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pro vyhledávání: '"Mei A."'
Enabling Large Language Models (LLMs) to understand the 3D physical world is an emerging yet challenging research direction. Current strategies for processing point clouds typically downsample the scene or divide it into smaller parts for separate an
Externí odkaz:
http://arxiv.org/abs/2411.19774
Autor:
Ni, Chaojun, Zhao, Guosheng, Wang, Xiaofeng, Zhu, Zheng, Qin, Wenkang, Huang, Guan, Liu, Chen, Chen, Yuyin, Wang, Yida, Zhang, Xueyang, Zhan, Yifei, Zhan, Kun, Jia, Peng, Lang, Xianpeng, Wang, Xingang, Mei, Wenjun
Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods strug
Externí odkaz:
http://arxiv.org/abs/2411.19548
Space-based gravitational wave detectors have the capability to detect signals from very high redshifts. It is interesting to know if such capability can be used to study the global structure of the cosmic space. In this paper, we focus on one partic
Externí odkaz:
http://arxiv.org/abs/2411.17177
Autor:
Zhang, Rongrong, Wan, Shengjie, Xiong, Jiarui, Ni, Lei, Li, Ye, Huang, Yajia, Li, Bing, Li, Mei, Yang, Shuai, Jin, Fan
Natural biological systems process environmental information through both amplitude and frequency-modulated signals, yet engineered biological circuits have largely relied on amplitude-based regulation alone. Despite the prevalence of frequency-encod
Externí odkaz:
http://arxiv.org/abs/2411.17158
High-order semi-Lagrangian methods for kinetic equations have been under rapid development in the past few decades. In this work, we propose a semi-Lagrangian adaptive rank (SLAR) integrator in the finite difference framework for linear advection and
Externí odkaz:
http://arxiv.org/abs/2411.17963
Autor:
Wang, Sheng, Tian, Yao, Mei, Xiaodong, Sun, Ge, Cheng, Jie, Ma, Fulong, Sander, Pedro V., Liang, Junwei
Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge historical trajectories with present observations t
Externí odkaz:
http://arxiv.org/abs/2411.17253
Autor:
Pearce-Casey, R., Nagam, B. C., Wilde, J., Busillo, V., Ulivi, L., Andika, I. T., Manjón-García, A., Leuzzi, L., Matavulj, P., Serjeant, S., Walmsley, M., Barroso, J. A. Acevedo, O'Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Cabanac, R., Courtois, H. M., Crook-Mansour, J., Delchambre, L., Despali, G., Ecker, L. R., Franco, A., Holloway, P., Jahnke, K., Mahler, G., Marchetti, L., Melo, A., Meneghetti, M., Müller, O., Nucita, A. A., Pearson, J., Rojas, K., Scarlata, C., Schuldt, S., Sluse, D., Suyu, S. H., Vaccari, M., Vegetti, S., Verma, A., Vernardos, G., Bolzonella, M., Kluge, M., Saifollahi, T., Schirmer, M., Stone, C., Paulino-Afonso, A., Bazzanini, L., Hogg, N. B., Koopmans, L. V. E., Kruk, S., Mannucci, F., Bromley, J. M., Díaz-Sánchez, A., Dickinson, H. J., Powell, D. M., Bouy, H., Laureijs, R., Altieri, B., Amara, A., Andreon, S., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Battaglia, P., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., George, K., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Burigana, C., Calabrese, M., Mora, A., Pöntinen, M., Scottez, V., Viel, M., Margalef-Bentabol, B.
The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen sc
Externí odkaz:
http://arxiv.org/abs/2411.16808
Autor:
Jiang, Yuncheng, Feng, Chun-Mei, Ren, Jinke, Wei, Jun, Zhang, Zixun, Hu, Yiwen, Liu, Yunbi, Sun, Rui, Tang, Xuemei, Du, Juan, Wan, Xiang, Xu, Yong, Du, Bo, Gao, Xin, Wang, Guangyu, Zhou, Shaohua, Cui, Shuguang, Goh, Rick Siow Mong, Liu, Yong, Li, Zhen
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal
Externí odkaz:
http://arxiv.org/abs/2411.16380
Large multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning over a large number of images. Existing benchmarks for multi-image que
Externí odkaz:
http://arxiv.org/abs/2411.16740
Most existing Dynamic Gaussian Splatting methods for complex dynamic urban scenarios rely on accurate object-level supervision from expensive manual labeling, limiting their scalability in real-world applications. In this paper, we introduce SplatFlo
Externí odkaz:
http://arxiv.org/abs/2411.15482