@article{LOPEZGALDO2023106840,

title = {Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks},

author = {Ant\'{i}a L\'{o}pez Galdo and Alejandro Guerrero-L\'{o}pez and Pablo M Olmos and Mar\'{i}a Jes\'{u}s G\'{o}mez Garc\'{i}a},

url = {https://www.sciencedirect.com/science/article/pii/S0952197623010242},

doi = {https://doi.org/10.1016/j.engappai.2023.106840},

issn = {0952-1976},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {126},

pages = {106840},

abstract = {Maintaining railway axles is crucial to prevent catastrophic failures and enormous human and economic costs. In recent years, there has been a growing interest in the railway industry to adopt condition monitoring techniques to enhance the safety and efficiency of the rail transport system, which maintenance is currently based on periodic inspections. In this context, this work presents a technique for real-time crack diagnosis on railway axles, based on advanced 2D-Convolutional Neural Network (CNN) architectures applied to time\textendashfrequency representations of vibration signals. One of the critical novelties is introducing a differential CNN structure that captures the system’s statistical properties, enabling generalisation between different mechanical sets and conditions. The proposed system has been trained with data corresponding to a unique wheelset assembly, showing that the model is able to diagnose cracks on the three different wheelset tested in operation under 32 different combinations of conditions, such as load, speed, sense of rotation and vibration direction. Four different crack levels have been introduced, representing the maximum one a 0.08% of the axle diameter, and the method proposed achieved Area Under the Curve (AUC) score of 0.85, significantly outperforming results obtained with other architectures proposed in the state-of-the-art, the score of the next below is 0.76. The results demonstrate the effectiveness and practicality of this approach to accurately classify the four crack levels tested within a condition monitoring system for non-stationary conditions, that would enable reliable real-time diagnosis, thus paving the way towards a more robust and efficient railway axle maintenance system.},

keywords = {Condition monitoring, Convolutional Neural Networks, Crack detection, Deep learning, Railway axles, Vibration signal},

pubstate = {published},

tppubtype = {article}

}

@article{10132455,

title = {Handling Ill-Conditioned Omics Data With Deep Probabilistic Models},

author = {Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Pablo M Olmos},

doi = {10.1109/JBHI.2023.3279493},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {27},

number = {9},

pages = {4601-4610},

keywords = {Data models;Biological system modeling;Probabilistic logic;Bayes methods;Bioinformatics;Mars;Feature extraction;Bayesian;classification;deep generative model;dimensionality reduction;latent space model;missing data;semi-supervised;VAE},

pubstate = {published},

tppubtype = {article}

}

@article{SANCHEZMARTIN2023118928,

title = {Enhancing diversity in GANs via non-uniform sampling},

author = {Pablo S\'{a}nchez-Mart\'{i}n and Pablo M Olmos and Fernando Perez-Cruz},

url = {https://www.sciencedirect.com/science/article/pii/S002002552300498X},

doi = {https://doi.org/10.1016/j.ins.2023.04.007},

issn = {0020-0255},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Information Sciences},

volume = {637},

pages = {118928},

abstract = {Recent advances in Generative Adversarial Networks (GANs) have led to impressive results in generating realistic data. However, GANs training is still challenging, often leading to mode-collapse, where a certain type of samples dominates the generated output. To address this issue, we propose a novel training algorithm based on bidirectional GANs (BiGANs) that can be generalized to any implicit generative model. Our algorithm relies on a non-uniform sampling scheme, where data points in a minibatch are sampled with probability inversely proportional to their log-evidence. However, estimating log-evidence is computationally expensive. Instead, we propose to use the reconstruction error, which directly correlates with the log-evidence and only requires a BiGAN network evaluation. Additionally, we combine the aforementioned method with a regularization in the empirical distribution of the encoder that further boosts the performance. Our empirical results show that the proposed methods improve both the quality and diversity of the generated samples.},

keywords = {Deep generative models, Generative adversarial networks, Mode-collapse},

pubstate = {published},

tppubtype = {article}

}

@article{articlee,

title = {Automatic patient functionality assessment from multimodal data using deep learning techniques \textendash Development and feasibility evaluation},

author = {Emese S\"{u}kei and Santiago Leon-Martinez and Pablo Olmos and Antonio Art\'{e}s Rodr\'{i}guez},

doi = {10.1016/j.invent.2023.100657},

year  = {2023},

date = {2023-01-01},

journal = {Internet Interventions},

volume = {33},

pages = {100657},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GUERREROLOPEZ2023105644,

