Physical Sciences and Mathematics Commons^™

Simulation And Optimization Of Task Offloading In Mine Edge Computing For Low-Concurrency Devices, Zhaolu Guo, Qianhui Liu

Journal of System Simulation

Abstract: With the advancement of mine intelligence, task offloading technology has become a core technology of mine edge computing (MEC). For edge computing systems deployed with mine low-concurrency devices (MLCD), a discrete-event simulation model for MEC task offloading oriented to MLCD was constructed to solve the collaborative optimization problem of task processing latency and load balancing of mine edge servers (MES). By dynamically simulating the queuing, transmission, and computation processes of tasks, the objective of minimizing task processing latency was achieved. An improved evolutionary algorithm, ISBT-EA, was proposed, which integrated a task-characteristic-driven initialization strategy and local search operators, enhancing the …

Prediction Of Industrial Concentration Parameters Based On Caudformer Model, Kaipeng Xu, Yan Wang, Xin Zhang, Yang Liu, Zhenzhong Wang, Xiang Liu, Zhicheng Ji

Journal of System Simulation

Abstract: To solve the problems in the industrial concentration process of traditional Chinese medicine that traditional prediction methods are difficult to deal with complex characteristics such as nonlinearity, high-dimensional coupling, and highly skewed distribution and that existing deep learning models insufficiently consider causal relationships among variables, ignore frequency-domain periodic characteristics, and lack long-range dependency modeling capabilities, an improved multivariate time series prediction model CauDformer was proposed in this paper. Based on the iTransformer architecture, a causal multi-head self-attention mechanism (C-MHSA) was introduced to compulsorily constrain the dependency direction among variables by utilizing a lower triangular causal mask, guiding the model …

There Is No Free Benchmark: An Institutional View Of Legal Ai Benchmarking, Neel Guha, Andy K. Zhang, Christine Tsang, Christopher D. Manning, Julian Nyarko, Daniel E. Ho

Faculty Scholarship

Despite substantial excitement around the use of AI in law, little information exists on the performance and associated risks of the domain’s widely marketed tools. Recent work, for instance, has demonstrated the significant potential for “hallucinations” — wherein models make up facts, law, and precedent — leading Chief Justice Roberts to spotlight this risk in his annual report on the judiciary. We argue that there is a need for public AI benchmarking in law. First, relative to other AI application domains, the legal AI ecosystem lacks legibility — there is little information about the design and performance of many commercial …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude

Research Collection School Of Computing and Information Systems

AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools.In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development …

Variational Speculative Decoding: Rethinking Draft Training From Token Likelihood To Sequence Acceptance, Xiandong Zou, Jianshu Li, Jing Huang, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variational inference over latent proposals (draft paths). VSD maximizes the marginal probability of target-model acceptance, yielding an ELBO that promotes high-quality latent proposals while minimizing divergence from the target distribution. To enhance quality and reduce variance, we incorporate a path-level utility and optimize via an Expectation-Maximization procedure. The E-step draws MCMC samples from an oracle-filtered posterior, while the M-step maximizes weighted likelihood using …

Knowledge-State Generative Agents For Pre-Assessment Question Evaluation, Ping Fan Ke, Yi Meng Lau, Siaw Ling Lo

Research Collection School Of Computing and Information Systems

This paper introduces a Knowledge‑State Generative Agent framework for evaluating the quality of pre‑assessment questions. The framework employs large language model (LLM)–based agents prompted to adopt a teacher persona to simulate the responses of students with and without mastery of targeted knowledge components. A preliminary empirical study using archival data from 424 students enrolled in an Information Systems Management course indicates that the proposed approach yields interpretable metrics under Classical Test Theory. Results further show that agents instantiated with the relevant mastered knowledge components exhibit systematically higher performance than agents lacking such mastery. In addition, the study suggests that teacher-persona …

What We Know About Accounting Ratios: Methodological Considerations, Wojciech Kuryłek, Oskar Kowalewski

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: This paper provides a comprehensive literature review of the methodological aspects of financial ratio analysis, consolidating dispersed knowledge on the computation, statistical properties, and appropriate usage of accounting ratios.

Design/Methodology/Approach: The study adopts a narrative literature review methodology, systematically surveying published research on financial ratio distributions, normality testing, data transformations, outlier handling, the proportionality assumption, dimensionality reduction techniques, compositional data analysis, and recommended ratio sets for corporate financial research.

