Zobrazeno 1 - 10
of 2 913
pro vyhledávání: '"P. Stowell"'
Autor:
Collaboration, Super-Kamiokande, Taniuchi, N., Abe, K., Abe, S., Asaoka, Y., Bronner, C., Harada, M., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakahata, M., Nakayama, S., Noguchi, Y., Pronost, G., Okamoto, K., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Mirabito, J., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Kropp, W. R., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Kim, J. Y., Lee, S. H., Lim, I. T., Moon, D. H., Park, R. G., Yang, B. S., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Bernard, L., Coffani, A., Drapier, O., Hedri, S. El, Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Rogly, R., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Feltre, M., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Quilain, B., Fujisawa, C., Horiuchi, S., Kobayashi, M., Liu, Y. M., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Yrey, A. Portocarrero, Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Boschi, T., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Taani, M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Yamamoto, S., Zhong, H., Feng, J., Feng, L., Han, S., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Yanagisawa, C., Hino, Y., Ishino, H., Ito, S., Kitagawa, H., Koshio, Y., Ma, W., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Stone, O., Stowell, P., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Lee, M. W., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Koshiba, M., Eguchi, A., Goto, S., Iwamoto, K., Mizuno, Y., Muro, T., Nakagiri, K., Nakajima, Y., Shima, S., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Marti, Ll., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Terada, K., Asaka, R., Inomoto, M., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Nakano, Y., Martin, J. F., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Wu, Y., Xu, B. D., Zhang, A. Q., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Minamino, A., Pintaudi, G., Sano, S., Sasaki, R., Shibayama, R., Shimamura, R., Suzuki, S., Wada, K.
A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intr
Externí odkaz:
http://arxiv.org/abs/2409.19633
Autor:
Ghani, Burooj, Kalkman, Vincent J., Planqué, Bob, Vellinga, Willem-Pier, Gill, Lisa, Stowell, Dan
Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performan
Externí odkaz:
http://arxiv.org/abs/2409.15383
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hie
Externí odkaz:
http://arxiv.org/abs/2409.08673
Autor:
Schäfer-Zimmermann, Julian C., Demartsev, Vlad, Averly, Baptiste, Dhanjal-Adams, Kiran, Duteil, Mathieu, Gall, Gabriella, Faiß, Marius, Johnson-Ulrich, Lily, Stowell, Dan, Manser, Marta B., Roch, Marie A., Strandburg-Peshkin, Ariana
Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them t
Externí odkaz:
http://arxiv.org/abs/2406.01253
With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta, as they are critical for biodiversity monitoring and at the base of many eco
Externí odkaz:
http://arxiv.org/abs/2404.03474
Detecting the presence of animal vocalisations in nature is essential to study animal populations and their behaviors. A recent development in the field is the introduction of the task known as few-shot bioacoustic sound event detection, which aims t
Externí odkaz:
http://arxiv.org/abs/2403.18638
Autor:
Andrej, Julian, Atallah, Nabil, Bäcker, Jan-Phillip, Camier, John, Copeland, Dylan, Dobrev, Veselin, Dudouit, Yohann, Duswald, Tobias, Keith, Brendan, Kim, Dohyun, Kolev, Tzanio, Lazarov, Boyan, Mittal, Ketan, Pazner, Will, Petrides, Socratis, Shiraiwa, Syun'ichi, Stowell, Mark, Tomov, Vladimir
The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and en
Externí odkaz:
http://arxiv.org/abs/2402.15940
The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting
Externí odkaz:
http://arxiv.org/abs/2312.09269
Autor:
Tosato, Giulio, Shehata, Abdelrahman, Janssen, Joshua, Kamp, Kees, Jati, Pramatya, Stowell, Dan
This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning models. Using the Western Medite
Externí odkaz:
http://arxiv.org/abs/2311.04945
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains severely limited. Transformers on
Externí odkaz:
http://arxiv.org/abs/2311.01526