title = {Automatic antibiotic resistance prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra},

author = {Alejandro Guerrero-L\'{o}pez and Carlos Sevilla-Salcedo and Ana Candela and Marta Hern\'{a}ndez-Garc\'{i}a and Emilia Cercenado and Pablo M. Olmos and Rafael Cant\'{o}n and Patricia Mu\~{n}oz and Vanessa G\'{o}mez-Verdejo and Rosa Campo and Bel\'{e}n Rodr\'{i}guez-S\'{a}nchez},

url = {https://www.sciencedirect.com/science/article/pii/S0952197622006340},

doi = {https://doi.org/10.1016/j.engappai.2022.105644},

issn = {0952-1976},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {118},

pages = {105644},

abstract = {Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available.},

keywords = {Antibiotic Resistance, Bayesian model, CP, ESBL, Klebsiella pneumoniae, MALDI-TOF},

pubstate = {published},

tppubtype = {article}

}

@article{2022A\&A...660A..75P,

title = {X-ray variability of HD 189733 across eight years of XMM-Newton observations},

author = {I. Pillitteri and G Micela and A. Maggio and S Sciortino and J L\'{o}pez-Santiago},

doi = {10.1051/0004-6361/202142232},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Astronomy and Astrophysics},

volume = {660},

pages = {A75},

keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, planetary systems, stars: activity, stars: flare, stars: individual: HD 189733, X-rays: stars},

pubstate = {published},

tppubtype = {article}

}

@article{vazquez2022validation,

title = {Validation of scientific topic models using graph analysis and corpus metadata},

author = {Manuel A V\'{a}zquez and Jorge Pereira-Delgado and J Cid-Sueiro and Jer\'{o}nimo Arenas-Garc\'{i}a},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Scientometrics},

pages = {1--18},

publisher = {Springer},

keywords = {yo},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoRamirezSchreier-2022-One-bittargetdetectionincollocated,

title = {One-bit target detection in collocated MIMO Radar and performance degradation analysis},

author = {Y -H Xiao and David Ram\'{i}rez and Peter J Schreier and C. Qian and L. Huang},

doi = {10.1109/TVT.2022.3178285},

issn = {0018-9545},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Trans. Vehicular Techn. (To appear)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ravi-TIT2022,

title = {Scaling Laws for Gaussian Random Many-Access Channels},

author = {Jithin Ravi and Tobias Koch},

doi = {10.1109/TIT.2021.3139430},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {68},

number = {4},

pages = {2429-2459},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PORRASSEGOVIA2022145,

title = {Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort},

author = {Alejandro Porras-Segovia and Isaac D\'{i}az-Oliv\'{a}n and Maria Luisa Barrig\'{o}n and Manon Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez and Enrique Baca-Garc\'{i}a},

url = {https://www.sciencedirect.com/science/article/pii/S0022395622001078},

doi = {https://doi.org/10.1016/j.jpsychires.2022.02.026},

issn = {0022-3956},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Journal of Psychiatric Research},

volume = {149},

pages = {145-154},

abstract = {Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.},

keywords = {Ecological momentary assessment, eHealth, Mhealth, Suicide, Suicide attempt, Suicide ideation},

pubstate = {published},

tppubtype = {article}

}

@article{romero2022multinomial,

title = {Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection},

author = {Lorena Romero-Medrano and P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Journal of Signal Processing Systems},

volume = {94},

number = {2},

pages = {215--227},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9594658b,

title = {Medical Data Wrangling With Sequential Variational Autoencoders},

author = {Daniel Barrej\'{o}n and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.1109/JBHI.2021.3123839},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {26},

number = {6},

pages = {2737-2745},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Llorente2022OnTS,

title = {On the safe use of prior densities for Bayesian model selection},

author = {Fernando Llorente and Luca Martino and E. Curbelo and J L\'{o}pez-Santiago and David Delgado},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {WIREs Computational Statistics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CANO2022,

title = {Covariance determination for improving uncertainty realism in orbit determination and propagation},

author = {Alejandro Cano and Alejandro Pastor and Diego Escobar and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo},

url = {https://www.sciencedirect.com/science/article/pii/S0273117722007190},

doi = {https://doi.org/10.1016/j.asr.2022.08.001},

issn = {0273-1177},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Advances in Space Research},

