334 research outputs found
Human Mobility Trends during the COVID-19 Pandemic in the United States
In March of this year, COVID-19 was declared a pandemic and it continues to
threaten public health. This global health crisis imposes limitations on daily
movements, which have deteriorated every sector in our society. Understanding
public reactions to the virus and the non-pharmaceutical interventions should
be of great help to fight COVID-19 in a strategic way. We aim to provide
tangible evidence of the human mobility trends by comparing the day-by-day
variations across the U.S. Large-scale public mobility at an aggregated level
is observed by leveraging mobile device location data and the measures related
to social distancing. Our study captures spatial and temporal heterogeneity as
well as the sociodemographic variations regarding the pandemic propagation and
the non-pharmaceutical interventions. All mobility metrics adapted capture
decreased public movements after the national emergency declaration. The
population staying home has increased in all states and becomes more stable
after the stay-at-home order with a smaller range of fluctuation. There exists
overall mobility heterogeneity between the income or population density groups.
The public had been taking active responses, voluntarily staying home more, to
the in-state confirmed cases while the stay-at-home orders stabilize the
variations. The study suggests that the public mobility trends conform with the
government message urging to stay home. We anticipate our data-driven analysis
offers integrated perspectives and serves as evidence to raise public awareness
and, consequently, reinforce the importance of social distancing while
assisting policymakers.Comment: 11 pages, 9 figure
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
Class imbalance is a common challenge in real-world recognition tasks, where
the majority of classes have few samples, also known as tail classes. We
address this challenge with the perspective of generalization and empirically
find that the promising Sharpness-Aware Minimization (SAM) fails to address
generalization issues under the class-imbalanced setting. Through investigating
this specific type of task, we identify that its generalization bottleneck
primarily lies in the severe overfitting for tail classes with limited training
data. To overcome this bottleneck, we leverage class priors to restrict the
generalization scope of the class-agnostic SAM and propose a class-aware
smoothness optimization algorithm named Imbalanced-SAM (ImbSAM). With the
guidance of class priors, our ImbSAM specifically improves generalization
targeting tail classes. We also verify the efficacy of ImbSAM on two
prototypical applications of class-imbalanced recognition: long-tailed
classification and semi-supervised anomaly detection, where our ImbSAM
demonstrates remarkable performance improvements for tail classes and anomaly.
Our code implementation is available at
https://github.com/cool-xuan/Imbalanced_SAM.Comment: Accepted by International Conference on Computer Vision (ICCV) 202
BIOMECHANICS TRENDS IN GRIP AND PINCH STRENGTH IN TWO AGE GROUPS OF CHINESE
Hands are one of the most complex and useful systems of the human body with grip and pinch strength being the most important biomechanics factor to assess the hand functions. Many diseases including malfunction of nervous systems and osteoarthritis may lead to weakness or abnormality of hand grip and pinch strength. The measurement of grip and pinch strength could be used to assess the degree of injury degree, treatment effect and recovery, thus making it necessary to build a biomechanics normative database of grip and pinch strength for use by researchers (such as doctors and sport researchers) from different fields (i.e. professional injury assessment, ergonomics and product design). The database is expected to provide detailed features of grip and pinch strength of the Chinese people
Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future
Air pollution is severely focused due to its distinct effect on climate change and adverse effect on human health, ecological system, etc. Eastern China is one of the most polluted areas in the world and many actions were taken to reduce air pollution. Numerical forecast of air quality was proved to be one of the effective ways to help to deal with air pollution. This chapter will present the development, uncertainty and thinking about the future of the numerical air quality forecast emphasized in eastern China region. Brief history of numerical air quality modeling including that of Shanghai Meteorological Service (SMS) was reviewed. The operational regional atmospheric environmental modeling system for eastern China (RAEMS) and its performance on forecasting the major air pollutants over eastern China region was introduced. Uncertainty was analyzed meanwhile challenges and actions to be done in the future were suggested to provide better service of numerical air quality forecast
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation studies have shown promise in
training models with limited labeled data. However, current dominant
teacher-student based approaches can suffer from the confirmation bias. To
address this challenge, we propose AD-MT, an alternate diverse teaching
approach in a teacher-student framework. It involves a single student model and
two non-trainable teacher models that are momentum-updated periodically and
randomly in an alternate fashion. To mitigate the confirmation bias from the
diverse supervision, the core of AD-MT lies in two proposed modules: the Random
Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module
(CCM). The RPA schedules the alternating diverse updating process with
complementary data batches, distinct data augmentation, and random switching
periods to encourage diverse reasoning from different teaching perspectives.
The CCM employs an entropy-based ensembling strategy to encourage the model to
learn from both the consistent and conflicting predictions between the
teachers. Experimental results demonstrate the effectiveness and superiority of
our AD-MT on the 2D and 3D medical segmentation benchmarks across various
semi-supervised settings.Comment: code:https://github.com/ZhenZHAO/AD-M
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