@article{10.3389/fimmu.2024.1396284,

title = {Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation},

author = {Paula Mu\~{n}iz and Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Rebeca Bail\'{e}n and Mar\'{i}a Chicano and Gillen Oarbeascoa and Juan Carlos Trivi\~{n}o and Ismael Iglesia-San Sebastian and Sara Fern\'{a}ndez de C\'{o}rdoba and Javier Anguita and Mi Kwon and Jos\'{e} Luis D\'{i}ez-Mart\'{i}n and Pablo M. Olmos and Carolina Mart\'{i}nez-Laperche and Ismael Bu\~{n}o},

url = {https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1396284},

doi = {10.3389/fimmu.2024.1396284},

issn = {1664-3224},

year  = {2024},

date = {2024-01-01},

journal = {Frontiers in Immunology},

volume = {Volume 15 - 2024},

abstract = {\<sec\>\<title\>Backgrounds\</title\>\<p\>Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS).\</p\>\</sec\>\<sec\>\<title\>Methods\</title\>\<p\>Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS).\</p\>\</sec\>\<sec\>\<title\>Results\</title\>\<p\>A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in non-coding regions. All models had a statistical significance of p\<0.0001.\</p\>\</sec\>\<sec\>\<title\>Conclusion\</title\>\<p\>Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients.\</p\>\</sec\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lopez-Castroman_Art\'{e}s-Rodr\'{i}guez_Courtet_Hanon_Gondek_Baca-Garc\'{i}a_Volpe_2024,

title = {The growing need to integrate digital mental health into psychiatric and medical education},

author = {Jorge Lopez-Castroman and Antonio Art\'{e}s-Rodr\'{i}guez and Philippe Courtet and Cecile Hanon and Tomasz Gondek and Enrique Baca-Garc\'{i}a and Umberto Volpe},

doi = {10.1192/j.eurpsy.2024.1802},

year  = {2024},

date = {2024-01-01},

journal = {European Psychiatry},

volume = {67},

number = {1},

pages = {e90},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MiguezJoaqu\'{i}n2024Mcsf,

title = {Master-slave coupling scheme for synchronization and parameter estimation in the generalized Kuramoto-Sivashinsky equation},

author = {Joaqu\'{i}n Miguez and Harold Molina-Bulla and In\'{e}s P Mari\~{n}o},

issn = {2470-0045},

year  = {2024},

date = {2024-01-01},

journal = {Physical review special topics. PRST-AB.},

volume = {110},

number = {5},

pages = {054206},

publisher = {American Physical Society,},

address = {Ridge, N.Y. :},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ram\'{i}rezDavid2024PDoa,

title = {Passive Detection of a Random Signal Common to Multi-Sensor Reference and Surveillance Arrays},

author = {David Ram\'{i}rez and Ignacio Santamaria and Louis L Scharf},

issn = {0018-9545},

year  = {2024},

date = {2024-01-01},

journal = {IEEE transactions on vehicular technology.},

volume = {73},

number = {7},

pages = {10106\textendash10117},

publisher = {Institute of Electrical and Electronics Engineers},

address = {New York :},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lancho_2023,

title = {Cell-Free Massive MIMO for URLLC: A Finite-Blocklength Analysis},

author = {Alejandro Lancho and Giuseppe Durisi and Luca Sanguinetti},

url = {http://dx.doi.org/10.1109/TWC.2023.3265303},

doi = {10.1109/twc.2023.3265303},

issn = {1558-2248},

year  = {2023},

date = {2023-12-01},

journal = {IEEE Transactions on Wireless Communications},

volume = {22},

number = {12},

pages = {8723\textendash8735},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoHuangRamirez-2023-Covariancematrixrecoveryfromone-bit,

title = {Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis},

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

doi = {10.1109/TSP.2023.3325664},

issn = {1053-587X},

year  = {2023},

date = {2023-11-01},

journal = {IEEE Trans. Signal Process.},

volume = {71},

pages = {4060\textendash4076},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/47167,

