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Articles 27211 - 27240 of 291668

Full-Text Articles in Physical Sciences and Mathematics

Geochemical Analysis Of Basement Rock As A Potential Metal Source In Mississippi Valley-Type Ores, Southern Midcontinent U.S.A., Mackenzie Moorhead May 2024

Geochemical Analysis Of Basement Rock As A Potential Metal Source In Mississippi Valley-Type Ores, Southern Midcontinent U.S.A., Mackenzie Moorhead

Graduate Theses and Dissertations

Mississippi Valley-Type (MVT) ores are epigenetic, sedimentary hydrothermal base metal deposits that exist worldwide with type localities in the midcontinent United States. These Permian-aged sulfide ores represent an extraordinary concentration of economic metals, Pb and Zn. Ore deposits occur in vast districts that have a rich mining history. The Pb and Zn from these deposits account for 24% of worldwide reserves. The geologic processes responsible for MVT ore genesis are not fully understood, especially the sourcing of ore constituents. In the midcontinent, the Ouachita Orogeny expelled saline hydrothermal brines in response to high rates of subsidence. These fluids migrated through …

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Temporal Trends And Dental Metric Variation In The Macaca Sylvanus Lineage, Cristina Stan May 2024

Temporal Trends And Dental Metric Variation In The Macaca Sylvanus Lineage, Cristina Stan

Graduate Theses and Dissertations

The extensive collection of fossil macaques from Europe, spanning from the Late Miocene to the Late Pleistocene period, is believed to be of the species Macaca sylvanus. However, there has been a prolonged discussion regarding the classification of these specimens into a single taxonomic group as the existing dental sample exhibits a large amount of morphological and metric variation. Specifically, researchers have attempted to identify multiple subspecies based on chronological data of the paleontological locality and, secondly, based on dental metric variation. In this study, I assess temporal trends in the fossil record of Macaca sylvanus and compare overall variation …

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Two Studies: Aromatization-Driven Ring Opening Functionalization Of Unstrained Cycloalkanones Through Visible Light Catalysis, And Synthesis Of Amino Endoperoxides Using Self-Doped Titanium Dioxide (Ti3+@Tio2) As A Photocatalyst, Enoch Kudoahor May 2024

Two Studies: Aromatization-Driven Ring Opening Functionalization Of Unstrained Cycloalkanones Through Visible Light Catalysis, And Synthesis Of Amino Endoperoxides Using Self-Doped Titanium Dioxide (Ti3+@Tio2) As A Photocatalyst, Enoch Kudoahor

Graduate Theses and Dissertations

The pharmaceutical industry has been focused on discovering new innovative drugs over the last decade, but the syntheses are either inefficient or the approach to environmental sustainability raises significant concerns. Photochemistry and photocatalysis have found widespread applications in organic synthesis as a result of this pressing issue. Our role as organic chemists is to improve environmental quality by creating a "greener" world through the development of efficient synthetic methods and strategies. Solar energy is now one of the most abundant and renewable natural resources. The emergence of environmental sustainability concerns has led to the development of new techniques for harnessing …

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Approximation Via Degree Reduction Of Nonlinearities With Applications To Turbulent Flows, Flame Fronts, And Magnetohydrodynamics, Matthew Enlow May 2024

Approximation Via Degree Reduction Of Nonlinearities With Applications To Turbulent Flows, Flame Fronts, And Magnetohydrodynamics, Matthew Enlow

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We perform an analytical and computational investigation on the effectiveness of a locally bounded truncation function, which we call a calming function, when applied to the nonlinear terms of several dissipative partial differential equations. In particular, the 3D Navier-Stokes equations of incompressible fluid flow, the 2D Kuramoto-Sivashinsky equations of laminar flame fronts, and the 2D MHD-Boussinesq equations of magnetohydrodynamics. Each of these equations have open questions about the global existence and uniqueness of their solutions. These calming functions effectively reduce the algebraic degree of select nonlinear terms, thus one can verify global wellposedness for these "calmed systems." More specifically, in …

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Characterizing The Role Of Chemo-Mechanical Couplings In The Activation Process Of Proteins That Undergo Conformational Changes, Ugochi Isu May 2024

Characterizing The Role Of Chemo-Mechanical Couplings In The Activation Process Of Proteins That Undergo Conformational Changes, Ugochi Isu

Graduate Theses and Dissertations

Proteins are not static entities; rather, they are dynamic macromolecules that undergo conformational changes to perform their biological functions. These structural transitions are often coupled to chemical events such as lipid interactions, or changes in the cellular environment. Understanding the intricate interplay between chemical perturbations and the resulting mechanical response is crucial for elucidating the activation mechanisms of proteins. This dissertation aims to characterize the role of chemo-mechanical couplings in the activation process of two G protein-coupled receptors (GPCRs): Cannabinoid Receptor 1 (CB1) and Metabotropic Glutamate Receptor 1 (mGluR1). We employ molecular dynamics simulations to investigate the conformational landscapes of …

