The COVID-19 pandemic is one of the most challenging healthcare crises during
the 21st century. As the virus continues to spread on a global scale, the
majority of efforts have been on the development of vaccines and the mass
immunization of the public. While the daily case numbers were following a
decreasing trend, the emergent of new virus mutations and variants still pose a
significant threat. As economies start recovering and societies start opening
up with people going back into office buildings, schools, and malls, we still
need to have the ability to detect and minimize the spread of COVID-19.
Individuals with COVID-19 may show multiple symptoms such as cough, fever, and
shortness of breath. Many of the existing detection techniques focus on
symptoms having the same equal importance. However, it has been shown that some
symptoms are more prevalent than others. In this paper, we present a multimodal
method to predict COVID-19 by incorporating existing deep learning classifiers
using convolutional neural networks and our novel probability-based weighting
function that considers the prevalence of each symptom. The experiments were
performed on an existing dataset with respect to the three considered modes of
coughs, fever, and shortness of breath. The results show considerable
improvements in the detection of COVID-19 using our weighting function when
compared to an equal weighting function.

本文提出了一种多模式方法，通过结合现有的卷积神经网络深度学习分类器和一个新的考虑每种症状流行度的概率加权函数，以预测 COVID-19。实验结果表明，在考虑加权函数时，相比于等权重函数，我们的方法显著提高了 COVID-19 的检测效果。

基于深度学习的多模式 COVID-19 症状检测和概率加权模式

Multimodal Detection of COVID-19 Symptoms using Deep Learning &  Probability-based Weighting of Modes

We propose a novel approach for deformation-aware neural networks that learn
the weighting and synthesis of dense volumetric deformation fields. Our method
specifically targets the space-time representation of physical surfaces from
liquid simulations. Liquids exhibit highly complex, non-linear behavior under
changing simulation conditions such as different initial conditions. Our
algorithm captures these complex phenomena in two stages: a first neural
network computes a weighting function for a set of pre-computed deformations,
while a second network directly generates a deformation field for refining the
surface. Key for successful training runs in this setting is a suitable loss
function that encodes the effect of the deformations, and a robust calculation
of the corresponding gradients. To demonstrate the effectiveness of our
approach, we showcase our method with several complex examples of flowing
liquids with topology changes. Our representation makes it possible to rapidly
generate the desired implicit surfaces. We have implemented a mobile
application to demonstrate that real-time interactions with complex liquid
effects are possible with our approach.

提出了一种新颖的变形感知神经网络方法，用于学习密集体积变形场的加权合成，特别针对液体模拟的时空表征，成功地实现了实时流体效果的移动端交互。