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pro vyhledávání: '"Alvares AS"'
Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of heterogeneous subgroups, the standard JM may be inadequate and lead to mi
Externí odkaz:
http://arxiv.org/abs/2410.22534
In this paper, we introduce the concept of a nested family of torsion pairs and will prove that this concept is strongly related to the existence of stratifying systems. Specifically, every stratifying system induces a nested family of torsion pairs.
Externí odkaz:
http://arxiv.org/abs/2410.14016
Autor:
Alvares, Cecilia M. S., Semino, Rocio
Simulations have acted as a cornerstone to understand MOF/polymer interface structure, however, no molecular-level simulation has yet been performed at the nanoparticle scale. In this work, a hybrid MARTINI/Force Matching (FM) force field was develop
Externí odkaz:
http://arxiv.org/abs/2410.14708
Autor:
Alvares, Danilo, Barrett, Jessica K., Mercier, François, Schulze, Jochen, Yiu, Sean, Castro, Felipe, Roumpanis, Spyros, Zhu, Yajing
Joint models have proven to be an effective approach for uncovering potentially hidden connections between various types of outcomes, mainly continuous, time-to-event, and binary. Typically, longitudinal continuous outcomes are characterized by linea
Externí odkaz:
http://arxiv.org/abs/2407.14311
Autor:
Alvares, Calvin, Chakraborty, Souvik
Over the past few years, equation discovery has gained popularity in different fields of science and engineering. However, existing equation discovery algorithms rely on the availability of noisy measurements of the state variables (i.e., displacemen
Externí odkaz:
http://arxiv.org/abs/2407.13704
Autor:
Lima, Mateus Guimarães, Carvalho, Antony, Álvares, João Gabriel, Chagas, Clayton Escouper das, Goldschmidt, Ronaldo Ribeiro
In the context of cybersecurity of modern communications networks, Intrusion Detection Systems (IDS) have been continuously improved, many of them incorporating machine learning (ML) techniques to identify threats. Although there are researches focus
Externí odkaz:
http://arxiv.org/abs/2407.11105
Autor:
Alvares, Danilo, Barrett, Jessica K., Mercier, François, Roumpanis, Spyros, Yiu, Sean, Castro, Felipe, Schulze, Jochen, Zhu, Yajing
Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma
Externí odkaz:
http://arxiv.org/abs/2405.20418
Publikováno v:
Biometrical Journal, 63(1), January 2021, Pages 7-26
Fully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi-parametric choices allow for more flexible patterns but they can suffer from overfitting and in
Externí odkaz:
http://arxiv.org/abs/2401.18014
Autor:
Alvares, Calvin, Banerjee, Soumitro
Reasonably large perturbations may push a power grid from its stable synchronous state into an undesirable state. Identifying vulnerabilities in power grids by studying power grid stability against such perturbations can aid in preventing future blac
Externí odkaz:
http://arxiv.org/abs/2401.00552
Autor:
Alvares, Cecilia M. S., Semino, Rocio
Despite the intense activity at the electronic and atomistic resolutions, coarse grained (CG) modeling of MOFs remains largely unexplored. One of the main reasons for this is the lack of adequate CG force fields. In this work, we present Iterative Bo
Externí odkaz:
http://arxiv.org/abs/2312.05192