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Full-Text Articles in Physical Sciences and Mathematics

An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen Tullino, Andrew Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques Jul 2024

An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen Tullino, Andrew Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques

Faculty Publications

The proposed Integrated Space Test Lexicon is intended to amalgamate the numerous definitions of integrated (IT or IT&E), development test (DT or DT&E), and operational test (OT or OT&E) into unified, service-wide definitions, aligned with the Space Test Enterprise Vision. Refining such definitions will help distill the core characteristics of these fundamental test types to first identify space system activities composing what is traditionally known as DT and OT, then to provide a means of how these activities fit into the IT paradigm and support space system development. In forging a common understanding of how DT and OT support space …

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Mapping Of Pavement Conditions Using Smartphone/Tablet Lidar Case Study: Sensor Performance Comparison, Calvin Beavers, Chad Day, Austin Krietemeyer, Scott M. Peterson, Yushin Ahn, Xiaojun Li Jul 2024

Mapping Of Pavement Conditions Using Smartphone/Tablet Lidar Case Study: Sensor Performance Comparison, Calvin Beavers, Chad Day, Austin Krietemeyer, Scott M. Peterson, Yushin Ahn, Xiaojun Li

Mineta Transportation Institute

Poor road conditions affect millions of drivers, and assessing the condition of paved surfaces is a critical step towards repairing them. This project explores the feasibility of using the Apple iPad Pro LiDAR sensor as a cost-effective tool for assessing the damage and condition of paved surfaces. Our research aims to provide accurate and precise measurements using readily available consumer devices and compare the results to state-of-the-art equipment. This investigation involved visual inspection, identification, and classification of pavement distresses, followed by a comparison of the iPad and iPhone LiDAR data with a survey-grade terrestrial laser scanner. The project revealed several …

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Development Of The Roadway Pothole Management Program, Dingxin Cheng Jul 2024

Development Of The Roadway Pothole Management Program, Dingxin Cheng

Mineta Transportation Institute

Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, …

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True Contraction Decomposition And Almost Eth-Tight Bipartization For Unit-Disk Graphs, Sayan Bandyapadhyay, William Lochet, Daniel Lokshtanov, Saket Saurabh, Jie Xue Jul 2024

True Contraction Decomposition And Almost Eth-Tight Bipartization For Unit-Disk Graphs, Sayan Bandyapadhyay, William Lochet, Daniel Lokshtanov, Saket Saurabh, Jie Xue

Computer Science Faculty Publications and Presentations

We prove a structural theorem for unit-disk graphs, which (roughly) states that given a set D of n unit disks inducing a unit-disk graph ...

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Towards Automated Slide Augmentation To Discover Credible And Relevant Links, Dilan Dinushka Senarath Arachchige, Christopher M. Poskitt, Kwan Chin (Xu Guangjin) Koh, Heng Ngee Mok, Hady Wirawan Lauw Jul 2024

Towards Automated Slide Augmentation To Discover Credible And Relevant Links, Dilan Dinushka Senarath Arachchige, Christopher M. Poskitt, Kwan Chin (Xu Guangjin) Koh, Heng Ngee Mok, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to 'pin' the most important ones. To evaluate the effectiveness of the tool, we trialled the system …

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Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra Jul 2024

Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …

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Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves Jul 2024

Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves

Research Collection School Of Computing and Information Systems

We provide generalization bounds for matrix completion with Schatten $p$ quasi-norm constraints, which is equivalent to deep matrix factorization with Frobenius constraints. In the uniform sampling regime, the sample complexity scales like $\widetilde{O}\left( rn\right)$ where $n$ is the size of the matrix and $r$ is a constraint of the same order as the ground truth rank in the isotropic case. In the distribution-free setting, the bounds scale as $\widetilde{O}\left(r^{1-\frac{p}{2}}n^{1+\frac{p}{2}}\right)$, which reduces to the familiar $\sqrt{r}n^{\frac{3}{2}}$ for $p=1$. Furthermore, we provide an analogue of the weighted trace norm for this setting which brings the sample complexity down to $\widetilde{O}(nr)$ in all …

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How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang Jul 2024