title = {Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study},

author = {Emese S\"{u}kei and Lorena Romero-Medrano and Santiago Leon-Martinez and Jes\'{u}s Herrera L\'{o}pez and Juan Jos\'{e} Campa\~{n}a-Montes and Pablo M Olmos and Enrique Baca-Garcia and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.ncbi.nlm.nih.gov/pubmed/37902823},

doi = {10.2196/47167},

issn = {2561-326X},

year  = {2023},

date = {2023-10-30},

journal = {JMIR Form Res},

volume = {7},

pages = {e47167},

abstract = {Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning\textendashbased models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning\textendashbased methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions\textemdashan important aspect in clinical practice.},

keywords = {WHODAS; functional limitations; mobile sensing; passive ecological momentary assessment; predictive modeling; interpretable machine learning; machine learning; disability; clinical outcome},

pubstate = {published},

tppubtype = {article}

}

@article{Kislal_2023,

title = {Efficient Evaluation of the Error Probability for Pilot-Assisted URLLC With Massive MIMO},

author = {A. Oguz Kislal and Alejandro Lancho and Giuseppe Durisi and Erik G. Str\"{o}m},

url = {http://dx.doi.org/10.1109/JSAC.2023.3280972},

doi = {10.1109/jsac.2023.3280972},

issn = {1558-0008},

year  = {2023},

date = {2023-07-01},

journal = {IEEE Journal on Selected Areas in Communications},

volume = {41},

number = {7},

pages = {1969\textendash1981},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kislal_2023b,

title = {Efficient Evaluation of the Error Probability for Pilot-Assisted URLLC With Massive MIMO},

author = {A. Oguz Kislal and Alejandro Lancho and Giuseppe Durisi and Erik G. Str\"{o}m},

url = {http://dx.doi.org/10.1109/JSAC.2023.3280972},

doi = {10.1109/jsac.2023.3280972},

issn = {1558-0008},

year  = {2023},

date = {2023-07-01},

journal = {IEEE Journal on Selected Areas in Communications},

volume = {41},

number = {7},

pages = {1969\textendash1981},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/43719,

title = {One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study},

author = {Maria Luisa Barrigon and Lorena Romero-Medrano and Pablo Moreno-Mu\~{n}oz and Alejandro Porras-Segovia and Jorge Lopez-Castroman and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},

url = {http://www.ncbi.nlm.nih.gov/pubmed/37656498},

doi = {10.2196/43719},

issn = {1438-8871},

year  = {2023},

date = {2023-05-01},

journal = {J Med Internet Res},

volume = {25},

pages = {e43719},

abstract = {Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.},

keywords = {e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide},

pubstate = {published},

tppubtype = {article}

}

@article{MORENOPINO2022109014,

title = {Deep Autoregressive Models with Spectral Attention},

author = {Fernando Moreno-Pino and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {https://www.sciencedirect.com/science/article/pii/S0031320322004940},

doi = {https://doi.org/10.1016/j.patcog.2022.109014},

issn = {0031-3203},

year  = {2023},

date = {2023-01-01},

urldate = {2022-01-01},

journal = {Pattern Recognition},

pages = {109014},

abstract = {Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model’s embedded space. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series’s noise. The proposed architecture has a number of useful properties: it can be effectively incorporated into well-known forecast architectures, requiring a low number of parameters and producing explainable results that improve forecasting accuracy. We test the Spectral Attention Autoregressive Model (SAAM) on several well-known forecast datasets, consistently demonstrating that our model compares favorably to state-of-the-art approaches.},

keywords = {Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting},

pubstate = {published},

tppubtype = {article}

}

@article{peis2023unsupervised,

title = {Unsupervised learning of global factors in deep generative models},

author = {Ignacio Peis and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Pattern Recognition},

volume = {134},

pages = {109130},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{romero2023multi,

title = {Multi-Source Change-Point Detection over Local Observation Models},

author = {Lorena Romero-Medrano and Antonio Art\'{e}s-Rodr\'{i}guez},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Pattern Recognition},

volume = {134},

pages = {109116},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{guerrero2023automatic,

