Zobrazeno 1 - 10
of 13 655
pro vyhledávání: '"Vannucci A."'
Autor:
Song, Ziyi, Shen, Weining, Vannucci, Marina, Baldizon, Alexandria, Cinciripini, Paul M., Versace, Francesco, Guindani, Michele
Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' respons
Externí odkaz:
http://arxiv.org/abs/2410.22675
Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The param
Externí odkaz:
http://arxiv.org/abs/2409.17404
Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2409.17129
Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework w
Externí odkaz:
http://arxiv.org/abs/2407.20085
We study the impact of mechanical vibrations on the performance of the photonic "hourglass" structure, which is predicted to emit single photons on-demand with near-unity efficiency and indistinguishability. Previous investigations neglected the impa
Externí odkaz:
http://arxiv.org/abs/2407.17309
Publikováno v:
Phys. Rev. B 110, 115308 (2024)
While the semiconductor quantum dot placed in a solid-state material allows for deterministic emission of single photons, the photon indistinguishability is strongly influenced by the intrinsic coupling to lattice vibrations, phonons, of the solid-st
Externí odkaz:
http://arxiv.org/abs/2407.14462
The present work provides an application of Global Sensitivity Analysis to supervised machine learning methods such as Random Forests. These methods act as black boxes, selecting features in high--dimensional data sets as to provide accurate classifi
Externí odkaz:
http://arxiv.org/abs/2407.14194
Publikováno v:
Biometrics (2024)
In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genom
Externí odkaz:
http://arxiv.org/abs/2407.05089
Autor:
Vannucci, Luca, Gregersen, Niels
Publikováno v:
Opt. Express 32, 35381-35394 (2024)
The swing-up of quantum emitter population (SUPER) scheme allows to populate the excited state of a quantum emitter with near-unity fidelity using two red-detuned laser pulses. Its off-resonant, yet fully coherent nature has attracted significant int
Externí odkaz:
http://arxiv.org/abs/2406.17540
Autor:
Ricci, Federica Zoe, Sudderth, Erik B., Lee, Jaylen, Peters, Megan A. K., Vannucci, Marina, Guindani, Michele
We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associa
Externí odkaz:
http://arxiv.org/abs/2406.17131