How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Generative AI tools can provide people with the ability to create virtual environments and scenes with natural language prompts. Yet, how people will formulate such prompts is unclear---particularly when they inhabit the environment that they are designing. For instance, it is likely that a person might say, "Put a chair here,'' while pointing at a location. If such linguistic and embodied features are common to people's prompts, we need to tune models to accommodate them. In this work, we present a Wizard of Oz elicitation study with 22 participants, where we studied people's implicit expectations when verbally prompting such programming …

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A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang Jul 2024

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

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Large Language Model Powered Agents For Information Retrieval, An Zhang, Yang Deng, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua Jul 2024

Large Language Model Powered Agents For Information Retrieval, An Zhang, Yang Deng, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like …

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Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner Jul 2024

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

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Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude Jul 2024

Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Identifying security issues early is encouraged to reduce the latent negative impacts on software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code …

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Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang Jul 2024

Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang

Research Collection School Of Computing and Information Systems

Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based …

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Esem: To Harden Process Synchronization For Servers, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu Jul 2024

Esem: To Harden Process Synchronization For Servers, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Process synchronization primitives lubricate server computing involving a group of processes as they ensure those processes to properly coordinate their executions for a common purpose such as provisioning a web service. A malfunctioned synchronization due to attacks causes friction among processes and leads to unexpected, and often hard-to-detect, application transaction errors. Unfortunately, synchronization primitives are not naturally protected by existing hardware-assisted isolation techniques e.g., SGX, because their process-oriented isolation conflicts with the primitive's demand for cross-process operations.This paper introduces the Enclave-Semaphore service (ESem) which shelters application semaphores and their operations against kernel-privileged attacks. ESem encapsulates all semaphores in the platform …

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Final 2024 Residential Metals Abatement Program (Rmap) Park Soil Sampling Field Sampling Plan (Fsp) Submittal #14 [Silver Bow Homes Park, Butte Koa, Fairmont Rv Park, And Cccs Gymnasium], Pioneer Technical Services, Inc. Jul 2024

Final 2024 Residential Metals Abatement Program (Rmap) Park Soil Sampling Field Sampling Plan (Fsp) Submittal #14 [Silver Bow Homes Park, Butte Koa, Fairmont Rv Park, And Cccs Gymnasium], Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang Jul 2024

Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting …

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Strong Marine Heatwaves Trigger Flowering In Seagrass, Catalina A. García-Escudero, Victoria Litsi-Mizan, Pavlos T. Efthymiadis, Vasilis Gerakaris, Oscar Serrano, Eugenia T. Apostolaki Jul 2024

Strong Marine Heatwaves Trigger Flowering In Seagrass, Catalina A. García-Escudero, Victoria Litsi-Mizan, Pavlos T. Efthymiadis, Vasilis Gerakaris, Oscar Serrano, Eugenia T. Apostolaki

Research outputs 2022 to 2026

In recent decades, the global intensification of marine heatwaves has impacted several ecosystems and species, including the endemic Mediterranean seagrass Posidonia oceanica. However, the scarcity of research in Eastern Mediterranean meadows, where historical and present thermal conditions differ from those of the Western Mediterranean, hampers our ability to draw comprehensive conclusions regarding the species' response to elevated sea temperatures. Here, we studied flowering patterns of P. oceanica meadows (3–15 m depth) of the Greek seas and assessed their potential association with marine heatwaves, while also examining the effects on plant growth associated with the transition from vegetative to sexual reproduction. …

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Testing Multiple Environmental Dna Substrates For Detection Of The Cryptic And Critically Endangered Burrowing Freshwater Crayfish Engaewa Pseudoreducta, Kathryn L. Dawkins, Paul Nevill, Brian Chambers, Shane Herbert, Quinton F. Burnham Jul 2024

Testing Multiple Environmental Dna Substrates For Detection Of The Cryptic And Critically Endangered Burrowing Freshwater Crayfish Engaewa Pseudoreducta, Kathryn L. Dawkins, Paul Nevill, Brian Chambers, Shane Herbert, Quinton F. Burnham

