@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},

urldate = {2022-01-01},

journal = {BMJ open},

volume = {12},

number = {9},

pages = {e051807},

publisher = {British Medical Journal Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{porras2022six,

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},

keywords = {},

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}

}

@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}

}

@article{Martino_2021,

title = {Compressed Particle Methods for Expensive Models With Application in Astronomy and Remote Sensing},

author = {Luca Martino and Victor Elvira and J L\'{o}pez-Santiago and Gustau Camps-Valls},

url = {https://doi.org/10.1109%2Ftaes.2021.3061791},

doi = {10.1109/taes.2021.3061791},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {IEEE Transactions on Aerospace and Electronic Systems},

volume = {57},

number = {5},

pages = {2607-2621},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/30833,

title = {Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning--Based Ecological Momentary Assessment Study},

author = {J. Ryu and Emese S\"{u}kei and Agnes Norbury and H. Liu, S. and Juan Jos\'{e} Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez},

url = {http://www.ncbi.nlm.nih.gov/pubmed/34524091},

doi = {10.2196/30833},

issn = {2368-7959},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

journal = {JMIR Ment Health},

volume = {8},

number = {9},

pages = {e30833},

abstract = {Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning--based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F},

keywords = {\"{a}nxiety disorder; COVID-19; social media; public health; digital phenotype; ecological momentary assessment; smartphone; machine learning; hidden Markov model\"},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/26548,

title = {Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study},

author = {Javier-David Lopez-Morinigo and Mar\'{i}a Luisa Barrig\'{o}n and Alejandro Porras-Segovia and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and S. S\'{a}nchez-Alonso and L. Mata-Iturralde and Laura Mu\~{n}oz Lorenzo and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a},

url = {http://www.ncbi.nlm.nih.gov/pubmed/34309576},

doi = {10.2196/26548},

issn = {1438-8871},

year  = {2021},

date = {2021-07-26},

urldate = {2021-07-26},

journal = {J Med Internet Res},

volume = {23},

number = {7},

pages = {e26548},

abstract = {Background: Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients' behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability. Objective: The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance. Methods: The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on ``being user versus nonuser'' (acceptability), which was the main outcome measure. Results: Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability. Conclusions: Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment. Trial Registration: ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347},

keywords = {ecological momentary assessment; acceptability; schizophrenia spectrum disorders; eB2; digital tools; mental health; schizophrenia; real-time data; patients; digital health; internet; mobile apps},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/24465,

title = {Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach},

author = {Emese S\"{u}kei and Agnes Norbury and Mercedes M Perez-Rodriguez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.ncbi.nlm.nih.gov/pubmed/33749612},

doi = {10.2196/24465},

issn = {2291-5222},

year  = {2021},

date = {2021-03-22},

journal = {JMIR Mhealth Uhealth},

volume = {9},

number = {3},

pages = {e24465},

abstract = {Background: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning--based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.},

keywords = {mental health; affect; mobile health; mobile phone; digital phenotype; machine learning; Bayesian analysis; probabilistic models; personalized models},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/17116,

title = {Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study},

author = {Jorge Lopez-Castroman and Diana Abad-Tortosa and Aurora Cobo Aguilera and Philippe Courtet and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},

url = {http://www.ncbi.nlm.nih.gov/pubmed/33470943},

doi = {10.2196/17116},

issn = {2368-7959},

year  = {2021},

date = {2021-01-20},

journal = {JMIR Ment Health},

volume = {8},

number = {1},

pages = {e17116},

abstract = {Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.},

keywords = {mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping},

pubstate = {published},

tppubtype = {article}

}

@article{PORRASSEGOVIA2021,

title = {Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation},

author = {Alejandro Porras-Segovia and Aurora Cobo and Isaac D\'{i}az-Oliv\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Sofian Berrouiguet and Jorge Lopez-Castroman and Philippe Courtet and Maria Luisa Barrig\'{o}n and Mar\'{i}a A Oquendo and Enrique Baca-Garc\'{i}a},

url = {https://www.sciencedirect.com/science/article/pii/S0165032721001932},

doi = {https://doi.org/10.1016/j.jad.2021.02.059},

issn = {0165-0327},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Affective Disorders},

abstract = {Background 

: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings. 

Methods 

: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process. 

Results 

: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours). 

