The Buffered \pi-Calculus: A Model for Concurrent Languages

Message-passing based concurrent languages are widely used in developing large distributed and coordination systems. This paper presents the buffered $\pi$-calculus --- a variant of the $\pi$-calculus where channel names are classified into buffered and unbuffered: communication along buffered channels is asynchronous, and remains synchronous along unbuffered channels. We show that the buffered $\pi$-calculus can be fully simulated in the polyadic $\pi$-calculus with respect to strong bisimulation. In contrast to the $\pi$-calculus which is hard to use in practice, the new language enables easy and clear modeling of practical concurrent languages. We encode two real-world concurrent languages in the buffered $\pi$-calculus: the (core) Go language and the (Core) Erlang. Both encodings are fully abstract with respect to weak bisimulations

Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model

Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization of the correlation potential to achieve self-consistency, especially for heterogeneous systems of large size. We propose a new method, called projected density matrix embedding theory (p-DMET), which achieves self-consistency without needing to optimize a correlation potential. We demonstrate the performance of p-DMET on the two-dimensional Hubbard model.Comment: 25 pages, 8 figure

Auditor Bargaining Power and Audit Fee Lowballing

Incoming auditors usually charge less audit fees to obtain the business (audit fee lowballing). Prior research shows that industry expert auditors have better expertise and resources to perform higher quality audit than the non-expert auditors. Consistent with this literature, we predict and find empirical evidence that the magnitude of lowballing will be significantly smaller for industry expert auditors comparing with non-experts auditors. This result adds new evidence of the impact of auditors’ barging power to the audit fee lowballing literature. 

Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank

Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning approach for the "Unbiased Learning to Rank" task in WSDM Cup 2023. We find that the provided data is severely biased so neural models trained directly with the top 10 results with click information are unsatisfactory. So we extract multiple heuristic-based features for multi-fields of the results, adjust the click labels, add true negatives, and re-weight the samples during model training. Since the propensities learned by existing ULTR methods are not decreasing w.r.t. positions, we also calibrate the propensities according to the click ratios and ensemble the models trained in two different ways. Our method won the 3rd prize with a DCG@10 score of 9.80, which is 1.1% worse than the 2nd and 25.3% higher than the 4th.Comment: 5 pages, 1 figure, WSDM Cup 202

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Cognitive reappraisal and empathy chain-mediate the association between relative deprivation and prosocial behavior in adolescents

BackgroundRelative deprivation is one of the factors that influences the development of personality and behavior. However, it is still unclear whether and how relative deprivation decreases the prosocial behavior in adolescents. This study aimed to examine the association between relative deprivation and adolescent prosocial behavior and the role of emotion regulation strategies and empathy in modifying this association.MethodsThe present study included 609 secondary school students (M = 15.42 years, SD = 0.653) in Fujian Province, China. All participants completed the Relative Deprivation Questionnaire, Emotion Regulation Scale, the Basic Empathy Scale, and Prosocial Behavior Scale. The collected data were analyzed using SPSS 25.0 and Mplus 7.4.ResultsRelative deprivation was negatively correlated with cognitive reappraisal, but positively correlated with expressive suppression. Cognitive reappraisal was positively correlated with empathy and prosocial behavior, but expressive suppression was not. Empathy was positively correlated with prosocial behavior. Relative deprivation decreased prosocial behavior through (a) cognitive reappraisal, (b) empathy, and (c) chain mediation of cognitive reappraisal and empathy. No significant mediating effect of expressive suppression was found.ConclusionThe results indicate that relative deprivation decreases adolescent prosocial behavior, and that cognitive reappraisal and empathy are the potential psychological mechanisms that affect the association between relative deprivation and adolescent prosocial behavior