Digital Commons Network^™

Differential Gene Expression Study In Whole Blood Identifies Candidate Genes For Psychosis In African American Individuals, Emma M. Knowles, Juan M. Peralta, A. L. Rodrigue, Samuel R. Mathias, Josephine Mollon, Ana C. Leandro, Joanne E. Curran, John Blangero, David C. Glahn

School of Medicine Publications

Genome-wide association has identified regions of the genome that mediate risk for psychosis. It is possible that variants in these regions confer risk by altering gene expression. This work has predominantly been conducted in individuals of European descent and has focused narrowly on schizophrenia rather than psychosis as a syndrome. In the present study we investigated alterations in gene expression in African American individuals with a range of psychotic diagnoses to increase understanding of the etiology in an underserved population. We performed RNA-seq in whole bloody to survey the transcriptome in 126 patients with a psychosis-spectrum disorder and 217 healthy …

Building Services Engineering May/June 2025

Building Services Engineering

No abstract provided.

Moditector: Module-Directed Testing For Autonomous Driving Systems, Renzhi Wang, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Lei Ma

Research Collection School Of Computing and Information Systems

Testing Autonomous Driving Systems (ADSs) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse and challenging scenarios to uncover potential vulnerabilities, these methods often treat ADS as a black-box, primarily focusing on identifying system-level failures like collisions or near-misses without pinpointing the specific modules responsible for these failures. This lack of root causes understanding for the failures hinders effective debugging and subsequent system repair. Furthermore, current approaches often fall short in generating violations that adequately test the individual modules of an ADS from a system-level perspective, such as perception, prediction, planning, …

Demystifying Memorization In Llm-Based Program Repair Via A General Hypothesis Testing Framework, Jiaolong Kong, Xiaofei Xie, Shangqing Liu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in various applications, particularly in code-related tasks such as code generation and program repair, setting new performance benchmarks. However, the extensive use of large training corpora raises concerns about whether these achievements stem from genuine understanding or mere memorization of training data—a question often overlooked in current research. This paper aims to study the memorization issue within LLM-based program repair by investigating whether the correct patches generated by LLMs are the result of memorization. The key challenge lies in the absence of ground truth for confirming memorization, leading to various ad-hoc methods …

Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou

Research Collection School Of Computing and Information Systems

Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to …

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Efficient And Green Large Language Models For Software Engineering: Literature Review, Vision, And The Road Ahead, Jieke Shi, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This article aims to redirect the focus of the research community toward the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance …

Deepvec: State-Vector Aware Test Case Selection For Enhancing Recurrent Neural Network, Zhonghao Jiang, Meng Yan, Li Huang, Weifeng Sun, Chao Liu, Song Sun, David Lo

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test …

Collision Kinematics For Fast Positron Impact Ionization Of Argon, Robert D. Dubois, Károly Tőkési

Physics Faculty Research & Creative Works

Classical trajectory Monte Carlo calculations for 1 keV positron impact ionization of argon are presented. A combination of energy-weighted triple differential cross-sections is shown to provide good to excellent agreement compared with experimental data, depending on the ejected electron azimuthal angular acceptance range used to define in-plane scattering. For ejected electron energies less than 100 eV, information about the influence of pre- (the impact parameter) and post-(the direction of scattering) collision kinematics on the triple differential level is obtained. An overall picture of these kinematic properties is also presented for single differential cross-sections as a function of ejected electron energy …

Advancing Academic Advising With Knowledge Graphs: Integrating Machine Learning And Llms For Personalized Course Planning, Sara Alshamsi

Theses

Academic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand …

Numerical Methods For Approximating Line Integrals Over Implicitly Defined Curves, Raghd Alsaadawi

Theses

In this thesis, we develop and investigate a predictor-corrector method for the numerical tracing of implicitly defined curves. The study begins with the introduction of modified numerical integration techniques — specifically, the modified trapezoidal and modified midpoint rules — for evaluating the line integral of a vector field along an implicitly defined curve. Furthermore, we explore higher-order methods aimed at improving the accuracy of such integrals. Theoretical and numerical results, including asymptotic error expansions, are presented to support the analysis. In addition, several numerical experiments are carried out to illustrate the effectiveness and robustness of the proposed approaches.

