Can the Black Lives Matter Movement Reduce Racial Disparities? Evidence from Medical Crowdfunding

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM activities were most impacted. We construct innovative instrumental variable approaches that utilize weekends and rainfall to identify the global and local effects of BLM protests. Results suggest a broad social movement has a greater influence on charitable-giving behavior than a local event. Social media significantly magnifies the impact of protests

Groups with at most 13 nonpower subgroups

For a group G and positive interger m, Gm denotes the subgroup generated by the elements gm where g runs through G. The subgroups not of the form Gm are called nonpower subgroups. We extend the classification of groups with few nonpower subgroups from groups with at most 9 nonpower subgroups to groups with at most 13 nonpower subgroups.Comment: 16 pages, 0 figure

A general framework for quantile estimation with incomplete data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/1/rssb12309.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/2/rssb12309-sup-0001-TableS1-S4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/3/rssb12309_am.pd

Learning Discriminative Features with Multiple Granularities for Person Re-Identification

The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved state-of-the-art performances and outperformed any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.Comment: 9 pages, 5 figures. To appear in ACM Multimedia 201