Backpacker identity: Scale development and validation

Backpacker identity has attracted growing attention in backpacker tourism research. However, there still lacks a valid scale to measure backpacker identity. Guided by Social Identity Theory (SIT), this study aims to develop and validate a scale to measure backpacker identity in the Chinese context. The study used two Chinese backpacker samples in two stages (Study 1, n=190; Study 2, n=323) to establish the psychometric properties of a backpacker identity scale (BIS). Following the process of scale development, a three-dimension (i.e., self-categorization, group self-value, and group self-evaluation) backpacker identity measurement model was identified. The refined scale with 16 measurement items was finally identified with sufficient reliability and validity. Theoretical and practical implications were discussed

Cart with a Vertically Oriented Load

A cart with a vertically oriented load was constructed per design requirements, utilizing computer aided design software and simulations, as well as targeted specifications per industry consultation

Use of Public Benefits Over the First Year of Pandemic

In response to the economic crisis caused by the COVID-19 pandemic, the U.S. federal government enacted initiatives designed to help households weather the pandemic’s effects. These initiatives included expansions of existing programs, such as unemployment insurance, as well as new programs like the economic impact payments. In this brief, we investigate the extent to which households relied on an array of public benefit programs over the course of the pandemic, how they used their economic impact payments, and the extent to which the unemployment insurance expansion was effective in insulating recipients from hardship during the pandemic. We find that, in general, households were much more likely to report using their economic impact payments for essential purchases and savings than for other reported purposes. We also find while higher income households were more likely to save their economic impact payments, lower-income households were still able to save at least a portion of these funds. Evidence suggests enrollment in four different public benefits—SNAP, TANF, unemployment insurance, and social security payments—increased over the course of the pandemic. Yet, large percentages of unemployment recipients had to wait in excess of two weeks to receive their unemployment payments and relatedly, high rates of hardship among unemployment insurance recipients increased starkly over the first year of the pandemic. These results speak both to the importance of current and future policy responses to the pandemic in helping households maintain a measure of financial security, as well as to the potential gaps in this response

SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification

The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems. Since modern computing resources are unable to perform computations at this extremely large scale, current state of the art methods utilize patch-based processing to preserve the resolution of WSIs. However, these methods are often resource intensive and make significant compromises on processing time. In this paper, we demonstrate that conventional patch-based processing is redundant for certain WSI classification tasks where high resolution is only required in a minority of cases. This reflects what is observed in clinical practice; where a pathologist may screen slides using a low power objective and only switch to a high power in cases where they are uncertain about their findings. To eliminate these redundancies, we propose a method for the selective use of high resolution processing based on the confidence of predictions on downscaled WSIs --- we call this the Selective Objective Switch (SOS). Our method is validated on a novel dataset of 684 Liver-Kidney-Stomach immunofluorescence WSIs routinely used in the investigation of autoimmune liver disease. By limiting high resolution processing to cases which cannot be classified confidently at low resolution, we maintain the accuracy of patch-level analysis whilst reducing the inference time by a factor of 7.74.Comment: Accepted for publication at CVPR202