@article{Ramirez,

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

author = {David Ram\'{i}rez and Antonio G. Marques and S. 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{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 = {Ryu, J. and S\"{u}kei, E. and Norbury, A. 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 P\'{e}rez-Rodr\'{i}guez, M. M.},

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/10.2196/24465,

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{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{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{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{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{9362154,

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 Gustavo Camps-Valls},

doi = {10.1109/TAES.2021.3061791},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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

pages = {1-1},

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{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{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{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{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 L Martino and M 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{math9070784,

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

author = {Luca Martino and Fernando Llorente and Ernesto 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{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{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{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{AArtes20c,

title = {Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study},

author = {Alejandro Porras-Segovia and Rosa Mar\'{i}a Molina-Madue\~{n}o and Sofian Berrouiguet and Jorge L\'{o}pez-Castroman and Maria Luisa Barrig\'{o}n and Mar\'{i}a Sandra P\'{e}rez-Rodr\'{i}guez and Jos\'{e} Heliodoro Marco and Isaac D\'{i}az-Oliv\'{a}n and Santiago de Le\'{o}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},

doi = {https://doi.org/10.1016/j.jad.2020.05.067},

year  = {2020},

date = {2020-05-26},

journal = {Journal of Affective Disorders},

volume = {274},

pages = {733-741},

keywords = {Ecological momentary assessment, Wearable devices},

pubstate = {published},

tppubtype = {article}

}

@article{Tobi20,

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

author = {Alejandro Lancho and Johan \"{O}stman and Giuseppe Durisi and Tobias Koch and Gonzalo Vazquez-Vilar },

doi = {10.1109/TWC.2020.2987573},

year  = {2020},

date = {2020-04-20},

journal = {IEEE Transactions on Wireless Communications},

volume = {19},

number = {7},

pages = {4831 - 4846},

keywords = {fading channels, finite-blocklength information theory, saddlepoint approximations, short packets, Ultra-reliable low-latency communications},

pubstate = {published},

tppubtype = {article}

}

@article{JMiguez20b,

title = {Stable Approximation Schemes for Optimal Filters},

author = {Dan Crisan and Alberto L\'{o}pez-Yela and Joaqu\'{i}n Miguez},

doi = {10.1137/19M1255410},

year  = {2020},

date = {2020-03-26},

journal = {SIAM/ASA Journal on Uncertainty Quantification},

volume = {8},

number = {1},

pages = {483-509},

keywords = {optimal filters, stability analysis, State space models, truncated filters},

pubstate = {published},

tppubtype = {article}

}

@article{Ram\'{i}rez2020b,

title = {Two-Channel Passive Detection of Cyclostationary Signals},

author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},

doi = {10.1109/TSP.2020.2981767},

issn = {1053-587X},

year  = {2020},

date = {2020-03-18},

journal = {IEEE Trans. Signal Process.},

volume = {68},

pages = {2340-2355},

keywords = {Cyclostationarity, generalized likelihood ratio test (GLRT), locally most powerful invariant test (LMPIT), multiple-input multiple-output (MIMO) passive detection},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20,

title = {Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia},

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

doi = {https://doi.org/10.1186/s12888-020-2431-x},

year  = {2020},

date = {2020-01-29},

journal = {BMC Psychiatry},

volume = {20},

number = {30},

keywords = {Ecological momentary assessment, Insight, Metacognitive training, Schizophrenia spectrum disorders},

pubstate = {published},

tppubtype = {article}

}

@article{2020A\&A...635A.206C,

title = {Is there Na I in the atmosphere of HD 209458b?. Effect of the centre-to-limb variation and Rossiter-McLaughlin effect in transmission spectroscopy studies},

author = {N Casasayas-Barris and E Pall\'{e} and F Yan and G Chen and R Luque and M Stangret and E Nagel and M Zechmeister and M Oshagh and J Sanz-Forcada and L Nortmann and F J Alonso-Floriano and P J Amado and J A Caballero and S Czesla and S Khalafinejad and M L\'{o}pez-Puertas and J L\'{o}pez-Santiago and K Molaverdikhani and D Montes and A Quirrenbach and A Reiners and I Ribas and A S\'{a}nchez-L\'{o}pez and M R Zapatero Osorio},

