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FPGA-Based Overlay Accelerators with Massive Parallel Processing Units to Accelerate Deep Neural Networks
No full textDeep neural networks (DNNs) are widely used in applications such as classification, prediction, and regression. Various DNN architectures, such as convolutional neural networks (CNN), multilayer perceptrons (MLP), long short-term memory (LSTM), recurrent neural networks (RNN), and transformers, have become leading machine learning techniques in these applications. They require significant computational resources and have substantial memory demands due to intensive matrix-matrix multiplications and complex data flows. Hence, efficient utilization of on-chip computational and memory resources is essential to maximize parallelism and minimize latency. Designing an optimal tiling scheme that aligns effectively with the architecture is also necessary. Modern FPGAs are equipped with Block Random Access Memory (BRAM) for storing data, such as weights, intermediate results, or configurations during processing, and Digital Signal Processing (DSP) blocks for performing high-speed arithmetic operations like multiplication and accumulation, which are essential for deep learning tasks. Efficient parallel utilization of these resources while maintaining operational frequency is crucial for achieving low-latency inference. Moreover, achieving high performance and accuracy across diverse applications often necessitates an optimized network architecture, typically developed through iterative experimentation and evaluation across various network topologies. However, custom hardware accelerators lack scalability and flexibility, as they cannot adapt to different topologies at runtime. Additionally, designing custom hardware using FPGA vendor tools and programming languages is a time-intensive process that demands a deep understanding of hardware architecture. This research developed versatile accelerators to address diverse computational and memory demands, enabling support for various DNNs across multiple applications while striving to maximize processing unit utilization to achieve low latency. Due to their high parallelism, efficient tiling, and advanced coding techniques, they surpassed the performance of most custom accelerators and general-purpose processors
Stay Woke: Understanding Critical Consciousness Development in Adults and its Relationship to Psychological Constructs
No full textRacism confers numerous measurable health risks for people of color. Interventions to mitigate the impact of racism on health are necessary and important to reduce disease burden and improve health outcomes for people of color. While communities of color have developed resiliency tactics that have allowed them to survive, even thrive, in the face of racial oppression, the evaluation and promotion of promising tools that counteract racism’s negative impacts are essential (Phillips et al., 2015). Critical consciousness is just such a tool. Research with children suggests it holds promise (Diemer et al., 2016; Heberle et al., 2020; Maker-Castro et al., 2022); lacking is attention to whether critical consciousness develops and functions similarly in adults. The current studies aim to expand the field’s understanding of critical consciousness in adults. Study 1 was a qualitative study using a semi-structured interview of adult participants of color who experienced race-based discrimination and scored high on a measure of critical consciousness. The 12 interviews highlighted six themes that indicated that education and discrimination can help precipitate the development of critical consciousness, critical consciousness development can be painful and almost like a grief process, and critical consciousness development can be associated with various aspects of identity. Study 2 evaluated quantitative data from 260 adults (18 – 40 years) of color who reside in the United States. Participants in Study 2 completed a variety of measures that includes measures of critical consciousness development, racial identity, racial attitudes, mental health, and well-being. Results suggested that a three-factor model (i.e., critical reflection, critical motivation, and critical action) is the best representation of critical consciousness among this sample. I added questions from Study 1 to create a new and more robust short critical consciousness scale (i.e., the Adjusted Short Critical Consciousness Scale). Correlations between critical consciousness factors and various outcome measures suggest that critical action and critical motivation measure unique constructs. Meanwhile, critical reflection seems to overlap theoretically with other measures relevant to awareness of oppression. Critical reflection and critical motivation were associated with higher levels of negative mental