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},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {118},

pages = {105644},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AGUILERA2023,

title = {Regularizing transformers with deep probabilistic layers},

author = {Aurora Cobo Aguilera and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando P\'{e}rez-Cruz},

url = {https://www.sciencedirect.com/science/article/pii/S0893608023000448},

doi = {https://doi.org/10.1016/j.neunet.2023.01.032},

issn = {0893-6080},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Neural Networks},

abstract = {Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.},

keywords = {Deep learning, Missing data, Natural language processing, Regularization, Transformers, Variational auto-encoder},

pubstate = {published},

tppubtype = {article}

}

@article{SEDANOCAPDEVILA2023115090,

title = {Text mining methods for the characterisation of suicidal thoughts and behaviour},

author = {Alba Sedano-Capdevila and Mauricio Toledo-Acosta and Mar\'{i}a Luisa Barrigon and Eliseo Morales-Gonz\'{a}lez and David Torres-Moreno and Bol\'{i}var Mart\'{i}nez-Zaldivar and Jorge Hermosillo-Valadez and Enrique Baca-Garc\'{i}a and Fuensanta Aroca and Antonio Artes-Rodriguez and Enrique Baca-Garc\'{i}a and Sofian Berrouiguet and Romain Billot and Juan Jose Carballo-Belloso and Philippe Courtet and David Delgado Gomez and Jorge Lopez-Castroman and Mercedes Perez Rodriguez and Julia Aznar-Carbone and Fanny Cegla and Pedro Guti\'{e}rrez-Recacha and Leire Izaguirre-Gamir and Javier Herrera-Sanchez and Marta Migoya Borja and Nora Palomar-Ciria and Adela S\'{a}nchez-Escribano Mart\'{i}nez and Manuel Vasquez and Silvia Vallejo-O\~{n}ate and Constanza Vera-Varela and Susana Amodeo-Escribano and Elsa Arrua and Olga Bautista and Maria Luisa Barrig\'{o}n and Rodrigo Carmona and Irene Caro-Ca\~{n}izares and Sonia Carollo-Vivian and Jaime Chamorro and Marta Gonz\'{a}lez-Granado and Miren Iza and M\'{o}nica Jim\'{e}nez-Gim\'{e}nez and Ana L\'{o}pez-G\'{o}mez and Laura Mata-Iturralde and Carolina Miguelez and Laura Mu\~{n}oz-Lorenzo and Roc\'{i}o Navarro-Jim\'{e}nez and Santiago Ovejero and Mar\'{i}a Luz Palacios and Margarita P\'{e}rez-Fominaya and Inmaculada Pe\~{n}uelas-Calvo and Sonia P\'{e}rez-Colmenero and Ana Rico-Romano and Alba Rodriguez-Jover and Sergio S\'{a}nchezAlonso and Juncal Sevilla-Vicente and Carolina Vigil-L\'{o}pez and Luc\'{i}a Villoria-Borrego and Marisa Martin-Calvo and Ana Alc\'{o}n-Dur\'{a}n and Ezequiel Di Stasio and Juan Manuel Garc\'{i}a-Vega and Pedro Mart\'{i}n-Calvo and Ana Jos\'{e} Ortega and Marta Segura-Valverde and Sara Mar\'{i}a Ba\~{n}\'{o}n-Gonz\'{a}lez and Edurne Crespo-Llanos and Rosana Codesal-Juli\'{a}n and Ainara Frade-Ciudad and Elena Hernando Merino and Raquel \'{A}lvarez-Garc\'{i}a and Jose Marcos Coll-Font and Pablo Portillo-de Antonio and Pablo Puras-Rico and Alba Sedano-Capdevila and Leticia Serrano-Marug\'{a}n},

