Exploring Large Language Models for Specialist-level Oncology Care

Autor: Palepu, Anil, Dhillon, Vikram, Niravath, Polly, Weng, Wei-Hung, Prasad, Preethi, Saab, Khaled, Tanno, Ryutaro, Cheng, Yong, Mai, Hanh, Burns, Ethan, Ajmal, Zainub, Kulkarni, Kavita, Mansfield, Philip, Webster, Dale, Barral, Joelle, Gottweis, Juraj, Schaekermann, Mike, Mahdavi, S. Sara, Natarajan, Vivek, Karthikesalingam, Alan, Tu, Tao

Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, their applicability in subspecialist or complex medical settings remain

Externí odkaz: http://arxiv.org/abs/2411.03395

Dark Energy Survey Year 3: Blue Shear

Autor: McCullough, J., Amon, A., Legnani, E., Gruen, D., Roodman, A., Friedrich, O., MacCrann, N., Becker, M. R., Myles, J., Dodelson, S., Samuroff, S., Blazek, J., Prat, J., Honscheid, K., Pieres, A., Ferté, A., Alarcon, A., Drlica-Wagner, A., Choi, A., Navarro-Alsina, A., Campos, A., Malagón, A. A. Plazas, Porredon, A., Farahi, A., Ross, A. J., Rosell, A. Carnero, Yin, B., Flaugher, B., Yanny, B., Sánchez, C., Chang, C., Davis, C., To, C., Doux, C., Brooks, D., James, D. J., Cid, D. Sanchez, Hollowood, D. L., Huterer, D., Rykoff, E. S., Gaztanaga, E., Huff, E. M., Suchyta, E., Sheldon, E., Sanchez, E., Tarsitano, F., Andrade-Oliveira, F., Castander, F. J., Bernstein, G. M., Gutierrez, G., Giannini, G., Tarle, G., Diehl, H. T., Huang, H., Harrison, I., Sevilla-Noarbe, I., Tutusaus, I., Ferrero, I., Elvin-Poole, J., Marshall, J. L., Muir, J., Weller, J., Zuntz, J., Carretero, J., DeRose, J., Frieman, J., Cordero, J., De Vicente, J., García-Bellido, J., Mena-Fernández, J., Eckert, K., Romer, A. K., Bechtol, K., Herner, K., Kuehn, K., Secco, L. F., da Costa, L. N., Paterno, M., Soares-Santos, 21 M., Gatti, M., Raveri, M., Yamamoto, M., Smith, M., Kind, M. Carrasco, Troxel, M. A., Aguena, M., Jarvis, M., Swanson, M. E. C., Weaverdyck, N., Lahav, O., Doel, P., Wiseman, P., Miquel, R., Gruendl, R. A., Cawthon, R., Allam, S., Hinton, S. R., Bridle, S. L., Bocquet, S., Desai, S., Pandey, S., Everett, S., Lee, S., Shin, T., Palmese, A., Conselice, C., Burke, D. L., Buckley-Geer, E., Lima, M., Vincenzi, M., Pereira, M. E. S., Crocce, M., Schubnell, M., Jeffrey, N., Alves, O., Vikram, V., Zhang, Y., Collaboration, DES

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this b

Externí odkaz: http://arxiv.org/abs/2410.22272

Improving Galaxy Cluster Selection with the Outskirt Stellar Mass of Galaxies

Autor: Kwiecien, Matthew, Jeltema, Tesla, Leauthaud, Alexie, Huang, Song, Rykoff, Eli, Heydenreich, Sven, Lange, Johannes, Everett, Spencer, Zhou, Conghao, Kelly, Paige, Zhang, Yuanyuan, Shin, Tae-Hyeon, Golden-Marx, Jesse, Marshall, J. L., Aguena, M., Allam, S. S., Bocquet, S., Brooks, D., Rosell, A. Carnero, Carretero, J., da Costa, L. N., Pereira, M. E. S., Davis, T. M., De Vicente, J., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., García-Bellido, J., Gatti, M., Gaztanaga, E., Giannini, G., Gruen, D., Gruendl, R. A., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Lee, S., Miquel, R., Pieres, A., Malagón, A. A. Plazas, Romer, A. K., Samuroff, S., Sanchez, E., Santiago, B., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Tucker, D. L., Vikram, V., Weaverdyck, N., Wiseman, P.

The number density and redshift evolution of optically selected galaxy clusters offer an independent measurement of the amplitude of matter fluctuations, $S_8$. However, recent results have shown that clusters chosen by the redMaPPer algorithm show r

Externí odkaz: http://arxiv.org/abs/2410.20205

Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense

Autor: Singh, Aditya Vikram, Rathbun, Ethan, Graham, Emma, Oakley, Lisa, Boboila, Simona, Oprea, Alina, Chin, Peter

Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defending networks against sophisticated adversaries remains a challenging t

Externí odkaz: http://arxiv.org/abs/2410.17351

Finite Sample and Large Deviations Analysis of Stochastic Gradient Algorithm with Correlated Noise

Autor: Yin, George, Krishnamurthy, Vikram

We analyze the finite sample regret of a decreasing step size stochastic gradient algorithm. We assume correlated noise and use a perturbed Lyapunov function as a systematic approach for the analysis. Finally we analyze the escape time of the iterate

Externí odkaz: http://arxiv.org/abs/2410.08449