Tootfinder

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Language learning shapes visual category-selectivity in deep neural networks
Zitong Lu, Yuxin Wang

Crosslisted article(s) found for q-bio.MN. https://arxiv.org/list/q-bio.MN/new
[1/1]:
- Decoding the dark proteome: Deep learning-enabled discovery of druggable enzymes in Wuchereria ba...
Shawnak Shivakumar, Jefferson Hernandez

Crosslisted article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Weak Form Learning for Mean-Field Partial Differential Equations: an Application to Insect Movement
Seth Minor, Bret D. Elderd, Benjamin Van Allen, David M. Bortz, Vanja Dukic

Crosslisted article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
Adele Chinda, Richmond Azumah, Hemanth Demakethepalli Venkateswara

Q&A with NYC mayoral front-runner Zohran Mamdani on social media, tech leaders, Trump, fame, Apple's and Google's "wrong decision" to remove ICEBlock, and more (Katie Drummond/Wired)
https://www.wired.com/story/the-big-interview-podcast-zohran-mamd…

Zohran Mamdani, the Internet’s Mayor
Mere months ago, New York’s mayoral front-runner was polling right next to “Someone Else.” He spoke to WIRED about building a social media machine, Big Tech capitulation, and learning from Eric Adams.

Replaced article(s) found for q-fin.TR. https://arxiv.org/list/q-fin.TR/new
[1/1]:
- Language Model Guided Reinforcement Learning in Quantitative Trading
Adam Darmanin, Vince Vella

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- HelixVS: Deep Learning-Enhanced Structure-Based Platform for Screening and Design
Shanzhuo Zhang, Xianbin Ye, Donglong He, Yueyang Huang, Xiaonan Zhang, Xiaomin Fang

MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design
S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
https://arxiv.org/abs/2511.18980 https://arxiv.org/pdf/2511.18980 https://arxiv.org/html/2511.18980
arXiv:2511.18980v1 Announce Type: new
Abstract: Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that integrates metasurface geometry and spectra within a shared latent space. MOCLIP employs contrastive learning to align geometry and spectral representations using an experimentally acquired dataset with a sample density comparable to ImageNet-1K. The study demonstrates MOCLIP inverse design capabilities for high-throughput zero-shot prediction at a rate of 0.2 million samples per second, enabling the design of a full 4-inch wafer populated with high-density metasurfaces in minutes. It also shows generative latent-space optimization reaching 97 percent accuracy. Finally, we introduce an optical information storage concept that uses MOCLIP to achieve a density of 0.1 Gbit per square millimeter at the resolution limit, exceeding commercial optical media by a factor of six. These results position MOCLIP as a scalable and versatile platform for next-generation photonic design and data-driven applications.
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Crosslisted article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Learning the Exact SABR Model
Giorgia Rensi, Pietro Rossi, Marco Bianchetti

Crosslisted article(s) found for q-fin.MF. https://arxiv.org/list/q-fin.MF/new
[1/1]:
- Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park

Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
Saeid Mashhadi, Amirhossein Saghezchi, Vesal Ghassemzadeh Kashani

Q&A with Andrej Karpathy on AGI still being a decade away, why reinforcement learning is terrible, superintelligence, his AI education startup Eureka, and more (Dwarkesh Patel/Dwarkesh Podcast)
https://www.dwarkesh.com/p/andrej-karpathy

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilib...
Ken Ishihara, Hideaki Shimazaki
https://arxiv.org/abs/2502.15440 https://mastoxiv.page/@arXiv_qbioNC_bot/114057779012161849
- Mechanisms for anesthesia, unawareness, respiratory depression, memory replay and sleep: MHb > IP...
Karin Vadovi\v{c}ov\'a
https://arxiv.org/abs/2509.04454 https://mastoxiv.page/@arXiv_qbioNC_bot/115167812677714466
- Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
https://arxiv.org/abs/2512.09366 https://mastoxiv.page/@arXiv_qbioNC_bot/115699940165988688
- Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavi...
Sang, Huang, Zhong, Wang, Yu, Li, Feng, Wang, Chai, Menon, Wang, Fang, Wang
https://arxiv.org/abs/2512.09761 https://mastoxiv.page/@arXiv_qbioNC_bot/115700046994546552
- Proof of a perfect platonic representation hypothesis
Liu Ziyin, Isaac Chuang
https://arxiv.org/abs/2507.01098 https://mastoxiv.page/@arXiv_csLG_bot/114788750477759162
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