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Articles 1441 - 1470 of 144425

Full-Text Articles in Physical Sciences and Mathematics

Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang Mar 2024

Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.

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Quantum Computing And U.S. Cybersecurity: A Case Study Of The Breaking Of Rsa And Plan For Cryptographic Algorithm Transition, Helena Holland Mar 2024

Quantum Computing And U.S. Cybersecurity: A Case Study Of The Breaking Of Rsa And Plan For Cryptographic Algorithm Transition, Helena Holland

Honors Theses

The invention of a cryptographically relevant quantum computer would revolutionize computing power, transforming industry and national security. While a theoretical possibility at the time of this writing, the ability of quantum algorithms to solve the factoring and discrete logarithm problems, upon which all currently employed public-key cryptography depends, presents a serious threat to digital communications. This research examines both the mathematics and government policy behind these risks and their implications for cybersecurity. Specifically, a case study of RSA, Shor’s algorithm, and the American Intelligence Community’s plan to transition toward quantum-resistant algorithms is presented to analyze quantum threats and opportunities and …

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Light Curve And Hardness Tests For Millilensing In Grb 081122a, Grb 081126a, Grb 110517b, And Grb 210812a, Oindabi Mukherjee, Robert J. Nemiroff Mar 2024

Light Curve And Hardness Tests For Millilensing In Grb 081122a, Grb 081126a, Grb 110517b, And Grb 210812a, Oindabi Mukherjee, Robert J. Nemiroff

Michigan Tech Publications, Part 2

Analyses are given on four recent gravitational millilensing claims on gamma-ray bursts (GRBs): GRB 081122A, GRB 081126A, GRB 110517B, and GRB 210812A. Two tests, a light curve similarity test and a hardness similarity test, compare different temporal sections of a single GRB to see if they are statistically similar. The hardness similarity test shows that the ratio between the second and the first emission episodes in each energy channel differed from the same ratio averaged over all energy channels at above 90 per cent confidence level in GRB 081122A. Additionally, the light curve similarity test applied to GRB 081122A, GRB …

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Health And Safety Plan (Hasp) Butte Priority Soils Operable Unit, Woodard & Curran Mar 2024

Health And Safety Plan (Hasp) Butte Priority Soils Operable Unit, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Attachment A Demonstration Of Need For Butte Priority Soils Operable Unit (Bpsou) Temporary Construction Surface Water Performance Standards Variance Request For Grove Gulch Sedimentation Bay Remedial Action Site, Josh Bryson Mar 2024

Attachment A Demonstration Of Need For Butte Priority Soils Operable Unit (Bpsou) Temporary Construction Surface Water Performance Standards Variance Request For Grove Gulch Sedimentation Bay Remedial Action Site, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang Mar 2024

Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang

Computer Science Faculty Publications and Presentations

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …

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Investigating Communication Of Findings In Environmental Impact Assessment And Developing A Research Agenda For Improvement, Alan Bond, Francois Retief, Angus Morrison-Saunders, Jenny Pope, Reece C. Alberts, Claudine Roos, Dirk Cilliers Mar 2024

Investigating Communication Of Findings In Environmental Impact Assessment And Developing A Research Agenda For Improvement, Alan Bond, Francois Retief, Angus Morrison-Saunders, Jenny Pope, Reece C. Alberts, Claudine Roos, Dirk Cilliers

Research outputs 2022 to 2026

Environmental Impact Assessment (EIA) aims to embed consideration of the significance of predicted environmental consequences (the findings) of proposed developments into approval decision making. Achieving this aim relies on adequate communication of the findings of the EIA to the stakeholders, especially the decision makers responsible for the approval decision. However, the naïve assumption that this communication of findings can be effectively achieved through the publication of a written report pervades legislation worldwide, despite decades of evidence to the contrary. As a first step towards improving such communication, this research identifies the contingent conditions associated with effectively transferring EIA findings from …

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Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan Mar 2024

Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …

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Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo Mar 2024

Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present …

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Demystifying Faulty Code: Step-By-Step Reasoning For Explainable Fault Localization, Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo Mar 2024

Demystifying Faulty Code: Step-By-Step Reasoning For Explainable Fault Localization, Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo

Research Collection School Of Computing and Information Systems

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers …

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Numerical Simulations For Fractional Differential Equations Of Higher Order And A Wright-Type Transformation, Mariana Nacianceno, Tamer Oraby, Hansapani Rodrigo, Y. Sepulveda, Josef A. Sifuentes, Erwin Suazo, T. Stuck, J. Williams Mar 2024

Numerical Simulations For Fractional Differential Equations Of Higher Order And A Wright-Type Transformation, Mariana Nacianceno, Tamer Oraby, Hansapani Rodrigo, Y. Sepulveda, Josef A. Sifuentes, Erwin Suazo, T. Stuck, J. Williams

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this work, a new relationship is established between the solutions of higher fractional differential equations and a Wright-type transformation. Solutions could be interpreted as expected values of functions in a random time process. As applications, we solve the fractional beam equation, fractional electric circuits with special functions as external sources, and derive d’Alembert’s formula for the fractional wave equation. Due to this relationship, we present two methods for simulating solutions of fractional differential equations. The two approaches use the interpretation of the Caputo derivative of a function as a Wright-type transformation of the higher derivative of the function. In …

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Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo Mar 2024

Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo

Research Collection School Of Computing and Information Systems

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers’ interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built …

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Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra Mar 2024

Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We present the hardware design and software pipeline for an ultra-low power device, in the form factor of a wearable badge, that supports energy efficient sensing, processing and wireless transfer of human voice commands and interactions. The proposed system, called PA2BLO, is envisioned to support both: (a) real-time, scalable, authorized voice based interaction and control of devices and appliances, and (b) longitudinal, low-power logging of natural voice interactions. PA2BLO in-troduces two key novel capabilities. First, it includes a low power, low-complexity voice authentication module that is able to reliably authenticate an authorized user only using low sampling rate (500 Hz) …

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Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun Mar 2024

Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; …

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Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao Mar 2024

Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz , a machine learning guided smart contract fuzzing framework. …

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Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma Mar 2024

Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma

Research Collection School Of Computing and Information Systems

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selectorclassifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the …

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Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw Mar 2024

Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Similarity joins on multidimensional data are crucial operators for recommendation purposes. The classic ��-join problem finds all pairs of points within �� distance to each other among two ��-dimensional datasets. In this paper, we consider a novel and alternative version of ��-join named community similarity based on user profile joins (CSJ). The aim of CSJ problem is, given two communities having a set of ��-dimensional users, to find how similar are the communities by matching every single pair of users (a user can be matched with at most one other user) having an absolute difference of at most �� per …

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Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose Mar 2024

Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose

Research Collection School Of Computing and Information Systems

Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on …

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Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan Mar 2024

Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan

Research Collection School Of Computing and Information Systems

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …

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Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu Mar 2024

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

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On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman Mar 2024

On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman

Research Collection School Of Computing and Information Systems

We report on two studies that examine how social sentiment influences information asymmetry in digital currency markets. We also assess whether cryptocurrency can be an investment vehicle, as opposed to only an instrument for asset speculation. Using a dataset on transactions from an exchange in South Korea and sentiment from Korean social media in 2018, we conducted a study of different trading behavior under two cryptocurrency trading market microstructures: a bid-ask spread dealer's market and a continuous trading buy-sell, immediate trade execution market. Our results highlight the impacts of positive and negative trader social sentiment valences on the effects of …

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T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen Mar 2024

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …

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Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine Mar 2024

Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine

Research Collection School Of Computing and Information Systems

Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research …

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Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung Mar 2024

Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way …

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Iterative Graph Self-Distillation, Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing Mar 2024

Iterative Graph Self-Distillation, Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing

Research Collection School Of Computing and Information Systems

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by …

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Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan Mar 2024

Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

AdamW modifies Adam by adding a decoupled weight decay to decay network weights per training iteration. For adaptive algorithms, this decoupled weight decay does not affect specific optimization steps, and differs from the widely used ℓ2-regularizer which changes optimization steps via changing the first- and second-order gradient moments. Despite its great practical success, for AdamW, its convergence behavior and generalization improvement over Adam and ℓ2-regularized Adam (ℓ2-Adam) remain absent yet. To solve this issue, we prove the convergence of AdamW and justify its generalization advantages over Adam and ℓ2-Adam. Specifically, AdamW provably converges but minimizes a dynamically regularized loss that …

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Stability Verification In Stochastic Control Systems Via Neural Network Supermartingales, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger Mar 2024

Stability Verification In Stochastic Control Systems Via Neural Network Supermartingales, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking …

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Strongly Magnetized Accretion In Two Ultracompact Binary Systems, Thomas J. Maccarone, Thomas Kupfer, Edgar Najera Casarrubias, Liliana E. Rivera Sandoval, Aarran W. Shaw, Christoper T. Britt, Jan Van Roestel, David R. Zurek Mar 2024

Strongly Magnetized Accretion In Two Ultracompact Binary Systems, Thomas J. Maccarone, Thomas Kupfer, Edgar Najera Casarrubias, Liliana E. Rivera Sandoval, Aarran W. Shaw, Christoper T. Britt, Jan Van Roestel, David R. Zurek

Physics and Astronomy Faculty Publications and Presentations

We present the discoveries of two of AM CVn systems, Gaia14aae and SDSS J080449.49+161624.8, which show X-ray pulsations at their orbital periods, indicative of magnetically collimated accretion. Both also show indications of higher rates of mass transfer relative to the expectations from binary evolution driven purely by gravitational radiation, based on existing optical data for Gaia14aae, which show a hotter white dwarf temperature than expected from standard evolutionary models, and X-ray data for SDSS J080449.49+161624.8 which show a luminosity 10−100 times higher than those for other AM CVn at similar orbital periods. The higher mass transfer rates could be driven …

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Powerful Radio Sources In The Southern Sky. Iii. First Results Of The Optical Spectroscopic Campaign, A. García-Pérez, H. A. Peña-Herazo, A. Jimenez-Gallardo, V. Chavushyan, F. Massaro, S. V. White, A. Capetti, B. Balmaverde, W. R. Forman, Juan P. Madrid Mar 2024

Powerful Radio Sources In The Southern Sky. Iii. First Results Of The Optical Spectroscopic Campaign, A. García-Pérez, H. A. Peña-Herazo, A. Jimenez-Gallardo, V. Chavushyan, F. Massaro, S. V. White, A. Capetti, B. Balmaverde, W. R. Forman, Juan P. Madrid

Physics and Astronomy Faculty Publications and Presentations

We recently built the G4Jy-3CRE catalog of extragalactic radio sources. This catalog lists 264 powerful radio sources selected with similar criteria to those of the revised Third Cambridge Catalog, but visible from the Southern Hemisphere. A literature search revealed that 119 sources in the G4Jy-3CRE catalog (i.e., 45%) lack a firm spectroscopic redshift measurement. Here, we present a campaign aimed at acquiring optical spectra of G4Jy-3CRE sources and measuring their redshifts. We used single-slit observations obtained with the Víctor Blanco Telescope, the New Technology Telescope, the Southern Astrophysical Research Telescope, and the 2.1 m telescope of the Observatorio Astronómico Nacional …

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Comment On “Spectral Shifts In General Relativity,” [Am. J. Phys. 62(10), 903–907 (1994)], Joseph D. Romano, Teviet Creighton Mar 2024

Comment On “Spectral Shifts In General Relativity,” [Am. J. Phys. 62(10), 903–907 (1994)], Joseph D. Romano, Teviet Creighton

Physics and Astronomy Faculty Publications and Presentations

No abstract provided.

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