Improving Emotion Recognition Accuracy with Personalized Clustering

Autor: Gutierrez-Martin, Laura, Ongil, Celia Lopez, Lanza-Gutierrez, Jose M., Calero, Jose A. Miranda

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (Affective Computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense,

Externí odkaz: http://arxiv.org/abs/2410.03696

Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection

Autor: Pérez-Aracil, J., Peláez-Rodríguez, C., McAdam, Ronan, Squintu, Antonello, Marina, Cosmin M., Lorente-Ramos, Eugenio, Luther, Niklas, Torralba, Veronica, Scoccimarro, Enrico, Cavicchia, Leone, Giuliani, Matteo, Zorita, Eduardo, Hansen, Felicitas, Barriopedro, David, Garcia-Herrera, Ricardo, Gutiérrez, Pedro A., Luterbacher, Jürg, Xoplaki, Elena, Castelletti, Andrea, Salcedo-Sanz, S.

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are dif

Externí odkaz: http://arxiv.org/abs/2411.10108

Express Yourself: Enabling large-scale public events involving multi-human-swarm interaction for social applications with MOSAIX

Autor: Alhafnawi, Merihan, Gomez-Gutierrez, Maca, Hunt, Edmund R., Lemaignan, Severin, O'Dowd, Paul, Hauert, Sabine

Robot swarms have the potential to help groups of people with social tasks, given their ability to scale to large numbers of robots and users. Developing multi-human-swarm interaction is therefore crucial to support multiple people interacting with t

Externí odkaz: http://arxiv.org/abs/2411.09975

Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$

Autor: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. 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Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. 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Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{

Externí odkaz: http://arxiv.org/abs/2411.07730

Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

Autor: Huang, Chien-yu, Chen, Wei-Chih, Yang, Shu-wen, Liu, Andy T., Li, Chen-An, Lin, Yu-Xiang, Tseng, Wei-Cheng, Diwan, Anuj, Shih, Yi-Jen, Shi, Jiatong, Chen, William, Chen, Xuanjun, Hsiao, Chi-Yuan, Peng, Puyuan, Wang, Shih-Heng, Kuan, Chun-Yi, Lu, Ke-Han, Chang, Kai-Wei, Yang, Chih-Kai, Ritter-Gutierrez, Fabian, Chuang, Ming To, Huang, Kuan-Po, Arora, Siddhant, Lin, You-Kuan, Yeo, Eunjung, Chang, Kalvin, Chien, Chung-Ming, Choi, Kwanghee, Hsieh, Cheng-Hsiu, Lin, Yi-Cheng, Yu, Chee-En, Chiu, I-Hsiang, Guimarães, Heitor R., Han, Jionghao, Lin, Tzu-Quan, Lin, Tzu-Yuan, Chang, Homu, Chang, Ting-Wu, Chen, Chun Wei, Chen, Shou-Jen, Chen, Yu-Hua, Cheng, Hsi-Chun, Dhawan, Kunal, Fang, Jia-Lin, Fang, Shi-Xin, Chiang, Kuan-Yu Fang, Fu, Chi An, Hsiao, Hsien-Fu, Hsu, Ching Yu, Huang, Shao-Syuan, Wei, Lee Chen, Lin, Hsi-Che, Lin, Hsuan-Hao, Lin, Hsuan-Ting, Lin, Jian-Ren, Liu, Ting-Chun, Lu, Li-Chun, Pai, Tsung-Min, Pasad, Ankita, Kuan, Shih-Yun Shan, Shon, Suwon, Tang, Yuxun, Tsai, Yun-Shao, Wei, Jui-Chiang, Wei, Tzu-Chieh, Wu, Chengxi, Wu, Dien-Ruei, Yang, Chao-Han Huck, Yang, Chieh-Chi, Yip, Jia Qi, Yuan, Shao-Xiang, Noroozi, Vahid, Chen, Zhehuai, Wu, Haibin, Livescu, Karen, Harwath, David, Watanabe, Shinji, Lee, Hung-yi

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language i

Externí odkaz: http://arxiv.org/abs/2411.05361