Digital Commons Network^™

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 …

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 …

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 …

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 …

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 …

Metapopulation Dynamics Of Sars-Cov-2 Transmission In A Small-Scale Amazonian Society, Thomas Kraft, Edmond Seabright, Sarah Alami, Samuel M. Jenness, Paul L. Hooper, Bret Beheim, Helen Davis, Daniel K. Cummings, Daniel Eid Rodriguez, Maguin Gutierrez Cayuba, Emily J. Miner, Xavier De Lamballerie, Lucia Inchauste, Stéphane Priet, Benjamin Trumble, Jonathan Stieglitz, Hillard Kaplan, Michael Gurven

ESI Publications

The severity of infectious disease outbreaks is governed by patterns of human contact, which vary by geography, social organization, mobility, access to technology and healthcare, economic development, and culture. Whereas globalized societies and urban centers exhibit characteristics that can heighten vulnerability to pandemics, small-scale subsistence societies occupying remote, rural areas may be buffered. Accordingly, voluntary collective isolation has been proposed as one strategy to mitigate the impacts of COVID-19 and other pandemics on small-scale Indigenous populations with minimal access to healthcare infrastructure. To assess the vulnerability of such populations and the viability of interventions such as voluntary collective isolation, we …

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.

About Premedical Programs At Ud, Udayton

Pre-Health Resource Center Campus Communications

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 …

Aesthetics Of/And The Universe, Reza Tavakol

Contemporary Aesthetics (Journal Archive)

Modern cosmology has revealed a Universe that is wholly unexpected; a defining feature is that its largest constituent parts are either dark or hidden behind the cosmological horizon, making them in principle unobservable.

These discoveries raise the important question of what constitutes an aesthetic appreciation of the Universe? By considering various modes of appreciation, I argue that a fuller aesthetic appreciation of the Universe would need to augment its visual forms of appreciation with conceptual and imaginative modes of appreciation informed by scientific knowledge.

This argument can have a direct bearing on the long-standing debate concerning the role of cognitive …

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.

2007 Great Day Program, Suny Geneseo

GREAT Day Programs

SUNY Geneseo’s First Annual G.R.E.A.T. Day.

Performance Of Siso And Mimo Antenna Systems In Suburban And Urban Scenarios For V2i Communications At 5.9 Ghz And 28 Ghz, Oluwaseun Falade

Theses and Dissertations

As the need for Vehicle to Infrastructure (V2I) innovations increases, further research is needed to test the performance of antennas installed on vehicles in realistic scenarios including their radiation patterns and optimal location. This thesis considers realistic suburban and urban scenarios with SISO and MIMO antennas for V2I systems at 5.9 GHz and 28 GHz. The simulation is based on Ray tracing (RT) channel models to accurately model the effects of obstacles such as buildings and vehicles on the communication signals. Time-varying vehicular environments are simulated using a 3-D RT tool involving a vehicle and a base station. This research …

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, …

Simultaneous Multistep Transformer Architecture For Model Predictive Control, Junho Park, Mohammad Reza Babaei, Samuel Arce Munoz, Ashwin N. Venkat, John Hedengren

Faculty Publications

Transformer neural networks have revolutionized natural language processing by effectively addressing the vanishing gradient problem. This study focuses on applying Transformer models to time-series forecasting and customizing them for a simultaneous multistep-ahead prediction model in surrogate model predictive control (MPC). The proposed method showcases improved control performance and computational efficiency compared to LSTM-based MPC and one-step-ahead prediction models using both LSTM and Transformer networks. The study introduces three key contributions: (1) a new MPC system based on a Transformer time-series architecture, (2) a training method enabling multistep-ahead prediction for time-series machine learning models, and (3) validation of the enhanced time …

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

Theory & 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 …

Evangelical School Of Nursing Bulletin, 1970-1971, Advocate Aurora Health

Historical Documents - Combined

Evangelical School of Nursing, Oak Lawn, IL: Brochure advertising the Evangelical School of Nursing. Includes information on school policies, education requirements, and a course catalog.

Evangelical School Of Nursing Bulletin, 1973-1974, Advocate Aurora Health

Historical Documents - Combined

Evangelical School of Nursing, Oak Lawn, IL: Brochure advertising the Evangelical School of Nursing. Includes information on school policies, education requirements, and a course catalog.

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 …

Fostering Self-Efficacy And Autonomous Motivation To Aid In Mastering High-Order Writing Skills, Jessica Nowacki Van Denheuvel

Graduate Teacher Education

Students need to be able to write to be successful in school, personal life, and professional settings. In upper elementary, fourth- through fifth-grades, students shift from basic to high-order writing skills. The increased cognitive load can overwhelm students and even lead to task avoidance. Self-efficacy and autonomous motivation assist students in engaging and sustaining complex tasks. This literature evaluated fifteen primary research studies on how teachers may foster these constructs to aid in students’ development of high-order writing skills. Three strands were revealed. The first strand explored the state of writing instruction - the influence of standards-based educational systems and …

Game-Based Learning In Elementary Mathematics, Sophia Greenfield

Graduate Teacher Education

This paper analyzed 15 primary, peer-reviewed research articles about how to integrate game-based learning in elementary mathematics to increase conceptual understanding. The purpose of this paper was to inform teachers about how to integrate digital and traditional games, including factors to enhance effective implementation, in a way that increased conceptual understanding. Key factors were gleaned from the articles that had a significant impact on the effectiveness of game-based learning. In Chapter Two, traditional and digital games were divided into two sections to analyze the benefits and drawbacks of each method. These benefits and drawbacks were compared in the Appendices to …

Exploring Relationships Between Practicing Registered Nurses (Rns) Pharmacology Knowledge And Medication Errors, Coleen Kumar

Dissertations

Medication errors result in significant negative patient outcomes and major costs to the health care system. Registered nurses (RNs) assume a major role in the medication administration process demanding high-level knowledge and skills.

This study aimed to determine if there was a relationship between practicing RNs' pharmacology knowledge and medication error occurrence.

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 …

Enhanced Capabilities For Investigating Local Structure And Magnetism: Three Dimensional Magnetic Pair Distribution Function And Symmetry Mode Analysis, Parker Hamilton

Theses and Dissertations

The local structure, atomic and magnetic structure correlated over a small length scale, of a material has a strong impact on material properties. Pair distribution function (PDF) analysis is a strong tool to investigate local structure and magnetism of this nature. This work outlines extensions to current one dimensional magnetic pair distribution functions (1D-mPDF) and the fitting of structures with symmetry breaking local atomic distortions. 1DmPDF analysis has been used to study local magnetic structure, but requires a rotational averaging of the correlations so directional information is lost, as in powder diffraction experiments. Three dimensional difference magnetic pair distribution function …