| Records |
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; |
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Springernature |
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English |
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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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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 |
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| Author |
Folgado, M.G.; Sanz, V.; Hirn, J.; Lorenzo-Saez, E.; Urchueguia, J. |
| Title |
Deep learning for urban air quality: a traffic-based prediction and alert system for Valencia |
Type |
Journal Article |
| Year |
2025 |
Publication |
Neural Computing and Applications |
Abbreviated Journal |
Neural Comput. Appl. |
| Volume |
37 |
Issue |
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Pages |
15837-15854 |
| Keywords |
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| Abstract |
Urban traffic congestion is a critical issue with significant implications for mobility and urban planning. In this study, we develop a real-time predictive alarm system capable of forecasting whether a street is likely to experience unusually high traffic within the next 30 min. The system classifies road segments into three alert levels based on traffic data updated every 10 min, providing timely information that can support decision-making in traffic management. The prediction model is built using deep learning techniques trained on a whole year of traffic data in the city of Valencia, and tested with the following year’s data. We evaluated different neural network architectures, including long short-term memory (LSTM) networks, an extended LSTM variant (xLSTM), and Graph Neural Networks (GNNs). Our results show that LSTM provides the best balance between accuracy and computational efficiency, making it the most suitable model for real-time deployment. In addition to traffic data, we incorporate meteorological variables such as wind speed, wind direction, and precipitation to explore their potential impact on traffic dynamics. Although the relationship between traffic and environmental conditions warrants further study, this work demonstrates the feasibility of using real-time predictions to improve urban mobility strategies. The proposed system offers a data-driven approach that can be integrated into broader traffic management frameworks to improve efficiency and responsiveness. |
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Edition |
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| ISSN |
1433-3058 |
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Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
7201 |
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| Author |
Folgado, M.G.; Sanz, V.; Hirn, J.; Lorenzo-Saez, E.; Urchueguia, J.F. |
| Title |
Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia |
Type |
Journal Article |
| Year |
2025 |
Publication |
Applied AI Letters |
Abbreviated Journal |
Applied AI Lett. |
| Volume |
6 |
Issue |
1 |
Pages |
e106 - 15pp |
| Keywords |
LSTM; neural network; time series; traffic forecasting |
| Abstract |
Traffic congestion represents a significant urban challenge, with notable implications for public health and environmental well-being. Consequently, urban decision-makers prioritize the mitigation of congestion. This study delves into the efficacy of harnessing extensive data on urban traffic dynamics, coupled with comprehensive knowledge of road networks, to enable Artificial Intelligence (AI) in forecasting traffic flux well in advance. Such forecasts hold promise for informing emission reduction measures, particularly those aligned with Low Emission Zone policies. The investigation centers on Valencia, leveraging its robust traffic sensor infrastructure, one of the most densely deployed worldwide, encompassing approximately 3500 sensors strategically positioned across the city. Employing historical data spanning 2016 and 2017, we undertake the task of training and characterizing a Long Short-Term Memory (LSTM) Neural Network for the prediction of temporal traffic patterns. Our findings demonstrate the LSTM's efficacy in real-time forecasting of traffic flow evolution, facilitated by its ability to discern salient patterns within the dataset. |
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Series Issue |
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Edition |
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| ISSN |
2689-5595 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
7189 |
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| Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
| Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
Type |
Journal Article |
| Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
| Volume |
93 |
Issue |
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Pages |
2529-2542 |
| Keywords |
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| Abstract |
Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures. |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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| Author |
Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. |
| Title |
Unbinned multivariate observables for global SMEFT analyses from machine learning |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
03 |
Issue |
3 |
Pages |
033 - 66pp |
| Keywords |
SMEFT; Higgs Properties |
| Abstract |
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits. |
| Address |
[Ambrosio, Raquel Gomez] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy, Email: raquel.gomezambrosio@unito.it; |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1029-8479 |
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:000946004000003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5501 |
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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 |
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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 Title |
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Abbreviated Series Title |
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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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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 |
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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 Title |
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Abbreviated Series Title |
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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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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 |
