Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence without anatomy, sourc...
Jia-Ying Wang, Shou-Qian Shi, Yu-Tong Chen et al.· 0 citations
High-quality representations are essential for a wide range of downstream tasks. Dedicated embedding models are explicitly optimized for representation learning, yet their training data are often more limited in scale and diversity than the massive corpora used to pretrain modern large language models and multimodal la...
Xin-Ran Liu, Shouqian Shi, Yi-Xian Chen et al.· 0 citations
Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions. CLIP provides a strong foundation for these tasks by learning a shared image-text embedding space from large-s...
The proposed IRIS (Identity Representations from Internal States), a training-free framework that constructs for each entity an iris-like signature encoding its distinctive and stable identity characteristics, thereby forming a shared space in which each entity is encoded once and can be aligned across different KGs th...
Xin-Ran Liu, Shengtao Li, Shouqian Shi et al.· arXiv.org· 0 citations
Penelope is introduced, an efficient latent-reasoning framework for pretrained decoder-only Transformers that localizes recurrent computation to a selected decoder interval and attains competitive accuracy relative to established latent-reasoning models while reducing measured inference latency.
Yutong Chen, Shouqian Shi, Xin-Ran Liu et al.· arXiv.org· 0 citations
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