Exploiting correlations across trials and behavioral sessions to improve neural decoding

Posted by dbos Columbia

Kavli Affiliate: Liam Paninski | Authors: Yizi Zhang, Hanrui Lyu, Cole Hurwitz, Shuqi Wang, Charles Lincoln Findling, Felix Hubert, Alexandre Pouget, International Brain Laboratory, Erdem Varol and Liam Paninski | Summary: Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and […]


Continue.. Exploiting correlations across trials and behavioral sessions to improve neural decoding

The kinematic bimodality: Efficient feedback and cold gas deficiency in slow-rotating galaxies

Posted by dbos Peking

Kavli Affiliate: Yingjie Peng | First 5 Authors: Bitao Wang, Yingjie Peng, , , | Summary: The bimodality in the stellar spin of low redshift (massive) galaxies, ubiquitously existing at all star formation levels and in diverse environment, suggests that galaxies grow and quench through two diverged evolutionary pathways. For spheroid-dominated galaxies of slow stellar […]


Continue.. The kinematic bimodality: Efficient feedback and cold gas deficiency in slow-rotating galaxies

Long-lived Sterile Neutrino Searches at Future Muon Colliders

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Qi Bi, Jinhui Guo, Jia Liu, Yan Luo, Xiao-Ping Wang | Summary: We explore the potential of studying sterile neutrinos at a future high-energy muon collider, where these particles can generate small active neutrino masses via the seesaw mechanism and exhibit long-lived particle signatures. A Dirac sterile […]


Continue.. Long-lived Sterile Neutrino Searches at Future Muon Colliders

Long-lived Sterile Neutrino Searches at Future Muon Colliders

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Qi Bi, Jinhui Guo, Jia Liu, Yan Luo, Xiao-Ping Wang | Summary: We explore the potential of studying sterile neutrinos at a future high-energy muon collider, where these particles can generate small active neutrino masses via the seesaw mechanism and exhibit long-lived particle signatures. A Dirac sterile […]


Continue.. Long-lived Sterile Neutrino Searches at Future Muon Colliders

Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Xinrui Zhou, Yuhao Huang, Haoran Dou, Shijing Chen, Ao Chang | Summary: In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative augmentation approaches present a promising solution to this issue, having been proven effective […]


Continue.. Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Xinrui Zhou, Yuhao Huang, Haoran Dou, Shijing Chen, Ao Chang | Summary: In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative augmentation approaches present a promising solution to this issue, having been proven effective […]


Continue.. Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training

Posted by dbos Peking

Kavli Affiliate: Jiansheng Chen | First 5 Authors: Kun Song, Zhiquan Tan, Bochao Zou, Jiansheng Chen, Huimin Ma | Summary: In this paper, we utilize information-theoretic metrics like matrix entropy and mutual information to analyze supervised learning. We explore the information content of data representations and classification head weights and their information interplay during supervised […]


Continue.. Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training