Physical Sciences and Mathematics Commons^™

Masked Diffusion Transformer Is A Strong Image Synthesizer, Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability to contextual relation learning among object semantic parts in an image. During training, MDT operates in the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion …

Learning Provably Stabilizing Neural Controllers For Discrete-Time Stochastic Systems, Matin Ansaripour, Krishnendu Chatterjee, A. Thomas Henzinger, Mathias Lechner, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problem of learning control policies in discrete-time stochastic systems which guarantee that the system stabilizes within some specified stabilization region with probability 1. Our approach is based on the novel notion of stabilizing ranking supermartingales (sRSMs) that we introduce in this work. Our sRSMs overcome the limitation of methods proposed in previous works whose applicability is restricted to systems in which the stabilizing region cannot be left once entered under any control policy. We present a learning procedure that learns a control policy together with an sRSM that formally certifies probability 1 stability, both learned as neural …

Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach …

Remedial Action Work Plan - East Middle School, Environmental Protection Agency

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – First Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Residential Metals Abatement Program – Interior School Dust – Remedial Action Work Plan – East Middle School, Environmental Resource Management (Erm)

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Second Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Quality Assurance Project Plan: Long-Term Operation And Maintenance Of Railroad Assets For Bnsf Railway Company And Union Pacific Railroad Butte Priority Soils Operable Unit, Kennedy Jenks

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Lightning Forecast From Chaotic And Incomplete Time Series Using Wavelet De-Noising And Spatiotemporal Kriging, Jared K. Nystrom, Raymond Hill, Andrew J. Geyer, Joseph J. Pignatiello Jr., Eric Chicken

Faculty Publications

Purpose: Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Design/Methodology/Approach: Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lighting prediction.

Findings: The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

Abstract © Emerald Publishing …

Green Promise Or Environmental Threat?: Corn Ethanol’S Positive And Negative Effects, Sydney Rosensaft

The Synapse: Intercollegiate science magazine

No abstract provided.

Utahns Strongly Support Renewable Energy Sources Such As Solar And Wind, Elizabeth Brunner, Stacia Ryder

Utah People and Environment Poll (UPEP)

Utah was the fastest-growing state in the nation by population between 2010 and 2020.¹ This growing population is bringing increased demand for energy. The build out of Utah's electric vehicle (EV) infrastructure, with the state aiming to site electric vehicle charging stations at least every 50 miles along its interstate highway system by the end of 2025, will also increase energy demand. This growth will equate to increased carbon emissions if Utah does not change its electricity mix, which is currently composed of primarily carbon-emitting sources. As of 2022 (see Figure 1), 53% of Utah's total electricity net generation …

Computerized Psychological Testing: Designing And Developing An Efficient Test Suite Using Hci And Reinforcement Learning Techniques, William Henry Hoskins

Theses and Dissertations

In this work we discuss the design and development of the Carolina Automated Reading Evaluation (CARE), created to facilitate the finding of deficits in the reading ability of children from four to nine years of age. Designed to automate the process of screening for reading deficits, the CARE is an interactive computer-based tool that helps eliminate the need for one-on-one evaluations of pupils to detect dyslexia and other reading deficits and facilitates the creation of new reading tests within the platform. While other tests collect specific data points in order to determine whether a pupil has dyslexia, they typically focus …

Effects Of Changing Climate Extremes And Vegetation Phenology On Wildlife Associated With Grasslands In The Southwestern United States, Tyler G. Creech, Matthew A. Williamson, Steven E. Sesnie, Esther S. Rubin, Daniel R. Cayan, Erica Fleishman

Human-Environment Systems Research Center Faculty Publications and Presentations

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species' occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of …

Robust Underwater State Estimation And Mapping, Bharat Joshi

Theses and Dissertations

The ocean covers two-thirds of Earth, which is relatively unexplored compared to the landmass. Mapping underwater structures is essential for both archaeological and conservation purposes. This dissertation focuses on employing a robot team to map underwater structures using vision-based simultaneous localization and mapping (SLAM). The overarching goal of this research is to create a team of autonomous robots to map large underwater structures in a coordinated fashion. This requires maintaining an accurate robust pose estimate of oneself and knowing the relative pose of the other robots in the team. However, the GPS-denied and communication-constrained underwater environment, along with low visibility, …

