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Disentanglement with Holographic Reduced Representations
Announce Type: new Abstract: Disentanglement, the separation of factors of variation in data using neural networks, remains a long-standing challenge in machine learning. Prior work has addressed this problem with variational autoencoders and generative adversarial networks that incorporate ideas from variational inference and information-theoretic constraints.
Cross-Sectional Physiological and Neuromuscular Profiling of Elite and Recreational University Badminton Athletes: Preliminary Benchmarks for Exercise-Based Injury Risk Stratification
Background and Purpose: Badminton imposes considerable cardiovascular and musculoskeletal stress. Physiological profiling can identify modifiable injury risk factors and inform exercise-based prevention and rehabilitation. This study compared cardiovascular recovery, neuromuscular activation, and limb strength between elite and recreational male university badminton players to derive preliminary physiological benchmarks for injury risk stratification and exercise rehabilitation guidance.