doi = {10.1051/0004-6361/201937221},

year  = {2020},

date = {2020-01-01},

journal = {Astronomy and Astrophysics},

volume = {635},

pages = {A206},

keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, methods: observational, planetary systems, planets and satellites: atmospheres, planets and satellites: individual: HD 209458b, techniques: spectroscopic},

pubstate = {published},

tppubtype = {article}

}

@article{Ram\'{i}rez2020,

title = {Multi-channel factor analysis with common and unique factors},

author = {David Ram\'{i}rez and Ignacio Santamar\'{i}a and L L Scharf and Steven Van Vaerenbergh},

doi = {10.1109/TSP.2019.2955829},

issn = {1053-587X},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Trans. Signal Process.},

volume = {68},

pages = {113-126},

keywords = {Block minorization-maximization algorithms, expectation-maximization algorithms, maximum likelihood estimation, multi-channel factor analysis, multiple-input multiple-output channels, passive radar},

pubstate = {published},

tppubtype = {article}

}

@article{SantamariaScharfRamirez-2020,

title = {Scale-invariant subspace detectors based on first- and second-order statistical models},

author = {Ignacio Santamar\'{i}a and L L Scharf and David Ram\'{i}rez},

doi = {10.1109/TSP.2020.3036725},

issn = {1053-587X},

year  = {2020},

date = {2020-01-01},

journal = {IEEE {T}rans. Signal Process.},

volume = {68},

pages = {6432-6443},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{norbury2020social,

title = {Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients},

author = {Agnes Norbury and Shelley Liu and Juan Jos\'{e} Campa\~{n}a-Montes and Lorena Romero-Medrano and Mar\'{i}a Luisa Barrig\'{o}n and Emma Smith and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez},

year  = {2020},

date = {2020-01-01},

journal = {Molecular psychiatry},

pages = {1--11},

publisher = {Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nazabal2020handling,

title = {Handling incomplete heterogeneous data using vaes},

author = {Alfredo Nazabal and Pablo M Olmos and Zoubin Ghahramani and Isabel Valera},

year  = {2020},

date = {2020-01-01},

journal = {Pattern Recognition},

volume = {107},

pages = {107501},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9260147,

title = {Adaptive Quadrature Schemes for Bayesian Inference via Active Learning},

author = {Fernando Llorente Fern\'{a}ndez and Luca Martino and Victor Elvira and David Delgado and J L\'{o}pez-Santiago},

doi = {10.1109/ACCESS.2020.3038333},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {208462-208483},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8861117b,

title = {On Single-Antenna Rayleigh Block-Fading Channels at Finite Blocklength},

author = {Alejandro Lancho and Tobias Koch and Giuseppe Durisi},

doi = {10.1109/TIT.2019.2945782},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {66},

number = {1},

pages = {496-519},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ASHEGHAN202016457,

title = {On the Stability of a Stochastic Nonlinear Model of the Heart Beat Rate During a Treadmill Exercise},

author = {Mohammad Mostafa Asheghan and Bahram Shafai and Joaqu\'{i}n M\'{i}guez},

url = {https://www.sciencedirect.com/science/article/pii/S2405896320310545},

doi = {https://doi.org/10.1016/j.ifacol.2020.12.735},

issn = {2405-8963},

year  = {2020},

date = {2020-01-01},

journal = {IFAC-PapersOnLine},

volume = {53},

number = {2},

pages = {16457-16461},

abstract = {We investigate the stability properties of a nonlinear stochastic dynamical model of a person’s heart beat rate (HBR) during a treadmill exercise. The analysis is based on the Lyapunov direct method and it is valid for systems with either known or unknown parameters. Specifically, we characterize an upper bound on the norm of the cumulative noise that holds in the presence of bounded errors in the model parameters and guarantees p-stability. Numerical simulations are presented that corroborate the theoretical results.},

note = {21st IFAC World Congress},

keywords = {nonlinear dynamcs, parameter perturbation, stability analysis, Stochastic processes, system biology},

pubstate = {published},

tppubtype = {article}

}

@article{article,

title = {Nudging the Particle Filter},

author = {\"{O}mer Akyıldız and Joaqu\'{i}n M\'{i}guez},

doi = {10.1007/s11222-019-09884-y},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Statistics and Computing},