Findings: Financial ratios predominantly deviate from normal distributions, exhibiting skewness, excess kurtosis, and sensitivity to outliers. Transformation techniques such as logarithmic, square root, and Box‑Cox methods yield mixed results in …

Operational Agency: A Permeable Legal Fiction For Tracing Culpability In Ai Systems, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood—a paradox that fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA)—a permeable legal fiction structured as an ex post evidentiary framework—and Operational Agency Graph (OAG)—a tool for mapping causal interactions among human actors, organizations, and AI systems. OA evaluates an AI’s observable operational characteristics: its goal-directedness (as a proxy for intent), predictive processing (as a proxy for foresight), and safety architecture (as a proxy for standard of care). OAG operationalizes that analysis by embedding these …

Glacier Elevation Difference Data For The Conterminous U.S., Andrew G. Fountain, Bryce Allen Glenn, Christopher Mcneil, Brian Menounos

Geology Faculty Datasets

Historic and recent elevation data for the glaciers in the conterminous US were differenced to estimate glacier mass and volume change.

Historic glacier elevations were obtained from the USGS National Elevation Dataset (https://doi.org/10.15760/geology-data.04), a digitized product from the USGS 1:24,000 paper topographic maps that included the glacier extent (Gesch, 2007). Recent glacier elevations were derived from aerial lidar and from satellite radar. The lidar data were largely obtained from the USGS 3D Elevation Program (Stoker and Miller, 2022; US Geological Survey, 2024), and filtered by acquisition date using only those data collected between mid-summer and early autumn. The …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning, Brahmanage Janaka Chathuranga Thilakarathna, Akshat Kumar

Research Collection School Of Computing and Information Systems

Sequential decision-making using Markov Decision Process underpins many real-world applications. Both model-based and model-free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often conflicting objectives, that can lead to unstable min–max, adversarial optimization. A promising alternative is safety reachability analysis, which precomputes a forward-invariant safe state–action set, ensuring that an agent starting inside this set remains safe indefinitely. Yet, most reachability-based methods address only hard safety constraints, and little work extends reachability to cumulative cost constraints. To address this, first, we define a safety-conditioned reachability set that decouples reward maximization …

Spatiotemporal Sycophancy: Negation-Based Gaslighting In Video Large Language Models, Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleading user feedback under negation-based gaslighting. Rather than merely changing their answers, the models often fabricate unsupported temporal or spatial explanations to justify incorrect revisions. To systematically investigate this phenomenon, we propose a negation-based gaslighting evaluation framework and introduce GasVideo-1000, a curated benchmark designed to probe spatiotemporal sycophancy with clear visual grounding and temporal reasoning requirements. …

Ethos: A Computational Framework For Material Culture Research, Chelsea L. Grafe

Department of Textiles, Merchandising and Fashion Design: Dissertations, Theses, and Student Research

Museum catalog records make large collections searchable, but they do not always preserve the reasoning behind the claims they contain. A record may present a quilt's identity as settled while leaving the reasoning behind that claim invisible, making it difficult for later researchers to check, question, or reuse. This project asks whether museum catalog data can be analyzed in a way that keeps each analytical claim connected to the evidence that supports it. To test this question, the project applies a material-first computational framework named Ethos. Ethos formalizes an object-centered inquiry sequence in which material dependencies establish where analysis begins …

Butte Reclamation Evaluation System Corrective Action Plans For Reclaimed Sites 2025 Bres Site Specific Corrective Action Plans For Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Math 119: Math For Elementary School Teachers Syllabus, Seth Lehman

Open Educational Resources

OER course syllabus for Math 119, Math for Elementary School Teachers, at Queens College

Mengukur Capaian Dan Identifikasi Konvergensi Pembangunan Infrastruktur Antarprovinsi Di Indonesia, Ressa Isnaini Arumnisaa', Aisyah Fitri Yuniasih

Jurnal Ekonomi dan Pembangunan Indonesia

Indonesia’s economic challenges are characterized by stagnant economic growth and regional development disparities. This study analyzes the achievement and convergence of infrastructure development across provinces in Indonesia during 2011–2021 through the construction of an Infrastructure Development Index (IPI). The study employs panel data from 33 provinces, using factor analysis to construct the IPI and the First Difference Generalized Method of Moments (FD-GMM) to examine convergence and its determinants. The results show that many provinces still have IDI scores below the national average. Furthermore, the FD-GMM model shows that σ-convergence and β-convergence occur in Indonesia. In addition, GRDP per capita, inflation, …

Chi Meta-Project Ecosystem Overview - Spring 2026, David B. Smith

Publications and Research

This paper offers a high-level account of the Center for Holistic Integration’s (CHI) meta-project ecosystem as visualized in the included system map. CHI provides an organizational structure framed around persistent meta-projects that support and extend individual initiatives across curriculum, scholarly and applied research, infrastructure, artistic production, AI development, cultural inquiry, and external partnerships. Rather than presenting the map as a static inventory of projects, the paper examines how its core domains function as living systems through which knowledge, tools, documentation, participants, and collaborations can accumulate over time. It also considers how CHI-mediated connectivity, institutional integration, and external funding allow the …

Crustal Structure Of The Vøring Plateau And Vøring Spur From Joint Refraction And Reflection Travel-Time Tomography, Anika Nawar Mayeesha