Conclusions 

: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.},

keywords = {Mhealth, Sleep, Smartphone, Suicide, Suicide attempt, Suicide ideation},

pubstate = {published},

tppubtype = {article}

}

@article{math9070784,

title = {Automatic Tempered Posterior Distributions for Bayesian Inversion Problems},

author = {Luca Martino and Fernando Llorente and E. Curbelo and J L\'{o}pez-Santiago and Joaqu\'{i}n M\'{i}guez},

url = {https://www.mdpi.com/2227-7390/9/7/784},

doi = {10.3390/math9070784},

issn = {2227-7390},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Mathematics},

volume = {9},

number = {7},

abstract = {We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure with alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the current estimate of the noise power. A complete Bayesian study over the model parameters and the scale parameter can also be performed. Numerical experiments show the benefits of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/mnras/stab2303,

title = {A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power},

author = {J L\'{o}pez-Santiago and Luca Martino and Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},

url = {https://doi.org/10.1093/mnras/stab2303},

doi = {10.1093/mnras/stab2303},

issn = {0035-8711},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Monthly Notices of the Royal Astronomical Society},

volume = {507},

number = {3},

pages = {3351-3361},

abstract = {Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights – while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event that includes a damped oscillation and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelization, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9438636,

title = {Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled Lines},

author = {Jos\'{e} Carlos Aradillas and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos},

doi = {10.1109/ACCESS.2021.3082689},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {76674-76688},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9398939,

title = {Spatially Coupled Generalized LDPC Codes: Asymptotic Analysis and Finite Length Scaling},

author = {David G M Mitchell and Pablo M Olmos and Michael Lentmaier and Daniel J Costello},

doi = {10.1109/TIT.2021.3071743},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {67},

number = {6},

pages = {3708-3723},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SEVILLASALCEDO2021108141,

title = {Sparse semi-supervised heterogeneous interbattery bayesian analysis},

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

url = {https://www.sciencedirect.com/science/article/pii/S0031320321003289},

doi = {https://doi.org/10.1016/j.patcog.2021.108141},

issn = {0031-3203},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Pattern Recognition},

volume = {120},

pages = {108141},

abstract = {The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.},

keywords = {Bayesian model, Canonical correlation analysis, Factor analysis, Feature selection, Multi-task, Principal component analysis, Semi-supervised},

pubstate = {published},

tppubtype = {article}

}

@article{moreno2021modular,

title = {Modular Gaussian Processes for Transfer Learning},

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

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Advances in Neural Information Processing Systems},

volume = {34},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9594658,

title = {Medical data wrangling with sequential variational autoencoders},

author = {Daniel Barrejon and Pablo M Olmos and Antonio Artes-Rodr\'{i}guez},

doi = {10.1109/JBHI.2021.3123839},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PEREZVIEITES2021108295,

title = {Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models},

author = {Sara P\'{e}rez-Vieites and Joaqu\'{i}n M\'{i}guez},

url = {https://www.sciencedirect.com/science/article/pii/S0165168421003327},

doi = {https://doi.org/10.1016/j.sigpro.2021.108295},

issn = {0165-1684},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Signal Processing},

volume = {189},

pages = {108295},

abstract = {We introduce a new sequential methodology to calibrate the fixed parameters and track the stochastic dynamical variables of a state-space system. The proposed method is based on the nested hybrid filtering (NHF) framework of [1], that combines two layers of filters, one inside the other, to compute the joint posterior probability distribution of the static parameters and the state variables. In particular, we explore the use of deterministic sampling techniques for Gaussian approximation in the first layer of the algorithm, instead of the Monte Carlo methods employed in the original procedure. The resulting scheme reduces the computational cost and so makes the algorithms potentially better-suited for high-dimensional state and parameter spaces. We describe a specific instance of the new method and then study its performance and efficiency of the resulting algorithms for a stochastic Lorenz 63 model and for a stochastic volatility model with real data.},

keywords = {Bayesian inference, Filtering, Kalman, Monte Carlo, Parameter estimation},

pubstate = {published},

tppubtype = {article}

}

@article{9665783,

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

author = {Jithin Ravi and Tobias Koch},

doi = {10.1109/TIT.2021.3139430},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Information Theory},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{castellanos2021position,

title = {Position-Based Adaptive Power Back-Off for User Electromagnetic Exposure Management in 

