Repetitive head impacts pose a significant threat to brain health. However, their injury accumulation mechanism remains elusive. Here, we study how traumatic axonal injury might accumulate based on typical impact severities and frequencies obtained from University Men’s Ice Hockey. From N = 994 impact simulations over a season (N = 22 athletes), we identified 50th (‘‘mild’’, 7.0
We investigate coupled fluid flow, heat transfer, species transport, and surface reaction in a single reactive pore embedded within a low-permeability matrix layer separating adjacent aquifer strata, motivated by aquifer thermal energy storage (ATES) applications. Building on fracture-scale reactive-transport simulations, we develop a pore-scale model that resolves reaction-driven pore evolution, and assess whether such evolution can compromise the impermeability assumption employed in homogenized ATES models. Analysis of a spatially independent reduction reveals a robust thermochemical structure in which the dominant reaction mode is governed by thermal forcing, while reaction and matrix-fluid heat exchange control transient relaxation toward equilibrium. Numerical simulations of the full pore-scale system of partial differential equations (PDE) show how axial transport redistributes these local dynamics without altering their underlying structure. Using time-dependent thermal and chemical forcing extracted from a fracture-scale model, we identify parameter regimes in which reaction-driven pore evolution remains insufficient to exceed experimentally reported permeability thresholds. These results delineate a regime of validity for the impermeability assumption in ATES modeling and provide a mechanistic link between fracture-scale forcing and pore-scale transport processes.
Accurate recall from large-scale memories remains a core challenge for memory-augmented AI assistants performing question answering (QA), especially in similarity-dense scenarios where existing methods mainly rely on semantic distance to the query for retrieval. Inspired by how humans link information associatively, we propose AssoMem, a novel framework constructing an associative memory graph that anchors dialogue utterances to automatically extracted clues. This structure provides a rich organizational view of the conversational context and facilitates importance-aware ranking. Further, AssoMem integrates multi-dimensional retrieval signals—relevance, importance, and temporal alignment—using an adaptive mutual information (MI)-driven fusion strategy. Extensive experiments across three benchmarks and a newly introduced dataset, MeetingQA, demonstrate that AssoMem consistently outperforms state-of-the-art baselines, verifying its superiority in context-aware memory recall.
Monaural multi-speaker automatic speech recognition (ASR) remains challenging due to data scarcity and the intrinsic difficulty of recognizing and attributing words to individual speakers, particularly in overlapping speech. Recent advances have driven the shift from cascade systems to end-to-end (E2E) architectures, which reduce error propagation and better exploit the synergy between speech content and speaker identity. Despite rapid progress in E2E multi-speaker ASR, the field lacks a comprehensive review of recent developments. This survey provides a systematic taxonomy of E2E neural approaches for multi-speaker ASR, highlighting recent advances and comparative analysis. Specifically, we analyze: (1) architectural paradigms (single-input-multiple-output (SIMO) vs. single-input-single-output (SISO)) for pre-segmented audio, analyzing their distinct characteristics and trade-offs; (2) recent architectural and algorithmic improvements based on these two paradigms, including multi-modal inputs; (3) extensions to long-form speech, including segmentation strategy and speaker-consistent hypothesis stitching. Further, we (4) evaluate and compare methods across standard benchmarks. We conclude with a discussion of open challenges and future research directions towards building robust and scalable multi-speaker ASR.
Modern large vision-language models (LVLMs) convert each input image into a large set of tokens that far outnumber the text tokens. Although this improves visual perception, it also introduces severe image token redundancy. Because image tokens contain sparse information, many contribute little to reasoning but greatly increase inference cost. Recent image token pruning methods address this issue by identifying important tokens and removing the rest. These methods improve efficiency with only small performance drops. However, most of them focus on single-image tasks and overlook multimodal in-context learning (ICL), where redundancy is higher and efficiency is more important. Redundant tokens weaken the advantage of multimodal ICL for rapid domain adaptation and lead to unstable performance. When existing pruning methods are applied in this setting, they cause large accuracy drops, which exposes a clear gap and the need for new approaches. To address this, we propose Contextually Adaptive Token Pruning (CATP), a training-free pruning method designed for multimodal ICL. CATP uses two stages of progressive pruning that fully reflect the complex cross-modal interactions in the input sequence. After removing 77.8% of the image tokens, CATP achieves an average performance gain of 0.6% over the vanilla model on four LVLMs and eight benchmarks, clearly outperforming all baselines. At the same time, it improves efficiency by reducing inference latency by an average of 10.78%. CATP strengthens the practical value of multimodal ICL and lays the foundation for future progress in interleaved image-text settings.