abstract = {The reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the many sources of uncertainty in the space environment, the most relevant one is the inherent uncertainty of the dynamic models, which is generally not considered in the batch least-squares Orbit Determination (OD) processes in operational scenarios. A classical approach to account for these sources of uncertainty is the theory of consider parameters. In this approach, a set of uncertain parameters are included in the underlying dynamical model, in such a way that the model uncertainty is represented by the variances of these parameters. However, realistic variances of these consider parameters are not known a priori. This work introduces a methodology to infer the variance of consider parameters based on the observed distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically should follow a chi-square distribution under Gaussian assumptions. Empirical Distribution Function statistics such as the Cramer-von-Mises and the Kolmogorov\textendashSmirnov distances are used to determine optimum consider parameter variances. The methodology is presented in this paper and validated in a series of simulated scenarios emulating the complexity of operational applications.},

keywords = {Chi-square distribution, Covariance determination, Covariance realism, Cramer-von-Mises, Kolmogorov\textendashSmirnov, Mahalanobis distance, Space situational awareness, Uncertainty realism},

pubstate = {published},

tppubtype = {article}

}

@article{PMID:35936756,

title = {Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App},

author = {Vicente Escudero-Vilaplana and Lorena Romero-Medrano and Cristina Villanueva-Bueno and Marta Rodr\'{i}guez de Diago and Alberto Y\'{a}nez-Montesdeoca and Roberto Collado-Borrell and Juan Jos\'{e} Campa\~{n}a-Montes and Bel\'{e}n Marzal-Alfaro and Jos\'{e} Luis Revuelta-Herrero and Antonio Calles and Mar Galera and Rosa \'{A}lvarez and Ana Herranz and Mar\'{i}a Sanjurjo and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {https://europepmc.org/articles/PMC9351705},

doi = {10.3389/fonc.2022.880430},

issn = {2234-943X},

year  = {2022},

date = {2022-01-01},

journal = {Frontiers in oncology},

volume = {12},

pages = {880430},

abstract = {\<h4\>Background\</h4\>We have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.\<h4\>Methods\</h4\>The app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.\<h4\>Results\</h4\>The app developed provides a system for continuously collecting, merging, and processing data related to patient's health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.\<h4\>Conclusions\</h4\>We observed that the app's acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{deLeon-Martineze058486,

title = {Virtual reality and speech analysis for the assessment of impulsivity and decision-making: protocol for a comparison with neuropsychological tasks and self-administered questionnaires},

author = {Santiago de Le\'{o}n and Marta Ruiz and Elena Parra-Vargas and Irene Chicchi-Giglioli and Philippe Courtet and Jorge L\'{o}pez-Castrom\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia and Alejandro Porras-Segovia and Maria Luisa Barrigon},

url = {https://bmjopen.bmj.com/content/12/7/e058486},

doi = {10.1136/bmjopen-2021-058486},

issn = {2044-6055},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {BMJ Open},

volume = {12},

number = {7},

publisher = {British Medical Journal Publishing Group},

abstract = {Introduction Impulsivity is present in a range of mental disorders and has been associated with suicide. Traditional measures of impulsivity have certain limitations, such as the lack of ecological validity. Virtual reality (VR) may overcome these issues. This study aims to validate the VR assessment tool ‘Spheres \& Shield Maze Task’ and speech analysis by comparing them with traditional measures. We hypothesise that these innovative tools will be reliable and acceptable by patients, potentially improving the simultaneous assessment of impulsivity and decision-making.Methods and analysis This study will be carried out at the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (Madrid, Spain). Our sample will consist of adults divided into three groups: psychiatric outpatients with a history of suicidal thoughts and/or behaviours, psychiatric outpatients without such a history and healthy volunteers. The target sample size was established at 300 participants (100 per group). Participants will complete the Barratt Impulsiveness Scale 11; the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale; Iowa Gambling Task; Continuous Performance Test; Stop signal Task, and Go/no-go task, three questions of emotional affect, the Spheres \& Shield Maze Task and two satisfaction surveys. During these tasks, participant speech will be recorded. Construct validity of the VR environment will be calculated. We will also explore the association between VR-assessed impulsivity and history of suicidal thoughts and/or behaviour, and the association between speech and impulsivity and decision-making.Ethics and dissemination This study was approved by the Ethics Committee of the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (PIC128-21_FJD). Participants will be required to provide written informed consent. The findings will be presented in a series of manuscripts that will be submitted to peer-reviewed journals for publication.Trial registration number NCT05109845; Pre-results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9805755,

