Interference between smooth pursuit and color working memory

Spatial working memory (WM) and spatial attention are closely related, but the relationship between non-spatial WM and spatial attention still remains unclear. The present study aimed to investigate the interaction between color WM and smooth pursuit eye movements. A modified delayed-match-to-sample paradigm (DMS) was applied with 2 or 4 items presented in each visual field. Subjects memorized the colors of items in the cued visual field and smoothly moved eyes towards or away from memorized items during retention interval despite that the colored items were no longer visible. The WM performance decreased with higher load in general. More importantly, the WM performance was better when subjects pursued towards rather than away from the cued visual field. Meanwhile, the pursuit gain decreased with higher load and demonstrated a higher result when pursuing away from the cued visual field. These results indicated that spatial attention, guiding attention to the memorized items, benefits color WM. Therefore, we propose that a competition for attention resources exists between color WM and smooth pursuit eye movements

The impacts of functional leadership, authentic leadership on employee and team performance

With the rapid development of the economy, enterprise managers need to cultivate a new style of leadership to adapt to the current organizational environment. Especially with the new generation of employees entering the workplace constantly, managers need to coordi nate the relationships between inside and outside the organization. Our study is aimed at exploring the impact of functional leadership on team performance as well as authentic leadership on individual performance, the mediating effect of team psychologica l capital between functional leadership and team performance and the moderating effect of team cohesion therein, the mediating effect of individual psychological capital between authentic leadership and individual performance, and the moderating effect of organizational identification therein. Drawing on social cognitive theory and leadership contingency theory, we built up theoretical models at the team level and the individual level. We tested our hypotheses with 478 employee samples and 138 leader samples using SPSS 22.0 and Mplus 8.0 softwa re. Our study showed that: (1) Functional leadership has a positive effect on team performance; (2) Team psychological capital mediates the relationship between functional leadership and team performance; (3) Team cohesion positively moderates the relation ship between functional leadership and team psychological capital, and team psychological capital has a mediating effect therein; (4) Authentic leadership has a positive effect on individual performance; (5) Individual psychological capital mediates the re lationship between authentic leadership and individual performance. Our study enriches the relevant research of functional leadership and authentic leadership and provides management enlightenment to promote the practice of modern organization management.Com o rápido desenvolvimento da economia, os gestores de empresas precisam de cultivar um novo estilo de liderança para se adaptarem ao ambiente organizacional atual. Especialmente com a nova geração de funcionários entrando no local de trabalho, os gestores precisam de coordenar as relações entre o interior e o exterior da organização. O nosso estudo tem como objetivo explorar o impacto da liderança funcional no desempenho da equipa, o impacto da liderança autêntica no desempenho individual, o efeito mediador do capital psicológico da equipa entre a liderança funcional e o desempenho da equipa e o efeito moderador da coesão da equipa, o efeito mediador do capital psicológico individual entre a liderança autêntica e o desempenho individual e o efeito moderador da identificação organizacional. Com base na teoria social cognitiva e na teoria da contingência da liderança, construímos modelos teóricos ao nível da equipa e ao nível individual. Testámos as nossas hipóteses com 478 funcionários e 138 líderes, utilizando os programas SPSS 22.0 e Mplus 8.0. O nosso estudo mostrou que: (1) A liderança funcional tem um efeito positivo no desempenho da equipa; (2) O capital psicológico da equipa medeia a relação entre a liderança funcional e o desempenho da equipa; (3) A coesão da equipa modera positivamente a relação entre a liderança funcional e o capital psicológico da equipa, onde o capital psicológico da equipa tem um efeito mediador; (4) A liderança autêntica tem um efeito positivo no desempenho individual; (5) O capital psicológico individual medeia a relação entre a liderança autêntica e o desempenho individual. O nosso estudo enriquece a investigação relevante sobre liderança funcional e liderança autêntica e fornece implicações de gestão para o avanço da prática da gestão de organizações modernas

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45\%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive

Deuteron cross section evaluation for safety and radioprotection calculations of IFMIF/EVEDA accelerator prototype

under construction in Japan. Interaction of these deuterons with matter will generate high levels of neutrons and induced activation, whose predicted yields depend strongly on the models used to calculate the different cross sections. A benchmark test was performed to validate these data for deuteron energies up to 20 MeV and to define a reasonable methodology for calculating the cross sections needed for EVEDA. Calculations were performed using the nuclear models included in MCNPX and PHITS, and the dedicated nuclear model code TALYS. Although the results obtained using TALYS (global parameters) or Monte Carlo codes disagree with experimental values, a solution is proposed to compute cross sections that are a good fit to experimental data. A consistent computational procedure is also suggested to improve both transport simulations/prompt dose and activation/residual dose calculations required for EVEDA

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach

While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the era of large language models (LLMs)- based code generation, where LLMs, deemed a complex and powerful black-box model, is instructed by a high-level natural language specification, namely a prompt, to generate code. Nevertheless, effectively evaluating and explaining the code generation capability of LLMs is inherently challenging, given the complexity of LLMs and the lack of transparency. Inspired by the recent progress in causality analysis and its application in software engineering, this paper launches a causality analysis-based approach to systematically analyze the causal relations between the LLM input prompts and the generated code. To handle various technical challenges in this study, we first propose a novel causal graph-based representation of the prompt and the generated code, which is established over the fine-grained, human-understandable concepts in the input prompts. The formed causal graph is then used to identify the causal relations between the prompt and the derived code. We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies. The results of these studies illustrate the potential of our technique to provide insights into LLM effectiveness, and aid end-users in understanding predictions. Additionally, we demonstrate that our approach provides actionable insights to improve the quality of the LLM-generated code by properly calibrating the prompt

