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Caring for and Extracting Data from Rats with High-Thoracic Spinal Cord Injuries
Get PDFIndividuals who suffer from spinal cord injuries are very susceptible to autonomic dysreflexia, which unfortunately can lead to death in many cases. By studying this injury, a better understanding of the topic can be achieved, along with potential direction for assistive technologies. The purpose of this study was to ensure that rats with High-Thoracic Spinal Cord Injury were being properly cared for throughout the experimental acclimation process, with proper housing, surgical procedures, diet, and post-operative care. In the lab, these objectives were met, and then blood pressure and electrocardiography recordings were taken before and after the animal was paralyzed. This data was then analyzed with baseline and startle recordings to see the changes that these animals go through after a high-thoracic spinal cord injury. The technology developed from this data could help prevent autonomic dysreflexia and help those who suffer from spinal cord injuries have an easier and more tolerable lifestyle
Efficient Coding of Local 2D Shape
Get PDFEfficient coding provides a concise account of key early visual properties, but can it explain higher-level visual function such as shape perception? If curvature is a key primitive of local shape representation, efficient shape coding predicts that sensitivity of visual neurons should be determined by naturally-occurring curvature statistics, which follow a scale-invariant power-law distribution. To assess visual sensitivity to these power-law statistics, we developed a novel family of synthetic maximum-entropy shape stimuli that progressively match the local curvature statistics of natural shapes, but lack global structure. We find that humans can reliably identify natural shapes based on 4th and higher-order moments of the curvature distribution, demonstrating fine sensitivity to these naturally-occurring statistics. What is the physiological basis for this sensitivity? Many V4 neurons are selective for curvature and analysis of population response suggests that neural population sensitivity is optimized to maximize information rate for natural shapes. Further, we find that average neural response in the foveal confluence of early visual cortex increases as object curvature converges to the naturally-occurring distribution, reflecting an increased upper bound on information rate. Reducing the variance of the curvature distribution of synthetic shapes to match the variance of the naturally-occurring distribution impairs the linear decoding of individual shapes, presumably due to the reduction in stimulus entropy. However, matching higher-order moments improves decoding performance, despite further reducing stimulus entropy. Collectively, these results suggest that efficient coding can account for many aspects of curvature perception
A Dynamical Model of Binding in Visual Cortex During Incremental Grouping and Search
Get PDFBinding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the cortical module extracts stimulus features across a hierarchy of spatial scales, the thalamic module provides a purely spatial relevance map. Both modules interact bidirectionally to enter locations of task-relevance into thalamus, while allowing integration of context with local features within cortical maps. This integration realizes the model\u27s binding mechanism. It is implemented by pyramidal neurons with dynamical basal and apical compartments performing coincidence detection (Larkum, TINS, 2013). The basal compartment is driven by bottom-up feature information and produces the neuron\u27s output, the apical compartment computes top-down contextual information akin to an interaction skeleton (Roelfsema&Singer, Cereb Cortex, 1998). Apical-basal integration yields an up-modulation in neuron activity binding it to the attended configuration. Gating information from thalamus restricts this integration to task-relevant locations (Saalman&Kastner, Curr Op Neurobiol, 2009). By model simulations, we show how altering the apical compartment\u27s operation regime steers binding to either perform search or incremental grouping
A Model for Binocular Fusion
No full textThis study proposed a model for binocular fusion, which aims to minimize binocular misalignment. The model consists of an array of phase and position disparity detectors in each spatial-frequency channel that calculate position and phase disparity energies at each location. Binocular misalignment is defined as the offset of two eyes’ projections of a 3D target into a depth plane, and is evaluated using misalignment energy (MisaE). MisaE is calculated as a weighted summation of squared phase disparity energies with weights proportional to the absolute phase disparity. MisaE is always greater than 0, except on the depth plane of the 3D target, where it reaches its minimum value of 0. Binocular fusion is achieved by searching for a depth plane where the MisaE reaches the minimum. The readout of position disparity is driven by the MisaE gradient along position disparity space to reduce the MisaE until it reaches the minimum or falls below a threshold. Depth perception during this process is given by calculating the disparity energy, which combines both position and phase disparity energies. At a small stimulus disparity, the MisaE reaches the minimum, resulting in perfect fusion. However, at a large stimulus disparity, it may reach a local minimum that does not match the stimulus disparity, leading to ambiguous depth perception. Computational simulations of the proposed model using both random dot stereograms and a real stereo image show that the model works well to search for stimulus disparity at each location when the stimulus disparity varies smoothly in space
Second Reaction: Secrets of the Sea: The Story of Jeanne Power, Revolutionary Marine Scientist
Get PDFCulturally Responsive College Student Retention Theory & Practice
