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Autoregressive neural quantum states of Fermi Hubbard models
Get PDFNeural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz
Understanding the Prevalence of Mental Health and Behavioral Distress in Transit Assaults and Presenting Targeted Response and Mitigation Methods to Reduce Victimization Trends
Get PDFThis research project was performed to better understand the prevalence of mental health and behavioral distress in transit assaults and present corresponding targeted response and mitigation methods to reduce victimization trends. It includes a literature review, a data presentation and overview of reported public transit assault-related events, an examination of the contributing factors to those events, and documentation of 10 transit agency case studies. The literature review discusses some of the misconceptions of mental illness and associated stigmas and discrimination that can occur in response to these misconceptions. It discusses various approaches and strategies that transit agencies have used to prepare transit vehicle operators and other frontline workers to better recognize mental health presentations and engage appropriately. It also highlights evidence-based practices that transit agencies can use to better prepare their systems and provide tools employees can use to better communicate with individuals who may be having mental health-related episodes. The research team gathered additional data from case study sites, including the methods used to address assault-related challenges and the successful community engagement and partnerships, which are documented in this report. The report provides findings and policy and practice recommendations
Di Salvo, Christine Marie
Get PDFSan Jose State University, Public Administration, 2002 MPA
San Jose State University, Public Relations,1984 BA
SJSU International Salzburg Fellow, 2012
Additional Courses:
University of California, Santa Cruz, Writing for Publication,1988https://scholarworks.sjsu.edu/erfa_bios/1362/thumbnail.jp
Spartan Daily, March 26, 2025
Get PDFVolume 164, Issue 27https://scholarworks.sjsu.edu/spartan_daily_2025/1026/thumbnail.jp
Smart Highway Construction Site Monitoring Using Artificial Intelligence
Get PDFConstruction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes. Our goal was to find the optimized balance between the model capabilities in object detection and memory processing requirements. Due to the lack of a comprehensive image database specifically developed for transportation infrastructure construction projects, the first portion of this study focused on preparing a comprehensive database of annotated images for various classes of equipment and machinery that are commonly used in roadway construction and rehabilitation projects. The second part of the project focused on training the deep learning models and improving the accuracy of the classification and detection algorithms. The outcomes of the trained and improved deep learning classification model were promising in terms of the precision and accuracy of the model in detecting specific objects at a highway construction site. It should be noted that the scope of this project was limited to the image and video data recorded from the ground-level and cannot be extended to Uncrewed Aerial System (UAS) data. This study provides valuable insights on the potentials of AI and deep learning to improve the monitoring and thus safety and efficiency of transportation infrastructure construction
Sublett, Scott Winfield
No full textUniversity of California, Los Angeles, School of Theater, Film, and Television, Screenwriting, 1993, MFA
Northwestern University,Department of Radio/Television/Film, Radio, 1977 BShttps://scholarworks.sjsu.edu/erfa_bios/1402/thumbnail.jp
Spartan Daily, January 30, 2025
Get PDFVolume 164, Issue 4https://scholarworks.sjsu.edu/spartan_daily_2025/1003/thumbnail.jp
Restoring School Librarians: Challenges and Strategies
Get PDFThe article examines efforts to restore school librarian positions across eleven urban U.S. school districts, despite significant financial and administrative obstacles. It documents strategies used by district library leaders to advocate for staffing, funding, and policy changes, emphasizing equity in access to certified school librarians. Through structured interviews and analysis, the report highlights key barriers, advocacy methods, and funding mechanisms that have influenced school library staffing. The study\u27s findings contribute to the ongoing discourse on educational equity and policy reform in K-12 library services
“They\u27re Like Slash”: Multimodality and Embodied Agency in Students\u27 Critical Engagements with Texts
Get PDFDespite recent calls to more fully incorporate multimodal perspectives into literacies research, there is still limited scholarship examining how students critically engage in reading activities by drawing on embodied practices. Racially and linguistically minoritized students are particularly disadvantaged by dominant logocentric and developmentalist approaches, which privilege oral and written discourse and often position these students as less capable of performing complex literacy practices. Drawing from three independent ethnographic studies, our multimodal interactional analysis examines how students of a range of ages and raciolinguistic backgrounds use embodied actions and other semiotic resources to agentively navigate text, task, and ideological constraints in activities involving reading and analyzing texts. Our analysis demonstrates the crucial role of students\u27 embodied practices in expanding upon and challenging the constraints of literacy activities, focusing particularly on how students leveraged epistemic stance-taking, embodied affective responses, and embodied forms of argumentation to negotiate and co-construct meaning. Through a focus on embodied agency, this paper presents and applies an interactional perspective on the embodied nature of literacy activities; shows how students\u27 creative mobilizations of embodied and other semiotic resources contribute to their critical readings of texts; and offers pedagogical and methodological implications for ways educators and researchers can attend to the intricacies of students\u27 embodied sense-making in literacy activities
Image Denoising Using Deep Learning Models
No full textThis project focused on image denoising using deep Convolutional Neural Network (CNN) architecture and Large Language Models (LLMs). A study was conducted to investigate different CNN and LLM models and their effectiveness in denoising images. Large Language Models (LLMs) are mainly used for Natural Language Processing, but recent advances in LLMs have allowed some of them to accept images as input. One of the objectives of the project was to see if LLMs could be used to denoise images and compare the performance with that of CNN models. LLMs work differently from CNNs because the input is fed into an LLM as a prompt. Training and test datasets were created for this project by adding Gaussian noise samples to an existing image dataset from a public domain database called Kaggle. Existing models used for the investigation were ChatGPT, BRDNet, and ECNDNet. ChatGPT is an LLM, and it was observed that LLM models could be used for image denoising. A new model called the Enhanced Model was created, trained, tested, and compared with these models. The metrics used to measure how well each image was denoised were peak signal-to-noise ratios (PSNR) and structural similarity index measurement (SSIM). The Enhanced Model produced the highest average PSNR and SSIM values for all the images tested