Research outputs 2022 to 2026

Effective conservation of endangered species depends on knowledge of their distributions, but species detection can often be challenging. An example of this is provided by the Critically Endangered Margaret River burrowing crayfish (Engaewa pseudoreducta), which is highly cryptic. Due to the burrowing habit of this crayfish, detection of this species currently requires a great deal of effort, the results are often non-conclusive, and, as it involves manual excavation of their burrows, the habitat of this and other species is destroyed in the detection process. In response to these challenges, this study developed and optimized a species-specific probe-based qPCR assay targeting …

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Unveiling The Dynamics Of Ai Applications: A Review Of Reviews Using Scientometrics And Bertopic Modeling, Raghu Raman, Debidutta Pattnaik, Laurie Hughes, Prema Nedungadi Jul 2024

Unveiling The Dynamics Of Ai Applications: A Review Of Reviews Using Scientometrics And Bertopic Modeling, Raghu Raman, Debidutta Pattnaik, Laurie Hughes, Prema Nedungadi

Research outputs 2022 to 2026

In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The …

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On Angles In Higher Order Brillouin Tessellations And Related Tilings In The Plane, Herbert Edelsbrunner, Alexey Garber, Mohadese Ghafari, Teresa Heiss, Morteza Saghafian Jul 2024

On Angles In Higher Order Brillouin Tessellations And Related Tilings In The Plane, Herbert Edelsbrunner, Alexey Garber, Mohadese Ghafari, Teresa Heiss, Morteza Saghafian

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

For a locally finite set in R 2 , the order-k Brillouin tessellations form an infinite sequence of convex face-to-face tilings of the plane. If the set is coarsely dense and generic, then the corresponding infinite sequences of minimum and maximum angles are both monotonic in k. As an example, a stationary Poisson point process in R 2 is locally finite, coarsely dense, and generic with probability one. For such a set, the distributions of angles in the Voronoi tessellations, Delaunay mosaics, and Brillouin tessellations are independent of the order and can be derived from the formula for angles in …

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Cellulosic Rich Biomass Production With Optimized Process Parameters By Using Glycerol Pretreatment For Biofuels Applications, Muhammad Sulaiman, Hamayoun Mahmood, Haris M. Khan, Tanveer Iqbal, Nehar U. Khan, Muhammad M. Abbas, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar Jul 2024

Cellulosic Rich Biomass Production With Optimized Process Parameters By Using Glycerol Pretreatment For Biofuels Applications, Muhammad Sulaiman, Hamayoun Mahmood, Haris M. Khan, Tanveer Iqbal, Nehar U. Khan, Muhammad M. Abbas, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar

Research outputs 2022 to 2026

In this work, we conduct acidified aqueous glycerol pre-treatment (AAG) on rice husks (RH) and utilize the response surface methodology (RSM) to assess the impact of pre-treatment parameters. The primary objective of this research is to optimize the parameters to maximize the cellulose content within RH. The parameters under consideration encompassed temperature (ranging from 80 to 110 °C), retention time (spanning 15 to 45 min), and biomass loading (varying from 5 to 10 wt. %). To achieve this optimization, we perform the Box-Behnken Design (BBD) within the framework of RSM. Additionally, we scrutinize the interactive effects of these parameters on …

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The Application Of Carbonate And Sediment Budgets To Assess The Stability Of Marginal Reef Systems, Shannon Dee, Adi Zweifler, Michael Cuttler, Jake Nilsen, Joshua Bonesso, Michael O'Leary, Nicola K. Browne Jul 2024

The Application Of Carbonate And Sediment Budgets To Assess The Stability Of Marginal Reef Systems, Shannon Dee, Adi Zweifler, Michael Cuttler, Jake Nilsen, Joshua Bonesso, Michael O'Leary, Nicola K. Browne

Research outputs 2022 to 2026

Coral reefs and their associated landforms (carbonate islands and shorelines) are under increasing threat from the effects of anthropogenic climate change, including sea level rise (SLR). The ability of a reef to keep up with SLR depends on the rate of calcium carbonate accretion. Census-based carbonate budgets quantify rates of net calcium carbonate production on a reef and facilitate estimations of vertical reef accretion potential (RAP). To date, most carbonate budget studies have been undertaken in clear-water settings resulting in a limited understanding of how inshore reefs situated in more marginal environmental settings are functioning now and under future climate …