Millimeter Wave Systems},

author = {Miguel R Castellanos and Borja Peleato and David J Love},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Wireless Communications Letters},

volume = {11},

number = {1},

pages = {86--90},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kadirvelu2021circuit,

title = {A Circuit for Simultaneous Reception of Data and Power Using a Solar Cell},

author = {Sindhubala Kadirvelu and Walter D Leon-Salas and Xiaozhe Fan and Jongseok Kim and Borja Peleato and Saeed Mohammadi and B Vijayalakshmi},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Green Communications and Networking},

volume = {5},

number = {4},

pages = {2065--2075},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gvazquez-TIT2021,

title = {Error Probability Bounds for Gaussian Channels Under Maximal and Average Power Constraints},

author = {Gonzalo Vazquez-Vilar},

doi = {10.1109/TIT.2021.3063311},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {67},

number = {6},

pages = {3965-3985},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vazquez2021interpretable,

title = {An interpretable machine learning method for the detection of schizophrenia using EEG signals},

author = {Manuel A V\'{a}zquez and Arash Maghsoudi and In\'{e}s P Mari no},

year  = {2021},

date = {2021-01-01},

journal = {Frontiers in Systems Neuroscience},

volume = {15},

publisher = {Frontiers Media SA},

keywords = {yo},

pubstate = {published},

tppubtype = {article}

}

@article{articlec,

title = {On the performance of particle filters with adaptive number of particles},

author = {Victor Elvira and Joaqu\'{i}n M\'{i}guez and Petar M Djuric},

doi = {10.1007/s11222-021-10056-0},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Statistics and Computing},

volume = {31},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cobo_porras-segovia_p\'{e}rez-rodr\'{i}guez_art\'{e}s-rodr\'{i}guez_barrig\'{o}n_courtet_baca-garc\'{i}a_2021,

title = {Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study},

author = {Aurora Cobo and Alejandro Porras-Segovia and Mercedes M Perez-Rodriguez and Antonio Art\'{e}s-Rodr\'{i}guez and Maria Luisa Barrig\'{o}n and Philippe Courtet and Enrique Baca-Garc\'{i}a},

doi = {10.1192/bjo.2021.43},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {BJPsych Open},

volume = {7},

number = {3},

pages = {e82},

publisher = {Cambridge University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lopez-morinigo_luisa_porra_art\'{e}s-rodr\'{i}guez_etal._2021,

title = {Pending challenges to e-mental health in the COVID-19 era: Acceptability of a smartphone-based ecological momentary assessment application among patients with schizophrenia spectrum disorders},

author = {Javier-David Lopez-Morinigo and B. -E. Maria Luisa and Alejandro Porras-Segovia and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.1192/j.eurpsy.2021.920},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {European Psychiatry},

volume = {64},

number = {S1},

pages = {S343\textendashS343},

publisher = {Cambridge University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rust_xydis_heutschi_perraudin_casas_du_strauss_eggenschwiler_perez-cruz_gramazio_etal._2021,

title = {A data acquisition setup for data driven acoustic design},

author = {Romana Rust and Achilleas Xydis and Kurt Heutschi and Nathanael Perraudin and Gonzalo Casas and Chaoyu Du and J\"{u}rgen Strauss and Kurt Eggenschwiler and Fernando Perez-Cruz and Fabio Gramazio and Matthias Kohler},

doi = {10.1177/1351010X20986901},

year  = {2021},

date = {2021-01-01},

journal = {Building Acoustics},

volume = {28},

number = {4},

pages = {345-360},

publisher = {Sage},

note = {1. sco. 2. Yes. 3. No. 4. . 5. . 6. Yes. 7. . 8. Published. 9. . 10. .},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{\<LineBreak\>	10261_258325,

title = {Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition},

author = {Sebastian Landwehr and Michele Volpi and Alexander Haumann and Charlotte Mary Robinson and Iris Thurnherr and Valerio Ferracci and Andrea Baccarini and Jenny Thomas and Irina V. Gorodetskaya and Christian Tatzelt and Silvia Henning and Robin L. Modini and Heather J. Forrer and Yajuan Lin and Nicolas Cassar and Rafel Sim\'{o} and Christel S. Hassler and Alireza Moallemi and Sarah E. Fawcett and Neil R. P. Harris and Ruth Airs and Marzieh H. Derkani and Alberto Alberello and Alessandro Toffoli and G Chen and P. Rodr\'{i}guez-Ros and Marina Zamanillo Campos and Pau Cortes and Lei Xue and Conor G. Bolas and Katherine C. Leonard and Fernando Perez-Cruz and David Walton and Julia Schmale},

doi = {10.5194/esd-12-1295-2021},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

organization = {Rafel Sim\'{o}, Marina Zamanillo, Pau Cort\'{e}s-Greus, and Pablo Rodr\'{i}guez-Ros were supported by the Spanish Ministry of Science through the BIOGAPS project (CTM2016-81008-R) organization =With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)},

abstract = {The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean\textendashatmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info12030121,

