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of 3 391
pro vyhledávání: '"Diaconu IȘ"'
For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplications. This paper introduces LUTMUL, which harnesses the potential of look-up tables (LUTs) for performi
Externí odkaz:
http://arxiv.org/abs/2411.11852
Autor:
Shysheya, Aliaksandra, Diaconu, Cristiana, Bergamin, Federico, Perdikaris, Paris, Hernández-Lobato, José Miguel, Turner, Richard E., Mathieu, Emile
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning
Externí odkaz:
http://arxiv.org/abs/2410.16415
Many important problems require modelling large-scale spatio-temporal datasets, with one prevalent example being weather forecasting. Recently, transformer-based approaches have shown great promise in a range of weather forecasting problems. However,
Externí odkaz:
http://arxiv.org/abs/2410.06731
The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from con
Externí odkaz:
http://arxiv.org/abs/2409.12034
Autor:
Garcia, B. Ary dos Santos, Bergermann, D., Caldwell, A., Dabhi, V., Diaconu, C., Diehl, J., Dvali, G., Egge, J., Garutti, E., Heyminck, S., Hubaut, F., Ivanov, A., Jochum, J., Knirck, S., Kramer, M., Kreikemeyer-Lorenzo, D., Krieger, C., Lee, C., Leppla-Weber, D., Li, X., Lindner, A., Majorovits, B., Maldonado, J. P. A., Martini, A., Miyazaki, A., Öz, E., Pralavorio, P., Raffelt, G., Redondo, J., Ringwald, A., Schaffran, J., Schmidt, A., Steffen, F., Strandhagen, C., Usherov, I., Wang, H., Wieching, G.
This paper presents the first search for dark matter axions with mass in the ranges 76.56 to 76.82 $\mu$eV and 79.31 to 79.53 $\mu$eV using a prototype setup for the MAgnetized Disk and Mirror Axion eXperiment (MADMAX). The experimental setup employs
Externí odkaz:
http://arxiv.org/abs/2409.11777
Autor:
Egge, J., Leppla-Weber, D., Knirck, S., Garcia, B. Ary dos Santos, Bergermann, D., Caldwell, A., Dabhi, V., Diaconu, C., Diehl, J., Dvali, G., Ekmedžić, M., Gallo, F., Garutti, E., Heyminck, S., Hubaut, F., Ivanov, A., Jochum, J., Karst, P., Kramer, M., Kreikemeyer-Lorenzo, D., Krieger, C., Lee, C., Lindner, A., Maldonado, J. P. A., Majorovits, B., Martens, S., Martini, A., Miyazaki, A., Öz, E., Pralavorio, P., Raffelt, G., Ringwald, A., Redondo, J., Roset, S., Salama, N., Schaffran, J., Schmidt, A., Steffen, F., Strandhagen, C., Usherov, I., Wang, H., Wieching, G., Cancelo, G., Di Federico, M., Hoshino, G., Stefanazzi, L.
We report the first result from a dark photon dark matter search in the mass range from ${78.62}$ to $83.95~\mathrm{\mu eV}/c^2$ with a dielectric haloscope prototype for MADMAX (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons wou
Externí odkaz:
http://arxiv.org/abs/2408.02368
Autor:
The MADMAX Collaboration, Garcia, B. Ary Dos Santos, Bergermann, D., Caldwell, A., Dabhi, V., Diaconu, C., Diehl, J., Dvali, G., Egge, J., Ekmedzic, M., Gallo, F., Garutti, E., Heyminck, S., Hubaut, F., Ivanov, A., Jochum, J., Karst, P., Kramer, M., Kreikemeyer-Lorenzo, D., Krieger, C., Leppla-Weber, D., Lindner, A., Maldonado, J., Majorovits, B., Martens, S., Martini, A., Öz, E., Pralavorio, P., Raffelt, G., Redondo, J., Ringwald, A., Roset, S., Schaffran, J., Schmidt, A., Steffen, F., Strandhagen, C., Usherov, I., Wang, H., Wieching, G.
Publikováno v:
JINST 19 T11002 (2024)
MADMAX, a future experiment to search for axion dark matter, is based on a novel detection concept called the dielectric haloscope. It consists of a booster composed of several dielectric disks positioned with $\mu$m precision. A prototype composed o
Externí odkaz:
http://arxiv.org/abs/2407.10716
Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each dataset in a meta-dataset is sampled. There are many cases in which prac
Externí odkaz:
http://arxiv.org/abs/2406.13493
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. However, when modelling real-world data, learning problems are often not
Externí odkaz:
http://arxiv.org/abs/2406.13488
Autor:
Ashman, Matthew, Diaconu, Cristiana, Kim, Junhyuck, Sivaraya, Lakee, Markou, Stratis, Requeima, James, Bruinsma, Wessel P., Turner, Richard E.
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved since their inception. This improvement can be attributed to two principal
Externí odkaz:
http://arxiv.org/abs/2406.12409