Examination And Application Of Body Condition Methods In Cetaceans, Kira Anne Telford

Theses and Dissertations

Body condition assessments are a valuable tool for evaluating the relative health of a population through various metrics, indexes, or proxies. Long-term data collection can be used to examine the relationship between fluctuations in body condition and natural or anthropogenic drivers. Application of this information is vital for monitoring the success of the conservation management decisions for a species or population. Cetaceans have a variety of methods available to assess body condition, including invasive methods like biopsies and necropsies or observational methods such as photogrammetry. Exploration of the application of these methods in the literature revealed an emphasis on necropsies …

Application And Use Of Artificial Intelligence (Ai) For Library Services Delivery In Academic Libraries In Kwara State, Nigeria, Abdullahi Olayinka Isiaka

Library Philosophy and Practice (e-journal)

The application and use Artificial Intelligence (AI) in library services delivery and operations has modernized traditional practices, enabling libraries to adapt to the evolving information needs of patrons in the digital era. The main purpose of this study is to investigate the application and use of Artificial Intelligence (AI) Technologies for Library Services Delivery in Academic Libraries in Kwara State, Nigeria. The study used a descriptive survey approach. The population was the 108 librarians in academic libraries in Kwara State, Nigeria. A total enumeration technique was employed, and a questionnaire was used to collect data from the library staff. The …

Complexity Of Reconfiguration In Surface Chemical Reaction Networks, Robert M. Alaniz, Josh Brunner, Michael Coulombe, Erik D. Demaine, Yevhenii Diomidov, Ryan Knobel, Timothy Gomez, Elise Grizzell, Jayson Lynch, Andrew Rodriguez, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

We analyze the computational complexity of basic reconfiguration problems for the recently introduced surface Chemical Reaction Networks (sCRNs), where ordered pairs of adjacent species nondeterministically transform into a different ordered pair of species according to a predefined set of allowed transition rules (chemical reactions). In particular, two questions that are fundamental to the simulation of sCRNs are whether a given configuration of molecules can ever transform into another given configuration, and whether a given cell can ever contain a given species, given a set of transition rules. We show that these problems can be solved in polynomial time, are NP-complete, …

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Computational Investigation Of Mononuclear Iron Water Oxidation Catalyst Design, Kristal Stevens, Emily Jarvis

Chemistry and Biochemistry Faculty Works

Hydrogen production from non-carbon sources is an essential component of clean and sustainable technology for reducing greenhouse gas emissions from fuels. Water oxidation, which splits water molecules into hydrogen (protons) and molecular oxygen, is a thermodynamically challenging, multistep reaction achieved in photosynthetic organisms via photocatalysis by the Oxygen Evolving Complex (OEC) of Photosystem II. Mononuclear water oxidation catalysts that aim to mimic nature typically rely on heavy, rare metals such as ruthenium and iridium. Replacing these metals with iron is particularly appealing because it is abundant, benign, and inexpensive. We use density functional theory to characterize the catalytic ability of …

Improved Temperature Dependence Of Rate Coefficients For Rotational State-To-State Transitions In H2O + H2O Collisions, Bikramaditya Mandal, Dmitri Babikov

Chemistry Faculty Research and Publications

Aims. We present an improved database of temperature-dependent rate coefficients for rotational state-to-state transitions in H₂O + H₂O collisions. The database includes 231 transitions between the lower para-states of H₂O and 210 transitions between its lower ortho-states (up to j = 7) and can be employed in cometary and planetary applications up to the temperature of 1000 K.

Methods. We developed and applied a new general method that allows the generation of rate coefficients for excitation and quenching processes that automatically satisfy the principle of microscopic reversibility and also helps to cover …

Scandium Triflate-Catalyzed Aromatic Aldehydic C-H Activation, Nicholas Griffin

Honors Projects

Herein described is a scandium triflate-catalyzed C-H activation of commercially available aromatic aldehydes achieved in low yields. The reaction occured in a one-pot synthesis over a two-hour duration and required minimal purification. Inclusion of a fluorine-tagged phenol allowed for reaction monitoring via ¹⁹FNMR.

Metal Organic Frameworks (Mofs) Supported Single Atom Catalysts (Sacs) For Solar Fuel Conversion, Humphrey Chiromo

Dissertations (1934 -)

The continual reliance on non-renewable energy sources from fossil fuels to meet the world’s energy demand is causing serious environmental problems such as air pollution and global warming, hence there is a need of an alternative clean sustainable energy source. Exploration of clean sustainable renewable energies shows great promise to replace fossil fuels to meet global energy needs. Among the renewable energy sources, solar energy represents one of the most promising alternative energy sources due to its abundance and sustainability. However, the major challenge is the harvesting and storage of solar energy. One of the promising approaches to resolve these …

Visually Analyzing Company-Wide Software Service Dependencies: An Industrial Case Study, Sebastian Baltes, Brian Pfitzmann, Thomas Kowark, Christoph Treude, Fabian Beck

Research Collection School Of Computing and Information Systems

Managing dependencies between software services is a crucial task for any company operating cloud applications. Visualizations can help to understand and maintain these com-plex dependencies. In this paper, we present a force-directed service dependency visualization and filtering tool that has been developed and used within SAP. The tool's use cases include guiding service retirement as well as understanding service deployment landscapes and their relationship to the company's organizational structure. We report how we built and adapted the tool under strict time constraints to address the requirements of our users. We further share insights on how we enabled internal adoption. For …

The Encyclopedia Of Neutrosophic Researchers, 5th Volume, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy.

In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements.

There are about 7,000 neutrosophic researchers, within 89 countries around the …

Digital Scholarship And Data Science Intersect In Libraries: A Needs Assessment Report, Halie Kerns

Library Created Resources

The following report summarized the results of a needs assessment completed in the fall of 2023 at Binghamton University by the Libraries’ Digital Scholarship team. The aim was to understand how data science-focused programming, as part of the digital scholarship’s offerings, would be utilized on campus. The report evaluates existing literature, summarizes findings from twenty-eight interviews done across campus, and lays out an action plan for the Digital Scholarship team’s future planning.

Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu

Research Collection School Of Computing and Information Systems

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …

Supporting Artefact Awareness In Partially-Replicated Workspaces, Emran Poh, Anthony Tang, Jenanie S. Lee, Zhao Shengdong

Research Collection School Of Computing and Information Systems

Using Cross Reality (CR) approaches for remote collaboration will often result in partially-replicated workspaces. Here, workspace artefacts are not equally accessible - i.e. a physical artefact may only be manipulated by one collaborator - and in general, the artefacts become desynchronised over time. In this paper, we introduce a framework for artefact awareness that can help collaborators maintain an understanding of each others' manipulations with workspace artefacts. We illustrate our design explorations through sketches, and outline how we aim to study the effectiveness and utility of artefact awareness in cross reality remote collaboration. In our work, we expect to show …

Rigid Body Constrained Motion Optimization And Control On Lie Groups And Their Tangent Bundles, Brennan S. Mccann

Doctoral Dissertations and Master's Theses

Rigid body motion requires formulations where rotational and translational motion are accounted for appropriately. Two Lie groups, the special orthogonal group SO(3) and the space of quaternions H, are commonly used to represent attitude. When considering rigid body pose, that is spacecraft position and attitude, the special Euclidean group SE(3) and the space of dual quaternions DH are frequently utilized. All these groups are Lie groups and Riemannian manifolds, and these identifications have profound implications for dynamics and controls. The trajectory optimization and optimal control problem on Riemannian manifolds presents significant opportunities for theoretical development. Riemannian optimization is an attractive …

Rubin Observatory Lsst Transients And Variable Stars Roadmap, Kelly M. Hambleton, Federica B. Bianco, Rachel Street, Keaton Bell, David Buckley, Melissa Graham, Nina Hernitschek, Michael B. Lund, Elena Mason, Liliana Rivera Sandoval

Physics and Astronomy Faculty Publications and Presentations

The Vera C. Rubin Legacy Survey of Space and Time (LSST) holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document …