Pointing Accuracy Improvements for the South Pole Telescope with Machine Learning

Autor: Chichura, P. M., Rahlin, A., Anderson, A. J., Ansarinejad, B., Archipley, M., Balkenhol, L., Benabed, K., Bender, A. N., Benson, B. A., Bianchini, F., Bleem, L. E., Bouchet, F. R., Bryant, L., Camphuis, E., Carlstrom, J. E., Chang, C. L., Chaubal, P., Chokshi, A., Chou, T. -L., Coerver, A., Crawford, T. M., Daley, C., de Haan, T., Dibert, K. R., Dobbs, M. A., Doohan, M., Doussot, A., Dutcher, D., Everett, W., Feng, C., Ferguson, K. R., Fichman, K., Foster, A., Galli, S., Gambrel, A. E., Gardner, R. W., Ge, F., Goeckner-Wald, N., Gualtieri, R., Guidi, F., Guns, S., Halverson, N. W., Hivon, E., Holder, G. P., Holzapfel, W. L., Hood, J. C., Hryciuk, A., Huang, N., Kéruzoré, F., Khalife, A. R., Kim, J., Knox, L., Korman, M., Kornoelje, K., Kuo, C. -L., Levy, K., Lowitz, A. E., Lu, C., Maniyar, A., Marrone, D. P., Martsen, E. S., Menanteau, F., Millea, M., Montgomery, J., Nakato, Y., Natoli, T., Noble, G. I., Omori, Y., Padin, S., Pan, Z., Paschos, P., Phadke, K. A., Pollak, A. W., Prabhu, K., Quan, W., Rahimi, M., Reichardt, C. L., Rouble, M., Ruhl, J. E., Schiappucci, E., Sobrin, J. A., Stark, A. A., Stephen, J., Tandoi, C., Thorne, B., Trendafilova, C., Umilta, C., Veitch-Michaelis, J., Vieira, J. D., Vitrier, A., Wan, Y., Whitehorn, N., Wu, W. L. K., Young, M. R., Zagorski, K., Zebrowski, J. A.

We present improvements to the pointing accuracy of the South Pole Telescope (SPT) using machine learning. The ability of the SPT to point accurately at the sky is limited by its structural imperfections, which are impacted by the extreme weather at

Externí odkaz: http://arxiv.org/abs/2412.15167

Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration

Autor: Endo, Mark, Wang, Xiaohan, Yeung-Levy, Serena

Recent works on accelerating Vision-Language Models show that strong performance can be maintained across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of

Externí odkaz: http://arxiv.org/abs/2412.13180

Ant Nest Detection Using Underground P-Band TomoSAR

Autor: Oré, Gian, Santos, Alexandre, Ukan, Daniele, Zanetti, Ronald, Camargo, Mariane, Oliveira, Luciano P., Kemper, Guillermo, Sanchez, Alonso, Diaz, Aldo, Gonzalez, Jorge, Rubio-Noriega, Ruth, Boccato, Levy, Hernandez-Figueroa, Hugo E.

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the

Externí odkaz: http://arxiv.org/abs/2412.11865

Non-perturbative cathodoluminescence microscopy of beam-sensitive materials

Autor: Bogroff, Malcolm, Cowley, Gabriel, Nicastro, Ariel, Levy, David, Wu, Yueh-Chun, Mao, Nannan, Yang, Tilo H., Zhang, Tianyi, Kong, Jing, Vasudevan, Rama, Kelley, Kyle P., Lawrie, Benjamin J.

Cathodoluminescence microscopy is now a well-established and powerful tool for probing the photonic properties of nanoscale materials, but in many cases, nanophotonic materials are easily damaged by the electron-beam doses necessary to achieve reason

Externí odkaz: http://arxiv.org/abs/2412.11413

Apollo: An Exploration of Video Understanding in Large Multimodal Models

Autor: Zohar, Orr, Wang, Xiaohan, Dubois, Yann, Mehta, Nikhil, Xiao, Tong, Hansen-Estruch, Philippe, Yu, Licheng, Wang, Xiaofang, Juefei-Xu, Felix, Zhang, Ning, Yeung-Levy, Serena, Xia, Xide

Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decisions in this domain are made with

Externí odkaz: http://arxiv.org/abs/2412.10360