Physical Sciences and Mathematics Commons^™

On Euler's Solution Of The Simple Difference Equation, Alexander Aycock

Euleriana

In this note we will discuss Euler's solution of the simple difference equation that he gave in his paper{\it ``De serierum determinatione seu nova methodus inveniendi terminos generales serierum"} \cite{E189} (E189:``On the determination of series or a new method of finding the general terms of series") and also present a derivation for the values of the Riemann $\zeta$-function at positive integer numbers based on Euler's ideas.

Euler And The Legendre Polynomials, Alexander Aycock

Euleriana

In this note we will present how Euler's investigations on various different subjects lead to certain properties of the Legendre polynomials. More precisely, we will show that the generating function and the difference equation for the Legendre polynomials was already written down by Euler in at least two different papers. Furthermore, we will demonstrate that some familiar expressions for the Legendre polynomials are corollaries of the before-mentioned works. Finally, we will show that Euler's ideas on continued fractions lead to an integral representation for the Legendre polynomials that seems to be less generally known.

Solution Of The Diophantine Equation (Maa+Nbb)=Cd(Mcc+Ndd) Using Rational Numbers, Georg Ehlers

Euleriana

This paper (E716) was published in Nova acta Academiae scientiarum imperialis petropolitanae, Volume 13 (1795/96), pp. 45-63. It was also included in Commentationes Arithmeticae, Volume II, as Number LXVIII, pp. 281-293 (E791). Euler starts with Fermat's Last Theorem and mentions the proofs for the cases n=3 and n=4 which he had completed himself earlier. He then moves on to make the sum of powers conjecture, which was later disproved in the second half of the 20th century. In this context he discusses his discovery of 134^4+133^4=158^4+59^4, which he calls unexpected. Euler derives the title equation from A^4+B^4=C^4+D^4, generalizing it to …

On The Motion Of The Nodes Of The Moon And The Variation Of Its Inclination To The Ecliptic (An English Translation Of De Motu Nodorum Lunae Eiusque Inclinationis Ad Eclipticam Variatione), Patrick T. Headley

Euleriana

In this paper Euler attempts to explain some features of the motion of the Moon using Newton’s inverse-square law of gravity. He describes the evidence in favor of Newton’s theory but also the lack of progress in the study of lunar motion due to the difficulty of the three-body problem, arising here since both the Sun and the Earth have large effects on the Moon. He proceeds to investigate the line of intersection between the planes of the Earth's orbit and the Moon's orbit, as well as the angle between the two planes.

The Wide Scope Of Euler’S Work, Christopher Goff, Erik R. Tou

Euleriana

An introduction to the contents in Issue 2, Volume 3 of Euleriana.

Connectome-Constrained Artificial Neural Networks, Jacob Morra

Electronic Thesis and Dissertation Repository

In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron …

Online Data Transmission Reduction Scheme For Energy Conservation In Wireless Video Sensor Networks, Iman Kadhum Abbood, Ali Kadhum Idrees

Karbala International Journal of Modern Science

Wireless Video Sensor Networks (WVSNs) are networks of low-cost, low-power camera sensor nodes. These nodes communicate locally and process information to meet an application's goal. WVSNs are extensively used in diverse monitoring applications, such as security, military, industrial, medical, and environmental monitoring. However, the transmission of large amounts of data collected by video sensor nodes in WVSNs poses challenges in terms of energy consumption, bandwidth usage, and network congestion. Reducing energy for processing and transmitting data in WVSNs is difficult due to the huge amount of sensed data in real-time. To address this issue, this paper proposes an Online Data …

Predicting Network Failures With Ai Techniques, Chandrika Saha

Electronic Thesis and Dissertation Repository

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …

Predictive Model Of Seismic Vibrations’ Peak Value Induced By Multi-Face Blasting, Krzysztof Fuławka, Lech Stolecki, Piotr Mertuszka, Marcin Szumny, Arkadiusz Anderko