title = {A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators},

author = {Sichen Li and M\'{e}lissa Zacharias and Jochem Snuverink and Jaime Coello de Portugal and Fernando Perez-Cruz and Davide Reggiani and Andreas Adelmann},

url = {https://www.mdpi.com/2078-2489/12/3/121},

doi = {10.3390/info12030121},

issn = {2078-2489},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Information},

volume = {12},

number = {3},

abstract = {The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bonilla-EscribanoRamirezPorras-Segovia-2021-Assessmentofvariabilityinirregularly,

title = {Assessment of variability in irregularly sampled time series: Applications to mental healthcare},

author = {P. Bonilla-Escribano and D. Ram\'{i}rez and A. Porras-Segovia and A. Art\'{e}s-Rodr\'{i}guez},

doi = {10.3390/math9010071},

issn = {2227-7390},

year  = {2021},

date = {2021-01-01},

journal = {Mathematics (Special issue on Recent Advances in Data Science)},

volume = {9},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{akyildiz2021convergence,

title = {Convergence rates for optimised adaptive importance samplers},

author = {O. D. Akyildiz and Joaqu\'{i}n M\'{i}guez},

year  = {2021},

date = {2021-01-01},

journal = {Statistics and Computing},

volume = {31},

number = {2},

pages = {1--17},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bonilla-EscribanoRamirezPorras-Segovia-2021,

title = {Assessment of variability in irregularly sampled time series: Applications to mental healthcare},

author = {P Bonilla-Escribano and David Ram\'{i}rez and Alejandro Porras-Segovia and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.3390/math9010071},

issn = {2227-7390},

year  = {2021},

date = {2021-01-01},

journal = {Mathematics (Special issue on Recent Advances in {D}ata Science)},

volume = {9},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno-MunozRamirezArtes-Rodriguez-2021,

title = {Change-point detection in hierarchical circadian models},

author = {P Moreno-Mu\~{n}oz and David Ram\'{i}rez and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.1016/j.patcog.2021.107820},

issn = {0031-3203},

year  = {2021},

date = {2021-00-01},

journal = {Pattern Recognition},

volume = {113},

pages = {107820},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lopez_Santiago_2020,

title = {A Likely Magnetic Activity Cycle for the Exoplanet Host M Dwarf GJ 3512},

author = {J Lopez-Santiago and Luca Martino and Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},

url = {https://doi.org/10.3847/1538-3881/abc171},

doi = {10.3847/1538-3881/abc171},

year  = {2020},

date = {2020-11-01},

urldate = {2020-11-01},

journal = {The Astronomical Journal},

volume = {160},

number = {6},

pages = {273},

publisher = {American Astronomical Society},

abstract = {Current radial velocity data from specialized instruments contain a large amount of information that may pass unnoticed if their analysis is not accurate. The joint use of Bayesian inference tools and frequency analysis has been shown as effective in revealing exoplanets but they have been used less frequently to investigate stellar activity. We intend to use radial velocity data of the exoplanet host star GJ 3512 to investigate its magnetic activity. Our study includes the analysis of the photometric data available. The main objectives of our work are to constrain the orbital parameters of the exoplanets in the system, to determine the current level of activity of the star and to derive an activity cycle length for it. An adaptive importance sampling method was used to determine the parameters of the exoplanets orbit. Generalized Lomb\textendashScargle periodograms were constructed with both radial velocity curve and photometric data. A careful analysis of the harmonic frequencies was conducted in each periodogram. Our fit to multiple Keplerian orbits constrained the orbital parameters of two giant gas planets orbiting the star GJ 3512. The host star showed an increase of its magnetic activity during the last observing campaign. The accurate fit of the radial velocity curve data to the multi-Keplerian orbit permitted to reveal the star rotation in the residuals of the best fit and estimate an activity cycle length of ∼14 yr.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20h,

title = {Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge},

author = {Ignacio Peis and Javier-David L\'{o}pez-Mor\'{i}\~{n}igo and Mercedes M Perez-Rodriguez and Maria Luisa Barrig\'{o}n and Marta Ruiz-G\'{o}mez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a },

doi = {https://doi.org/10.1038/s41598-020-74425-x},

year  = {2020},

date = {2020-10-14},

journal = {Scientific Reports},

volume = {10},

number = {17286},

keywords = {Actigraphic recording},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20e,

title = {A Probabilistic Patient Scheduling Model with Time Variable Slots},

author = {Danae Carreras-Garc\'{i}a and David Delgado-G\'{o}mez and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {https://doi.org/10.1155/2020/9727096},

