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Articles 24061 - 24090 of 294560

Full-Text Articles in Physical Sciences and Mathematics

Beat-It : Beat-Synchronized Multi-Condition 3d Dance Generation, Zikai Huang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Chenxi Zheng, Jing Qin, Shengfeng He Oct 2024

Beat-It : Beat-Synchronized Multi-Condition 3d Dance Generation, Zikai Huang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Chenxi Zheng, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Dance, as an art form, fundamentally hinges on the precise synchronization with musical beats. However, achieving aesthetically pleasing dance sequences from music is challenging, with existing methods often falling short in controllability and beat alignment. To address these shortcomings, this paper introduces Beat-It, a novel framework for beat-specific, key pose-guided dance generation. Unlike prior approaches, Beat-It uniquely integrates explicit beat awareness and key pose guidance, effectively resolving two main issues: the misalignment of generated dance motions with musical beats, and the inability to map key poses to specific beats, critical for practical choreography. Our approach disentangles beat conditions from music …

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Onerestore : A Universal Restoration Framework For Composite Degradation, Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, Shengfeng He Oct 2024

Onerestore : A Universal Restoration Framework For Composite Degradation, Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite this reality, existing restoration methods typically target isolated degradation types, thereby falling short in environments where multiple degrading factors coexist. To bridge this gap, our study proposes a versatile imaging model that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios. In this context, we propose OneRestore, a novel transformer-based framework designed for adaptive, controllable scene restoration. The proposed framework leverages a unique cross-attention mechanism, merging degraded scene descriptors with image features, …

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Documenting Ethical Considerations In Open Source Ai Models, Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, Marc Cheong Oct 2024

Documenting Ethical Considerations In Open Source Ai Models, Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, Marc Cheong

Research Collection School Of Computing and Information Systems

Background: The development of AI-enabled software heavily depends on AI model documentation, such as model cards, due to different domain expertise between software engineers and model developers. From an ethical standpoint, AI model documentation conveys critical information on ethical considerations along with mitigation strategies for downstream developers to ensure the delivery of ethically compliant software. However, knowledge on such documentation practice remains scarce. Aims: The objective of our study is to investigate how developers document ethical aspects of open source AI models in practice, aiming at providing recommendations for future documentation endeavours. Method: We selected three sources of documentation on …

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An Empirical Study Of Api Misuses Of Data-Centric Libraries, Akalanda Galappaththi, Sarah Nadi, Christoph Treude Oct 2024

An Empirical Study Of Api Misuses Of Data-Centric Libraries, Akalanda Galappaththi, Sarah Nadi, Christoph Treude

Research Collection School Of Computing and Information Systems

Developers rely on third-party library Application Programming Interfaces (APIs) when developing software. However, libraries typically come with assumptions and API usage constraints, whose violation results in API misuse. API misuses may result in crashes or incorrect behavior. Even though API misuse is a well-studied area, a recent study of API misuse of deep learning libraries showed that the nature of these misuses and their symptoms are different from misuses of traditional libraries, and as a result highlighted potential shortcomings of current misuse detection tools. We speculate that these observations may not be limited to deep learning API misuses but may …

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A Survey Of Protocol Fuzzing, Xiaohan Zhang, Cen Zhang, Xinghua Li, Zhengjie Du, Bing Mao, Yeting Li, Pan Li Oct 2024

A Survey Of Protocol Fuzzing, Xiaohan Zhang, Cen Zhang, Xinghua Li, Zhengjie Du, Bing Mao, Yeting Li, Pan Li

Research Collection School Of Computing and Information Systems

Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to uncovering these vulnerabilities within protocol implementations. However, there still lacks a systematic overview of protocol fuzzing for answering the essential questions such as what the unique challenges are, how existing works solve them, and so on. To bridge this gap, we conducted a comprehensive investigation of related works from both academia and industry. Our study includes a detailed summary of the specific challenges in protocol fuzzing and provides a systematic categorization …

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Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui Oct 2024

Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui

Research Collection School Of Computing and Information Systems

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time …

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Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw Oct 2024

Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This article presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces …

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Graph Continual Learning With Debiased Lossless Memory Replay, Chaoxi Niu, Guansong Pang, Ling Chen Oct 2024

