Physical Sciences and Mathematics Commons^™

Relative Comparison Of Modern Computing To Computer Technology Of Ages, Iwasan D. Kejawa Dr., Hailly Rubio Ms.

School of Computing: Faculty Publications

Abstract

Abstract

Are there differences and similarities between the computer technology of today and yesterdays. Research had shown that there had been tremendous improvements from the computers of ages (traditional Computers) as we enter the 21st century. Both the physicality and the functionalities of computers have changed but some remain the same. The memory capacity and functions have changed, but all are still based on the old concepts of yesteryears.

Identifying And Analyzing Multi-Star Systems Among Tess Planetary Candidates Using Gaia, Katie E. Bailey

Electronic Theses and Dissertations

Exoplanets represent a young, rapidly advancing subfield of astrophysics where much is still unknown. It is therefore important to analyze trends among their parameters to learn more about these systems. More complexity is added to these systems with the presence of additional stellar companions. To study these complex systems, one can employ programming languages such as Python to parse databases such as those constructed by TESS and Gaia to bridge the gap between exoplanets and stellar companions. Data can then be analyzed for trends in these multi-star exoplanet systems and in juxtaposition to their single-star counterparts. This research was able …

Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan

Physics Faculty Publications

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

Symbolic Computation Of Squared Amplitudes In High Energy Physics With Machine Learning, Abdulhakim Alnuqaydan

Theses and Dissertations--Physics and Astronomy

The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These complex calculations are currently performed using domain-specific symbolic algebra tools, where the computational time escalates rapidly with an increase in the number of loops and final state particles. This dissertation introduces an innovative approach: employing a transformer-based sequence-to-sequence model capable of accurately predicting squared amplitudes of Standard Model processes up to one-loop order when trained on symbolic sequence pairs. The primary objective of this work is to significantly reduce the computational time and, more importantly, develop a model that …

Symplectically Integrated Symbolic Regression Of Hamiltonian Dynamical Systems, Daniel Dipietro

Computer Science Senior Theses

Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data. SISR employs a deep symbolic regression approach, using a multi-layer LSTMRNN with mutation to probabilistically sample Hamiltonian symbolic expressions. Using symplectic neural networks, we develop a model-agnostic approach for extracting meaningful physical priors from the data that can be imposed on-the-fly into the RNN output, limiting its search space. Hamiltonians generated by the RNN are optimized and assessed using a fourth-order symplectic integration scheme; prediction performance is used to train the LSTM-RNN to generate increasingly better functions via a risk-seeking policy gradients …

A Multidisciplinary Collaboration Between Graphic Design And Physics Classes Responding To Covid-19, Szilvia Kadas, Eric M. Edlund

The SUNY Journal of the Scholarship of Engagement: JoSE

Students from graphic design and physics classes at SUNY Cortland collaborated during the spring semester of 2020 on a multidisciplinary project related to the COVID-19 pandemic. In these collaborations, the students’ individual contributions were part of a larger project that required a diverse skill set, through which students learned how different skills can complement their own disciplines. The graphic design and physics instructors applied a project-based learning philosophy applying the Common Problem Pedagogy (CPP) framework to construct student-teams composed of both disciplines. This project explored how coordinated social actions can allow the public to exercise control in uncertain times. Students …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Physics Engine On The Gpu With Opengl Compute Shaders, Quan Huy Minh Bui

Master's Theses

Any kind of graphics simulation can be thought of like a fancy flipbook. This notion is, of course, nothing new. For instance, in a game, the central computing unit (CPU) needs to process frame by frame, figuring out what is happening, and then finally issues draw calls to the graphics processing unit (GPU) to render the frame and display it onto the monitor. Traditionally, the CPU has to process a lot of things: from the creation of the window environment for the processed frames to be displayed, handling game logic, processing artificial intelligence (AI) for non-player characters (NPC), to the …

Making Artificial Cips Data With A Generative Adversarial Neural Network, Austin Hedges

Fall Showcase for Research and Creative Inquiry

Polar mesospheric clouds (PMCs) have been studied for thirteen years by NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. The Cloud Imaging and Particle Size (CIPS) instrument onboard AIM has taken many images of PMCs over this time. Such a large number of images makes CIPS data ideal for training neural networks which require large datasets. CIPS images were used to train a Generative Adversarial Network (GAN) to train towards being able to generate purely artificial CIPS-like images.