year  = {2020},

date = {2020-09-01},

journal = {Computational and Mathematical Methods in Medicine},

volume = {2020},

number = {9727096},

pages = {10},

keywords = {e-health, patient scheduling systems, prediction theory},

pubstate = {published},

tppubtype = {article}

}

@article{Tobi20b,

title = {Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels},

author = {Taufiq A Asyhari and Tobias Koch and Albert Guill\'{e}n i F\`{a}bregas},

doi = {https://doi.org/10.3390/e22090971},

year  = {2020},

date = {2020-08-31},

journal = {Entropy},

volume = {22},

number = {9},

pages = {971},

keywords = {achievable rates, Fading, high signal-to-noise ratio (SNR), mismatched decoding, multiple antennas, multiple-access channels, nearest neighbor decoding, noncoherent, pilot-aided channel estimation},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20b,

title = {Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior},

author = {Patricia Carretero and Juan Jos\'{e} Campa\~{n}a-Montes and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {https://doi.org/10.1007/7854_2020_170},

year  = {2020},

date = {2020-08-15},

journal = {Current Topics in Behavioral Neurosciences},

keywords = {Big data, Digital footprint, Digital phenotype, e-health, Ecological momentary assessment, Machine learning, Mobile health, Suicidal risk, Wearable devices},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20d,

title = {Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms},

author = {Gonzalo R\'{i}os-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fern\'{a}ndez-Avil\'{e}s and \'{A}ngel Arenal},

doi = {https://doi.org/10.3389/fbioe.2020.00789},

year  = {2020},

date = {2020-07-30},

journal = {Frontiers in Bioengineering and Biotechnology},

volume = {8},

number = {789},

keywords = {atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms},

pubstate = {published},

tppubtype = {article}

}

@article{JMiguez20c,

title = {Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization},

author = {O. D. Akyildiz and Dan Crisan and Joaqu\'{i}n Miguez},

doi = {https://doi.org/10.1007/s11222-020-09964-4},

year  = {2020},

date = {2020-07-29},

journal = {Statistics and Computing},

keywords = {Gradient-free optimization, Nonconvex optimization, Sampling, Sequential Monte Carlo, Stochastic optimization},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20f,

title = {Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey},

author = {Pavel E Arenas-Casta\~{n}eda and Fuensanta Aroca and Ismael Martinez-Nicolas and Luis A Castillo Esp\'{i}ndola and Igor Barahona and Cynthya Maya-Hern\'{a}ndez and Martha Miriam Lavana Hern\'{a}ndez and Paulo C\'{e}sar Manrique Mir\'{o}n and Daniela Guadalupe Alvarado Barrera and Erik Trevi\~{n}o Aguilar and Axay\'{a}catl Barrios N\'{u}\~{n}ez and Giovanna De Jesus Carlos and Anabel Vildosola Garc\'{e}s and Josselyne Flores Mercado and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Santiago de Leon and Cristian Antonio Molina-Pizarro and Arsenio Rosado Franco and Mercedes M Perez-Rodriguez and Philippe Courtet and Gonzalo Mart\'{i}nez-Al\'{e}s and Enrique Baca-Garc\'{i}a},

doi = {10.1136/bmjopen-2019-035041},

year  = {2020},

date = {2020-07-19},

journal = {BMJ Open 2020},

volume = {10},

number = {e035041},

keywords = {Mental Health, Smartphone, Suicidal behavior},

pubstate = {published},

tppubtype = {article}

}

@article{2020AMT....13.3815M,

title = {Integration and calibration of non-dispersive infrared (NDIR) CO$_2$ low-cost sensors and their operation in a sensor network covering Switzerland},

author = {Michael M\"{u}ller and Peter Graf and Jonas Meyer and Anastasia Pentina and Dominik Brunner and Fernando Perez-Cruz and Christoph H\"{u}glin and Lukas Emmenegger},

doi = {10.5194/amt-13-3815-2020},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {Atmospheric Measurement Techniques},

volume = {13},

number = {7},

pages = {3815-3834},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lancho_2020b,

title = {Saddlepoint Approximations for Short-Packet Wireless Communications},

author = {Alejandro Lancho and Johan Ostman and Giuseppe Durisi and Tobias Koch and Gonzalo Vazquez-Vilar},

url = {http://dx.doi.org/10.1109/TWC.2020.2987573},

doi = {10.1109/twc.2020.2987573},

issn = {1558-2248},

year  = {2020},

date = {2020-07-01},

journal = {IEEE Transactions on Wireless Communications},

volume = {19},

number = {7},

pages = {4831\textendash4846},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}