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Application Of Event Cameras And Neuromorphic Computing To Vslam: A Survey, Sangay Tenzin, Alexander Rassau, Douglas Chai Jul 2024

Application Of Event Cameras And Neuromorphic Computing To Vslam: A Survey, Sangay Tenzin, Alexander Rassau, Douglas Chai

Research outputs 2022 to 2026

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, …

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Mvmoe: Multi-Task Vehicle Routing Solver With Mixture-Of-Experts, Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu Jul 2024

Mvmoe: Multi-Task Vehicle Routing Solver With Mixture-Of-Experts, Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu

Research Collection School Of Computing and Information Systems

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes …

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Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem, Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun Jul 2024

Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem, Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun

Research Collection School Of Computing and Information Systems

Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based …

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Adaptive Stabilization Based On Machine Learning For Column Generation, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Eberhard Andrew, Guangquan Zhang Jul 2024

Adaptive Stabilization Based On Machine Learning For Column Generation, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Eberhard Andrew, Guangquan Zhang

Research Collection School Of Computing and Information Systems

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. …

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Containerization On A Self-Supervised Active Foveated Approach To Computer Vision, Dario Dematties, Silvio Rizzi, George K. Thiruvathukal Jun 2024

Containerization On A Self-Supervised Active Foveated Approach To Computer Vision, Dario Dematties, Silvio Rizzi, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global …

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Enhancing Tumor Classification Through Machine Learning Algorithms For Breast Cancer Diagnosis, Lawrence Agbota, Edmund Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei- Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, Augustina Adjei Jun 2024

Enhancing Tumor Classification Through Machine Learning Algorithms For Breast Cancer Diagnosis, Lawrence Agbota, Edmund Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei- Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, Augustina Adjei

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In cancer diagnosis, machine learning helps improve cancer detection by providing doctors with a second perspective and allowing for faster and more accurate determination and decisions. Numerous studies have used both classic machine learning approaches and deep learning to address cancer classification. In this study, we examine the efficacy of five commonly used machine learning algorithms; both traditional and deep learning models namely, Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), Decision Tree and Deep Neural Networks (DNN). We analyze their ability to properly classify tumors as Benign or Malignant using the Wisconsin breast cancer dataset (WBCD). Random Forest …

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On The Size Of Maximal Binary Codes With 2, 3, And 4 Distances, Alexander Barg, Alexey Glazyrin, Wei-Jiun Kao, Ching-Yi Lai, Pin-Chieh Tseng, Wei-Hsuan Yu Jun 2024

On The Size Of Maximal Binary Codes With 2, 3, And 4 Distances, Alexander Barg, Alexey Glazyrin, Wei-Jiun Kao, Ching-Yi Lai, Pin-Chieh Tseng, Wei-Hsuan Yu

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We address the maximum size of binary codes and binary constant weight codes with few distances. Previous works established a number of bounds for these quantities as well as the exact values for a range of small code lengths. As our main results, we determine the exact size of maximal binary codes with two distances for all lengths n≥6 as well as the exact size of maximal binary constant weight codes with 2, 3, and 4 distances for several values of the weight and for all but small lengths.

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Pseudo-Siamese Network Combined With Label-Free Raman Spectroscopy For The Quantification Of Mixed Trace Amounts Of Antibiotics In Human Milk: A Feasibility Study, Jing Yi Mou, Muhammad Usman, Jia Wei Tang, Quan Yuan, Zhang Wen Ma, Xin Ru Wen, Zhao Liu, Liang Wang Jun 2024

Pseudo-Siamese Network Combined With Label-Free Raman Spectroscopy For The Quantification Of Mixed Trace Amounts Of Antibiotics In Human Milk: A Feasibility Study, Jing Yi Mou, Muhammad Usman, Jia Wei Tang, Quan Yuan, Zhang Wen Ma, Xin Ru Wen, Zhao Liu, Liang Wang

Research outputs 2022 to 2026

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network …

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