Graph Continual Learning With Debiased Lossless Memory Replay, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks. Memory replay-based methods, which aim to replay data of previous tasks when learning new tasks, have been explored as one principled approach to mitigate the forgetting of the knowledge learned from the previous tasks. In this paper we extend this methodology with a novel framework, called Debiased Lossless Memory replay (DeLoMe). Unlike …

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Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang Oct 2024

Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, …

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Pvp-Ssd: Point-Voxel Fusion With Partitioned Point Cloud Sampling For Anchor-Free Single-Stage Small 3d Object Detection, Xinlin Wu, Yibin Tian, Yin Pan, Zhiyuan Zhang, Xuesong Wu, Ruisheng Wang, Zhi Zeng Oct 2024

Pvp-Ssd: Point-Voxel Fusion With Partitioned Point Cloud Sampling For Anchor-Free Single-Stage Small 3d Object Detection, Xinlin Wu, Yibin Tian, Yin Pan, Zhiyuan Zhang, Xuesong Wu, Ruisheng Wang, Zhi Zeng

Research Collection School Of Computing and Information Systems

Single-stage object detection from 3D point clouds in autonomous driving faces significant challenges, particularly in accurately detecting small objects. To address this issue, we propose a novel method called Point-Voxel dual-branch feature extraction with Partitioned point cloud sampling for anchor-free Single-Stage Detection of 3D objects (PVP-SSD). The network comprises two branches: a point branch and a voxel branch. In the point branch, a partitioned point cloud sampling strategy leverages axial features to divide the point cloud. Then, it assigns different sampling weights to various segments to enhance the sampling accuracy. Additionally, a local feature enhancement module explicitly calculates the correlation …

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Video Editing For Video Retrieval, Bin Zhu, Kevin Flanagan, Adriano Fragomeni, Michael Wray, Dima Damen Oct 2024

Video Editing For Video Retrieval, Bin Zhu, Kevin Flanagan, Adriano Fragomeni, Michael Wray, Dima Damen

Research Collection School Of Computing and Information Systems

Though pre-training vision-language models have demonstrated significant benefits in boosting video-text retrieval performance from large-scale web videos, fine-tuning still plays a critical role with manually annotated clips with start and end times, which requires considerable human effort. To address this issue, we explore an alternative cheaper source of annotations, single timestamps, for video-text retrieval. We initialise clips from timestamps in a heuristic way to warm up a retrieval model. Then a video clip editing method is proposed to refine the initial rough boundaries to improve retrieval performance. A student-teacher network is introduced for video clip editing: the teacher model is …

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Stagedvulbert: Multi-Granular Vulnerability Detection With A Novel Pre-Trained Code Model, Yuan Jiang, Yujian Zhang, Xiaohong Su, Christoph Treude, Tiantian Wang Oct 2024

Stagedvulbert: Multi-Granular Vulnerability Detection With A Novel Pre-Trained Code Model, Yuan Jiang, Yujian Zhang, Xiaohong Su, Christoph Treude, Tiantian Wang

Research Collection School Of Computing and Information Systems

The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed …

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Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto Oct 2024

Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their “black-box” nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to …

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Kpiroot: Efficient Monitoring Metric-Based Root Cause Localization In Large-Scale Cloud Systems, Wenwei Gu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu Oct 2024

Kpiroot: Efficient Monitoring Metric-Based Root Cause Localization In Large-Scale Cloud Systems, Wenwei Gu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their run-time status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

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The Ontology Of Fields As A Foundation For Conceptual Modeling, Arturo Castellanos, Roman Lukyanenko, Binny M. Samuel, Jon W. Beard, Hsiang-Li (Roger) Chiang, Keng Siau, Veda C. Storey, Debra Vandermeer, Carson Woo Oct 2024

The Ontology Of Fields As A Foundation For Conceptual Modeling, Arturo Castellanos, Roman Lukyanenko, Binny M. Samuel, Jon W. Beard, Hsiang-Li (Roger) Chiang, Keng Siau, Veda C. Storey, Debra Vandermeer, Carson Woo

Research Collection School Of Computing and Information Systems

As any discipline matures, its research communities reflect on the epistemological foundations that support their research and discuss how the discipline can, and should, progress. This type of introspection is typically observed in the natural sciences, where researchers question the nature of their fields, the standards of rigor applied, and the impact of their work. As researchers in conceptual modeling, we should continually reflect on, and discuss, the theories and practice of conceptual modeling to ensure its ongoing relevance and consistency with the advancements in information technologies.

