Physical Sciences and Mathematics Commons^™

Understanding Political Polarization Using Language Models: A Dataset And Method, Samiran Gode, Supreeth Bare, Bhiksha Raj, Hyungon Yoo

Natural Language Processing Faculty Publications

Our paper aims to analyze political polarization in US political system using language models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates' views on the economy, healthcare, education, and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a language model-based method that helps analyze how polarized a candidate is. Our data are divided into two parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based …

Multi-Scale Attention Networks For Pavement Defect Detection, Junde Chen, Yuxin Wen, Yaser Ahangari Nanehkaran, Defu Zhang, Adan Zeb

Engineering Faculty Articles and Research

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. …

Disentangling Groundwater Dynamics At Oregon's Mckenzie River Headwaters, Taylor Nicole Salazar

Dissertations and Theses

Widespread mountain snowpack declines have been occurring across the western United States over the last century, and are increasing in duration and severity, with several record-breaking low-snowpack years in the last decade. In the Pacific Northwest, summer streamflow is largely a result of melting snow and groundwater discharge, as summers are typically dry and warm in this Mediterranean bioclimate, thus making spring-fed rivers critical for regional water resources. However, current trends and predictions in hydroclimate modeling indicate spring-fed rivers will experience reduced summer discharge driven by the increasingly larger impact of anthropogenic climate change (warming) on (decreased) mountain snowpack. A …

Comments On The Draft Butte Priority Soils Operable Unit (Bpsou) Buffalo Gulch 60 Percent Design (Dated December 9, 2022), Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Comments On The Draft Butte Priority Soils Operable Unit (Bpsou) Buffalo Gulch 60 Percent Design (Dated December 9, 2022), Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed (Ir) Sites Colorado Dump – West Lot Construction Completion Report (Ccr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Comments On The Draft Butte Priority Soils Operable Unit (Bpsou) Buffalo Gulch 60 Percent Design (Dated December 9, 2022), Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Spatial Variations In Ancient Meteoric Water: An Investigation Of The Rattlesnake Tuff, Julian Michael Cohen

Dissertations and Theses

Volcanic glass has been used extensively as a paleoclimate proxy. Deuterium (²H) concentrations in glass have been found to be stable over geologic timescales, making δD a reliable proxy for ancient water chemistry. However, continued work revolves around better understanding how different factors affect preserved water in ash. Here, I analyze δD in the Rattlesnake Tuff (RST), a widespread ca. 7 Ma ashflow tuff, and create an isoscape to assess variations in δD across Oregon during that time. Additionally, I examine compositional data from glass shards to explore the relationship between δD and shard composition. The RST exhibits …

Wave Optics Approach To Solar Cell Brdf Modeling With Experimental Results, Madilynn Compean, Todd V. Small, Milo W. Hyde Iv, Michael Marciniak

Faculty Publications

Light curve analysis is often used to discern information about satellites in geosynchronous orbits. Solar panels, comprising a large part of the satellite’s body, contribute significantly to these light curves. Historically, theoretical bidirectional reflectance distribution functions (BRDFs) have failed to capture key features in the scattered light from solar panels. In recently published work, a new solar cell BRDF was developed by combining specular microfacet and “two-slit” diffraction terms to capture specular and periodic/array scattering, respectively. This BRDF was experimentally motivated and predicted many features of the solar cell scattered irradiance. However, the experiments that informed the BRDF were limited …

Eliminating Adversarial Noise Via Information Discard And Robust Representation Restoration, Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Deep neural networks (DNNs) are vulnerable to adversarial noise. Denoising model-based defense is a major protection strategy. However, denoising models may fail and induce negative effects in fully white-box scenarios. In this work, we start from the latent inherent properties of adversarial samples to break the limitations. Unlike solely learning a mapping from adversarial samples to natural samples, we aim to achieve denoising by destroying the spatial characteristics of adversarial noise and preserving the robust features of natural information. Motivated by this, we propose a defense based on information discard and robust representation restoration. Our method utilize complementary masks to …

Which Is Better For Learning With Noisy Labels: The Semi-Supervised Method Or Modeling Label Noise?, Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu

Machine Learning Faculty Publications

In real life, accurately annotating large-scale datasets is sometimes difficult. Datasets used for training deep learning models are likely to contain label noise. To make use of the dataset containing label noise, two typical methods have been proposed. One is to employ the semi-supervised method by exploiting labeled confident examples and unlabeled unconfident examples. The other one is to model label noise and design statistically consistent classifiers. A natural question remains unsolved: which one should be used for a specific real-world application? In this paper, we answer the question from the perspective of causal data generative process. Specifically, the performance …

Waterbody Size Predicts Bank- And Boat-Angler Efforts, Derek S. Kane, Kevin L. Pope, Keith D. Koupal, Mark A. Pegg, Christopher J. Chizinski, Mark A. Kaemingk

