Zobrazeno 1 - 10
of 8 145
pro vyhledávání: '"De Sa, P."'
Autor:
Fraser-McKelvie, A., van de Sande, J., Gadotti, D. A., Emsellem, E., Brown, T., Fisher, D. B., Martig, M., Bureau, M., Gerhard, O., Battisti, A. J., Bland-Hawthorn, J., Catinella, B., Combes, F., Cortese, L., Croom, S. M., Davis, T. A., Falcón-Barroso, J., Fragkoudi, F., Freeman, K. C., Hayden, M. R., McDermid, R., Ciraulo, B. Mazzilli, Mendel, J. T., Pinna, F., Poci, A., Rutherford, T. H., de Sá-Freitas, C., Silva-Lima, L. A., Valenzuela, L. M., van de Ven, G., Wang, Z., Watts, A. B.
The vertical evolution of galactic discs is governed by the sub-structures within them. We examine the diversity of kinematic sub-structure present in the first 12 galaxies observed from the GECKOS survey, a VLT/MUSE large programme providing a syste
Externí odkaz:
http://arxiv.org/abs/2411.03430
Autor:
Ganem, Fabiana, Vacaro, Luã Bida, Araujo, Eduardo Correa, Alves, Leon Diniz, Bastos, Leonardo, Carvalho, Luiz Max, Almeida, Iasmim, de Sá, Asla Medeiros, Coelho, Flávio Codeço
Dengue is a climate-sensitive mosquito-borne disease with a complex transmission dynamic. Data related to climate, environmental and sociodemographic characteristics of the target population are important for project scenarios. Different datasets and
Externí odkaz:
http://arxiv.org/abs/2410.18945
Binary population synthesis (BPS) is an essential tool for extracting information about massive binary evolution from gravitational-wave (GW) detections of compact object mergers. It has been successfully used to constrain the most likely permutation
Externí odkaz:
http://arxiv.org/abs/2410.11830
We perform a first study of the impact of varying two components of the initial conditions in binary population synthesis of compact binary mergers - the initial mass function, which is made metallicity- and star formation rate-dependent, and the orb
Externí odkaz:
http://arxiv.org/abs/2410.01451
Autor:
Potapczynski, Andres, Qiu, Shikai, Finzi, Marc, Ferri, Christopher, Chen, Zixi, Goldblum, Micah, Bruss, Bayan, De Sa, Christopher, Wilson, Andrew Gordon
Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts focused on a small number of hand-crafted structured matrices and neglected to invest
Externí odkaz:
http://arxiv.org/abs/2410.02117
Autor:
Feng, Shuangquan, de Sa, Virginia R.
Automatic facial action unit (AU) recognition is used widely in facial expression analysis. Most existing AU recognition systems aim for cross-participant non-calibrated generalization (NCG) to unseen faces without further calibration. However, due t
Externí odkaz:
http://arxiv.org/abs/2409.00240
Autor:
de Sá, Alex G. C., Ascher, David B.
Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties -- including absorption, distribution, metabolism and excretion (ADME)-- are crucia
Externí odkaz:
http://arxiv.org/abs/2408.00421
Autor:
Hartung, Michael, Maier, Andreas, Delgado-Chaves, Fernando, Burankova, Yuliya, Isaeva, Olga I., Patroni, Fábio Malta de Sá, He, Daniel, Shannon, Casey, Kaufmann, Katharina, Lohmann, Jens, Savchik, Alexey, Hartebrodt, Anne, Chervontseva, Zoe, Firoozbakht, Farzaneh, Probul, Niklas, Zotova, Evgenia, Tsoy, Olga, Blumenthal, David B., Ester, Martin, Laske, Tanja, Baumbach, Jan, Zolotareva, Olga
Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarked almost ex
Externí odkaz:
http://arxiv.org/abs/2408.00200
Languages continually evolve in response to societal events, resulting in new terms and shifts in meanings. These changes have significant implications for computer applications, including automatic translation and chatbots, making it essential to ch
Externí odkaz:
http://arxiv.org/abs/2407.16624
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensive numerical experiments on the MNIST dataset and finetuning RoBERTa-La
Externí odkaz:
http://arxiv.org/abs/2406.14362