Zobrazeno 1 - 10
of 7 851
pro vyhledávání: '"A. Filali"'
Publikováno v:
Bulgarian Journal of Veterinary Medicine, Vol 25, Iss 1, Pp 161-165 (2022)
The present study is the first report aimed to determine the antibiotic susceptibility profiles of Mann-heimia haemolytica and Pasteurella multocida serogroup A Moroccan isolates. Each isolate was tested for sensitivity to amoxicillin (A), amoxicilli
Externí odkaz:
https://doaj.org/article/5547ff3173484e3fbf4fe5179f00efff
Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced fra
Externí odkaz:
http://arxiv.org/abs/2410.21203
Autor:
Li, Peiyu, Bahri, Omar, Hosseinzadeh, Pouya, Boubrahimi, Soukaïna Filali, Hamdi, Shah Muhammad
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in
Externí odkaz:
http://arxiv.org/abs/2410.20539
Autor:
FASER collaboration, Abraham, Roshan Mammen, Ai, Xiaocong, Anders, John, Antel, Claire, Ariga, Akitaka, Ariga, Tomoko, Atkinson, Jeremy, Bernlochner, Florian U., Bianchi, Emma, Boeckh, Tobias, Boyd, Jamie, Brenner, Lydia, Burger, Angela, Cadoux, Franck, Cardella, Roberto, Casper, David W., Cavanagh, Charlotte, Chen, Xin, Cho, Eunhyung, Chouhan, Dhruv, Coccaro, Andrea, Débieux, Stephane, D'Onofrio, Monica, Desai, Ansh, Dmitrievsky, Sergey, Dobre, Radu, Eley, Sinead, Favre, Yannick, Fellers, Deion, Feng, Jonathan L., Fenoglio, Carlo Alberto, Ferrere, Didier, Fieg, Max, Filali, Wissal, Firu, Elena, Garabaglu, Ali, Gibson, Stephen, Gonzalez-Sevilla, Sergio, Gornushkin, Yuri, Gwilliam, Carl, Hayakawa, Daiki, Holzbock, Michael, Hsu, Shih-Chieh, Hu, Zhen, Iacobucci, Giuseppe, Inada, Tomohiro, Iodice, Luca, Jakobsen, Sune, Joos, Hans, Kajomovitz, Enrique, Kawahara, Hiroaki, Keyken, Alex, Kling, Felix, Köck, Daniela, Kontaxakis, Pantelis, Kose, Umut, Kotitsa, Rafaella, Kuehn, Susanne, Kugathasan, Thanushan, Levinson, Lorne, Li, Ke, Liu, Jinfeng, Liu, Yi, Lutz, Margaret S., MacDonald, Jack, Magliocca, Chiara, Mäkelä, Toni, McCoy, Lawson, McFayden, Josh, Medina, Andrea Pizarro, Milanesio, Matteo, Moretti, Théo, Nakamura, Mitsuhiro, Nakano, Toshiyuki, Nevay, Laurie, Ohashi, Ken, Otono, Hidetoshi, Paolozzi, Lorenzo, Petersen, Brian, Preda, Titi, Prim, Markus, Queitsch-Maitland, Michaela, Rokujo, Hiroki, Rubbia, André, Sabater-Iglesias, Jorge, Sato, Osamu, Scampoli, Paola, Schmieden, Kristof, Schott, Matthias, Sfyrla, Anna, Sgalaberna, Davide, Shamim, Mansoora, Shively, Savannah, Takubo, Yosuke, Tarannum, Noshin, Theiner, Ondrej, Torrence, Eric, Martinez, Oscar Ivan Valdes, Vasina, Svetlana, Vormwald, Benedikt, Wang, Di, Wang, Yuxiao, Welch, Eli, Xu, Yue, Zahorec, Samuel, Zambito, Stefano, Zhang, Shunliang
The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity
Externí odkaz:
http://arxiv.org/abs/2410.10363
RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an accurate and efficient time series classification model. We use the fore
Externí odkaz:
http://arxiv.org/abs/2410.03098
Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field data through machine learning
Externí odkaz:
http://arxiv.org/abs/2410.00312
Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data prepro
Externí odkaz:
http://arxiv.org/abs/2409.14016
Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. T
Externí odkaz:
http://arxiv.org/abs/2409.14013
Autor:
Céspedes, Lucía, Kozlowski, Diego, Pradier, Carolina, Sainte-Marie, Maxime Holmberg, Shokida, Natsumi Solange, Benz, Pierre, Poitras, Constance, Ninkov, Anton Boudreau, Ebrahimy, Saeideh, Ayeni, Philips, Filali, Sarra, Li, Bing, Larivière, Vincent
Clarivate's Web of Science (WoS) and Elsevier's Scopus have been for decades the main sources of bibliometric information. Although highly curated, these closed, proprietary databases are largely biased towards English-language publications, underest
Externí odkaz:
http://arxiv.org/abs/2409.10633
Recent neutrino-nucleus cross-section measurements of observables characterising kinematic imbalance from the T2K, MicroBooNE and MINERvA experiments are used to benchmark predictions from widely used neutrino interaction event generators. Given the
Externí odkaz:
http://arxiv.org/abs/2407.10962