health outcomes and lower levels of positive health outcomes and well-being. Overall, these two studies add to the critical consciousness literature by 1) creating an adjusted and more robust critical consciousness scale, 2) providing insight into how critical consciousness develops, 3) highlighting unique features related to identity and critical consciousness development in adults of color, and 4) expanding our theoretical understanding of critical consciousness as a construct among adults of color. These findings help lessen the gap in the critical consciousness literature among adults. The implications of this study suggest that while critical consciousness development may disrupt the negative associations between racism and health, we still need more research with adult samples
Differential Drivers of Psychological Constructs: A Comparative Analysis Using Conditional Inference Tree and Spatially Varying Coefficient Models
No full textPsychological constructs such as Anxiety, Depression, Fatalism, Divine Control, Luck, Helplessness, and Internality are pressing subjects in the United States (US). Although several studies have explored how specific covariates influence these constructs, less is known about how combinations of predictors interact to predict these constructs and how these relationships vary across different geographic regions. This study addresses the gap by combining Conditional Inference Trees (CIT) and Geographically Weighted Regression (GWR) to explore the interaction effects of the covariates in predicting the constructs and spatial variability in the determinants of psychological constructs respectively Using a nationally representative survey of 2000 respondents, CIT was used to explore how covariates such as Adverse Childhood Experience (ACE), age, gender, race, education, and urbanicity interact to predict each construct. Geographically Weighted Regression (GWR) was employed to explore the spatial variation in the effects of covariates on psychological constructs in the U.S. Key findings reveal that age and Adverse Childhood Experiences (ACE) were strong predictors of different levels of Depression and Anxiety. Education, age, and race played critical roles in predicting different levels of Fatalism. Regional patterns also emerged: for example, education and urbanicity were more influential on Anxiety in the West and Central U.S., while gender had stronger effects in the Northeast and Midwest. Urbanicity more strongly affected Depression in the Northeast and Midwest, whereas gender was more impactful in the West and Central regions. Similarly, education and urbanicity were linked to Luck in the Northeast and Midwest, while ACE was more predictive in the West and Central U.S. Helplessness was associated with gender in the Northeast and Midwest and with race in the West. Education influenced Internality in the West and Central U.S., while gender was more impactful in the North and Midwest. This study highlights the importance of considering both interactions among mixed-type covariates and geographic variability when examining psychological constructs. By incorporating CIT and GWR, we were able to discover significant interaction between covariates in predicting psychological constructs and regional differences in how covariates such as education, gender, race, and urbanicity influence constructs like Anxiety, Depression, Fatalism, and Divine Control
The Upcoming Moral Crisis in Primitive Artificial Intelligence
No full textAs we continue to develop artificially intelligent systems, there is an increasingly high chance that we will develop a system that is both conscious and capable of suffering. Furthermore, it is likely that the development of this conscious machine will be entirely unintentional. While this machine will have moral status, identifying it will be extremely difficult, leading to it being treated the same as its inert predecessors. For these reasons I believe that a crisis in ethics is looming. This paper aims to argue that it is possible for a machine to have moral status, that the first such machine will likely be produced unintentionally, and that identifying this machine will involve significant difficulties
Factors Affecting Diet Selection in Sheep fed Endophyte-Infected Toxic Fescue
No full textTall fescue infected with endophyte‑producing fungi can induce fescue toxicosis in grazing ruminants, reducing performance and altering forage intake. Isoflavones—such as biochanin A, a major compound in red clover (Trifolium pratense)—have been proposed to mitigate these effects through improved palatability and vascular relaxation. This study examined whether inclusion of ground red clover (GRC)—either directly or combined with hydroxypropyl methylcellulose (HPMC) and polyethylene glycol (PEG)—affected voluntary diet selection in lambs offered toxic fescue.
Thirty‑two lambs were housed individually and assigned randomly to one of four diets: (1) toxic fescue silage (CONT), (2) silage + 5% GRC (RC5), (3) silage + 10% GRC (RC10), or (4) silage + 5% GRC with HPMC and PEG (RC5+) during two experimental periods. Diet refusals were measured daily from each lamb over a 7-d period following a 14-day dietary adaptation, and neutral detergent fiber (NDF) content was analyzed for all feed and refusal. Differences in percent refusal and NDF intake among diets were not statistically significant (p \u3e 0.05). Linear mixed‑model and stepwise regression analyses confirmed that diet refusal was unrelated to red clover inclusion or NDF change, though significant period and animal effects indicated that temporal or cohort factors influenced variation in diet selection.
Therefore, under the tested conditions, GRC supplementation did not significantly alter diet selection. Further research should explore alternate delivery methods or concentrations, greater diet diversity, or other phytoestrogen sources such as biochanin A analogs. Another interesting finding is that reliable diet‑selection data can still be obtained when sheep are fed below the typical 10% refusal threshold
Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification
No full textIn today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation
The Case Against Finean Essentialism
No full textIn this thesis, I survey, critique, and add to recent arguments against Finean Essentialism: the view that all de re modal facts are metaphysically explained by essences. Arguments against Finean Essentialism generally fall into two categories: (a) Finean Essentialism risks contradicting commonly held views or suppositions, or (b) Finean Essentialism involves an explanatory gap, vicious circularity, or a vicious regress. While the details of the arguments within each category differ significantly, it is useful to group them in this way given the relevant implications they have for one another. I will argue that the proposed critiques from category (a) are ultimately unsuccessful, whereas the critiques from category (b) pose a serious problem for Finean Essentialism
Plowing with a Pencil: Policy Approaches for States Squaring Agricultural Interests with Select Public Interests
No full textFrom a fifth-generation wheat farmer on the Kansas plains to a cattle rancher in Montana to a beginning peach producer in Georgia, American agriculture produces, provides, and protects sources of food, fiber, fuel, and shelter. American agriculture, once solely supported by rural family farmers and steeped in an agrarian system, now relies heavily on industrialized operations, creating an arena ripe for the clash of diverse policy perspectives. As farmers produce the food enjoyed at dinner tables across the country, the inevitable impacts of agriculture production on the environment have led to policy and legal arguments surrounding the regulation of agriculture. Concerning the promulgation and implementation of regulatory measures, two unique perspectives color the majority of policy approaches. The first perspective focuses on protecting the interests of the agriculture industry by virtue of special treatment, exemptions, and exclusions, while the second perspective focuses on regulating harm from agricultural production through policy or law.
The guiding question of this Note considers how to best advocate for agricultural interests in the promulgation and enforcement of regulations that will inherently impact aspects of the agricultural industry. Part II, and the subsequent sections focus on agency oversight of the agriculture industry at the intersection of agricultural law, environmental regulations, and farmland ownership laws. Additionally, subsection E explores two applicable theories, agricultural exceptionalism and regulatory capture, underpinned with agrarian and industrialized views, considering certain sectors of agriculture. Part III studies the policy actions of several states concerning environmental regulation, right-to-farm statutes, and foreign ownership of U.S. farmland. After exploring the motivations for implementing each policy tool, Part IV dissects each tool in relation to the state’s action or inaction to offer examples of the approach’s alignment with agricultural values to determine the motivations for utilizing each tool. Finally, Part V offers a discussion of the concerns and alter- native solutions following implementation of each policy tool to offer the most collaborative, efficient, and effective actions a policy- maker may make when attempting to balance agricultural values with other values
Compositions Comprising Peptides that Block Transmission of Orthotospoviruses
No full textOrthotospovirus virions travel through the thrips foregut and enter midgut epithelial cells through the interaction between virus glycoproteins and cellular receptors with several protein motifs thought to be involved in the interaction. Single, double and triple mutant polypeptides in the soybean vein necrosis virus (SVNV)/Neohydatothrips variabilis system are provided herein and several are shown to block viral transmission from the thrips to the soybean plants. Methods for inhibiting viral transmission using these polypeptides or constructs comprising polynucleotides encoding peptides are also provided herein