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A Quantitative Study Assessing The Impact Of Financial Sector Safeguards Rules On Us Publicly Traded Companies' Cybersecurity Breach Frequency And Severity, Alan Dinerman Oct 2024

A Quantitative Study Assessing The Impact Of Financial Sector Safeguards Rules On Us Publicly Traded Companies' Cybersecurity Breach Frequency And Severity, Alan Dinerman

School of Public Service Theses & Dissertations

Cybersecurity failure in the private sector is a growing federal policy priority. Nevertheless, US policy makers struggle with policy instrumentation in this domain. Behavioral public policy theory asserts that a linkage exists between policy target behavioral & cultural attributes and effective policy tool instrumentation. The federal government is now embracing a regulation heavy approach, but little empirical study exists at the nexus of behavioral public policy theory and use of regulatory tools in the cybersecurity policy domain. Since the early 2000s, the financial sector has employed a regulation heavy approach using the Safeguards regulations to facilitate cybersecurity in financial private …

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Refinement Of Alphafold-Predicted Models Using Cryo-Em Density Maps And Enhancement Of Protein Secondary Structure Topologies With Residue Contacts, Maytha Naif Alshammari Oct 2024

Refinement Of Alphafold-Predicted Models Using Cryo-Em Density Maps And Enhancement Of Protein Secondary Structure Topologies With Residue Contacts, Maytha Naif Alshammari

Computer Science Theses & Dissertations

Proteins play an important role in almost every biological process. Understanding the mechanism of protein function requires knowledge of three-dimensional (3D) structures. Traditionally, the determination of 3D structures has presented significant challenges. However, Cryo-Electron Microscopy (Cryo-EM) has revolutionized the field of structural biology, providing a powerful technique for atomic structure determination. This dissertation delves into the potential of cryoEM in two ways. First, this dissertation presents a new flexible fitting approach, utilizing Normal Mode Analysis (NMA) and Elastic Network Models (ENMs) to refine AlphaFold-predicted models by optimizing the structures to match cryo-EM density maps. This approach identifies the optimal mode …

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Optical Investigation Of A Low-Pressure Plasma In A Superconducting Radio-Frequency Cavity, Zachary A. Caudell Oct 2024

Optical Investigation Of A Low-Pressure Plasma In A Superconducting Radio-Frequency Cavity, Zachary A. Caudell

Electrical & Computer Engineering Theses & Dissertations

The state-of-the-art cryomodules for particle accelerators use superconducting radio-frequency (SRF) cavities. It is known that accumulation of hydrocarbon impurities reduces the cavity surface work function, decreasing cryomodule performance metrics such as field emission, thermal stability, and accelerating gradients. An in-situ method of plasma processing has recently been developed to clean the cavities and restore cryomodule performance [1]. This study is to understand the properties of the low-pressure plasmas generated in a C100 SRF cavity and ultimately to improve the performance of plasma surface cleaning using optical emission spectroscopy. Using a 500 mm focal length Czerny-Turner monochromator coupled with a photomultiplier …

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Symbolic Regression For Data-Driven Equation Discovery: A Physics-Informed Approach, Anusha Reddy Singireddy Oct 2024

Symbolic Regression For Data-Driven Equation Discovery: A Physics-Informed Approach, Anusha Reddy Singireddy

Computer Science Theses & Dissertations

Symbolic Regression (SR) is a cutting-edge machine learning technique that discovers mathematical expressions representing the underlying patterns in data. Unlike traditional regression, SR explores a wide range of mathematical models, allowing for flexible and interpretable solutions. We utilize PySR, a highly customizable symbolic regression package, which combines genetic programming and modern optimization methods to efficiently search for interpretable equations. PySR balances model complexity with performance by penalizing overly complex expressions while optimizing accuracy. In this thesis, we apply Physics-informed Symbolic Regression through PySR to model the x and t dependence of the flavor isovector combination Hu − d( …

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Simulating The Compound Impacts Of Climate Change And Sea-Level Rise On Coastal Shallow Groundwater Using An Integrated Surface-Subsurface Hydrologic Model, Zhaoyi Cai Oct 2024

