Exact Solvability Of Entanglement For Arbitrary Initial State in an Infinite-Range Floquet System

Autor: Sharma, Harshit, Bhosale, Udaysinh T.

Sharma and Bhosale [\href{https://journals.aps.org/prb/abstract/10.1103/PhysRevB.109.014412}{Phys. Rev. B \textbf{109}, 014412 (2024)}; \href{https://journals.aps.org/prb/abstract/10.1103/PhysRevB.110.064313}{Phys. Rev. B \textbf{110}, 064313,(2024)}

Externí odkaz: http://arxiv.org/abs/2411.16670

Anomaly detection using Diffusion-based methods

Autor: Bhosale, Aryan, Mukherjee, Samrat, Banerjee, Biplab, Cuzzolin, Fabio

This paper explores the utility of diffusion-based models for anomaly detection, focusing on their efficacy in identifying deviations in both compact and high-resolution datasets. Diffusion-based architectures, including Denoising Diffusion Probabili

Externí odkaz: http://arxiv.org/abs/2412.07539

3D Audio-Visual Segmentation

Autor: Sokolov, Artem, Bhosale, Swapnil, Zhu, Xiatian

Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. To that end, Audio-Visual Segmentation (AVS), taking as condition an audio signal to identify the masks of the

Externí odkaz: http://arxiv.org/abs/2411.02236

Measurements of hadron production in 90 GeV/c proton-carbon interactions

Autor: Adhikary, H., Adrich, P., Allison, K. K., Amin, N., Andronov, E. V., Arsene, I. -C., Bajda, M., Balkova, Y., Battaglia, D., Bazgir, A., Bhosale, S., Bielewicz, M., Blondel, A., Bogomilov, M., Bondar, Y., Bryliński, W., Brzychczyk, J., Buryakov, M., Camino, A. F., Chandak, Y., Ćirković, M., Csanád, M., Cybowska, J., Czopowicz, T., Dalmazzone, C., Davis, N., Dmitriev, A., von Doetinchem, P., Dominik, W., Dumarchez, J., Engel, R., Feofilov, G. A., Fields, L., Fodor, Z., Friend, M., Gaździcki, M., Gollwitzer, K. E., Golosov, O., Golovatyuk, V., Golubeva, M., Grebieszkow, K., Guber, F., Hurh, P. G., Ilieva, S., Ivashkin, A., Izvestnyy, A., Karpushkin, N., Kiełbowicz, M., Kireyeu, V. A., Kolesnikov, R., Kolev, D., Koshio, Y., Kowalski, S., Kozłowski, B., Krasnoperov, A., Kucewicz, W., Kuchowicz, M., Kuich, M., Kurepin, A., László, A., Lewicki, M., Lykasov, G., Lyubushkin, V. V., Maćkowiak-Pawłowska, M., Makhnev, A., Maksiak, B., Malakhov, A. I., Marcinek, A., Marino, A. D., Mathes, H. -J., Matulewicz, T., Matveev, V., Melkumov, G. L., Merzlaya, A., Mik, Ł., Morozov, S., Nagai, Y., Nakadaira, T., Naskręt, M., Nishimori, S., Olivier, A., Ozvenchuk, V., Panova, O., Paolone, V., Petukhov, O., Pidhurskyi, I., Płaneta, R., Podlaski, P., Popov, B. A., Pórfy, B., Prokhorova, D. S., Pszczel, D., Puławski, S., Renfordt, R., Ren, L., Ortiz, V. Z. Reyna, Röhrich, D., Rondio, E., Roth, M., Rozpłochowski, Ł., Rumberger, B. T., Rumyantsev, M., Rustamov, A., Rybczynski, M., Rybicki, A., Rybka, D., Sakashita, K., Schmidt, K., Seryakov, A., Seyboth, P., Shah, U. A., Shiraishi, Y., Shukla, A., Słodkowski, M., Staszel, P., Stefanek, G., Stepaniak, J., Świderski, Ł., Szewiński, J., Szukiewicz, R., Taranenko, A., Tefelska, A., Tefelski, D., Tereshchenko, V., Tsenov, R., Turko, L., Tveter, T. S., Unger, M., Urbaniak, M., Veberič, D., Vitiuk, O., Volkov, V., Wickremasinghe, A., Witek, K., Wójcik, K., Wyszyński, O., Zherebtsova, A. Zaitsev E., Zimmerman, E. D., Zviagina, A.

This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV$/c$ proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via dE/dx was performed for the charged hadrons $

Externí odkaz: http://arxiv.org/abs/2410.23098

Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following

Autor: He, Yun, Jin, Di, Wang, Chaoqi, Bi, Chloe, Mandyam, Karishma, Zhang, Hejia, Zhu, Chen, Li, Ning, Xu, Tengyu, Lv, Hongjiang, Bhosale, Shruti, Zhu, Chenguang, Sankararaman, Karthik Abinav, Helenowski, Eryk, Kambadur, Melanie, Tayade, Aditya, Ma, Hao, Fang, Han, Wang, Sinong

Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expectations. However, evaluating LLMs' ability to follow instructions rem

Externí odkaz: http://arxiv.org/abs/2410.15553