No full textThe three studies included in this dissertation collectively aim to highlight the negative impact that culturally homogenous academic, social, and financial systems on college campuses have on the experiences of culturally diverse college students. Currently the academic, social, and financial experiences of college students are not structured to support culturally diverse student groups, thus inhibiting their development of a sense of belonging and contributing to the low retention rates of culturally diverse students. The first study, “Examining Individualism in College Student Retention Theory and Practice: A Transition from Student Integration to Institutional Adjustment,” is a meta synthesis that explains that contemporary retention theories and practices are based on antiquated retention theories and are inadequate in addressing the needs of culturally diverse college student populations. The second study, “Financial Literacy Programming in American Higher Education: What’s There and What’s Missing,” is a qualitative study that addressed the research gap regarding financial literacy and wellness by more clearly defining the financial experiences and interactions that college students have within the financial systems of college. As a follow-up to the second study, the third study, “Values and Value: A Qualitative Study on Culturally Responsive Financial Literacy Programming,” examines more closely the content of financial literacy programs in higher education to identify ways that financial literacy programming on college campuses is responsive to students’ cultural identities. The findings of the included studies collectively inform foundational principles of a culturally responsive retention theory through which the college environment can be examined and potentially transformed to be more inclusive in access and support for culturally diverse students
Image Restoration Methods for Imaging Through Atmospheric Turbulence
No full textThe performance of long-range imaging systems often suffers due to the presence of atmospheric turbulence. One way to alleviate the degradation caused by atmospheric turbulence is to apply post-processing mitigation algorithms, where a high-quality frame is reconstructed from a single degraded image or a sequence of degraded frames. The image processing algorithms for atmospheric turbulence mitigation have been studied for decades, yet some critical problems remain open. This dissertation addresses the problem of image reconstruction through atmospheric turbulence from three unique perspectives: 1. Reconstruction with the presence of moving objects. By re-designing the reference frame extraction step, our method can keep the moving object in the reference frame, and thus the conventional pipeline can be extended to dynamic scenes. We also introduce a robust way to perform lucky region fusion. In the end, we propose a physics-constrained prior model of the point spread function for blind deconvolution. Our reconstruction method achieves state-of-the-art performance among classic optimization-based methods in both static and dynamic scene cases. 2. A fast turbulence simulation scheme for generating large-scale datasets. We propose the Phase-to-Space transform, a lightweight neural network module that transforms the Zernike Polynomial weights to learned dictionary weights to synthesize the point spread functions efficiently. Our approach is 1000 times faster than the classic split-step simulation methods on GPU while keeping the essential turbulence statistics. 3. We propose a physics-inspired transformer model for single-frame image reconstruction through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map and restore a turbulence-free image. We also present a real-world dataset for better evaluation of turbulence mitigation algorithm
Counting Closed Geodesics in Orbit Closures
No full textThe moduli space of Abelian differentials on Riemann surfaces admits a natural action by SL (2, R). This thesis is concerned with using the classification of invariant measures for this action due to Eskin and Mirzakhani, to study the growth of closed geodesics in the support of an invariant measure coming from the closure of an orbit for the SL (2, R)-action. These are always subvarieties of moduli space. For 0 ≤ θ ≤ 1, we obtain an exponential bound on the number of closed geodesics in the orbit closure, of length at most R, that have at least θ-fraction of their length in a region with short saddle connections
Factors Influencing Student Outcomes in K-12 Integrated STEM Education: A Systematic Review
Get PDFEarlier integrated science, technology, engineering, and mathematics (STEM) education research has shown effects on students’ attitudes toward STEM careers, actual and perceived learning, and interest in pursuing a STEM career in their future endeavors. The current systematic review purported to review the recent K-12 integrated STEM education research to determine (a) the factors that influence student outcomes and (b) the general characteristics of reviewed studies. Overall, the results (a) showed that most studies focused on integrating at least three subject areas; (b) highlighted four main factors (i.e., instructional, teacher-related, student-related, and extracurricular factors) that jointly influence student outcomes; and (c) revealed that science is the most frequently integrated main field followed by engineering. Engineering also turned out to be a connector in integrated STEM together with technology. The results led to various implications for both in-class practice and future research on K-12 integrated STEM education
A Strategic Assessment of Needs and Opportunities for the Wider Adoption of Electric Vehicles in Indiana
Get PDFINDOT plans to invest nearly $100 million to build a statewide electric vehicle (EV) charging network as part of the National Electric Vehicle Infrastructure Formula Program. SPR-4509 Phase-I identified energy EV charging deserts in Indiana for long-distance trips. SPR-4509 Phase-II further examines the charging stations\u27 impact on EV long-distance trips in Indiana. Using an agent-based simulation model, the number of charges, vehicle miles traveled, energy used during the trip, and energy used during charging were estimated for nine different cases. High EV daily charging demand areas in Indiana were shown in ArcGIS based on multiple scenarios of different charging station construction phases and EV market penetration rates. The study findings can inform the state’s EV charging plan development