Causality-Aided Trade-off Analysis for Machine Learning Fairness

There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To ractically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widelyused fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies

InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models

Large vision-language models (LVLMs) have demonstrated their incredible capability in image understanding and response generation. However, this rich visual interaction also makes LVLMs vulnerable to adversarial examples. In this paper, we formulate a novel and practical targeted attack scenario that the adversary can only know the vision encoder of the victim LVLM, without the knowledge of its prompts (which are often proprietary for service providers and not publicly available) and its underlying large language model (LLM). This practical setting poses challenges to the cross-prompt and cross-model transferability of targeted adversarial attack, which aims to confuse the LVLM to output a response that is semantically similar to the attacker's chosen target text. To this end, we propose an instruction-tuned targeted attack (dubbed \textsc{InstructTA}) to deliver the targeted adversarial attack on LVLMs with high transferability. Initially, we utilize a public text-to-image generative model to "reverse" the target response into a target image, and employ GPT-4 to infer a reasonable instruction $\boldsymbol{p}^\prime$ from the target response. We then form a local surrogate model (sharing the same vision encoder with the victim LVLM) to extract instruction-aware features of an adversarial image example and the target image, and minimize the distance between these two features to optimize the adversarial example. To further improve the transferability with instruction tuning, we augment the instruction $\boldsymbol{p}^\prime$ with instructions paraphrased from GPT-4. Extensive experiments demonstrate the superiority of our proposed method in targeted attack performance and transferability. The code is available at https://github.com/xunguangwang/InstructTA

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)

Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable in practice. Moreover, privacy concerns arise when excessive CI tests are performed. Despite the distinct nature between unreliable and excessive CI tests, this paper identifies a unified and principled approach to addressing both of them. Generally, CI statements, the outputs of CI tests, adhere to Pearl's axioms, which are a set of well-established integrity constraints on conditional independence. Hence, we can either detect erroneous CI statements if they violate Pearl's axioms or prune excessive CI statements if they are logically entailed by Pearl's axioms. Holistically, both problems boil down to reasoning about the consistency of CI statements under Pearl's axioms (referred to as CIR problem). We propose a runtime verification tool called CICheck, designed to harden causal discovery algorithms from reliability and privacy perspectives. CICheck employs a sound and decidable encoding scheme that translates CIR into SMT problems. To solve the CIR problem efficiently, CICheck introduces a four-stage decision procedure with three lightweight optimizations that actively prove or refute consistency, and only resort to costly SMT-based reasoning when necessary. Based on the decision procedure to CIR, CICheck includes two variants: ED-CICheck and ED-CICheck, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively. [abridged due to length limit

Motion Integration for Ocular Pursuit Does Not Hinder Perceptual Segregation of Moving Objects

When confronted with a complex moving stimulus, the brain can integrate local element velocities to obtain a single motion signal, or segregate the elements to maintain awareness of their identities. The integrated motion signal can drive smooth-pursuit eye movements (Heinen and Watamaniuk, 1998), whereas the segregated signal guides attentive tracking of individual elements in multiple-object tracking tasks (MOT; Pylyshyn and Storm, 1988). It is evident that these processes can occur simultaneously, because we can effortlessly pursue ambulating creatures while inspecting disjoint moving features, such as arms and legs, but the underlying mechanism is unknown. Here, we provide evidence that separate neural circuits perform the mathematically opposed operations of integration and segregation, by demonstrating with a dual-task paradigm that the two processes do not share attentional resources. Human observers attentively tracked a subset of target elements composing a small MOT stimulus, while pursuing it ocularly as it translated across a computer display. Integration of the multidot stimulus yielded optimal pursuit. Importantly, performing MOT while pursuing the stimulus did not degrade performance on either task compared with when each was performed alone, indicating that they did not share attention. A control experiment showed that pursuit was not driven by integration of only the nontargets, leaving the MOT targets free for segregation. Nor was a predictive strategy used to pursue the stimulus, because sudden changes in its global velocity were accurately followed. The results suggest that separate neural mechanisms can simultaneously segregate and integrate the same motion signals

Motion Integration for Ocular Pursuit Does Not Hinder Perceptual Segregation of Moving Objects

When confronted with a complex moving stimulus, the brain can integrate local element velocities to obtain a single motion signal, or segregate the elements to maintain awareness of their identities. The integrated motion signal can drive smooth-pursuit eye movements (Heinen and Watamaniuk, 1998), whereas the segregated signal guides attentive tracking of individual elements in multiple-object tracking tasks (MOT; Pylyshyn and Storm, 1988). It is evident that these processes can occur simultaneously, because we can effortlessly pursue ambulating creatures while inspecting disjoint moving features, such as arms and legs, but the underlying mechanism is unknown. Here, we provide evidence that separate neural circuits perform the mathematically opposed operations of integration and segregation, by demonstrating with a dual-task paradigm that the two processes do not share attentional resources. Human observers attentively tracked a subset of target elements composing a small MOT stimulus, while pursuing it ocularly as it translated across a computer display. Integration of the multidot stimulus yielded optimal pursuit. Importantly, performing MOT while pursuing the stimulus did not degrade performance on either task compared with when each was performed alone, indicating that they did not share attention. A control experiment showed that pursuit was not driven by integration of only the nontargets, leaving the MOT targets free for segregation. Nor was a predictive strategy used to pursue the stimulus, because sudden changes in its global velocity were accurately followed. The results suggest that separate neural mechanisms can simultaneously segregate and integrate the same motion signals