The Impact of Employment Discrimination on Job Search Performance

This study is to explore the impact of perceived employment discrimination on job search performance. Based on the trait activation theory, this article proposes that people’s perceived employment discrimination when searching jobs online has a direct effect on job search performance, moderated by self-efficacy of job applicants. A total of 97 valid questionnaires were collected in this study. Through data analysis, we have concluded that the perceived impact of employment discrimination on job search performance is significantly negatively correlated, and job applicants’ self-efficacy is not significant. Such results have implications for the human resource managers and job applicants to adopt positive attitudes to deal with the possible facing discrimination generated during searching a job in the internet era

Growing season net ecosystem CO2 exchange of two desert ecosystems with alkaline soils in Kazakhstan

Central Asia is covered by vast desert ecosystems, and the majority of these ecosystems have alkaline soils. Their contribution to global net ecosystem CO(2) exchange (NEE) is of significance simply because of their immense spatial extent. Some of the latest research reported considerable abiotic CO(2) absorption by alkaline soil, but the rate of CO(2) absorption has been questioned by peer communities. To investigate the issue of carbon cycle in Central Asian desert ecosystems with alkaline soils, we have measured the NEE using eddy covariance (EC) method at two alkaline sites during growing season in Kazakhstan. The diurnal course of mean monthly NEE followed a clear sinusoidal pattern during growing season at both sites. Both sites showed significant net carbon uptake during daytime on sunny days with high photosynthetically active radiation (PAR) but net carbon loss at nighttime and on cloudy and rainy days. NEE has strong dependency on PAR and the response of NEE to precipitation resulted in an initial and significant carbon release to the atmosphere, similar to other ecosystems. These findings indicate that biotic processes dominated the carbon processes, and the contribution of abiotic carbon process to net ecosystem CO(2) exchange may be trivial in alkaline soil desert ecosystems over Central Asia

Attitudes and knowledge about naloxone and overdose prevention among detained drug users in Ningbo, China

Abstract Background To date there has been limited research on both the prevalence of overdose and drug user knowledge about overdose prevention and response methods in China. In addition, there has been no effort to integrate naloxone information and distribution into pre-release services for drug users detained in isolated compulsory detoxification facilities in China. Methods The authors conducted a survey of 279 heroin users in isolated compulsory detoxification centers in Ningbo, China in an attempt to evaluate the possibility of conducting prelease peer naloxone programs in Ningbo isolated compulsory detoxification centers. Respondents' demographic background, history of heroin overdoses, and attitudes/knowledge about overdose prevention and response were collected. Results While drug users in Ningbo's compulsory detoxification centers have limited understandings of how to effectively respond to overdoses, they expressed concern about the possibility of overdose, interest in participating in overdose prevention and response programs, and a willingness to help their peers. In general, there was no significant difference in history and attitudes/knowledge of overdose between male and female participants. Conclusion Based on the findings of this research, our survey provides preliminary evidence that detained drug users have considerable interest in overdose prevention and response information and willingness to help peers. However, drug users in Ningbo isolated compulsory detoxification centers currently have limited understandings of effective ways of helping to prevent overdose deaths

Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion

Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with potentially copyrighted or dangerous content based on corresponding textual concepts information. This includes specific intellectual property (IP), human faces, and various artistic styles. However, Negative Prompt, a widely used method for content removal, frequently fails to conceal this content due to inherent limitations in its inference logic. In this work, we propose a novel strategy named \textbf{Degeneration-Tuning (DT)} to shield contents of unwanted concepts from SD weights. By utilizing Scrambled Grid to reconstruct the correlation between undesired concepts and their corresponding image domain, we guide SD to generate meaningless content when such textual concepts are provided as input. As this adaptation occurs at the level of the model's weights, the SD, after DT, can be grafted onto other conditional diffusion frameworks like ControlNet to shield unwanted concepts. In addition to qualitatively showcasing the effectiveness of our DT method in protecting various types of concepts, a quantitative comparison of the SD before and after DT indicates that the DT method does not significantly impact the generative quality of other contents. The FID and IS scores of the model on COCO-30K exhibit only minor changes after DT, shifting from 12.61 and 39.20 to 13.04 and 38.25, respectively, which clearly outperforms the previous methods

Decoupled measurement and modeling of interface reaction kinetics of ion-intercalation battery electrodes

Ultrahigh rate performance of active particles used in lithium-ion battery electrodes has been revealed by single-particle measurements, which indicates a huge potential for developing high-power batteries. However, the charging/discharging behaviors of single particles at ultrahigh C-rates can no longer be described by the traditional electrochemical kinetics in such ion-intercalation active materials. In the meantime, regular kinetic measuring methods meet a challenge due to the coupling of interface reaction and solid-state diffusion processes of active particles. Here, we decouple the reaction and diffusion kinetics via time-resolved potential measurements with an interval of 1 ms, revealing that the classical Butler-Volmer equation deviates from the actual relation between current density, overpotential, and Li+ concentration. An interface ion-intercalation model is developed which considers the excess driving force of Li+ (de)intercalation in the charge transfer reaction for ion-intercalation materials. Simulations demonstrate that the proposed model enables accurate prediction of charging/discharging at both single-particle and electrode scales for various active materials. The kinetic limitation processes from single particles to composite electrodes are systematically revealed, promoting rational designs of high-power batteries

Backward Reasoning in Large Language Models for Verification

Chain-of-Though (CoT) prompting has shown promising performance in various reasoning tasks. Recently, Self-Consistency \citep{wang2023selfconsistency} proposes to sample a diverse set of reasoning chains which may lead to different answers while the answer that receives the most votes is selected. In this paper, we propose a novel method to use backward reasoning in verifying candidate answers. We mask a token in the question by ${\bf x}$ and ask the LLM to predict the masked token when a candidate answer is provided by \textit{a simple template}, i.e., ``\textit{\textbf{If we know the answer of the above question is \{a candidate answer\}, what is the value of unknown variable ${\bf x}$?}}'' Intuitively, the LLM is expected to predict the masked token successfully if the provided candidate answer is correct. We further propose FOBAR to combine forward and backward reasoning for estimating the probability of candidate answers. We conduct extensive experiments on six data sets and three LLMs. Experimental results demonstrate that FOBAR achieves state-of-the-art performance on various reasoning benchmarks.Comment: Preprin

Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models

Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models. Though effective, it is particularly problematic in the few-shot scenario, where prompt tuning performance is sensitive to the initialization and requires a time-consuming process to find a good initialization, thus restricting the fast adaptation ability of the pre-training models. In addition, prompt tuning could undermine the generalizability of the pre-training models, because the learnable prompt tokens are easy to overfit to the limited training samples. To address these issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data. Rather than designing a specific prompt tuning method, our GRAM can be easily incorporated into various prompt tuning methods in a model-agnostic way, and comprehensive experiments show that GRAM brings about consistent improvement for them in several settings (i.e., few-shot learning, cross-domain generalization, cross-dataset generalization, etc.) over 11 datasets. Further, experiments show that GRAM enables the orthogonal methods of textual and visual prompt tuning to work in a mutually-enhanced way, offering better generalizability beyond the uni-modal prompt tuning methods.Comment: Accepted by ICCV 202