url = {https://www.sciencedirect.com/science/article/pii/S0165178123000434},

doi = {https://doi.org/10.1016/j.psychres.2023.115090},

issn = {0165-1781},

year  = {2023},

date = {2023-01-01},

journal = {Psychiatry Research},

volume = {322},

pages = {115090},

abstract = {Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question “how are you feeling today?” were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients’ free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.},

keywords = {Machine learning, Mobile health, Natural language processing, Suicidal ideation, Suicide, Suicide attempt},

pubstate = {published},

tppubtype = {article}

}

@article{bonilla2023multidimensional,

title = {Multidimensional variability in ecological assessments predicts two clusters of suicidal patients},

author = {Pablo Bonilla-Escribano and David Ram\'{i}rez and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castrom\'{a}n},

year  = {2023},

date = {2023-01-01},

journal = {Scientific reports},

volume = {13},

number = {1},

pages = {3546},

publisher = {Nature Publishing Group UK London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SUKEI2023100657,

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 M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {https://www.sciencedirect.com/science/article/pii/S221478292300057X},

doi = {https://doi.org/10.1016/j.invent.2023.100657},

issn = {2214-7829},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Internet Interventions},

volume = {33},

pages = {100657},

abstract = {Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.},

keywords = {Attention models, Digital phenotyping, Ecological momentary assessment, In-situ patient monitoring, Time-series modelling, Transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1137/21M1431497,

title = {Polynomial Propagation of Moments in Stochastic Differential Equations},

author = {Alberto L\'{o}pez Yela and Joaqu\'{i}n M\'{i}guez},

url = {https://doi.org/10.1137/21M1431497},

doi = {10.1137/21M1431497},

year  = {2023},

date = {2023-01-01},

journal = {SIAM Journal on Applied Dynamical Systems},

volume = {22},

number = {2},

pages = {1153-1181},

abstract = {Abstract. We address the problem of approximating the moments of the solution, (boldsymbolX(t)) , of an It\^{o} stochastic differential equation (SDE) with drift and diffusion terms over a time grid (t_0, t_1, …, t_n) . In particular, we assume an explicit numerical scheme for the generation of sample paths (hatboldsymbolX(t_0), hatboldsymbolX(t_1), …, hatboldsymbolX(t_n), …) and then obtain recursive equations that yield any desired noncentral moment of (hatboldsymbolX(t_n)) as a function of the initial condition (hatboldsymbolX(t_0) = boldsymbolX_0) . The core of the methodology is the decomposition of the numerical solution (hatboldsymbolX(t_n)) into a “central part” and an “effective noise” term. The central term is computed deterministically from the ordinary differential equation (ODE) that results from eliminating the diffusion term in the SDE, while the effective noise accounts for the stochastic deviation from the numerical solution of the ODE. For simplicity, we describe the proposed methodology based on an Euler\textendashMaruyama integrator, but other explicit numerical schemes can be exploited in the same way. We also apply the moment approximations to construct estimates of the 1-dimensional marginal probability density functions of (hatboldsymbolX(t_n)) based on a Gram\textendashCharlier expansion. Both for the approximation of moments and 1-dimensional densities, we describe how to handle the cases in which the initial condition is fixed (i.e., (boldsymbolX_0 = boldsymbolx_0) for some deterministic and known (boldsymbolx_0) ) or random. In the latter case, we resort to polynomial chaos expansion (PCE) schemes in order to approximate the target moments. The methodology has been inspired by the PCE and differential algebra methods used for uncertainty propagation in astrodynamics problems. Hence, we illustrate its application for the quantification of uncertainty in a 2-dimensional Keplerian orbit perturbed by a Wiener noise process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cano2023catalog,

title = {Catalog-based atmosphere uncertainty quantification},

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

year  = {2023},

date = {2023-01-01},

journal = {The Journal of the Astronautical Sciences},

volume = {70},

number = {5},

pages = {42},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cano2023covariance,

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},

year  = {2023},

date = {2023-01-01},

journal = {Advances in Space Research},

volume = {72},

number = {7},

pages = {2759\textendash2777},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}