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

Bank- and boat-angler efforts are logistically difficult and costly to estimate, preventing landscape-scale estimates that are required to address current and future challenges (e.g., climate change, invasive species) for inland recreational fisheries. Using a large Nebraska, USA, recreational fishery dataset (N = 67 waterbodies), we demonstrate that waterbody size can be used to predict bank- and boat-angler efforts across a heterogeneous landscape of extra small (< 104 ha) and large (> 647 ha) waterbodies. Bank and boat anglers respond to waterbody size, however these relationships appear to be unique between the two angler types. Boat-angler efforts increased as a function of waterbody size, whereas bank-angler …

Advancing Our Understanding Of Martian Proton Aurora Through A Coordinated Multi-Model Comparison Campaign, Andrea C. G. Hughes, Michael Scott Chaffin, Edwin J. Mierkiewicz, Justin Deighan, Rebecca Jolitz, Esa Kallio, Guillaume Gronof, Valery I. Shematovich, Dmitry Bisikalo, Cyril L. Simon Wedlund, Jasper S. Halekas, Nicholas M. Schneider, Birgit Ritter, Zachary Girazian, Sonal Jain, Jean-Claude M. C. G´Erard, Bradley Michael Hegyi

Publications

Proton aurora are the most commonly observed yet least studied type of aurora at Mars. In order to better understand the physics and driving processes of Martian proton aurora, we undertake a multi-model comparison campaign. We compare results from four different proton/hydrogen precipitation models with unique abilities to represent Martian proton aurora: Jolitz model (3-D Monte Carlo), Kallio model (3-D Monte Carlo), Bisikalo/Shematovich et al. model (1-D kinetic Monte Carlo), and Gronoff et al. model (1-D kinetic). This campaign is divided into two steps: an inter-model comparison and a data-model comparison. The inter-model comparison entails modeling five different representative cases …

She Is An Expert In This Research Field: The Signal Of Recent Publications' Relevance, Gil Zeevi, Osnat Mokryn

Northeast Journal of Complex Systems (NEJCS)

Assessing the expertise of researchers has garnered increased interest recently. This heightened focus arises from the growing emphasis on interdisciplinary science and the subsequent need to form expert teams. When forming these teams, the coordinators need to assess expertise in fields that are often very different from theirs. The conventional reliance on signals of success, prestige, and academic impact can unintentionally perpetuate biases within the assessment process. This traditional approach favors senior researchers and those affiliated with prestigious institutions, potentially overlooking talented individuals from underrepresented backgrounds or institutions. This paper addresses the challenge of determining expertise by proposing a methodology …

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

The Spectrum Of Nim-Values For Achievement Games For Generating Finite Groups, Bret J. Benesh, Dana C. Ernst, Nándor Sieben

Mathematics Faculty Publications

We study an impartial achievement game introduced by Anderson and Harary. The game is played by two players who alternately select previously unselected elements of a finite group. The game ends when the jointly selected elements generate the group. The last player able to make a move is the winner of the game. We prove that the spectrum of nim-values of these games is {0, 1, 2, 3, 4}. This positively answers two conjectures from a previous paper by the last two authors.

Sentiment Analysis Before And During The Covid-19 Pandemic, Emily Musgrove

Mathematics Summer Fellows

This study examines the change in connotative language use before and during the Covid-19 pandemic. By analyzing news articles from several major US newspapers, we found that there is a statistically significant correlation between the sentiment of the text and the publication period. Specifically, we document a large, systematic, and statistically significant decline in the overall sentiment of articles published in major news outlets. While our results do not directly gauge the sentiment of the population, our findings have important implications regarding the social responsibility of journalists and media outlets especially in times of crisis.

Self-Inductance And Magnetic Flux, Diego Castano, Teresa M. Castano

Chemistry and Physics Faculty Articles

The canonical equation for self-inductance involving magnetic flux is examined, and a more general form is presented that can be applied to continuous current distributions. We attempt to clarify and extend the use of the standard equation by recasting it in its more versatile form.

Density Dependence And Dynamics Of Dipole-Dipole Interactions Among Rydberg Atoms, Hannah Conley

Physics and Astronomy Summer Fellows

After trapping atoms, exciting them to an initial high-energy Rydberg state, and allowing them a brief time to exchange energy with each other through dipole-dipole interactions, we observe how their energies are redistributed among various energy levels. The measurements we make in this physical experiment do not give insight into how individual atoms interact or metrics like fidelity and entanglement entropy, which impact our results. For this reason, it is useful to compare our results from the physical experiment to those of our simulation on a supercomputer, in which we can track the final and initial energy of individual atoms …

Gamma-Ray Spectroscopy Of One-Neutron Knockout From 72se, Alyssa Himmelreich

Physics and Astronomy Summer Fellows

We studied single-neuron excitations of the exotic nucleus ⁷¹Se with the one-neutron knockout reaction from a ⁷²Se beam at the National Superconducting Cyclotron Laboratory. We measured gamma rays de-exciting states in ⁷¹Se using the Gretina gamma-ray tracking array and the momentum of the ⁷¹Se reaction product with the S800 Magnetic Spectrograph. We utilize a supercomputer to run multiple simulations and fit them to our gamma-ray spectrum. Using gamma-ray yields and level schemes, we can determine the number of times each excited state was populated by neutron knockout. We deduced cross sections for populating single-neutron excitations of …

Enhanced Quantum Chemistry With Machine Learning, Brock Dyer

Physics and Astronomy Summer Fellows

This file is a catalogue of the relevant quantum mechanical and computer programming topics that I learned during the summer which will be helping me to generate an artificial intelligence that will be able to perform computational chemical calculations at a much faster rate and comparable or better accuracy than current methods.