Simulating The Compound Impacts Of Climate Change And Sea-Level Rise On Coastal Shallow Groundwater Using An Integrated Surface-Subsurface Hydrologic Model, Zhaoyi Cai

Civil & Environmental Engineering Theses & Dissertations

As climate change intensifies and sea levels rise, coastal aquifers become increasingly vulnerable to interacting oceanic and terrestrial processes. This dissertation examines the combined impacts of climate change and sea-level rise (SLR) on coastal shallow groundwater using an integrated surface-subsurface hydrologic model, focusing on the low-lying Little Creek watershed (LCW) in Norfolk, Virginia. The SWAT-MODFLOW model, a coupled system, unifies land surface, streamflow, and groundwater processes, providing a comprehensive framework for watershed-aquifer analysis. Model parameters were derived from U.S. Geological Survey datasets, relevant literature, station records, and technical reports, followed by meticulous calibration. The validated model first simulated baseline groundwater …

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Measuring Clas12 D(E, E′Π±) Cross Sections For E4nu, Caleb Fogler Oct 2024

Measuring Clas12 D(E, E′Π±) Cross Sections For E4nu, Caleb Fogler

Physics Theses & Dissertations

Neutrino experiments need neutrino event generators such as GENIE to simulate neutrino-nucleus (νA) interactions in order to measure neutrino oscillations. We need eA data to validate GENIE. GENIE d(e, e′ ) cross sections do not match data in the pion production region. Further analysis of this region can help constrain GENIE models. The goal of this project was to compare 4.244 GeV CLAS12 d(e, e′π±) cross sections to GENIE predictions. We analyzed data from the Fall 2019 run period of Run Group B (RG-B). We applied particle identification, fiducial, and vertex …

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Etdsuite: A Toolkit To Mine Electronic Theses And Dissertations To Enrich Scholarly Big Data Using Natural Language Processing And Computer Vision, Muntabir Hasan Choudhury Oct 2024

Etdsuite: A Toolkit To Mine Electronic Theses And Dissertations To Enrich Scholarly Big Data Using Natural Language Processing And Computer Vision, Muntabir Hasan Choudhury

Computer Science Theses & Dissertations

In the past decades, there has been a growing interest in mining scientific documents to obtain domain knowledge automatically. One of the understudied types of scientific documents is Electronic Theses and Dissertations (ETDs), as ETDs have distinct features compared with conference proceedings and journal articles. ETDs usually serve as partial requirements of academic degrees for students pursuing higher education. They are book-length documents (i.e., 100 – 400 pages long), and the topics may shift across chapters, exhibit the significant contribution of a student’s research over the entire degree pursuing period, and have unique metadata schema and page layouts. However, the …

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Machine Learning And Simulation Techniques For Detecting Buoys From Lidar Data, Christopher Adolphi Oct 2024

Machine Learning And Simulation Techniques For Detecting Buoys From Lidar Data, Christopher Adolphi

Electrical & Computer Engineering Theses & Dissertations

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive the external environment through onboard sensors. The main sensor utilized in this research is a LiDAR sensor. This sensor is able to generate point clouds of the surrounding environment, of which a machine learning model is used to label each point in …

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A Self Consistent 2d Simulation Of Coherent Synchrotron Radiation Effects On Beam Dynamics, Dallan Duffin Oct 2024

A Self Consistent 2d Simulation Of Coherent Synchrotron Radiation Effects On Beam Dynamics, Dallan Duffin

Physics Theses & Dissertations

An increasing interest in high quality and high current electron beams necessitates a thorough understanding and prediction of coherent synchrotron radiation effects. The self-interaction of charged particles in a beam undergoing synchrotron motion is a physically significant process that is all too often computationally intensive with very little analytical results to rely on for the general case. The coherent spectrum of this interaction is of utmost importance to the design of free electron lasers (FELs) and an accurate assessment is imperative for their design. This work presents a novel implementation to the numerical simulation of charged particle beams. The simulation …

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Suppression Of Dynamic Aeroelastic Response Of A Semispan Wing Using Traditional And Distributed Sensing, James Ramey Oct 2024

Suppression Of Dynamic Aeroelastic Response Of A Semispan Wing Using Traditional And Distributed Sensing, James Ramey