Qwasi: The Quantum Walk Simulator, Warren V. Wilson

Theses and Dissertations

As quantum computing continues to evolve, the ability to design and analyze novel quantum algorithms becomes a necessary focus for research. In many instances, the virtues of quantum algorithms only become evident when compared to their classical counterparts, so a study of the former often begins with a consideration of the latter. This is very much the case with quantum walk algorithms, as the success of random walks and their many, varied applications have inspired much interest in quantum correlates. Unfortunately, finding purely algebraic solutions for quantum walks is an elusive endeavor. At best, and when solvable, they require simple …

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

Improving 3d Printed Prosthetics With Sensors And Motors, Rachel Zarin

Honors Projects

A 3D printed hand and arm prosthetic was created from the idea of adding bionic elements while keeping the cost low. It was designed based on existing models, desired functions, and materials available. A tilt sensor keeps the hand level, two motors move the wrist in two different directions, a limit switch signals the fingers to open and close, and another motor helps open and close the fingers. All sensors and motors were built on a circuit board, programmed using an Arduino, and powered by a battery. Other supporting materials include metal brackets, screws, guitar strings, elastic bands, small clamps, …

An Analysis Of Frenkel Defects And Backgrounds Modeling For Supercdms Dark Matter Searches, Matthew Stein

Physics Theses and Dissertations

Years of astrophysical observations suggest that dark matter comprises more than ~80 % of all matter in the universe. Particle physics theories favor a weakly-interacting particle that could be directly detected in terrestrial experiments. The Super Cryogenic Dark Matter Search (SuperCDMS) Collaboration operates world-leading experiments to directly detect dark matter interacting with ordinary matter. The SuperCDMS Soudan experiment searched for weakly interacting massive particles (WIMPs) via their elastic-scattering interactions with nuclei in low-temperature germanium detectors.

During the operation of the SuperCDMS Soudan experiment, ²¹⁰Pb sources were installed to study background rejection of the Ge detectors. Data from these sources …

Quantifying Effects Of Using Thermally Thin Fuel Approximations On Modelling Fire Propagation In Woody Fuels, David Blasen, Jesse Johnson, William Jolly, Russell Parsons

Graduate Student Theses, Dissertations, & Professional Papers

In this paper, we quantify the effects of the thermally thin fuel approximations commonly made in numerical models that eliminate temperature gradients within a heated object. This assumption is known to affect the modeled ignition and burn behavior, but there is little research on its impact, particularly in larger fuels or in numerical models including moisture and chemical decomposition of fuels.

We begin by comparing modeled to observed ignition times and burn rates. To constrain variability in the material properties of wood and focus on variability caused by fuels assumed to be thermally thin, we conduct experiments using thermogravimetric analysis …

Underwater Acoustic Signal Analysis Toolkit, Kirk Bienvenu Jr

University of New Orleans Theses and Dissertations

This project started early in the summer of 2016 when it became evident there was a need for an effective and efficient signal analysis toolkit for the Littoral Acoustic Demonstration Center Gulf Ecological Monitoring and Modeling (LADC-GEMM) Research Consortium. LADC-GEMM collected underwater acoustic data in the northern Gulf of Mexico during the summer of 2015 using Environmental Acoustic Recording Systems (EARS) buoys. Much of the visualization of data was handled through short scripts and executed through terminal commands, each time requiring the data to be loaded into memory and parameters to be fed through arguments. The vision was to develop …

Hardware Design Theory (Using Raspberry Pi), Anthony Kelly, Thomas Blum Dr.

