Southwest Baptist University (SBU) is a private Baptist university in Bolivar, Missouri. It is affiliated with the Missouri Baptist Convention, which is part of the Southern Baptist Convention. In 2019, it had a total enrollment of 3,280 students attending at one of SBU's four Missouri campuses in Bolivar, Mountain View, Salem, or Springfield.
Images from high-resolution cameras are mapped onto a sparse pattern of low spatial resolution and intensity in the retina, which limits visual perception in retinal prosthetic vision. When the entire scene is converted into phosphenes, it may allow unnecessary background information to be retained and may cause visual clutter, which may make it hard for prosthetic vision users to interpret the scene. In order to tackle this issue, this paper presents a context-, depth-, and user-preference-aware method for selecting the objects of interest in the generation of phosphene images. The proposed method does not show all the objects equally but learns to sort the objects according to their relevance to prosthetic vision. Manual annotation of a subset of COCO images was conducted where the most salient object was selected based on environment type, scene type, user mode, safety, navigation relevance, task importance, and distance. All of the candidate objects are described by full-scene visual features, object-crop features, handcrafted priority features, context embeddings, and monocular depth features. To predict object-level importance scores and identify the Top-1 and Top-4 important objects in unseen scenes, a hybrid deep learning model combining twin ResNet-18 backbones for scene and object feature extraction with embedding-based context encoding was trained. Priority maps and phosphene images were then created using the selected object masks and were depth-weighted. Two types of phosphene representations were also produced: Canny-edge-based and direct full images. The proposed framework is designed to suppress irrelevant background areas and improve important and closer objects in order to obtain a simplified and informative prosthetic-vision representation of the scene. The experimental evaluation, including Top-1 accuracy, Top-3 accuracy, mean reciprocal rank (MRR), and visual comparison, demonstrates the effectiveness of the proposed framework, achieving a Top-1 accuracy of 90.12%, a Top-3 accuracy of 97.45%, and an MRR of 0.9368. Furthermore, the proposed Canny-priority phosphene representation achieved an average human-participant recognition accuracy of approximately 86%. The proposed method offers a user-adaptive strategy for selecting and visualizing the information of a scene under the severe constraint of the bandwidth of retinal prosthetic vision.
The study of strong electron correlations has significantly advanced the frontiers of condensed matter physics, especially in relation to correlation-driven quantum phase transitions (QPTs). In the vicinity of QPTs, quantum critical fluctuations of multiple degrees of freedom enable the emergence of exotic many-body states and quantum critical behaviours beyond the Landau paradigm. Recently, magnetic frustration, traditionally associated with insulating magnets, has been recognized as pivotal to investigating new phases of matter in correlation-driven Kondo breakdown QPTs that are not clearly associated with broken symmetry. The nature of these new phases, however, remains underexplored. Here, we report quantum criticalities emerging from a cluster spin-glass in the heavy-fermion metal TiFexCu2x-1Sb, where frustration originates from intrinsic disorder. Specific heat and magnetic Grüneisen parameter measurements under varying magnetic fields exhibit quantum critical scaling, indicating a quantum critical point (QCP) near 0.13 Tesla. As the magnetic field increases, the cluster spin-glass phase is progressively suppressed. Upon crossing the QCP, resistivity and Hall effect measurements reveal enhanced screening of local moments and an expanding Fermi surface, consistent with the Kondo breakdown scenario. Our findings uncover a new family of iron-based heavy-fermion metals with intricate interplay of multiple degrees of freedom, enabling the exploration of unconventional excitations and exotic quantum critical states and behaviours.
Multiple choice questions (MCQs) are frequently used in medical education for assessment. Automated generation of MCQs in board-exam format could potentially save significant effort for faculty and generate a wider set of practice materials for student use. The goal of this study was to explore the feasibility of using ChatGPT by OpenAI to generate USMLE/COMLEX-USA-style practice quiz items as study aids. Researchers gave second year medical students studying renal physiology access to a set of practice quizzes with ChatGPT generated questions. The exam items generated were evaluated by independent experts for quality and adherence to NBME/NBOME guidelines. Forty-nine percent of questions contained item writing flaws, and 22% contained factual or conceptual errors. However, 59/65 (91%) were categorized as a reasonable starting point for revision. These results demonstrate the feasibility of large language model (LLM)-generated practice questions in medical education, but only when supervised by a subject matter expert with training in exam item writing.
This study addresses the ethical dilemmas arising from changes in family structure and institutionalized elderly care, systematically exploring the increasingly prominent phenomenon of "care poverty" in the context of globalization. The study explores the contradictions between population aging, labor migration, and increased care demands. It identifies the root cause of care poverty as the conflict between weakening traditional caregiving functions due to family structure changes and lagging development of institutionalized elderly care systems. Drawing on successful experiences from countries like Japan and Germany, the research proposes targeted multi-level solutions to address care poverty, offering new theoretical perspectives.