Mechanical & Aerospace Engineering Theses & Dissertations

Characterization and management of aeroelastic effects are a vital part of aircraft design. Mitigation of these effects often includes control systems to suppress the vibrations. Control systems are constantly being developed and improved, with one such avenue of development being new sensing systems that can increase the information fed into the controller. Fiber Optic Strain Sensing (FOSS) technology is one such system that utilizes Fiber Bragg Gratings (FBGs) on a fiber optic cable to sense the strain on structural elements. This system has promise as a high-density feedback device, and the maturation of the technology has allowed assessment of its …

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An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla Oct 2024

An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla

Engineering Management & Systems Engineering Theses & Dissertations

Healthcare workers, either clinical or non-clinical, are obligated to serve patients. However, lack of a sufficient number of professionals leads to burnout, severe stress, and, consequently, decreased quality of services. In this context, very few countries have been successful in employing service robots to perform dull, dirty, and/or dangerous tasks related to patient wellbeing/healthcare, while most countries are still skeptical about it. As robotics advances, there is an opportunity for healthcare to take advantage of this technology to reduce personnel workload and to reduce the possibility of exposure to contagious pathogens. However, healthcare is a vulnerable environment and requires critical …

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Error And Correction Analysis For The Ffa@Cebaf Energy Upgrade, Alexander Mattison Coxe Oct 2024

Error And Correction Analysis For The Ffa@Cebaf Energy Upgrade, Alexander Mattison Coxe

Physics Theses & Dissertations

An energy upgrade design for the Continuous Electron Beam Accelerator Facility (CEBAF) is under development, using fixed field alternating gradient (FFA) return arcs to recirculate electron beam up to an additional five times through the accelerating structures at CEBAF. A necessary component of any large accelerator is a beam steering and optical correction system. Small environmental changes and system errors can lower beam quality or even shut down the machine; and in pursuit of the scientific mission of JLab, high quality electron beams must be delivered to the experimental halls on a predictable schedule. Correction in the novel FFA arcs …

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Resilient Tcp Variant Enabling Smooth Network Updates For Software-Defined Data Center Networks, Abdul Basit Dogar, Sami Ullah, Yiran Zhang, Hisham Alasmary, Muhammad Waqas, Sheng Chen Oct 2024

Resilient Tcp Variant Enabling Smooth Network Updates For Software-Defined Data Center Networks, Abdul Basit Dogar, Sami Ullah, Yiran Zhang, Hisham Alasmary, Muhammad Waqas, Sheng Chen

Research outputs 2022 to 2026

Network updates have become increasingly prevalent since the broad adoption of software-defined networks (SDNs) in data centers. Modern TCP designs, including cutting-edge TCP variants DCTCP, CUBIC, and BBR, however, are not resilient to network updates that provoke flow rerouting. In this paper, we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates, because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance deterioration. We look into the causes and propose a network update-friendly TCP (NUFTCP), which is an extension of …

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Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth Ph.D., James Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich Oct 2024

Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth Ph.D., James Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich

Faculty Publications

Merriam-Webster and Oxford define a xenobiotic as any substance foreign to living systems. Allura Red AC (a.k.a., E129; FD&C Red No. 40), a synthetic food dye extensively used in manufacturing ultra-processed foods and therefore highly prevalent in our food supply, falls under this category. The surge in synthetic food dye consumption during the 70s and 80s was followed by an epidemic of metabolic diseases and the emergence of early-onset colorectal cancer in the 1990s. This temporal association raises significant concerns, particularly given the widespread inclusion of synthetic food dyes in ultra-processed products, notably those marketed toward children. Given its interactions …

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Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth, James R. Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich Oct 2024

Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth, James R. Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich

Faculty Publications

Merriam-Webster and Oxford define a xenobiotic as any substance foreign to living systems. Allura Red AC (a.k.a., E129; FD&C Red No. 40), a synthetic food dye extensively used in manufacturing ultra-processed foods and therefore highly prevalent in our food supply, falls under this category. The surge in synthetic food dye consumption during the 70s and 80s was followed by an epidemic of metabolic diseases and the emergence of early-onset colorectal cancer in the 1990s. This temporal association raises significant concerns, particularly given the widespread inclusion of synthetic food dyes in ultra-processed products, notably those marketed toward children. Given its interactions …

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