Undergraduate Research

The concept for this research proposal is focused on achieving three main objectives:

1) To understand the logic and design behind the Raspberry Pi (RbP) mini-computer model, including: all hardware components and their functions, the capabilities [and limits] of the RbP, and the circuit engineering for these components.

2) To be able to, using the Python high-level language, duplicate, manipulate, and create RbP projects ranging from basic user-input and response systems to the theories behind more intricate and complicated observatory sensors.

3) Simultaneously, in order to combine a mutual shared interest of History and to blend in work done within …

Grasping The Void: Immersion Tactics Using Gesture Controlled Physics Interaction Systems In Virtual Reality, Avery Rapson

Senior Independent Study Theses

This thesis uses the HTC Vive in Unity to compare two different types of object interaction systems in order to determine the effectiveness of physics based interaction systems in a virtual environment. The research problem that motivates this project is the fact that there is no standardized method for defining successful object interaction techniques in VR. There are numerous interaction techniques in VR that fall short of simulating realistic object interaction. This project explores a physics based interaction system and examines how effective it is by comparing it to a non-physics based system. A model house with various interactable objects …

The Parallelization And Optimization Of The N-Body Problem Using Openmp And Openmpi, Nicholas J. Carugati

Student Publications

The focus of this research is exploring the efficient ways we can implement the NBody problem. The N-Body problem, in the field of physics, is a problem in which predicts or simulates the movements of planets and how they interact with each other gravitationally. For this research, we are viewing if the simulation can execute efficiently by delegating the heavy computational work through different cores of a CPU. The approach that is being used to figure this out is by integrating the parallelization API OpenMP and the message-passing library OpenMPI into the code. Rather than all the code executing on …

Spatio-Temporal Generalization Of The Harris Criterion And Its Application To Diffusive Disorder, Thomas Vojta, Ronald Dickman

Physics Faculty Research & Creative Works

We investigate how a clean continuous phase transition is affected by spatiotemporal disorder, i.e., by an external perturbation that fluctuates in both space and time. We derive a generalization of the Harris criterion for the stability of the clean critical behavior in terms of the space-time correlation function of the external perturbation. As an application, we consider diffusive disorder, i.e., an external perturbation governed by diffusive dynamics, and its effects on a variety of equilibrium and nonequilibrium critical points. We also discuss the relation between diffusive disorder and diffusive dynamical degrees of freedom in the example of model C of …

Strong-Disorder Magnetic Quantum Phase Transitions: Status And New Developments, Thomas Vojta

Physics Faculty Research & Creative Works

This article reviews the unconventional effects of random disorder on magnetic quantum phase transitions, focusing on a number of new experimental and theoretical developments during the last three years. On the theory side, we address smeared quantum phase transitions tuned by changing the chemical composition, for example in alloys of the type A_1-xB_x. We also discuss how the interplay of order parameter conservation and overdamped dynamics leads to enhanced quantum Griffiths singularities in disordered metallic ferromagnets. Finally, we discuss a semiclassical theory of transport properties in quantum Griffiths phases. Experimental examples include the ruthenates Sr_{1-x …}

Rare Regions And Griffiths Singularities At A Clean Critical Point: The Five-Dimensional Disordered Contact Process, Thomas Vojta, John Igo, José A. Hoyos

Physics Faculty Research & Creative Works

We investigate the nonequilibrium phase transition of the disordered contact process in five space dimensions by means of optimal fluctuation theory and Monte Carlo simulations. We find that the critical behavior is of mean-field type, i.e., identical to that of the clean five-dimensional contact process. It is accompanied by off-critical power-law Griffiths singularities whose dynamical exponent z' saturates at a finite value as the transition is approached. These findings resolve the apparent contradiction between the Harris criterion, which implies that weak disorder is renormalization-group irrelevant, and the rare-region classification, which predicts unconventional behavior. We confirm and illustrate our theory by …

