultaneously obtain the fusion
spectral embedding from the multi-view data and the fusion structure as
adjacent matrix to input graph convolutional networks for semi-supervised
classification. Experiments demonstrate that the performance of SF-GCN
outperforms that of the state of the arts on three challenging datasets, which
are Cora,Citeseer and Pubmed in citation networks
Practical Block-wise Neural Network Architecture Generation
Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performance networks using the Q-Learning paradigm with epsilon-greedy
exploration strategy. The optimal network block is constructed by the learning
agent which is trained sequentially to choose component layers. We stack the
block to construct the whole auto-generated network. To accelerate the
generation process, we also propose a distributed asynchronous framework and an
early stop strategy. The block-wise generation brings unique advantages: (1) it
performs competitive results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by
BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing
auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of
the search space in designing networks which only spends 3 days with 32 GPUs,
and (3) moreover, it has strong generalizability that the network built on
CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201
Mixed-permutation channel with its application to estimate quantum coherence
Quantum channel, as the information transmitter, is an indispensable tool in
quantum information theory. In this paper, we study a class of special quantum
channels named the mixed-permutation channels. The properties of these channels
are characterized. The mixedpermutation channels can be applied to give a lower
bound of quantum coherence with respect to any coherence measure. In
particular, the analytical lower bounds for l1-norm coherence and the relative
entropy of coherence are shown respectively. The extension to bipartite systems
is presented for the actions of the mixed-permutation channels.Comment: 19 page
Single-Curvature Sandwich Panels with Aluminum Foam Cores under Impulsive Loading
Single-curvature sandwich panels combine the advantages of the shell and sandwich structure and are therefore envisaged to possess good potential to resist blast and shock or impact loads. This study presents a comprehensive report on the dynamic response and shock resistance of single-curvature sandwich panels, comprising two aluminum alloy face-sheets and an aluminum foam core, subjected to air-blast loading, in terms of the experimental investigation and numerical simulation. The deformation modes, shock resistance capability, and energy absorption performance are studied, and the influences of specimen curvature, blast impulse, and geometrical configuration are discussed. Results indicate that the deformation/failure, deflection response, and energy absorption of curved sandwich panels are sensitive to the loading intensity and geometric configuration. These results are significant to guide the engineering applications of sandwich structures with metallic foam cores subjected to air-blast loading
Corporate Governance Roles of Information Quality and Corporate Takeovers
We examine the corporate governance roles of information quality and the takeover market with asymmetric information regarding the value of the target firm. Increasing information quality improves the takeover efficiency however, a highly efficient takeover market also discourages the manager from exerting effort. We find that perfect information quality is not optimal for either current shareholders’ expected payoff maximization or expected firm value maximization. Furthermore, current shareholders prefer a lower level of information quality than the level that maximizes expected firm value, because of a misalignment between current shareholders’ value and total firm value. We also analyze the impact of antitakeover laws, and find that the passage of antitakeover laws may induce current shareholders to choose a higher level of information quality and thus increase expected firm value
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