BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices

Autor: Lu, Xudong, Chen, Yinghao, Chen, Cheng, Tan, Hui, Chen, Boheng, Xie, Yina, Hu, Rui, Tan, Guanxin, Wu, Renshou, Hu, Yan, Zeng, Yi, Wu, Lei, Bian, Liuyang, Wang, Zhaoxiong, Liu, Long, Yang, Yanzhou, Xiao, Han, Zhou, Aojun, Wen, Yafei, Chen, Xiaoxin, Ren, Shuai, Li, Hongsheng

The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essenti

Externí odkaz: http://arxiv.org/abs/2411.10640

Personalize to generalize: Towards a universal medical multi-modality generalization through personalization

Autor: Tan, Zhaorui, Yang, Xi, Pan, Tan, Liu, Tianyi, Jiang, Chen, Guo, Xin, Wang, Qiufeng, Nguyen, Anh, Qi, Yuan, Huang, Kaizhu, Cheng, Yuan

The differences among medical imaging modalities, driven by distinct underlying principles, pose significant challenges for generalization in multi-modal medical tasks. Beyond modality gaps, individual variations, such as differences in organ size an

Externí odkaz: http://arxiv.org/abs/2411.06106

Can Personalized Medicine Coexist with Health Equity? Examining the Cost Barrier and Ethical Implications

Autor: Francisco, Kishi Kobe Yee, Apuhin, Andrane Estelle Carnicer, Tan, Myles Joshua Toledo, Byers, Mickael Cavanaugh, Maravilla, Nicholle Mae Amor Tan, Karim, Hezerul Abdul, AlDahoul, Nouar

Personalized medicine (PM) promises to transform healthcare by providing treatments tailored to individual genetic, environmental, and lifestyle factors. However, its high costs and infrastructure demands raise concerns about exacerbating health disp

Externí odkaz: http://arxiv.org/abs/2411.02307

The neutron veto of the XENONnT experiment: Results with demineralized water

Autor: XENON Collaboration, Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morá, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M.

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equ

Externí odkaz: http://arxiv.org/abs/2412.05264

SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization

Autor: Tan, Xiaofeng, Wang, Hongsong, Geng, Xin, Zhou, Pan

Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor high-quality, hu

Externí odkaz: http://arxiv.org/abs/2412.05095