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Publikováno v:
TÜBA-AR: Turkish Academy of Sciences, Journal of Archaeology; 2024, Vol. 34 Issue 34, p9-23, 15p
Autor:
World Bank
This assessment report presents the results of a study focused on the Sioni Reservoir watershed, which is subject to seasonal sediment loads affecting the sustainability of water for hydropower generation and irrigation. The study reveals the major c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2456::e3015ebb175780fea2f3f6d0fef85147
http://documents.worldbank.org/curated/en/099340011072214901/P1717380b56fd201c0b65508906272c86b3
http://documents.worldbank.org/curated/en/099340011072214901/P1717380b56fd201c0b65508906272c86b3
Autor:
Odagiu, Patrick, Que, Zhiqiang, Duarte, Javier, Haller, Johannes, Kasieczka, Gregor, Lobanov, Artur, Loncar, Vladimir, Luk, Wayne, Ngadiuba, Jennifer, Pierini, Maurizio, Rincke, Philipp, Seksaria, Arpita, Summers, Sioni, Sznajder, Andre, Tapper, Alexander, Aarrestad, Thea K.
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the inpu
Externí odkaz:
http://arxiv.org/abs/2402.01876
The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high level physic
Externí odkaz:
http://arxiv.org/abs/2310.08062
Autor:
Sah, Malvika1 malvikasah98@gmail.com, Sioni, Supriya1 supriyasioni15@gmail.com
Publikováno v:
Indian Journal of Positive Psychology. Sep2024, Vol. 15 Issue 3, p331-337. 7p.
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-8. 8p.
Autor:
Khoda, Elham E, Rankin, Dylan, de Lima, Rafael Teixeira, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of impl
Externí odkaz:
http://arxiv.org/abs/2207.00559
Autor:
Pappalardo, Alessandro, Umuroglu, Yaman, Blott, Michaela, Mitrevski, Jovan, Hawks, Ben, Tran, Nhan, Loncar, Vladimir, Summers, Sioni, Borras, Hendrik, Muhizi, Jules, Trahms, Matthew, Hsu, Shih-Chieh, Hauck, Scott, Duarte, Javier
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantizat
Externí odkaz:
http://arxiv.org/abs/2206.07527