Improving The Efficiency Of Liquid-Hydrogen Simulation Via Event Storage, Jake Kosa

Physics and Astronomy Summer Fellows

We contributed to the analysis of gamma-ray spectroscopy data collected at the Facility for Rare Isotope Beams at Michigan State University by speeding up the UCGretina simulation code, used in the analysis and planning of experiments. Simulating beam-target interactions in a liquid-hydrogen target system is a time intensive task, even when parallelized. In the process of analyzing data, a large number of simulations must be run for different gamma-ray energies, target positions, and lifetimes of excited states. We are addressing the most computationally intensive component of the simulations by adding the ability to simulate a large sample of beam particles …

Carbon Nanotubes Animated, Nicholas Onyemeke

Chemistry Summer Fellows

In this project, I implemented the knowledge about carbon nanotubes into an animated short. The animated short is meant to simplify the research so that anyone can understand regardless of their background in science. The research I did was on how nanotubes can be used in the medical field as a measure to inhibit bacteria growth. A nanotube dye solution was synthesized to inoculate bacteria along with an antibiotic solution with the nanotubes. This was done to observe how well the nanotubes can inhibit bacteria growth without the need for a drug compared to the effectiveness when it has a …

On Training Neurons With Bounded Compilations, Lance Kennedy

Master of Science in Computer Science Theses

Knowledge compilation offers a formal approach to explaining and verifying the behavior of machine learning systems, such as neural networks. Unfortunately, compiling even an individual neuron into a tractable representation such as an Ordered Binary Decision Diagram (OBDD), is an NP-hard problem. In this thesis, we consider the problem of training a neuron from data, subject to the constraint that it has a compact representation as an OBDD. Our approach is based on the observation that a neuron can be compiled into an OBDD in polytime if (1) the neuron has integer weights, and (2) its aggregate weight is bounded. …

One Formula For Non-Prime Numbers: Motivations And Characteristics, Mahmoud Mansour, Kamal Hassan Prof.

Basic Science Engineering

Primes are essential for computer encryption and cryptography, as they are fundamental units of whole numbers and are of the highest importance due to their mathematical qualities. However, identifying a pattern of primes is not easy. Thinking in a different way may get benefits, by considering the opposite side of the problem which means focusing on non-prime numbers. Recently, researchers introduced, the pattern of non-primes in two maximal sets while in this paper, non-primes are presented in one formula. Getting one-way formula for non-primes may pave the way for further applications based on the idea of primes.

The Screening Of The Protective Antigens Of Aeromonas Hydrophila Using The Reverse Vaccinology Approach: Potential Candidates For Subunit Vaccine Development, Xiaoheng Zhao, Hanliang Cheng, Xiangning Chen, Jianhe Xu, Zhujin Ding

Faculty, Staff and Student Publications

The threat of bacterial septicemia caused by Aeromonas hydrophila infection to aquaculture growth can be prevented through vaccination, but differences among A. hydrophila strains may affect the effectiveness of non-conserved subunit vaccines or non-inactivated A. hydrophila vaccines, making the identification and development of conserved antigens crucial. In this study, a bioinformatics analysis of 4268 protein sequences encoded by the A. hydrophila J-1 strain whole genome was performed based on reverse vaccinology. The specific analysis included signal peptide prediction, transmembrane helical structure prediction, subcellular localization prediction, and antigenicity and adhesion evaluation, as well as interspecific and intraspecific homology comparison, thereby screening …

The Impact Framework And Implementation For Accessible In Silico Clinical Phenotyping In The Digital Era, Kurt Miller, Liwei Wang, Kirk E Roberts, Steven D Bedrick, William R Hersh, Hongfang Liu

Faculty, Staff and Student Publications

Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping …

Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Dulin Wang, Wentao Li, Sean I Savitz, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

BACKGROUND: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct …

Ortho-Fluoro Effect On The C–C Bond Activation Of Benzonitrile Using Zerovalent Nickel, Sébastien Lachaize, Dominique C. Gallegos, Juliana J. Antonio, Abdurrahman C. Atesin, Tülay A. Ateşin, William D. Jones

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

The effect of fluoro substitution on the C–C bond activation of aromatic nitriles has been studied by reacting a variety of fluorinated benzonitriles with the nickel(0) fragment [Ni(dippe)] and by locating the reaction intermediates and transition-state structures on the potential energy surface by using density functional theory calculations with the [Ni(dmpe)] fragment (dippe = 1,2-bis(diisopropylphosphino)ethane, dmpe = 1,2-bis(dimethylphosphino)ethane). As in the previous reports, the reaction of fluorinated benzonitriles with the [Ni(dippe)] fragment initially formed an η2-nitrile complex, which then converted to the C–CN bond activation product. Thermodynamic parameters for the equilibrium between these complexes have been determined experimentally in both …