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Vascular Brain Digital Twins For Medical Training And Education In Metaverse, Shamma Khaled Alghafri May 2024

Vascular Brain Digital Twins For Medical Training And Education In Metaverse, Shamma Khaled Alghafri

Theses

This study addresses the need for innovative educational tools in the field of anatomy, specifically focusing on brain anatomy. The objective is to develop a virtual reality application and a 3D visualization that offer immersive and interactive learning experiences for students. The VR application, developed using Unity and designed for the Oculus Quest 2 headset, creates an immersive virtual laboratory environment. This environment includes interactive elements such as a table with buttons for displaying brain models and a whiteboard for user interaction. Users can manipulate and explore different brain structures, enhancing their understanding of complex anatomical features. Additionally, we integrated …

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Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia May 2024

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia

Research Collection School Of Computing and Information Systems

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …

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Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang May 2024

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

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Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan May 2024

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …

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An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo May 2024

An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …

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Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen May 2024

Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen

Research Collection School Of Computing and Information Systems

With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …

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Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude May 2024

Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude

Research Collection School Of Computing and Information Systems

The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.

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Instant3d: Instant Text-To-3d Generation, Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Shuicheng Yan, Xiangyu Xu May 2024

Instant3d: Instant Text-To-3d Generation, Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Shuicheng Yan, Xiangyu Xu

Research Collection School Of Computing and Information Systems

Text-to-3D generation has attracted much attention from the computer vision community. Existing methods mainly optimize a neural field from scratch for each text prompt, relying on heavy and repetitive training cost which impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core …

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Knowledge Enhanced Multi-Intent Transformer Network For Recommendation, Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo May 2024

Knowledge Enhanced Multi-Intent Transformer Network For Recommendation, Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo

Research Collection School Of Computing and Information Systems

Incorporating Knowledge Graphs (KGs) into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and interpretability for the underlying models. However, simply integrating KG into recommendation usually brings in negative feedback in industry, mainly due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG. For example, in e-commerce scenarios, users may exhibit preferences for specific styles, brands, or colors. ii) knowledge noise, which is a prevalent issue in Knowledge Enhanced Recommendation (KGR) and even more severe in industry scenarios. The irrelevant …

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Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An May 2024

Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an elegant tool to understand the rationalities. Computing NE in two- or multi-player general-sum games is PPAD-Complete. Therefore, in this work, we propose REinforcement Nash Equilibrium Solver (RENES), which trains a single policy to modify the games with different sizes and applies the solvers on the modified games where the obtained solution is evaluated on the original games. Specifically, our contributions are threefold. i) We represent the games as ��-rank response graphs and leverage graph neural network (GNN) to handle the games with different sizes as inputs; …

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Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua May 2024

Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as …

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Plug-And-Play Policy Planner For Large Language Model Powered Dialogue Agents, Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, Tat-Seng Chua May 2024

Plug-And-Play Policy Planner For Large Language Model Powered Dialogue Agents, Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs. Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback. However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases. In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue …

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Reinforcement Learning With Maskable Stock Representation For Portfolio Management In Customizable Stock Pools, Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An May 2024

Reinforcement Learning With Maskable Stock Representation For Portfolio Management In Customizable Stock Pools, Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential to train profitable agents for PM through interacting with financial markets. However, existing work mostly focuses on fixed stock pools, which is inconsistent with investors’ practical demand. Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e.g., adding one popular stocks), which lead to customizable stock pools (CSPs). Existing …

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Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An May 2024

Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-training and fine-tuning paradigm in Policy-Space Response Oracles (PSRO) to improve scalability in solving large-scale PEGs. However, these methods primarily focus on specific PEGs with fixed initial conditions that may vary substantially in real-world scenarios, which significantly hinders the applicability of the traditional methods. To address this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs. Our contributions are threefold: …

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Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas May 2024

Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas

Research Collection School Of Computing and Information Systems

In this article, we describe our experience in developing an undergraduate Linear Algebra course tailored to highlight its relevance and applicability in Computer Science. Over the course of three years, the course transitioned from a traditional direct-instruction format to a flipped-classroom design, resulting in positive student learning outcomes. This article covers the course design philosophy, its syllabus, learning objectives, and the incorporation of both quantitative and qualitative student feedback in shaping the course. Furthermore, the article shares the insights gleaned from our experience, which can serve as best practices for instructors aiming to deliver a successful Linear Algebra course for …

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From Tweets To Token Sales: Assessing Ico Success Through Social Media Sentiments, Donghao Huang, S. Samuel, Quoc Toan Huynh, Zhaoxia Wang May 2024

From Tweets To Token Sales: Assessing Ico Success Through Social Media Sentiments, Donghao Huang, S. Samuel, Quoc Toan Huynh, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

