Echo State Networks for Spatio-Temporal Area-Level Data

Autor: Wang, Zhenhua, Holan, Scott H., Wikle, Christopher K.

Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecasting of these datasets can be extremely useful for policymakers to deve

Externí odkaz: http://arxiv.org/abs/2410.10641

Chevalley groups over $\Z$: A representation-theoretic approach

Autor: Ali, Abid, Carbone, Lisa, Murray, Scott H.

Let $G(\mathbb{Q})$ be a simply connected Chevalley group over $\mathbb{Q}$ corresponding to a simple Lie algebra $\mathfrak g$ over $\mathbb{C}$. Let $V$ be a finite dimensional faithful highest weight $\mathfrak g$-module and let $V_\mathbb{Z}$ be

Externí odkaz: http://arxiv.org/abs/2408.16895

Pictures Of MIDI: Controlled Music Generation via Graphical Prompts for Image-Based Diffusion Inpainting

Autor: Hawley, Scott H.

Recent years have witnessed significant progress in generative models for music, featuring diverse architectures that balance output quality, diversity, speed, and user control. This study explores a user-friendly graphical interface enabling the dra

Externí odkaz: http://arxiv.org/abs/2407.01499

Scintillation Light in SBND: Simulation, Reconstruction, and Expected Performance of the Photon Detection System

Autor: SBND Collaboration, Abratenko, P., Acciarri, R., Adams, C., Aliaga-Soplin, L., Alterkait, O., Alvarez-Garrote, R., Andreopoulos, C., Antonakis, A., Arellano, L., Asaadi, J., Badgett, W., Balasubramanian, S., Basque, V., Beever, A., Behera, B., Belchior, E., Betancourt, M., Bhat, A., Bishai, M., Blake, A., Bogart, B., Bogenschuetz, J., Brailsford, D., Brandt, A., Brickner, S., Bueno, A., Camilleri, L., Caratelli, D., Carber, D., Carlson, B., Carneiro, M., Castillo, R., Cavanna, F., Chen, H., Chung, S., Cicala, M. F., Coackley, R., Crespo-Anadón, J. I., Cuesta, C., Dalager, O., Darby, R., Del Tutto, M., Di Benedetto, V., Djurcic, Z., Duffy, K., Dytman, S., Ereditato, A., Evans, J. J., Ezeribe, A., Fan, C., Filkins, A., Fleming, B., Foreman, W., Franco, D., Furic, I., Furmanski, A., Gao, S., Garcia-Gamez, D., Gardiner, S., Ge, G., Gil-Botella, I., Gollapinni, S., Green, P., Griffith, W. C., Guenette, R., Guzowski, P., Hagaman, L., Hamer, A., Hamilton, P., Hernandez-Morquecho, M., Hilgenberg, C., Howard, B., Imani, Z., James, C., Jones, R. S., Jung, M., Junk, T., Kalra, D., Karagiorgi, G., Kelly, K., Ketchum, W., King, M., Klein, J., Kotsiopoulou, L., Kroupová, T., Kudryavtsev, V. A., Larkin, J., Lay, H., LaZur, R., Li, J. -Y., Lin, K., Littlejohn, B., Louis, W. C., Luo, X., Machado, A., Machado, P., Mariani, C., Marinho, F., Mastbaum, A., Mavrokoridis, K., McConkey, N., McCusker, B., Meddage, V., Mendez, D., Mooney, M., Moor, A. F., Moura, C. A., Mulleriababu, S., Navrer-Agasson, A., Nebot-Guinot, M., Nguyen, V. C. L., Nicolas-Arnaldos, F., Nowak, J., Oh, S., Oza, N., Palamara, O., Pallat, N., Pandey, V., Papadopoulou, A., Parkinson, H. B., Paton, J., Paulucci, L., Pavlovic, Z., Payne, D., Pelegrina-Gutiérrez, L., Pimentel, V. L., Plows, J., Psihas, F., Putnam, G., Qian, X., Rajagopalan, R., Ratoff, P., Ray, H., Reggiani-Guzzo, M., Roda, M., Ross-Lonergan, M., Safa, I., Sanchez-Castillo, A., Sanchez-Lucas, P., Schmitz, D. W., Schneider, A., Schukraft, A., Scott, H., Segreto, E., Sensenig, J., Shaevitz, M., Slater, B., Soares-Nunes, M., Soderberg, M., Söldner-Rembold, S., Spitz, J., Spooner, N. J. C., Stancari, M., Stenico, G. V., Strauss, T., Szelc, A. M., Totani, D., Toups, M., Touramanis, C., Tung, L., Valdiviesso, G. A., Van de Water, R. G., Vázquez-Ramos, A., Wan, L., Weber, M., Wei, H., Wester, T., White, A., Wilkinson, A., Wilson, P., Wongjirad, T., Worcester, E., Worcester, M., Yadav, S., Yandel, E., Yang, T., Yates, L., Yu, B., Yu, J., Zamorano, B., Zennamo, J., Zhang, C.

Publikováno v: Eur. Phys. J. C 84, 1046 (2024)

SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This p

Externí odkaz: http://arxiv.org/abs/2406.07514

Bayesian Methods to Improve The Accuracy of Differentially Private Measurements of Constrained Parameters

Autor: Janicki, Ryan, Holan, Scott H., Irimata, Kyle M., Livsey, James, Raim, Andrew

Formal disclosure avoidance techniques are necessary to ensure that published data can not be used to identify information about individuals. The addition of statistical noise to unpublished data can be implemented to achieve differential privacy, wh

Externí odkaz: http://arxiv.org/abs/2406.04448