Skip The Grid Bringing Light To The Darkness And Bridging The Educational Gap In Navajo Nation, Robert Joseph Byrne

Construction Management

Skip The Grid is a unique project done in collaboration with many groups, including California Polytechnic State University (Cal Poly) students and faculty members, representatives from Solv Energy, Nextracker, Tribal Liaisons, Heart of America, and members of the Navajo Nation. This group delivered and installed solar panel systems accompanied by batteries, lights, and a small refrigerator. The Navajo Nation faces many problems, including higher poverty and unemployment rates when compared to the rest of the United States. The goal of the Skip The Grid project was to deliver sustainable, safe, and reliable power to homes of school-aged children in the …

Assessing The Impact Of The Women In Construction Leadership Course And Disc Training On The Construction Industry For Students And Recent Graduates, Amanda E. Mcdowell

Construction Management

This study examined the impact of the Women in Construction Leadership Course, paired with DISC training, on students transitioning into the construction industry. The DISC instrument classifies behaviors into four personality types (i.e., Dominant, Influencer, Steady, Conscientious) and the training helped students identify each personality type to improve their communication skills. Although technical skills are essential for completing daily tasks, effective leadership and communication are crucial to project success. This research explored how DISC training can improve new hires’ perceptions of their abilities to interact and communicate with their project team members. Through a survey of course participants, the study …

Skip The Grid: Energizing Navajo Nation, Eiley Q. Eichten

Construction Management

Skip the Grid is an interdisciplinary service project aimed at providing reliable and clean energy to families living in Navajo Nation. Cal Poly partnered with Heart of America, SOLV Energy, and Nextracker to help bring this plan to fruition. Twenty students were selected to contribute to the planning, coordinating, and fundraising for the project. After the preparation was completed, Cal Poly students joined representatives from Heart of America, SOLV Energy, and Nextracker. The trip commenced with a hands-on, productive five day visit to Chinle, Arizona. The first working day commenced with Cal Poly students attending a total of five schools …

A Scan Of Pleiotropic Immune Mediated Disease Genes Identifies Novel Determinants Of Baseline Fviii Inhibitor Status In Hemophilia A, Marcio Almeida, Vincent P. Diego, Kevin R. Viel, Bernadette W. Luu, Eron G. Manusov, Juan M. Peralta, Satish Kumar, Sarah Williams-Blangero, John Blangero, Tom Howard

School of Medicine Publications

Hemophilia-A (HA) is the X-linked bleeding disorder caused by heterogeneous factor (F)VIII gene (F8)-mutations and deficiencies in plasma-FVIII-activity that prevent intrinsic-pathway mediated coagulation-amplification. Severe-HA patients (HAPs) require life-long infusions of therapeutic-FVIII-proteins (tFVIIIs) but ~30% develop neutralizing-tFVIII-antibodies called “FVIII-inhibitors (FEIs)”. We investigated the genetics underlying the variable risk of FEI-development in 450 North American HAPs (206 and 244 respectively self-reporting black-African- or white-European-ancestry) by analyzing the genotypes of single-nucleotide-variations (SNVs) in candidate immune-mediated-disease (IMD)-genes using a binary linear-mixed model of genetic association with baseline-FEI-status, the dependent variable, while simultaneously accounting for their genetic relationships and heterogeneous-F8-mutations to …

Multi-Point And Multi-Station Orbit Propagation For Non-Functional Drifting Geo Satellites, Hritik Mitra

Theses

Currently, there are thousands of man-made space objects that are orbiting the Earth. These satellites serve a host of essential purposes (scientific research, technical applications, services) and they are all required to remain within their mission parameters. Most critical for them is to maintain the orbital parameters that are specified to achieve a particular mission’s objectives. If a satellite deviates beyond a certain limit, there is not only a risk of mission failure but there is a major hazard for possible collisions with other objects such as active satellites, space debris and even natural objects in some cases. There is …

Investigation And Prediction Of Excessive Water Production In Bottom Water-Drive Naturally Fractured Reservoirs Using Machine And Deep Learning, Sami Abderraouf Belkhir

