Records |
Author |
Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C. |
Title |
Machine Learning aided 3D-position reconstruction in large LaCl3 crystals |
Type |
Journal Article |
Year |
2021 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
Volume |
1001 |
Issue |
|
Pages |
165249 - 17pp |
Keywords |
Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section |
Abstract |
We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements. |
Address |
[Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: javier.balibrea@ific.uv.es |
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 |
0168-9002 |
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:000641308300007 |
Approved |
no |
Is ISI |
yes |
International Collaboration ![sorted by International Collaboration field, ascending order (up)](img/sort_asc.gif) |
no |
Call Number |
IFIC @ pastor @ |
Serial |
4803 |
Permanent link to this record |
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Author |
Blanes-Selva, V.; Ruiz-Garcia, V.; Tortajada, S.; Benedi, J.M.; Valdivieso, B.; Garcia-Gomez, J.M. |
Title |
Design of 1-year mortality forecast at hospital admission: A machine learning approach |
Type |
Journal Article |
Year |
2021 |
Publication |
Health Informatics Journal |
Abbreviated Journal |
Health Inform. J. |
Volume |
27 |
Issue |
1 |
Pages |
13pp |
Keywords |
machine learning; palliative care; hospital admission data; mortality forecast |
Abstract |
Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion. |
Address |
[Blanes-Selva, Vicent; Benedi, Jose-Miguel; Garcia-Gomez, Juan M.] Univ Politecn Valencia, Valencia, Spain, Email: viblasel@upv.es |
Corporate Author |
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Thesis |
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Publisher |
Sage Publications Inc |
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 |
1460-4582 |
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:000645567000008 |
Approved |
no |
Is ISI |
yes |
International Collaboration ![sorted by International Collaboration field, ascending order (up)](img/sort_asc.gif) |
no |
Call Number |
IFIC @ pastor @ |
Serial |
5182 |
Permanent link to this record |
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Author |
Ferrer-Sanchez, A.; Martin-Guerrero, J.; Ruiz de Austri, R.; Torres-Forne, A.; Font, J.A. |
Title |
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics |
Type |
Journal Article |
Year |
2024 |
Publication |
Computer Methods in Applied Mechanics and Engineering |
Abbreviated Journal |
Comput. Meth. Appl. Mech. Eng. |
Volume |
424 |
Issue |
|
Pages |
116906 - 18pp |
Keywords |
Riemann problem; Euler equations; Machine learning; Neural networks; Relativistic hydrodynamics |
Abstract |
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions. |
Address |
[Ferrer-Sanchez, Antonio; Martin-Guerrero, JoseD.] ETSE UV, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain, Email: Antonio.Ferrer-Sanchez@uv.es |
Corporate Author |
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Thesis |
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Publisher |
Elsevier Science 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 |
0045-7825 |
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:001221797400001 |
Approved |
no |
Is ISI |
yes |
International Collaboration ![sorted by International Collaboration field, ascending order (up)](img/sort_asc.gif) |
no |
Call Number |
IFIC @ pastor @ |
Serial |
6126 |
Permanent link to this record |
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Author |
Kasieczka, G. et al; Sanz, V. |
Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
Volume |
84 |
Issue |
12 |
Pages |
124201 - 64pp |
Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
Abstract |
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
Address |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
Corporate Author |
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Thesis |
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Publisher |
IOP Publishing Ltd |
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 |
0034-4885 |
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:000727698500001 |
Approved |
no |
Is ISI |
yes |
International Collaboration ![sorted by International Collaboration field, ascending order (up)](img/sort_asc.gif) |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5039 |
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 ![sorted by International Collaboration field, ascending order (up)](img/sort_asc.gif) |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5077 |
Permanent link to this record |