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| Author |
Hirn, J.; Sanz, V.; Lorite, J.; Martinez-Hernandez, F.; Mendoza-Fernandez, A.J.; Mota, J.F.; Navarro-Cano, J.A.; Perez-Garcia, F.J.; Prieto-Rubio, J.; Sanchez-Martin, R.; Verdu, M. |
| Title |
Evaluating the restoration of plant ecological interactions in gypsum mines with species co-occurrences analyses |
Type |
Journal Article |
| Year |
2026 |
Publication |
Journal of Applied Ecology |
Abbreviated Journal |
J. Appl. Ecol. |
| Volume |
63 |
Issue |
5 |
Pages |
e70397 - 15pp |
| Keywords |
competition; facilitation; gypsum mines; nurse plants; plant-plant interactions; seed-based restoration; seedling-based restoration |
| Abstract |
Ecological interactions are a crucial component of biodiversity, and their loss can disrupt ecosystem functions. Therefore, restoring these interactions is essential for effective ecological restoration. Although positive interactions between plants (i.e. plant facilitation) have gained attention in restoration ecology, most research has concentrated on population and community outcomes, with limited focus on the restoration of plant-plant interactions. We used Markov network models to evaluate pairwise and higher-order plant interactions in gypsum shrublands in southern Spain and compared them with interactions in adjacent areas restored after mining activities. Restoration efforts, involving seed-based and seedling-based approaches from 13 years ago, were assessed to determine whether positive (facilitation) and negative (competition) interactions had been restored within each community. Given the stressful conditions of gypsum soils, we hypothesize that facilitation is the main force shaping the interactions in both reference and restored areas. Positive interactions between the most abundant species dominated over negative ones in both natural and restored areas. In both the seed-based and the seedling-based restorations, about half of the observed pairwise interactions in the natural area were restored with the same sign. Generally, facilitation interactions that were not restored became neutral, but rarely turned negative. Synthesis and applications. These case studies suggest that both seed-based and seedling-based approaches can restore pairwise species interactions to a similar extent. The seedling-based method, although more expensive, provides immediate visual impact by directly introducing canopy-forming species into the system. In contrast, the seed-based approach, which is more cost-effective, can establish highly dense plant communities, enhancing soil protection, though it may also lead to increased emergent competition through third-order interactions. |
| Address |
[Hirn, Johannes; Prieto-Rubio, Jorge; Sanchez-Martin, Ricardo; Verdu, Miguel] Univ Valencia, Ctr Invest Desertificac CIDE, CSIC, Generalitat Valenciana, Valencia, Spain, Email: miguel.verdu@ext.uv.es |
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Thesis |
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| Publisher |
Wiley |
Place of Publication |
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English |
Summary Language |
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Series Issue |
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Edition |
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0021-8901 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
WOS:001757331700001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
7218 |
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| Author |
Hirsch, M.; Mantani, L.; Sanz, V. |
| Title |
Data-Driven Discovery Strategy for Standard Model Effective Field Theory Searches |
Type |
Journal Article |
| Year |
2025 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
| Volume |
135 |
Issue |
24 |
Pages |
241801 - 8pp |
| Keywords |
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| Abstract |
We present a novel strategy to uncover indirect signs of new physics in collider data using the standard model effective field theory (SMEFT) framework, offering notably improved sensitivity compared to traditional global analyses. Our approach leverages genetic algorithms to efficiently navigate the high-dimensional space of operator subsets, identifying deformations that improve agreement with data without relying on prior ultraviolet (UV) assumptions. This enables the systematic detection of SMEFT scenarios that outperform the standard model in explaining observed deviations. We validate the approach on current large hadron collider and large electron-positron collider measurements, perform closure tests with injected UV signals, and assess performance under high-luminosity projections. The algorithm successfully recovers relevant operator subsets and highlights directions in parameter space where deviations are most likely to emerge. Our results demonstrate the potential of SMEFT-based discovery searches driven by model selection, providing a scalable framework for future data analyses. |
| Address |
[Hirsch, Martin; Mantani, Luca; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, E-46980 Valencia, Spain |
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Thesis |
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| Publisher |
Amer Physical Soc |
Place of Publication |
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Editor |
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English |
Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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0031-9007 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
WOS:001645065400021 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
6997 |
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| Author |
Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
| Title |
LIGO as a probe of dark sectors |
Type |
Journal Article |
| Year |
2021 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
| Volume |
104 |
Issue |
10 |
Pages |
095001 - 9pp |
| Keywords |
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| Abstract |
We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential. |
| Address |
[Huang, Fei; Shu, Jing; Xue, Xiao] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China, Email: huangf4@uci.edu; |
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Amer Physical Soc |
Place of Publication |
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English |
Summary Language |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
2470-0010 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
WOS:000716446500001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5021 |
| Permanent link to this record |
