Zobrazeno 1 - 10
of 76 371
pro vyhledávání: '"A. KOHN"'
With the large diversity of platforms and devices used by students, web applications increasingly suggest themselves as the solution of choice. Developing adequate educational programming environments in the browser, however, remains a challenge and
Externí odkaz:
http://arxiv.org/abs/2410.07001
We study convolutional neural networks with monomial activation functions. Specifically, we prove that their parameterization map is regular and is an isomorphism almost everywhere, up to rescaling the filters. By leveraging on tools from algebraic g
Externí odkaz:
http://arxiv.org/abs/2410.00722
Autor:
Amiri, Hossein, Kohn, Will, Ruan, Shiyang, Kim, Joon-Seok, Kavak, Hamdi, Crooks, Andrew, Pfoser, Dieter, Wenk, Carola, Zufle, Andreas
We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the sim
Externí odkaz:
http://arxiv.org/abs/2410.00185
We propose a seasonal AR model with time-varying parameter processes in both the regular and seasonal parameters. The model is parameterized to guarantee stability at every time point and can accommodate multiple seasonal periods. The time evolution
Externí odkaz:
http://arxiv.org/abs/2409.18640
We consider function spaces defined by self-attention networks without normalization, and theoretically analyze their geometry. Since these networks are polynomial, we rely on tools from algebraic geometry. In particular, we study the identifiability
Externí odkaz:
http://arxiv.org/abs/2408.17221
Dynamic linear regression models forecast the values of a time series based on a linear combination of a set of exogenous time series while incorporating a time series process for the error term. This error process is often assumed to follow an autor
Externí odkaz:
http://arxiv.org/abs/2408.09096
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. Inference is carried out using these summaries in place of the original dataset,
Externí odkaz:
http://arxiv.org/abs/2408.04419
Autor:
Daya Bay collaboration, An, F. P., Bai, W. D., Balantekin, A. B., Bishai, M., Blyth, S., Cao, G. F., Cao, J., Chang, J. F., Chang, Y., Chen, H. S., Chen, H. Y., Chen, S. M., Chen, Y., Chen, Y. X., Chen, Z. Y., Cheng, J., Cheng, Y. -C., Cheng, Z. K., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Dalager, O., Deng, F. S., Ding, X. Y., Ding, Y. Y., Diwan, M. V., Dohnal, T., Dolzhikov, D., Dove, J., Duyang, H. Y., Dwyer, D. A., Gallo, J. P., Gonchar, M., Gong, G. H., Gong, H., Gu, W. Q., Guo, J. Y., Guo, L., Guo, X. H., Guo, Y. H., Guo, Z., Hackenburg, R. W., Han, Y., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, J. R., Hu, T., Hu, Z. J., Huang, H. X., Huang, J. H., Huang, X. T., Huang, Y. B., Huber, P., Jaffe, D. E., Jen, K. L., Ji, X. L., Ji, X. P., Johnson, R. A., Jones, D., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Langford, T. J., Lee, J., Lee, J. H. C., Lei, R. T., Leitner, R., Leung, J. K. C., Li, F., Li, H. L., Li, J. J., Li, Q. J., Li, R. H., Li, S., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, S., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, J. C., Liu, J. L., Liu, J. X., Lu, C., Lu, H. Q., Luk, K. B., Ma, B. Z., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mandujano, R. C., Marshall, C., McDonald, K. T., McKeown, R. D., Meng, Y., Napolitano, J., Naumov, D., Naumova, E., Nguyen, T. M. T., Ochoa-Ricoux, J. P., Olshevskiy, A., Park, J., Patton, S., Peng, J. C., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, J., Reveco, C. Morales, Rosero, R., Roskovec, B., Ruan, X. C., Russell, B., Steiner, H., Sun, J. L., Tmej, T., Treskov, K., Tse, W. -H., Tull, C. E., Tung, Y. C., Viren, B., Vorobel, V., Wang, C. H., Wang, J., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Wei, H. Y., Wei, L. H., Wei, W., Wen, L. J., Whisnant, K., White, C. G., Wong, H. L. H., Worcester, E., Wu, D. R., Wu, Q., Wu, W. J., Xia, D. M., Xie, Z. Q., Xing, Z. Z., Xu, H. K., Xu, J. L., Xu, T., Xue, T., Yang, C. G., Yang, L., Yang, Y. Z., Yao, H. F., Ye, M., Yeh, M., Young, B. L., Yu, H. Z., Yu, Z. Y., Yue, B. B., Zavadskyi, V., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, F. Y., Zhang, H. H., Zhang, J. L., Zhang, J. W., Zhang, Q. M., Zhang, S. Q., Zhang, X. T., Zhang, Y. M., Zhang, Y. X., Zhang, Y. Y., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, R. Z., Zhou, L., Zhuang, H. L., Zou, J. H.
Publikováno v:
Physical Review Letters 133, 151801 (2024)
This Letter reports the first measurement of the oscillation amplitude and frequency of reactor antineutrinos at Daya Bay via neutron capture on hydrogen using 1958 days of data. With over 3.6 million signal candidates, an optimized candidate selecti
Externí odkaz:
http://arxiv.org/abs/2406.01007
Directed acyclic graph (DAG) learning is a rapidly expanding field of research. Though the field has witnessed remarkable advances over the past few years, it remains statistically and computationally challenging to learn a single (point estimate) DA
Externí odkaz:
http://arxiv.org/abs/2405.15167
Autor:
Baumeister, Jan, Finkbeiner, Bernd, Kohn, Florian, Löhr, Florian, Manfredi, Guido, Schirmer, Sebastian, Torens, Christoph
This paper reports on the integration of runtime monitoring into fully-electric aircraft designed by Volocopter, a German aircraft manufacturer of electric multi-rotor helicopters. The runtime monitor recognizes hazardous situations and system faults
Externí odkaz:
http://arxiv.org/abs/2404.12035