title = {Coded Caching with Heterogeneous User Profiles},

author = {Ciyuan Zhang and Su Wang and Vaneet Aggarwal and Borja Peleato},

doi = {10.1109/TIT.2022.3186210},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Transactions on Information Theory},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9791424,

title = {Coded Caching with Full Heterogeneity: Exact Capacity of The Two-User/Two-File Case},

author = {Chih-Hua Chang and Borja Peleato and Chih-Chun Wang},

doi = {10.1109/TIT.2022.3181411},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Transactions on Information Theory},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PANTOJAROSERO2022104430,

title = {Generating LOD3 building models from structure-from-motion and semantic segmentation},

author = {B. G. Pantoja-Rosero and R. Achanta and M. Kozinski and P. Fua and Fernando Perez-Cruz and K. Beyer},

url = {https://www.sciencedirect.com/science/article/pii/S092658052200303X},

doi = {https://doi.org/10.1016/j.autcon.2022.104430},

issn = {0926-5805},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Automation in Construction},

volume = {141},

pages = {104430},

abstract = {This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation techniques. Given multiple-view images of a building, we compute a three-dimensional (3D) planar abstraction (LOD2 model) of its point cloud using SfM techniques. To obtain LOD3 models, we use deep learning to perform semantic segmentation of the openings in the two-dimensional (2D) images. Unlike existing approaches, we do not rely on complex input, pre-defined 3D shapes or manual intervention. To demonstrate the robustness of our method, we show that it can generate 3D building shapes from a collection of building images with no further input. For evaluating reconstructions, we also propose two novel metrics. The first is a Euclidean\textendashdistance-based correlation of the 3D building model with the point cloud. The second involves re-projecting 3D model facades onto source photos to determine dice scores with respect to the ground-truth masks. Finally, we make the code, the image datasets, SfM outputs, and digital twins reported in this work publicly available in github.com/eesd-epfl/LOD3_buildings and doi.org/10.5281/zenodo.6651663. With this work we aim to contribute research in applications such as construction management, city planning, and mechanical analysis, among others.},

keywords = {3D building models, Deep learning, Digital twin, LOD models, Masonry buildings, Structure from motion},

pubstate = {published},

tppubtype = {article}

}

@article{PANTOJAROSERO2022128264,

title = {TOPO-Loss for continuity-preserving crack detection using deep learning},

author = {B. G. Pantoja-Rosero and D. Oner and M. Kozinski and R. Achanta and P. Fua and Fernando Perez-Cruz and K. Beyer},

url = {https://www.sciencedirect.com/science/article/pii/S0950061822019250},

doi = {https://doi.org/10.1016/j.conbuildmat.2022.128264},

issn = {0950-0618},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Construction and Building Materials},

volume = {344},

pages = {128264},

abstract = {We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address these problems by adapting an approach previously proposed for reconstructing roads in aerial images, in which a Convolutional Neural Network is trained with a loss function specifically designed to encourage the continuity of thin structures and to accommodate imprecise annotations. We evaluate combinations of three loss functions (the Mean Squared Error, the Dice loss and the new connectivity-oriented loss) on two datasets using TernausNet, a deep network shown to attain state-of-the-art accuracy in crack detection. We herein show that combining these three losses significantly improves the topology of the predictions quantitatively and qualitatively. We also propose a new continuity metric, named Cracks Per Patch (CPP), and share a new dataset of images of earthquake-affected urban scenes accompanied by crack annotations. The dataset and implementations are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/topo_crack_detection and https://doi.org/10.5281/zenodo.6769028).},

keywords = {Crack detection, Deep learning, Masonry buildings, Post-earthquake assessment},

pubstate = {published},

tppubtype = {article}

}

@article{nhess-22-2031-2022,

title = {Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland},

author = {C. P\'{e}rez-Guill\'{e}n and Frank Techel and M. Hendrick and Michele Volpi and A. Herwijnen and T. Olevski and G. Obozinski and Fernando Perez-Cruz and J. Schweizer},

url = {https://nhess.copernicus.org/articles/22/2031/2022/},

doi = {10.5194/nhess-22-2031-2022},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Natural Hazards and Earth System Sciences},

volume = {22},

number = {6},

pages = {2031--2056},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/sac/ZhangWP22,

title = {Accelerated parallel non-conjugate sampling for Bayesian non-parametric  models},

