The field of machine learning is witnessing significant developments in healthcare and language modeling. Researchers are exploring innovative approaches to improve the accuracy and efficiency of models in these domains. One notable trend is the integration of sensor monitoring data and machine learning algorithms to track disease progression and provide personalized interventions. Another area of focus is the development of novel architectures and techniques to enhance the scalability and reliability of language models. State space models, in particular, are gaining attention for their ability to process long-context inputs and improve performance on consumer hardware. Furthermore, generative modeling paradigms are being applied to cognitive diagnosis and language modeling, offering promising results and new avenues for research. Notable papers in this area include:

• Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring, which presents a novel approach to tracking ALS progression using semi-supervised regression models.
• Generative Cognitive Diagnosis, which introduces a generative diagnosis paradigm for cognitive diagnosis, achieving excellent performance improvements over traditional methods.
• Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving, which designs an array of State-update Processing Units to enable efficient execution of state update and attention operations.
• Recognizing Dementia from Neuropsychological Tests with State Space Models, which proposes a novel framework for automatic dementia classification using state space models, outperforming prior approaches in fine-grained dementia classification.
• Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length, which presents a comprehensive benchmarking of Transformer, SSM, and hybrid models for long-context inference on consumer and embedded GPUs.