Reducing Objectification Could Tackle Stigma in the COVID-19 Pandemic: Evidence from China

Stigmatization associated with the coronavirus disease 2019 (COVID-19) is expected to be a complex issue and to extend into the later phases of the pandemic, which impairs social cohesion and relevant individuals\u27 well-being. Identifying contributing factors and learning their roles in the stigmatization process may help tackle the problem. This study quantitatively assessed the severity of stigmatization against three different groups of people: people from major COVID-19 outbreak sites, those who had been quarantined, and healthcare workers; explored the factors associated with stigmatization within the frameworks of self-categorization theory and core social motives; and proposed solutions to resolve stigma. The cross-sectional online survey was carried out between April 21 and May 7, 2020, using a convenience sample, which yielded 1,388 valid responses. Employing data analysis methods like multivariate linear regression and moderation analysis, this study yields some main findings: (1) those from major COVID-19 outbreak sites received the highest level of stigma; (2) factors most closely associated with stigmatization, in descending order, are objectification and epidemic proximity in an autonomic aspect and fear of contracting COVID-19 in a controllable aspect; and (3) superordinate categorization is a buffering moderator in objectification-stigmatization relationship. These findings are important for further understanding COVID-19-related stigma, and they can be utilized to develop strategies to fight against relevant discrimination and bias. Specifically, reinforcing superordinate categorization by cultivating common in-group identity, such as volunteering and donating for containment of the pandemic, could reduce objectification and, thus, alleviate stigma

An experimental study on the rotational accuracy of variable preload spindle-bearing system

The rotational performance of the spindle-bearing system has critical influence upon the geometric shape and surface roughness of the machined parts. The effects of preload and preload method on the rotational performance of the spindle-bearing system is explored experimentally to reveal the role of preload and preload method in spindle rotational performances under different speeds. A test rig on which both the rigid preload and elastic preload can be realized, equipped with variable preload spindle-bearing system, is developed. Based on the mechanical model, the relationship of the axial preload and negative axial clearance of the spindle-bearing system is provided. Rotating sensitive radial error motion tests are conducted for evaluating synchronous and asynchronous radial errors of the variable preload spindle-bearing system under different rotating speeds and preload methods. The change regularity of synchronous and asynchronous radial errors with preloads under different rotating speeds are given. The results show that the preload plays an important role on the rotational performance of spindle-bearing system. The rigid preload is more efficient in achieving better rotational performance than elastic preload under the same rotating speed. Furthermore, this article significantly guides the preload designing and assembling of the new spindle-bearing system

Influence of external heat sources on volumetric thermal errors of precision machine tools

Volumetric accuracy is susceptible to thermal gradient caused by internal heat source (IHS) and external heat source (EHS). A temperature-structure multi-step calculation method is presented to investigate the influences of EHSs on volumetric thermal errors of precision machine tools. The temperature and structure of the machine tool are simulated first, and then, the volumetric thermal errors are calculated using multi-body theory method. Simulations are completed to study the effects of different EHSs on a machine tool, and series of validating experiments are carried out to verify the modeling method. The test results in specific position and working condition revealed that EHSs contribute 53, 21, and 68% of thermal deviations in X, Y, and Z directions individually. It is illustrated that the EHS is an important factor affecting the volumetric accuracy of precision machine tools. The methods provided in this paper are valuable for machine tool designers to evaluate the EHS effects on volumetric thermal errors during designing process; furthermore, some insulating measures are suggested to improve the accuracy and accuracy stability of precision machine tools by reducing the EHS influences

Improved Fine-Tuning by Better Leveraging Pre-Training Data

As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the number of training samples is increased in some vision tasks. In this work, we revisit this phenomenon from the perspective of generalization analysis by using excess risk bound which is popular in learning theory. The result reveals that the excess risk bound may have a weak dependency on the pre-trained model. The observation inspires us to leverage pre-training data for fine-tuning, since this data is also available for fine-tuning. The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning. With the theoretical motivation, we propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task. Extensive experimental results for image classification tasks on 8 benchmark data sets verify the effectiveness of the proposed data selection based fine-tuning pipeline

Compassion, Discrimination, and Prosocial Behaviors: Young Diasporic Chinese During the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has fueled anti-Asian, especially anti-Chinese sentiments worldwide, which may negatively impact diasporic Chinese youths\u27 adjustment and prosocial development. This study examined the association between compassion, discrimination and prosocial behaviors in diasporic Chinese youths during the COVID-19 pandemic. 360 participants participated and completed the multi-country, cross-sectional, web-based survey between April 22 and May 9, 2020, the escalating stage of the pandemic. This study found compassion as prosocial behaviors\u27 proximal predictor, while discrimination independently predicted participation in volunteering, and could potentially enhance the association between compassion and charitable giving. These findings suggest that prosociality among young people is sensitive to social context, and that racial discrimination should be considered in future prosocial studies involving young members of ethnic and racial minorities