Neutrosophic Theory And Its Applications : Collected Papers - Vol. 1, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic Theory means Neutrosophy applied in many fields in order to solve problems related to indeterminacy. Neutrosophy is a new branch of philosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. This theory considers every entity together with its opposite or negation and with their spectrum of neutralities in between them (i.e. entities supporting neither nor ). The and ideas together are referred to as . Neutrosophy is a generalization of Hegel's dialectics (the last one is based on and only). According to this theory every entity tends to be …

Gravity Evolved, Clark Duvall

Computer Science and Software Engineering

Gravity Evolved is a galactic physics game, created for mobile devices. In the game, you make your own solar system by creating planets, and then placing weapons on the planets. The physics of these planets and projectiles shot by the weapons are then simulated. Gravity Evolved has a Battle mode, where your solar system is pitted against an opposing solar system. In Battle mode, you earn money to unlock, create, and upgrade items in your solar system. Gravity Evolved will be on the Apple App Store and Google Play Store in the summer of 2013.

Meter-Sized Moonlet Population In Saturn's C Ring And Cassini Division, K. Baillié, J. E. Colwell, L. W. Esposito, Mark C. Lewis

Computer Science Faculty Research

Stellar occultations observed by the Cassini Ultraviolet Imaging Spectrograph reveal the presence of transparent holes a few meters to a few tens of meters in radial extent in otherwise optically thick regions of the C ring and the Cassini Division. We attribute the holes to gravitational disturbances generated by a population of ~10 m boulders in the rings that is intermediate in size between the background ring particle size distribution and the previously observed ~100 m propeller moonlets in the A ring. The size distribution of these boulders is described by a shallower power-law than the one that describes the …

Autonomous Entropy-Based Intelligent Experimental Design, Nabin Kumar Malakar

Legacy Theses & Dissertations (2009 - 2024)

The aim of this thesis is to explore the application of probability and information theory in experimental design, and to do so in a way that combines what we know about inference and inquiry in a comprehensive and consistent manner.

Motion In Augmented Reality Games: An Engine For Creating Plausible Physical Interactions In Augmented Reality Games, Brian Mac Namee, David Beaney, Qingqing Dong

Articles

The next generation of Augmented Reality (AR) games will require real and virtual objects to coexist in motion in immersive game environments. This will require the illusion that real and virtual objects interact physically together in a plausible way. The Motion in Augmented Reality Games (MARG) engine described in this paper has been developed to allow these kinds of game environments. The paper describes the design and implementation of the MARG engine and presents two proof-of-concept AR games that have been developed using it. Evaluations of these games have been performed and are presented to show that the MARG engine …

Forked:A Demonstration Of Physics Realism In Augmented Reality, David Beaney, Brian Mac Namee

Conference papers

In making fully immersive augmented reality (AR) applications, real and virtual objects will have to be seen to physically interact together in a realistic and believable way. This paper describes Forked! a system that has been developed to show how physical interactions between real and virtual objects can be simulated re- alistically and believably through appropriate use of a physics en- gine. The system allows users control a robotic forklift to manipu- late virtual crates in an AR environment. The paper also describes a evaluation experiment in which it is shown that the physical inter- actions between the forklift and …

Nonlinear Dynamics In Combinatorial Games: Renormalizing Chomp, Eric J. Friedman, Adam S. Landsberg

WM Keck Science Faculty Papers

We develop a new approach to combinatorial games that reveals connections between such games and some of the central ideas of nonlinear dynamics: scaling behaviors, complex dynamics and chaos, universality, and aggregation processes. We take as our model system the combinatorial game Chomp, which is one of the simplest in a class of "unsolved" combinatorial games that includes Chess, Checkers, and Go. We discover that the game possesses an underlying geometric structure that "grows" (reminiscent of crystal growth), and show how this growth can be analyzed using a renormalization procedure adapted from physics. In effect, this methodology allows one to …