| Records |
| Author |
Bonilla, J. et al; Vos, M. |
| Title |
Jets and Jet Substructure at Future Colliders |
Type |
Journal Article |
| Year |
2022 |
Publication |
Frontiers in Physics |
Abbreviated Journal |
Front. Physics |
| Volume |
10 |
Issue |
|
Pages |
897719 - 17pp |
| Keywords |
jets; jet substructure; collider; artificial intelligence; machine learning; snowmass; top quark; Higgs boson |
| Abstract |
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program. |
| Address |
[Bonilla, Johan; Erbacher, Robin] Univ Calif, Dept Phys & Astron, Davis, CA USA, Email: bpnachman@lbl.gov; |
| Corporate Author |
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Thesis |
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| Publisher |
Frontiers Media Sa |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2296-424x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000822618100001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5464 |
| Permanent link to this record |
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| Author |
Folgado, M.G.; Sanz, V. |
| Title |
Exploring the political pulse of a country using data science tools |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Computational Social Science |
Abbreviated Journal |
J. Comput. Soc. Sci. |
| Volume |
5 |
Issue |
|
Pages |
987-1000 |
| Keywords |
Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP) |
| Abstract |
In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis. |
| Address |
[Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Valencia 46980, Spain, Email: migarfol@upvnet.upv.es; |
| Corporate Author |
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Thesis |
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| Publisher |
Springernature |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2432-2717 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000742263500002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5077 |
| Permanent link to this record |
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| Author |
Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
| Title |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
Type |
Journal Article |
| Year |
2022 |
Publication |
Methods in Ecology and Evolution |
Abbreviated Journal |
Methods Ecol. Evol. |
| Volume |
13 |
Issue |
|
Pages |
1052-1061 |
| Keywords |
artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders |
| Abstract |
Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge. |
| Address |
[Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es |
| Corporate Author |
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Thesis |
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| Publisher |
Wiley |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2041-210x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000765239700001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5155 |
| Permanent link to this record |
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| Author |
Hirn, J.; Sanz, V.; Garcia Navarro, J.E.; Goberna, M.; Montesinos-Navarro, A.; Navarro-Cano, J.A.; Sanchez-Martin, R.; Valiente-Banuet, A.; Verdu, M. |
| Title |
Transfer learning of species co-occurrence patterns between plant communities |
Type |
Journal Article |
| Year |
2024 |
Publication |
Ecological Informatics |
Abbreviated Journal |
Ecol. Inform. |
| Volume |
83 |
Issue |
|
Pages |
102826 - 8pp |
| Keywords |
Generative artificial intelligence; Patchy vegetation; Plant communities; Restoration ecology; Species co-occurrence; Variational autoencoders |
| Abstract |
Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates. |
| Address |
[Hirn, Johannes; Montesinos-Navarro, Alicia; Sanchez-Martin, Ricardo; Verdu, Miguel] Univ Valencia Generalitat Valenciana, Ctr Invest Desertificac CIDE, CSIC, Valencia, Spain |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1574-9541 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:001327519900001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
6278 |
| Permanent link to this record |
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| Author |
Mendez, V.; Amoros, G.; Garcia, F.; Salt, J. |
| Title |
Emergent algorithms for replica location and selection in data grid |
Type |
Journal Article |
| Year |
2010 |
Publication |
Future Generation Computer Systems |
Abbreviated Journal |
Futur. Gener. Comp. Syst. |
| Volume |
26 |
Issue |
7 |
Pages |
934-946 |
| Keywords |
Grid computing; Algorithms; Optimization methods; Artificial intelligence |
| Abstract |
Grid infrastructures for e-Science projects are growing in magnitude terms. Improvements in data Grid replication algorithms may be critical in many of these infrastructures. This paper shows a decentralized replica optimization service, providing a general Emergent Artificial Intelligence (EAI) algorithm for the problem definition. Our aim is to set up a theoretical framework for emergent heuristics in Grid environments. Further, we describe two EAI approaches, the Particle Swarm Optimization PSO-Grid Multiswarm Federation and the Ant Colony Optimization ACO-Grid Asynchronous Colonies Optimization replica optimization algorithms, with some examples. We also present extended results with best performance and scalability features for PSO-Grid Multiswarrn Federation. |
| Address |
[Mendez Munoz, Victor; Amoros Vicente, Gabriel; Salt Cairols, Jose] CSIC, Grid & E Sci Grp, Inst Fis Corpuscular IFIC, Mixed Inst, E-46071 Valencia, Spain, Email: vmendez@ific.uv.es |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier Science Bv |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0167-739x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISI:000279804200004 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ elepoucu @ |
Serial |
411 |
| Permanent link to this record |