With the advent of social network technology, the influence of collective opinions has significantly impacted business, marketing, and fundraising. Particularly in the blockchain space, Initial Coin Offerings (ICOs) gain substantial exposure across various online platforms. Yet, the intricate relationships among these elements remain largely unexplored. This study aims to investigate the relationships between social media sentiment, engagement metrics, and ICO success. We hypothesize a positive correlation between favorable sentiment in ICO-related tweets and overall project success. Additionally, we recognize social media engagement indicators (mentions, retweets, likes, follower counts) as critical factors affecting ICO performance. Employing machine learning techniques, we conduct …

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Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher May 2024

Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher

Research Collection School Of Computing and Information Systems

Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized …

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Analyzing And Revivifying Function Signature Inference Using Deep Learning, Yan Lin, Trisha Singhal, Debin Gao, David Lo May 2024

Analyzing And Revivifying Function Signature Inference Using Deep Learning, Yan Lin, Trisha Singhal, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Function signature plays an important role in binary analysis and security enhancement, with typical examples in bug finding and control-flow integrity enforcement. However, recovery of function signatures by static binary analysis is challenging since crucial information vital for such recovery is stripped off during compilation. Although function signature recovery using deep learning (DL) is proposed in an effort to handle such challenges, the reported accuracy is low for binaries compiled with optimizations. In this paper, we first perform a systematic study to quantify the extent to which compiler optimizations (negatively) impact the accuracy of existing DL techniques based on Recurrent …

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Robust Auto-Scaling With Probabilistic Workload Forecasting For Cloud Databases, Haitian Hang, Xiu Tang, Jianling Sun, Lingfeng Bao, David Lo, Haoye Wang May 2024

Robust Auto-Scaling With Probabilistic Workload Forecasting For Cloud Databases, Haitian Hang, Xiu Tang, Jianling Sun, Lingfeng Bao, David Lo, Haoye Wang

Research Collection School Of Computing and Information Systems

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we …

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Automatic Grading Of Short Answers Using Large Language Models In Software Engineering Courses, Nguyen Binh Duong Ta, Yi Meng Chai May 2024

Automatic Grading Of Short Answers Using Large Language Models In Software Engineering Courses, Nguyen Binh Duong Ta, Yi Meng Chai

Research Collection School Of Computing and Information Systems

Short-answer based questions have been used widely due to their effectiveness in assessing whether the desired learning outcomes have been attained by students. However, due to their open-ended nature, many different answers could be considered entirely or partially correct for the same question. In the context of computer science and software engineering courses where the enrolment has been increasing recently, manual grading of short-answer questions is a time-consuming and tedious process for instructors. In software engineering courses, assessments concern not just coding but many other aspects of software development such as system analysis, architecture design, software processes and operation methodologies …

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Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin May 2024

Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

Research Collection School Of Computing and Information Systems

The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is …

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Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao May 2024

Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao

Research Collection School Of Computing and Information Systems

Owing to the widespread deployment of smartphones and networked devices, massive amount of data in different types are generated every day, including numeric data, locations, text data, images, etc. Nearest neighbour search in multi-metric spaces has attracted much attention, as it can accommodate any type of data and support search on flexible combinations of multiple metrics. However, most existing methods focus on single metric queries, failing to answer multi-metric queries efficiently due to the complex metric combinations. In this paper, for the first time, we study the approximate nearest neighbour search (ANNS) in multi-metric spaces, and propose HJG, a hierarchical …

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Reinforcement Retrieval Leveraging Fine-Grained Feedback For Fact Checking News Claims With Black-Box Llm, Xuan Zhang, Wei Gao May 2024

Reinforcement Retrieval Leveraging Fine-Grained Feedback For Fact Checking News Claims With Black-Box Llm, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant challenges under the setting of black-box LLM. We propose an approach leveraging Fine-grained Feedback with Reinforcement Retrieval (FFRR) to enhance fact-checking on news claims by using black-box LLM. FFRR adopts a two-level strategy to gather fine-grained feedback from the LLM, which serves as a reward for optimizing the retrieval policy, by rating the retrieved documents …

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Deep Reinforcement Learning Guided Improvement Heuristic For Job Shop Scheduling, Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang May 2024

Deep Reinforcement Learning Guided Improvement Heuristic For Job Shop Scheduling, Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph-Neural-Network-based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, …

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Escaping Saddle Points In Heterogeneous Federated Learning Via Distributed Sgd With Communication Compression, Sijin Chen, Zhize Li, Yuejie Chi May 2024

Escaping Saddle Points In Heterogeneous Federated Learning Via Distributed Sgd With Communication Compression, Sijin Chen, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

We consider the problem of finding second-order stationary points in the optimization of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm PowerEF-SGD that only communicates compressed information via a novel error-feedback scheme. To our knowledge, PowerEF-SGD is the first distributed and compressed SGD algorithm that provably escapes saddle points …

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