author = {Michael Minyi Zhang and Sinead A. Williamson and Fernando Perez-Cruz},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Stat. Comput.},

volume = {32},

number = {3},

pages = {50},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9764678,

title = {Generalized Perfect Codes for Symmetric Classical-Quantum Channels},

author = {Andreu Blasco Coll and Gonzalo Vazquez-Vilar and Javier Rodr\'{i}guez Fonollosa},

doi = {10.1109/TIT.2022.3170868},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {68},

number = {9},

pages = {5923-5936},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cano2022improving,

title = {Improving Orbital Uncertainty Realism Through Covariance Determination in GEO},

author = {Alejandro Cano and Alejandro Pastor and Sergio Fern\'{a}ndez and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo and Diego Escobar},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {The Journal of the Astronautical Sciences},

volume = {69},

number = {5},

pages = {1394--1420},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{barrigon2022smartphone,

title = {Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V. 2.0 randomised clinical trial},

author = {Maria Luisa Barrigon and Alejandro Porras-Segovia and Philippe Courtet and Jorge Lopez-Castroman and Sofian Berrouiguet and Mar\'{i}a-Mercedes P\'{e}rez-Rodr\'{i}guez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},

year  = {2022},

date = {2022-01-01},

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journal = {BMJ open},

volume = {12},

number = {9},

pages = {e051807},

publisher = {British Medical Journal Publishing Group},

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pubstate = {published},

tppubtype = {article}

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title = {Six-month clinical and ecological momentary assessment follow-up of patients at high risk of suicide: a survival analysis},

author = {Alejandro Porras-Segovia and Manon Moreno and Mar\'{i}a Luisa Barrig\'{o}n and Jorge L\'{o}pez Castroman and Philippe Courtet and Sofian Berrouiguet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {The Journal of Clinical Psychiatry},

volume = {84},

number = {1},

pages = {44594},

publisher = {Physicians Postgraduate Press, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sevilla2022bayesian,

title = {Bayesian sparse factor analysis with kernelized observations},

author = {Carlos Sevilla-Salcedo and Alejandro Guerrero-L\'{o}pez and Pablo M Olmos and Vanessa G\'{o}mez-Verdejo},

year  = {2022},

date = {2022-01-01},

journal = {Neurocomputing},

volume = {490},

pages = {66--78},

publisher = {Elsevier},

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pubstate = {published},

tppubtype = {article}

}

@article{10.1182/blood-2022-168461,

title = {Identification of Predictive Models Including Polymorphisms in Cytokines Genes Associated with Post-Transplant Complications after Identical HLA-Allogeneic Stem Cell Transplantation},

author = {Paula Mu\~{n}iz Sevilla and Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Mi Kwon and Rebeca Bail\'{e}n and Gillen Oarbeascoa and Diego Carbonell and Julia Su\'{a}rez Gonz\'{a}lez and Mar\'{i}a Chicano Lavilla and Cristina Andres and Juan Carlos Trivi\~{n}o and Javier Anguita and Jos\'{e} Luis D\'{i}ez-Mart\'{i}n and Pablo M Olmos and Carolina Martinez-Laperche and Ismael Bu\~{n}o},

url = {https://doi.org/10.1182/blood-2022-168461},

doi = {10.1182/blood-2022-168461},

issn = {0006-4971},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Blood},

volume = {140},

number = {Supplement 1},

pages = {4795-4796},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PerezViaVielva-2022-OnlinedetectionandSNRestimation,

title = {Online detection and SNR estimation in cooperative spectrum sensing},

author = {J. P\'{e}rez and Javier V\'{i}a and L. Vielva and David Ram\'{i}rez},

doi = {10.1109/TWC.2021.3113089},

issn = {1536-1276},

year  = {2022},

date = {2022-00-01},

urldate = {2022-00-01},

journal = {IEEE Trans. Wireless Comm.},

volume = {21},

number = {4},

pages = {2521--2533},

keywords = {},

pubstate = {published},

tppubtype = {article}

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@article{Ramirez,

title = {Graph-signal reconstruction and blind deconvolution for structured inputs},

author = {David Ram\'{i}rez and Antonio G Marques and Santiago Segarra},

doi = {10.1016/j.sigpro.2021.108180},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Signal Process. (Special issue on Processing and Learning over Graphs)},

volume = {188},

pages = {108180},

keywords = {},

pubstate = {published},

tppubtype = {article}

}