Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

Autor: Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, Johannes Kästner

Publikováno v: npj Computational Materials, Vol 10, Iss 1, Pp 1-18 (2024)

Abstract Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate

Externí odkaz: https://doaj.org/article/b80bd493f23e40339f5d153468a09724

SARS-CoV-2 in Myelodysplastic Syndromes: A Snapshot From Early Italian Experience

Autor: Sandra Mossuto, Enrico Attardi, Francesco Alesiani, Emanuele Angelucci, Enrico Balleari, Massimo Bernardi, Gianni Binotto, Costanza Bosi, Anna Calvisi, Isabella Capodanno, Antonella Carbone, Andrea Castelli, Marco Cerrano, Rosanna Ciancia, Daniela Cilloni, Marino Clavio, Cristina Clissa, Elena Crisà, Monica Crugnola, Matteo G. Della Porta, Nicola Di Renzo, Ambra Di Veroli, Roberto Fattizzo, Carmen Fava, Susanna Fenu, Ida L. Ferrara, Luana Fianchi, Carla Filì, Carlo Finelli, Valentina Giai, Francesco Frattini, Valentina Gaidano, Gianluca Guaragna, Svitlana Gumenyuk, Roberto Latagliata, Stefano Mancini, Emanuela Messa, Alfredo Molteni, Pellegrino Musto, Pasquale Niscola, Esther Oliva, Giuseppe A. Palumbo, Annamaria Pelizzari, Federica Pilo, Antonella Poloni, Marta Riva, Flavia Rivellini, Chiara Sarlo, Mariarita Sciumé, Roberto Secchi, Carmine Selleri, Agostino Tafuri, Valeria Santini

Publikováno v: HemaSphere, Vol 4, Iss 5, p e483 (2020)

Externí odkaz: https://doaj.org/article/213f028c785b49d69a37c0f28f6d7838

Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

Autor: Konstantinos Gkiotsalitis, Gulcin Ermis, Francesco Alesiani

Publikováno v: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 1004-1010
STARTPAGE=1004;ENDPAGE=1010;TITLE=2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
IEEE Xplore
ITSC
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::825069b397d474fab1d10cbe2b6f8cb2
https://doi.org/10.36227/techrxiv.16622896

Towards Interpretable Multi-task Learning Using Bilevel Programming

Autor: Wenzhe Yin, Francesco Alesiani, Ammar Shaker, Shujian Yu

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676605
ECML/PKDD (2)

Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural phenomenon exhibit sparse structures, enforcing sparsity on learned m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1dba4fbc181c5e2acd9acd5932591c54
https://doi.org/10.1007/978-3-030-67661-2_35

Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications

Autor: Francesco Alesiani, Ammar Shaker, Shujian Yu, Jose C. Principe

Publikováno v: IJCAI

We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions. The new statistic avoids the explicit estimation of the underlying distributions in highdimensional space and it operates on the cone o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12647af8013a951fa9211eca887dc7dd
http://arxiv.org/abs/2005.02196