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Crustal architecture of the Vøring Plateau and Vøring Spur in the mid-Norwegian continental margin remains debated because the geophysical data in the area do not uniquely constrain whether the crust is continental, oceanic, or transitional. This study reanalyzes wide-angle ocean-bottom seismometer data from Profile 11-03 across the Vøring passive continental margin using joint travel-time tomography of crustal refractions and Moho reflections. A total of 6,760 first-arrival picks and 542 PmP picks from sixteen instruments were inverted for P-wave velocity and reflector depth. Three starting models were inverted independently to test sensitivity to the initial crustal configuration. The first two used …

Observing System Simulation Experiments To Investigate Impacts Of Deep-Tropospheric Uas Flights On Forecasts, Kenneth Alexander Swan

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Uncrewed aircraft systems have emerged as a promising option to address meteorological gaps in observations in the lower troposphere. With recent advancements, UAS may be able to address gaps aloft in areas that lack coverage from radiosondes and commercial aircraft. UAS have shown the potential to significantly benefit NWP forecasts from profiles in the lowest 2 km AGL, but the impact of UAS profiles to the tropopause has yet to be quantified. Additionally, the impacts of hazardous flying conditions that UAS would encounter need to be addressed to guide future UAS capabilities. To address these questions, we designed an observing …

An Emergent Seafood Nationalism Takes Hold In The United States, Owen Temby

School of Earth, Environmental, & Marine Sciences Faculty Publications

This paper introduces the concept of ‘seafood nationalism’ to describe how, since 2024–2025, the United States increasingly uses tariffs, trade remedies, food safety enforcement and origin-based rules to favour American seafood over foreign products. Federal actions include Executive Order 14276, Restoring American Seafood Competitiveness, which directs agencies to address unfair trade practices, unsafe imports and to develop a coordinated seafood trade strategy. Additional measures include a tariff package imposing a 10% duty on nearly all seafood imports and 30% on seafood from China, as well as new anti-dumping and countervailing duty orders on warm-water shrimp. This paper presents Gulf …

Preliminary Findings From Intermountain West County Emergency Manager Survey, Bailey Holdaway, Courtney Flint, Gina Gilson, Richard Rushforth

Environment and Society Student Research

In the Intermountain West, water resources and weather conditions are increasingly plagued by uncertainty. Population growth throughout the region may further intensify many of these water-related hazards. County emergency managers are responsible for managing hazards and coordinating interactions among entities at various management levels. They are important entities in efforts to safely and effectively manage water-related hazards in the region.

2026 Final Insufficiently Reclaimed Sites Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto

School of Accountancy Faculty Publications

This study examines whether large language models exhibit systematic country-contingent differential treatment in financial fraud detection. Analyzing 30,000 synthetic transactions with identical statistical properties across three country attributions (United States, Great Britain, and China), we find LLMs assign significantly higher fraud probabilities to Chinese-attributed transactions (36.2%) compared to Western countries (≈30–31%), resulting in accuracy disparities of 67% versus 74%. The gap remains stable across five independent experimental replications and persists when using Chinese language prompts, ruling out linguistic effects. Bias mitigation strategies, such as requiring explanations or explicit country neutrality instructions, reduce but fail to eliminate these disparities. Testing across …

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items.CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and(2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficultto generalize to …

Late-Night And Early-Morning Train Scheduling With Non-Traffic Hour Maintenance Window In Urban Rail Transit Systems, Yaochen Ma, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Regular maintenance during non-traffic hours (NTH) is vital for the resilience of urban rail transit (URT) systems, yet an insufficient NTH maintenance window poses a challenge for URT systems in various cities. For instance, the Hong Kong MTR Corporation has noted that the required NTH maintenance time often exceeds the available window, prompting service adjustments such as earlier late-night closures and/or later early-morning starts. To address this challenge, this study develops an optimal scheduling framework that links late-night and early-morning URT services through the NTH maintenance window requirement to maximize public welfare. A Decoupled Optimization Model (DOM) first derives closed-form …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Operationalizing Ethics For Ai Agents: How Developers Encode Values Into Repository Context Files, Christoph Treude, Sebastian Baltes, Marc Cheong

Research Collection School Of Computing and Information Systems

As AI coding agents become embedded in software development workflows, developers are beginning to operationalize ethical principles by encoding behavioral rules into repository-level context files for AI agents, such as AGENTS.md files. Rather than examining the ethics of AI agents in the abstract, this vision paper investigates how ethics and values are already being translated for AI agents into actionable instructions that shape agent behavior. Through a preliminary investigation, we find that developers are already embedding guidance related to fairness, accessibility, sustainability, tone, and privacy. These artifacts function as a developer-authored governance layer, translating abstract principles into situated, natural-language directives …

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …