Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.Comment: 12 pages, 7 figure

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras. To our knowledge, this is the first dataset for cross-platform intelligent surveillance applications, where the UAVs could work as a powerful complement for the ground surveillance cameras. To more realistically simulate the actual cross-platform Ground-to-Aerial surveillance scenarios, the surveillance cameras are fixed about 2 meters above the ground, while the UAVs capture videos of persons at different location, with a variety of view-angles, flight attitudes and flight modes. Therefore, the dataset has the following unique characteristics: 1) drastic view-angle changes between query and gallery person images from cross-platform cameras; 2) diverse resolutions, poses and views of the person images under 9 rich real-world scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed analysis about current two-step and end-to-end person search methods, and further propose a simple yet effective knowledge distillation scheme on the head of the ReID network, which achieves state-of-the-art performances on both of the G2APS and the previous two public person search datasets, i.e., PRW and CUHK-SYSU. The dataset and source code available on \url{https://github.com/yqc123456/HKD_for_person_search}.Comment: Accepted by ACM MM 202

Activation of Serotonin 2C Receptors in Dopamine Neurons Inhibits Binge-like Eating in Mice

Acknowledgments and Disclosures This work was supported by the National Institutes of Health (Grant Nos. R01DK093587 and R01DK101379 [to YX], R01DK092605 to [QT], R01DK078056 [to MM]), the Klarman Family Foundation (to YX), the Naman Family Fund for Basic Research (to YX), Curtis Hankamer Basic Research Fund (to YX), American Diabetes Association (Grant Nos. 7-13-JF-61 [to QW] and 1-15-BS-184 [to QT]), American Heart Association postdoctoral fellowship (to PX), Wellcome Trust (Grant No. WT098012 [to LKH]), and Biotechnology and Biological Sciences Research Council (Grant No. BB/K001418/1 [to LKH]). The anxiety tests (e.g., open-field test, light-dark test, elevated plus maze test) were performed in the Mouse Neurobehavior Core, Baylor College of Medicine, which was supported by National Institutes of Health Grant No. P30HD024064. PX and YH were involved in experimental design and most of the procedures, data acquisition and analyses, and writing the manuscript. XC assisted in the electrophysiological recordings; LV-T assisted in the histology study; XY, KS, CW, YY, AH, LZ, and GS assisted in surgical procedures and production of study mice. MGM, QW, QT, and LKH were involved in study design and writing the manuscript. YX is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no biomedical financial interests or potential conflicts of interest.Peer reviewedPublisher PD