Journal of Sustainable Mining

The seismicity level induced by blasting in the Polish copper mines is very important inlight of the efficiency of active rockburst prevention and safe conduct of blasting operations in the vicinity of the mining infrastructure such as shafts, workings, or function chambers (e.g., workshops, storages, etc.). Knowledge of the seismic vibrations’ peak value might be the basis for designing blasting works in a way that ensures desired seismic effect. However, current experiences show that Peak Particle Velocity prediction models developed so far do not apply to multi-face blasting, where there are many vibrations’ sources at the same time dotted across …

Biogenesis Synthesis Of Zno Nps: Its Adsorption And Photocatalytic Activity For Removal Of Acid Black 210 Dye, Zahraa A. Najm, Mohammed A. Atiya, Ahmed K. Hassan

Karbala International Journal of Modern Science

This study investigated the treatment of textile wastewater contaminated with Acid Black 210 dye (AB210) using zinc oxide nanoparticles (ZnO NPs) through adsorption and photocatalytic techniques. ZnO NPs were synthesized using a green synthesis process involving eucalyptus leaves as reducing and capping agents. The synthesized ZnO NPs were characterized using UV-Vis spectroscopy, SEM, EDAX, XRD, BET, Zeta potential, and FTIR techniques. The BET analysis revealed a specific surface area and total pore volume of 26.318 m²/g. SEM images confirmed the crystalline and spherical nature of the particles, with a particle size of 73.4 nm. A photoreactor was designed …

Ifseg: Image-Free Semantic Segmentation Via Vision-Language Model, Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo, Jinwoo Shin

Machine Learning Faculty Publications

Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates …

How I Read An Article That Uses Machine Learning Methods, Aziz Nazha, Olivier Elemento, Shannon Mcweeney, Moses Miles, Torsten Haferlach

Kimmel Cancer Center Faculty Papers

No abstract provided.

Initial Experience With An Electron Flash Research Extension (Flex) For The Clinac System, Kyuhak Oh, Kyle J. Gallagher, Megan Hyun, Diane Schott, Sarah Wisnoskie, Yu Lei, Samuel Hendley, Jeffrey Wong, Shuo Wang, Brendan Graff, Christopher Jenkins, Frank Rutar, Md Ahmed, Joshua Mcneur, Jeffrey Taylor, Marty Schmidt, Lasitha Senadheera, Wendy Smith, Subodh M. Lele, Ran Dai, Dong Jianghu (James), Y Yan, Su-Min Zhou

Department of Physics and Astronomy: Faculty Publications

Purpose: Radiotherapy delivered at ultra-high-dose-rates (≥40 Gy/s), that is, FLASH, has the potential to effectively widen the therapeutic window and considerably improve the care of cancer patients. The underlying mechanism of the FLASH effect is not well understood, and commercial systems capable of delivering such dose rates are scarce. The purpose of this study was to perform the initial acceptance and commissioning tests of an electron FLASH research product for preclinical studies.

Methods: A linear accelerator (Clinac 23EX) was modified to include a nonclinical FLASH research extension (the Clinac-FLEX system) by Varian, a Siemens Healthineers company (Palo Alto, …

Unsupervised Sampling Promoting For Stochastic Human Trajectory Prediction, Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang

Machine Learning Faculty Publications

The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler, to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is …

Burstormer: Burst Image Restoration And Enhancement Transformer, Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming Hsuan Yang

Computer Vision Faculty Publications

On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complementary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication …

Clip2protect: Protecting Facial Privacy Using Text-Guided Makeup Via Adversarial Latent Search, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar

Computer Vision Faculty Publications

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate 'naturalistic' images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low- dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the …

Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan

Computer Vision Faculty Publications

Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make over-confident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and …

3d-Aware Multi-Class Image-To-Image Translation With Nerfs, Senmao Li, Joost Van De Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang

Computer Vision Faculty Publications

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we …

Kd-Dlgan: Data Limited Image Generation Via Knowledge Distillation, Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric Xing

Machine Learning Faculty Publications

Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded generation especially in generation diversity. Inspired by the recent advances in knowledge distillation (KD), we propose KD-DLGAN, a knowledge-distillation based generation framework that introduces pre-trained vision-language models for training effective data-limited generation models. KD-DLGAN consists of two innovative designs. The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models. …

Discriminative Co-Saliency And Background Mining Transformer For Co-Salient Object Detection, Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation …

Dynamic Graph Enhanced Contrastive Learning For Chest X-Ray Report Generation, Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang

Computer Vision Faculty Publications

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes …

3d Semantic Segmentation In The Wild: Learning Generalized Models For Adverse-Condition Point Clouds, Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing

Computer Vision Faculty Publications

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. We study all-weather 3DSS modeling under two setups: 1) domain adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2) domain generalizable 3DSS that learns all-weather 3DSS models from normal-weather data. Our studies reveal …

Final 2023 Rmap Stodden Park Soil Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Undergraduate Students' Conceptions Of Ngss Science And Engineering Practices, Jessie Webb

Theses and Dissertations

In 2013, a new set of science standards was introduced for K-12 science education, called the Next Generation Science Standards (NGSS), which focused on three dimensions of science learning that work together: disciplinary core ideas, crosscutting concepts, and science and engineering practices. These standards are novel in their emphasis on students needing more than only content knowledge to learn science and engineering. The NGSS science and engineering practices (SEPs) stress the importance of students engaging in the authentic practices of scientists and engineers to help them think like a scientist, practice science themselves, and overcome the misconception that science is …

The Gamma-Signless Laplacian Adjacency Matrix Of Mixed Graphs, Omar Alomari, Mohammad Abudayah, Manal Ghanem

Theory and Applications of Graphs

The α-Hermitian adjacency matrix H_α of a mixed graph X has been recently introduced. It is a generalization of the adjacency matrix of unoriented graphs. In this paper, we consider a special case of the complex number α. This enables us to define an incidence matrix of mixed graphs. Consequently, we define a generalization of line graphs as well as a generalization of the signless Laplacian adjacency matrix of graphs. We then study the spectral properties of the gamma-signless Laplacian adjacency matrix of a mixed graph. Lastly, we characterize when the signless Laplacian adjacency matrix of …

Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani

Electronic Thesis and Dissertation Repository

In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in …

Scale Model Experiments Of Toxic Gas Production From The Combustion Of Polymers When Applied With Different Droplet Sizes Of Water Mist, Nicharee Thinnakornsutubutr, Masayuki Mizuno, Kazunori Kuwana

Progress in Scale Modeling, an International Journal

This research experimentally investigated the combustion of polymeric materials with water mist application in an enclosure, with an emphasis on the production of toxic gases. Two different diameters, ~100 and ~260 μm, were tested. The experimental conditions were determined based on Froude similarity laws for low drop Reynolds number conditions. Droplets and polymers’ physical and chemical properties influence the burning/extinguishing behavior and toxic-gas evolution. In general, larger droplets can extinguish a fire in a shorter time, and toxic gas concentrations in a test chamber decreased more rapidly. However, the large droplets tended to cause the flame expansion phenomenon for thermoplastics …

Optimization Of Electrode Configurations For Calibration-Free, Remote Sensing Of Heavy Metals In Water Using Double-Potenital Step Anodic Coulometry, Jessica Bone

Online Theses and Dissertations

Many areas around the world are known and predicted to suffer from arsenic-contaminated drinking water resulting in elevated medical issues. Current arsenic detection techniques require that a sample be taken at the site, carried to the lab, and then tested by a skilled technician, which is not practical for remote, hard to reach places. In collaboration with researchers at the University of Louisville and the University of Kentucky, we are designing an electrochemical cell for a calibration-free detection technique that can be performed remotely, eliminating the need for on-site technicians, and helping to prevent chronic arsenic poisoning. A validated and …

Bpsou Buffalo Gulch Forebay Parcel 2023 Expedited Well Abandonment Plan, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Undetermined Coefficients With Hyperbolic Sines And Cosines, Laurie A. Florio, George L. Fischer

CODEE Journal

The method of undetermined coefficients is commonly applied to solve linear, constant coefficient, non-homogeneous ordinary differential equations when the forcing function is from a selected class of functions. Often the hyperbolic sine and cosine functions are not explicitly included in this list of functions. Through a set of guided examples, this work argues that the hyperbolic sine and cosine ought to be included in the select class of functions. Careful explanation is provided for the necessary treatment of the cases where the argument of the hyperbolic sine and/or cosine functions matches one or both of the roots of the characteristic …