Theses

Naturally Fractured Reservoirs (NFRs) are characterized by dual-porosity and dual-permeability systems, posing significant challenges in managing water production due to highly conductive fracture networks that facilitate rapid water migration from bottom aquifers, often bypassing oil stored in the matrix, thus resulting in early water breakthrough and water channeling phenomena. The main objective of this thesis is to develop and validate deep learning and machine learning models to predict water production, water breakthrough time (tbt), and ultimate water cut (WCult) in NFRs, thereby enabling more effective reservoir management strategies. This work also aims to evaluate the sensitivity of water behavior to …

Do Government Benefit Programs Reduce Child Labor?: Causal Evidence From The Midday Meal Scheme In India, Vardaan Chawla, Tomoki Fujii, Arpita Khanna, Ray Rohan

Research Collection School Of Economics

This study evaluates the impact of the midday meal program in India on the prevalence of child labor for upper primary school going children. The staggered implementation of the largest school feeding program in the world across states provides exogenous variation in exposure. Using the nationally representative National Sample Survey Office (NSSO) data, we find that exposure to the program reduced prevalence of child labor by 47 percent relative to children in never-treated states, with no significant difference in impact between boys and girls. The effects were primarily driven by a decline in household labor supply, especially performance of household …

Comparative Genotoxicological Analysis Of Occupational Pesticide Exposures: An Experimental Approach And Systematic Review, Moustafa Samy Sherif

Dissertations

This dissertation investigates the genotoxic and cytotoxic effects of pesticide exposure through three complementary research components: a systematic review of occupational pesticide exposure in Arab countries (AraSys), a systematic review on encapsulated versus conventional pesticide formulations (Encap-Sys), and experimental toxicological assessments of various pesticide formulations in human cell lines (Pyr-Ex). This experimental component includes cytotoxicity assays using propidium iodide and DNA damage assessments via alkaline comet assays in HL60 and HepG2 cell lines exposed to pyrethroid insecticides and glyphosate-based formulations.

The AraSys systematic review revealed a critical positive association between occupational pesticide exposure and DNA damage in agricultural workers in …

Genetic Counseling For Adult-Onset Neurogenetic Conditions In Hispanic/Latine Communities: A Qualitative Study Of Barriers And Facilitators From Hispanic/Latine Genetic Counselors' Perspectives, Elizabeth Aleman

All Publications

Hispanic/Latine (H/Le) individuals, despite being at higher risk for certain adult-onset neurological conditions (AONCs) compared to non-Hispanic white individuals, experience delays in accessing neurologic health services and are significantly underrepresented in clinical research for conditions such as Alzheimer's disease and Parkinson's disease. While existing studies have highlighted barriers to genetic health services for H/Le groups, there is limited research on their experiences with adult-onset conditions beyond cancer. This study aimed to explore the perspectives of H/Le genetic counselors on factors that influence adult neurogenetic counseling (NGC) access for H/Le individuals, and suggestions for expanding access as subject matter experts of …

On-Site Water Management Field Sampling Plan (Fsp), 2025 Update, Mark Thompson, Rampart Solutions

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

Ai Governance And Algorithmic Auditing In Financial Institutions: Lessons From Singapore, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This paper examines the role of algorithmic auditing as a mechanism for responsible AI development and deployment in the financial sector, with a particular focus on Singapore’s regulatory and institutional initiatives. Against the backdrop of fragmented global artificial intelligence (AI) governance frameworks, the study analyses how Singapore has developed operational tools — such as the Veritas Toolkit, AI Verify, Project Moonshot and Project Mindforge — that go beyond abstract ethical principles to provide measurable, use-case-specific standards for auditing AI systems. These initiatives contribute to standardising audit practices, enhancing transparency and bridging trust gaps between financial institutions, regulators and stakeholders. The …

Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang

Research Collection School Of Computing and Information Systems

AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …

Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …

De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do not produce any error messages. To address this problem, multiple white-box techniques were proposed, but they are inapplicable in many practical scenarios. Black-box techniques are more practical, but the existing ones are less effective as they often rely on irrelevant syntactic information. To bridge this gap, we propose a novel black-box technique (BLADE), which …

A Comprehensive Study Of Oop-Related Bugs In C++ Compilers, Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …