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The Emergence of Emotion Categories
No full textWhat are concepts, and what mechanisms categorizes these concepts based on their associated properties? This thesis explores the cognitive and computational processes that underlie our mental representations of emotion concepts. Emotions are understood not as isolated events but as emergent patterns resulting from interactions among physiological, behavioral, and contextual inputs. Building on the Interactive Activation and Competition for Emotion (IAC-E) model developed by Suri and Gross (2022), this work investigates how emotions arise from the activation of multiple, interconnected feature pools within a neural network (Suri & Gross, 2022). To further test the theory of emergent patterns of activation, this paper draws upon the principles of the Parallel Distributed Processing (PDP) framework, which posits that concepts are not stored in a single unit but arise from patterns of activation distributed across networks of interconnected neurons (Rumelhart et al., 1986). Using the PDP principles, we developed a computational model to simulate how emotion categories form and how distinct patterns of activation emerge in the hidden layers of the network. The findings offer insight into the representational structure of emotion concepts and support the view that knowledge is not localized but emerge from patterns of activation across interconnected neural units.https://doi.org/10.46569/8g84mw90
A Geometric Interpretation of the Characteristic Polynomial
No full textMatroids, introduced by Whitney in 1935, serve as a unifying framework for un- derstanding independence across various mathematical fields, including graph theory, linear algebra, and projective geometry. One of the most important invariants of a matroid is its characteristic polynomial, as it encodes vital combinatorial information about the structure of the matroid. Recent work by Nathanson and Ross [NR23] established a an interpretation of the characteristic polynomial in terms of a geometric object called normal com- plexes—constructed from the matroids Bergman fan—and the coefficients of the characteristic polynomial. Building on this idea, [NOR23] later gave a new proof of the Heron–Rota–Welsh conjecture, which states the the coefficients of the char- acteristic polynomial are log-concave, by demonstrating that the volumes of normal complexes satisfies the Alexandrov–Fenchel inequalities, thus connecting deep geo- metric results to one of the central recent results in matroid theory [AHK199]. In this work, we take a further step in bridging geometry and matroid theory by explicitly studying the normal complex whose mixed volume polynomial is precisely the reduced characteristic polynomial of a given matroid. Using this geometric con- struction, we provide a new formula for computing the characteristic polynomial's coefficients through volume calculations of the normal complex. This new perspec- tive not only offers a refined geometric viewpoint on matroid invariants but also underscores the powerful synergy between combinatorial structures and geometric analysis. We begin in Chapter 1 by motivating the idea of the characteristic polynomial for a matroid through an exploration of the chromatic polynomial of a graph. Chapter 2 introduces polyhedral complexes and explains how we build a normal complex for a given matroid. Finally, in Chapter 3, we study the specific normal complex of interest and derive its volume formula.https://doi.org/10.46569/mw22vg11
Model-Based Force Estimation of a Cable Driven Exoglove
No full textThis work presents the development of a soft, cable-driven hand exoskeleton designed to assist individuals with impaired hand function, particularly stroke survivors, who represent over 80 million people worldwide, with many experiencing lasting upper-limb motor defcits. The proposed system features a lightweight, 3D-printed actuation unit that transmits torque through bowden cables. paired with fexible 3D-printed, crisscross shaped fnger tendons allowing for compliant motion during both fexion and extension. A key component is the use of model-based force estimation without relying on traditional tactile sensors. The glove integrates multiple sensing methods: electromyography (EMG) detects the user's muscle activity to signal intent, while computer vision (CV) identifes objects and guides grasp initiation. The system uses control logic that shifts between modes; grasping when an object is detected, holding with stable force, and releasing upon EMG classifcation. The apple, and object associated with tasks of active daily living (ADL) was successfully grasped, held, and released, with measured average fnger forces ranging from 8.33 N to 11.72 N. Plots of motor current over time confrmed accurate grasping behavior and clear transition between sensing modes. This work introduces a multimodal, user-centered control system for assistive hand rehabilitation and daily function. The results demonstrate both the feasibility and potential impact of this approach, particularly as this system takes an important step toward making home-based rehabilitation and everyday assistance more natural and accessible.https://doi.org/10.46569/h415pm03
Objectivity and Usefulness of Simplify Writing's Diagnostic Pre-Assessment
No full textTraditional writing rubrics often provide broad, subjective evaluations that obscure students' specific strengths and weaknesses. This limits teachers' ability to deliver targeted instruction. This mixed-methods study examined the objectivity and instructional usefulness of the Simplify Writing® Pre-Assessment, a diagnostic tool designed to reduce subjectivity and provide skill-specific data for instructional planning. Participants included 291 educators across the United States who completed a Qualtrics survey and 10 educators who participated in an inter-rater reliability study scoring three student writing samples (grades 3–5) using both a state-standard rubric and the Simplify Writing® scoring tool. Quantitative survey data were analyzed descriptively, while qualitative responses were coded thematically. Inter-rater reliability data were compared using mean scores, standard deviation, and score range. Findings revealed that 94% of participants agreed the scoring sheet was clear, and 90% reported that the 0–2 scale was more objective than traditional rubrics. Teachers reported using the data to form small groups, plan targeted mini-lessons, and better communicate strengths and areas for growth to students and families. Inter-rater reliability analyses showed that Simplify Writing® consistently produced narrower scoring ranges and lower standard deviations than the traditional rubric, indicating improved consistency among raters. Five key themes emerged: (1) increased scoring clarity and consistency, (2) actionable data for instructional planning, (3) enhanced teacher confidence, (4) usability considerations and training needs, and (5) improved equity and fairness, particularly for multilingual learners and students with disabilities. Results suggest that the Simplify Writing® Pre-Assessment offers a more objective, equitable, and instructionally valuable alternative to traditional rubrics. Keywords: writing assessment subjectivity, assessing writing, diagnostic assessment, inter-rater reliability, instructional planning, equity, Simplify Writing
Mitigating the Social-Emotional Impacts of Learning with Dyslexia: Inspiring Literacy Motivation through Identifying with Characters in Literature
No full textDyslexia is a neurobiological learning difference that affects much more than the ability to decode words; it shapes self-perception, social relationships, and overall motivation to engage in literacy activities. While structured literacy instruction remains essential, the emotional toll of repeated academic struggles is often overlooked. This curricular project addresses the lack of self-efficacy by combining high-quality, dyslexia-inclusive literature with a Social Emotional Learning (SEL) Project-Based Learning (PBL) unit. The curated booklist features diverse, authentic representations of dyslexia across genres to serve as mirrors for students with orthographic processing difficulties to see themselves in the literature and windows for their peers to foster empathy and understanding. The accompanying PBL unit guides students to research real-life mentors who also have dyslexia in order to reframe their learning differences as a source of strength and uniqueness. This project is designed for use in elementary and middle school settings to support literacy skill development while affirming identity and building resilience. By integrating inclusive storytelling and intentional social-emotional learning practices, it provides students, parents, caregivers, educators, and librarians with tools to cultivate confident, motivated readers who recognize their unique abilities and feel a genuine connection in their literacy journey
Empowering BIPOC students: cultivating the educational path to be and become
No full textThis study investigated the role of teacher–student relationships in cultivating a sense of belonging and promoting academic success through the lens of culturally relevant pedagogy (CRP). It critically examined barriers that hinder effective CRP implementation and analyzed institutional conditions, systems, and structures required to sustain culturally responsive practices in K–12 educational settings. Teacher and student relationships are foundational to the success of CRP because they create the interpersonal context in which culturally responsive teaching can thrive. When teachers build genuine connections with students, they validate students' cultural identities and lived experiences, fostering an environment where learners feel seen, respected, and empowered. This sense of belonging not only supports social–emotional well-being but also enhances motivation and engagement, which are key drivers of academic achievement. However, these relationships must be nurtured in a broader pedagogical framework that prioritizes cultural relevance and equity, ensuring teaching practices affirm diverse identities rather than merely acknowledging difference superficially. Despite its promise, the effective implementation of CRP faces significant challenges. Educators often encounter systemic barriers, such as limited professional development focused on cultural competence, rigid curriculum standards, and school policies that do not support adaptive or inclusive practices. Moreover, institutional structures may inadvertently perpetuate inequities by privileging dominant cultural norms and marginalizing students from diverse backgrounds. This study explored how school leadership, policy frameworks, and collaborative professional learning communities can work together to create sustainable conditions for CRP. By identifying the critical supports and systemic changes necessary, this research aimed to inform strategies that not only initiate culturally relevant practices but also embed them deeply and enduringly in the educational system
What the Reaper Claims
No full textThis creative project is a part of a novel following the two characters, Connor (the older sibling) on his quest through technologically advanced side of Gilton City to expose the most perturbing parts of people at the order of his anonymous patron in exchange for vast riches and dreams coming true ultimately leads to a decision between his sister's future and his own safety ignites one last flame in him before he burns out. Leslie (the younger sibling) begins her own journey through the rundown side of Gilton, first to seek help for her grandfather, then, to rekindle the nonexistent bond between herself and her brother by reconstructing the person he was in life
Enhancing alzheimer's disease detection through deep learning models and corpus callosum segmentation
No full textAlzheimer's disease affects multiple areas of the brain, one of which is the Corpus Callosum—a compact, yet critical structure whose size and shape make its modelling quite challenging. Specifically, I was interested in investigating whether Alzheimer's can be identified using only this area. In my initial experiments, I utilized the OASIS-2 dataset [1], which holds 350 brain images labelled as Alzheimer's, MCI, or normal. Several CNN models, i.e., VGG, ResNet, and EfficientNet, were trained using various preprocessing strategies such as grayscale conversion, data normalization, contrast stretching, data augmentation, and SMOTE. Yet, low model performance was caused by insufficient data and the compact size of the Corpus Callosum. To address this, I moved to the ADNI dataset [2], comprising more than 3,000 brain scans. I had to transform the 3D. niftii scans into 2D slices and tried various resolutions and CNN architectures. I also tried applying a 3D CNN to unsegmented scans, although this gave mixed results due to added brain tissue causing noise and decreasing precision. Isolating the Corpus Callosum became apparent as essential for enhancing performance.To do so, I employed a U-Net segmentation model [3] for extracting Corpus Callosum from the scans. Classification was limited to this segmented area, and this improved precision to about 80%. This result proves the viability of using the Corpus Callosum as an accurate biomarker for detecting Alzheimer's disease in support of using specific brain areas for disease diagnosis
Implementation of real-time traffic light and color detection using FPGA-based acceleration
No full textThis project uses the Ultra96 FPGA board to detect real-time traffic signals and recognize objects with the help of hardware acceleration and Python-based algorithms. It showcases the FPGA's processing ability by handling inputs from two USB cameras. A Logitech USB camera and a monochrome USB camera. At the same time. The implementation incorporates sophisticated detection algorithms such as TensorFlow Lite, YOLO, and Haar Cascade, resulting in speed and accuracy [39]. The system for detecting is based on a TensorFlow Lite model that has been trained and optimized for edge devices using methods like quantization. The training data consisted of 1,000 images of traffic lights taken under various lighting and weather conditions. To improve the model's robustness, data augmentation techniques like rotation, scale modifications, and brightness alterations were used.The system identifies traffic signals as objects. Understands their color (red or green), allowing for accurate on-the-spot detection. This project highlights the advantages of FPGA hardware acceleration over processors such as Raspberry Pi. The Ultra96 FPGA demonstrates superiority in speed and efficiency while offering scalability suitable for utilization in intelligent traffic control systems, self-driving vehicles such as autonomous cars, and roadway safety surveillance. System deployment is performed via Jupyter Notebook to provide an adaptable development workflow. The images capture the system's strengths well. The Logitech camera succeeds in displaying resolution and correctly detecting colors, whereas the monochrome camera does well in low-light conditions. This clever blend of FPGA acceleration with a software framework and real-time object detection provides a flexible and feasible answer for advanced traffic control systems
Parent-school partnerships: Enhancing success and reducing behavioral issues through training
No full textParent-school collaboration plays a crucial role in promoting student success and addressing behavioral challenges in secondary schools. Research indicates that parenting styles significantly influence adolescent behavior, academic performance, and emotional well-being. Additionally, strong home-school connections impact students' motivation, self-regulation, and engagement while reducing externalizing behaviors. Grounded in evidence-based strategies, this project introduces a structured, school-led workshop designed to strengthen parental involvement by providing psychoeducation on parenting styles, behavior management strategies, and effective home-school communication. This intervention aims to equip parents with practical tools to foster a supportive learning environment that enhances student academic achievement and social-emotional development, ultimately helping to bridge the gap between families and schools to establish sustainable partnerships that promote long-term student success and well-being