32,934 research outputs found
DIANet: Dense-and-Implicit Attention Network
Attention networks have successfully boosted the performance in various
vision problems. Previous works lay emphasis on designing a new attention
module and individually plug them into the networks. Our paper proposes a
novel-and-simple framework that shares an attention module throughout different
network layers to encourage the integration of layer-wise information and this
parameter-sharing module is referred as Dense-and-Implicit-Attention (DIA)
unit. Many choices of modules can be used in the DIA unit. Since Long Short
Term Memory (LSTM) has a capacity of capturing long-distance dependency, we
focus on the case when the DIA unit is the modified LSTM (refer as DIA-LSTM).
Experiments on benchmark datasets show that the DIA-LSTM unit is capable of
emphasizing layer-wise feature interrelation and leads to significant
improvement of image classification accuracy. We further empirically show that
the DIA-LSTM has a strong regularization ability on stabilizing the training of
deep networks by the experiments with the removal of skip connections or Batch
Normalization in the whole residual network. The code is released at
https://github.com/gbup-group/DIANet
New Real-Variable Characterizations of Musielak-Orlicz Hardy Spaces
Let be such that
is an Orlicz function and is a
Muckenhoupt weight. The Musielak-Orlicz Hardy space
is defined to be the space of all such that the grand maximal function belongs to the
Musielak-Orlicz space . Luong Dang Ky established its
atomic characterization. In this paper, the authors establish some new
real-variable characterizations of in terms of the
vertical or the non-tangential maximal functions, or the Littlewood-Paley
-function or -function, via first establishing a
Musielak-Orlicz Fefferman-Stein vector-valued inequality. Moreover, the range
of in the -function characterization of
coincides with the known best results, when
is the classical Hardy space , with
, or its weighted variant.Comment: J. Math. Anal. Appl. (to appear
Range-based attacks on links in random scale-free networks
and play keys on the problem of attacking on links in random
scale-free (RSF) networks. In this Brief Report we obtain the relation between
and in RSF networks analytically by the generating function
theory, and then give an estimation about the impact of attacks on the
of the network. The analytical results show that short range
attacks are more destructive for RSF networks, and are confirmed numerically.
Further our results are consistent with the former literature (Physical Review
E \textbf{66}, 065103(R) (2002))
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