Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sebastian Risau Gusman"'
Autor:
Sabrina Riva, Juan Ignacio Ispizua, María Trinidad Breide, Sofía Polcowñuk, José Ricardo Lobera, María Fernanda Ceriani, Sebastian Risau-Gusman, Diana Lorena Franco
Publikováno v:
PLoS Genetics, Vol 18, Iss 12, p e1010258 (2022)
After mating, the physiology of Drosophila females undergo several important changes, some of which are reflected in their rest-activity cycles. To explore the hypothesis that mating modifies the temporal organization of locomotor activity patterns,
Externí odkaz:
https://doaj.org/article/1c335177397246b6a0ac6bea3b33b393
Autor:
Guadalupe Cascallares, Sabrina Riva, D Lorena Franco, Sebastian Risau-Gusman, Pablo M Gleiser
Publikováno v:
PLoS ONE, Vol 13, Iss 8, p e0202505 (2018)
In many animals the circadian rhythm of locomotor activity is controlled by an endogenous circadian clock. Using custom made housing and video tracking software in order to obtain high spatial and temporal resolution, we studied the statistical prope
Externí odkaz:
https://doaj.org/article/8a94d4e910f7417086216bd0ad9aee27
Publikováno v:
Journal of Statistical Physics. 124:1231-1253
We characterize the topology of the phase space of the Berlin-Kac spherical model in the context of the so called Topological Hypothesis, for spins lying in hypercubic lattices of dimension d. For zero external field we are able to characterize the t
Publikováno v:
Machine Learning. 46:53-70
We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that takes into account the number of high order features relative to the input space dimension. We analyze the effect of different features
Publikováno v:
Physical review letters. 95(14)
The topological hypothesis states that phase transitions should be related to changes in the topology of configuration space. The necessity of such changes has already been demonstrated. We characterize exactly the topology of the configuration space
Autor:
Marco Idiart, Sebastian Risau-Gusman
Publikováno v:
Physical review. E, Statistical, nonlinear, and soft matter physics. 72(4 Pt 1)
We perform an extensive numerical investigation on the retrieval dynamics of the synchronous Hopfield model, also known as Little-Hopfield model, up to sizes of 2(18) neurons. Our results correct and extend much of the early simulations on the model.
Publikováno v:
Repositório Institucional da UFRGS
Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
ResearcherID
Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
ResearcherID
A random walk is performed over a disordered media composed of $N$ sites random and uniformly distributed inside a $d$-dimensional hypercube. The walker cannot remain in the same site and hops to one of its $n$ neighboring sites with a transition pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98fe816517611a2653e3574c392fa4f2
Publikováno v:
Repositório Institucional da UFRGS
Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
ResearcherID
Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
ResearcherID
A random walk is performed on a disordered landscape composed of $N$ sites randomly and uniformly distributed inside a $d$-dimensional hypercube. The walker hops from one site to another with probability proportional to $\exp [- \beta E(D)]$, where $
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1af3408a642537d6452b44e8bca18b02
http://arxiv.org/abs/cond-mat/0210563
http://arxiv.org/abs/cond-mat/0210563
Publikováno v:
Physical review. E, Statistical, nonlinear, and soft matter physics. 64(3 Pt 1)
We study the typical learning properties of the recently introduced soft margin classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of statistical mechanics. We derive analytically the behavior of the learning curves in the
Publikováno v:
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, 2000, 62, pp.7092-7099
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, American Physical Society, 2000, 62, pp.7092-7099
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, 2000, 62, pp.7092-7099
Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, American Physical Society, 2000, 62, pp.7092-7099
The learning properties of finite size polynomial Support Vector Machines are analyzed in the case of realizable classification tasks. The normalization of the high order features acts as a squeezing factor, introducing a strong anisotropy in the pat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fe9a57a0e3ee3482c407bf281661a9c
https://hal.science/hal-00124372
https://hal.science/hal-00124372