The coronavirus disease (COVID-19) is rapidly spreading all over the world,
and has infected more than 1,436,000 people in more than 200 countries and
territories as of April 9, 2020. Detecting COVID-19 at early stage is essential
to deliver proper healthcare to the patients and also to protect the uninfected
population. To this end, we develop a dual-sampling attention network to
automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in
chest computed tomography (CT). In particular, we propose a novel online
attention module with a 3D convolutional network (CNN) to focus on the
infection regions in lungs when making decisions of diagnoses. Note that there
exists imbalanced distribution of the sizes of the infection regions between
COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom
onset. Therefore, we develop a dual-sampling strategy to mitigate the
imbalanced learning. Our method is evaluated (to our best knowledge) upon the
largest multi-center CT data for COVID-19 from 8 hospitals. In the
training-validation stage, we collect 2186 CT scans from 1588 patients for a
5-fold cross-validation. In the testing stage, we employ another independent
large-scale testing dataset including 2796 CT scans from 2057 patients. Results
show that our algorithm can identify the COVID-19 images with the area under
the receiver operating characteristic curve (AUC) value of 0.944, accuracy of
87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With
this performance, the proposed algorithm could potentially aid radiologists
with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19
outbreak.

该研究提出了一种基于双采样关注网络用于肺部 CT 扫描的 COVID-19 诊断自动化算法，使用了一种新颖的在线注意力模块来诊断 COVID-19 ，并提出了双重采样策略来缓解诊断上的数据不平衡性问题。此算法在 COVID-19 早期诊断方面的性能表现非常棒。

双采样关注网络用于诊断社区获得性肺炎相关 COVID-19

Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community  Acquired Pneumonia

Geoscience data often have to rely on strong priors in the face of
uncertainty. Additionally, we often try to detect or model anomalous sparse
data that can appear as an outlier in machine learning models. These are
classic examples of imbalanced learning. Approaching these problems can benefit
from including prior information from physics models or transforming data to a
beneficial domain. We show an example of including physical information in the
architecture of a neural network as prior information. We go on to present
noise injection at training time to successfully transfer the network from
synthetic data to field data.

介绍了在神经网络体系结构中包含物理信息作为先验信息，并在训练时进行噪声注入以成功地将网络从合成数据转移到现场数据的示例。

将物理学应用到深度学习 —— 以 4D 地震压力饱和度反演为例

Including Physics in Deep Learning -- An example from 4D seismic  pressure saturation inversion

Cyber security has grown up to be a hot issue in recent years. How to
identify potential malware becomes a challenging task. To tackle this
challenge, we adopt deep learning approaches and perform flow detection on real
data. However, real data often encounters an issue of imbalanced data
distribution which will lead to a gradient dilution issue. When training a
neural network, this problem will not only result in a bias toward the majority
class but show the inability to learn from the minority classes. In this paper,
we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN)
which classifies the data in a layer-wise manner. To better learn from the
minority classes, we propose a Quantity Dependent Backpropagation (QDBP)
algorithm which incorporates the knowledge of the disparity between classes. We
evaluate our method on an imbalanced data set. Experimental result demonstrates
that our approach outperforms the state-of-the-art methods and justifies that
the proposed method is able to overcome the difficulty of imbalanced learning.
We also conduct a partial flow experiment which shows the feasibility of
real-time detection and a zero-shot learning experiment which justifies the
generalization capability of deep learning in cyber security.

本文提出了一种基于层次分类和数量相关的反向传播算法的树形深度神经网络（TSDNN），以解决不平衡数据学习的问题，并在实验中表明其优于现有方法，可用于实时检测和深度学习在网络安全中的普适性能力。