ニュースアーカイブの時制情報活用のための有効な手法に関する研究

京都大学新制・課程博士博士(情報学)甲第24259号情博第803号京都大学大学院情報学研究科社会情報学専攻(主査)教授 吉川 正俊, 教授 田島 敬史, 教授 黒橋 禎夫, 特定准教授 LIN Donghui学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

A Resource Perspective Linking Personality Traits and Work-Family Conflict and Enrichment: Examination of the Indirect Effect Through Resource Development

Challenging the prior research viewing employees as passive beings who respond to work-family conflict, this dissertation studies employees as active agents who shape their experience of work-family conflict and enrichment through developing resources based on their attentional efforts. Specifically, drawing on Conservation of Resources Theory, this dissertation proposes and tests a resource-based process model that explain the indirect effect of key resources (conscientiousness, extraversion, and agreeableness) on work-family conflict and work-family enrichment through differential resource development processes (human capital development, social capital development, and altruistic development). The results show support that agreeableness is associated with work-family conflict and enrichment through its unique effect on altruistic capital development. The indirect effect of conscientiousness on work-family conflict and enrichment operates through human capital development and altruistic capital development. Extraversion is associated with work-family conflict and enrichment through all three types of resource development. Moreover, the supplementary analysis using a longitudinal mediation design reveals a pattern of reverse causality—the positive relationship between conscientiousness and human capital development is attributable to the indirect effect through work-family enrichment

Spatio-Temporal Branching for Motion Prediction using Motion Increments

Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy

Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation

Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. To effectively mitigate this concern, this paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective. We introduce SecuCoGen\footnote{SecuCoGen has been uploaded as supplemental material and will be made publicly available after publication.}, a meticulously curated dataset targeting 21 critical vulnerability types. SecuCoGen comprises 180 samples and serves as the foundation for conducting experiments on three crucial code-related tasks: code generation, code repair and vulnerability classification, with a strong emphasis on security. Our experimental results reveal that existing models often overlook security concerns during code generation, leading to the generation of vulnerable code. To address this, we propose effective approaches to mitigate the security vulnerabilities and enhance the overall robustness of code generated by LLMs. Moreover, our study identifies weaknesses in existing models' ability to repair vulnerable code, even when provided with vulnerability information. Additionally, certain vulnerability types pose challenges for the models, hindering their performance in vulnerability classification. Based on these findings, we believe our study will have a positive impact on the software engineering community, inspiring the development of improved methods for training and utilizing LLMs, thereby leading to safer and more trustworthy model deployment

Mechanism, prevention and treatment of cognitive impairment caused by high altitude exposure

Hypobaric hypoxia (HH) characteristics induce impaired cognitive function, reduced concentration, and memory. In recent years, an increasing number of people have migrated to high-altitude areas for work and study. Headache, sleep disturbance, and cognitive impairment from HH, severely challenges the physical and mental health and affects their quality of life and work efficiency. This review summarizes the manifestations, mechanisms, and preventive and therapeutic methods of HH environment affecting cognitive function and provides theoretical references for exploring and treating high altitude-induced cognitive impairment

A hydrated deep eutectic electrolyte with finely-tuned solvation chemistry for high-performance zinc-ion batteries

Despite their cost-effectiveness and intrinsic safety, aqueous zinc-ion batteries have faced challenges with poor reversibility originating from various active water-induced side reactions. After systematically scrutinizing the effects of water on the evolution of solvation structures, electrolyte properties, and electrochemical performances through experimental and theoretical approaches, a hydrated deep eutectic electrolyte with a water-deficient solvation structure ([Zn(H2O)2(eg)2(otf)2]) and reduced free water content in the bulk solution is proposed in this work. This electrolyte can dramatically suppress water-induced side reactions and provide high Zn2+ mass transfer kinetics, resulting in highly reversible Zn anodes (∼99.6% Coulombic efficiency over 1000 cycles and stable cycling over 4500 h) and high capacity Zn//NVO full cells (436 mA h g−1). This work will aid the understanding of electrolyte solvation structure–electrolyte property–electrochemical performance relationships of aqueous electrolytes in aqueous zinc-ion batteries

Metal–organic frameworks and their derivatives for optimizing lithium metal anodes

Lithium metal anodes (LMAs) have been considered the ultimate anode materials for next-generation batteries. However, the uncontrollable lithium dendrite growth and huge volume expansion that can occur during charge and discharge seriously hinder the practical application of LMAs. Metal–organic framework (MOF) materials, which possess the merits of huge specific surface area, excellent porosity, and flexible composition/structure tunability, have demonstrated great potential for resolving both of these issues. This article first explores the mechanism of lithium dendrite formation as described by four influential models. Subsequently, based on an in-depth understanding of these models, we propose potential strategies for utilizing MOFs and their derivatives to suppress lithium dendrite growth. We then provide a comprehensive review of research progress with respect to various applications of MOFs and their derivatives to suppress lithium dendrites and inhibit volume expansion. The paper closes with a discussion of perspectives on future modifications of MOFs and their derivatives to achieve stable, dendrite-free lithium metal batteries

Trace Amounts of Triple-Functional Additives Enable Reversible Aqueous Zinc-Ion Batteries from a Comprehensive Perspective

Although their cost-effectiveness and intrinsic safety, aqueous zinc-ion batteries suffer from notorious side reactions including hydrogen evolution reaction, Zn corrosion and passivation, and Zn dendrite formation on the anode. Despite numerous strategies to alleviate these side reactions have been demonstrated, they can only provide limited performance improvement from a single aspect. Herein, a triple-functional additive with trace amounts, ammonium hydroxide, was demonstrated to comprehensively protect zinc anodes. The results show that the shift of electrolyte pH from 4.1 to 5.2 lowers the HER potential and encourages the in situ formation of a uniform ZHS-based solid electrolyte interphase on Zn anodes. Moreover, cationic NH4+ can preferentially adsorb on the Zn anode surface to shield the "tip effect" and homogenize the electric field. Benefitting from this comprehensive protection, dendrite-free Zn deposition and highly reversible Zn plating/stripping behaviors were realized. Besides, improved electrochemical performances can also be achieved in Zn//MnO2 full cells by taking the advantages of this triple-functional additive. This work provides a new strategy for stabilizing Zn anodes from a comprehensive perspective

Distinct pattern of TP53 mutations in human immunodeficiency virusâ related head and neck squamous cell carcinoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142251/1/cncr31063.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142251/2/cncr31063_am.pd