volume = {30},

pages = {305-330},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez2020estimacion,

title = {Estimaci\'{o}n del desplazamiento horizontal del detector en un sistema de rayos X utilizando aprendizaje por transferencia},

author = {Carlos Fern\'{a}ndez del Cerro and RC Gimenez and Pablo M Olmos and Alessandro Piol and Manuel Desco Men\'{e}ndez and M\'{o}nica Abella Garc'ia},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

publisher = {Sociedad Espa~nola de Ingenier'ia Biom\'{e}dica},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes19d,

title = {Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea},

author = {Fernando Moreno-Pino and Alejandro Porras-Segovia and Pilar L\'{o}pez-Esteban and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},

doi = {10.5664/jcsm.8032},

year  = {2019},

date = {2019-11-15},

journal = {Journal of Clinical Sleep Medicine},

volume = {15},

number = {11},

pages = {1645-1653},

keywords = {e-health, sleep apnea, Sleep disorders, Wearables},

pubstate = {published},

tppubtype = {article}

}

@article{Bonilla-Escribano2019,

title = {Assessment of e-social activity in psychiatric patients},

author = {P Bonilla-Escribano and David Ram\'{i}rez and Alba Sedano-Capdevila and Juan Jose Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.1109/JBHI.2019.2918687},

issn = {2168-2194},

year  = {2019},

date = {2019-11-01},

journal = {IEEE J. Biomedical and Health Informatics},

volume = {23},

number = {6},

pages = {2247-2256},

keywords = {E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes},

pubstate = {published},

tppubtype = {article}

}

@article{Geiger-TIT2019a,

title = {On the Information Dimension of Stochastic Processes},

author = {Bernhard C Geiger and Tobias Koch},

issn = {0018-9448},

year  = {2019},

date = {2019-10-01},

journal = {IEEE Transactions on Information Theory},

volume = {65},

number = {10},

pages = {6496-6518},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FPerez18,

title = {Probabilistic Time of Arrival Localization},

author = {Fernando P\'{e}rez-Cruz and Pablo M Olmos and Michael Minyi Zhang and Howard Huang},

doi = {10.1109/LSP.2019.2944005},

year  = {2019},

date = {2019-09-26},

journal = {IEEE Signal Processing Letters},

volume = {26},

number = {11},

pages = {1683 - 1687},

keywords = {Probabilistic modeling, Time of arrival localization},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes19c,

title = {Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol},

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

doi = {https://doi.org/10.1186/s12888-019-2260-y},

year  = {2019},

date = {2019-09-07},

journal = {BMC Psychiatry},

volume = {19},

number = {277},

keywords = {Data Mining, sensors, Smartphone, Suicide, Wearables},

pubstate = {published},

tppubtype = {article}

}

@article{gvazquez-tit19,

title = {The Error Probability of Generalized Perfect Codes via the Meta-Converse},

author = {Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Sergio Verd\'{u}},

doi = {10.1109/TIT.2019.2906227},

issn = {0018-9448},

year  = {2019},

date = {2019-09-01},

journal = {IEEE Transactions on Information Theory},

volume = {65},

number = {9},

pages = {5705-5717},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{liu-TIT2019a,

title = {A Probabilistic Peeling Decoder to Efficiently Analyze Generalized LDPC Codes Over the BEC},

author = {Yanfang Liu and Pablo M Olmos and Tobias Koch},

issn = {0018-9448},

year  = {2019},

date = {2019-08-01},

journal = {IEEE Transactions on Information Theory},

volume = {65},

number = {8},

pages = {4831-4853},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JMiguez20,

title = {Nudging the particle Filter},

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

doi = {https://doi.org/10.1007/s11222-019-09884-y},

year  = {2019},

date = {2019-07-13},

journal = {Statistics and Computing},

volume = {30},

pages = {305-330},

keywords = {approximation errors, Data assimilation, Model errors, Nudging, Particle filtering, Robust filtering},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes19b,

title = {Onset of schizophrenia diagnoses in a large clinical cohort},

author = {Jorge L\'{o}pez-Castrom\'{a}n and Jos\'{e} M Leiva-Murillo and Fanny Cegla-Schvartzman and Hilario Blasco-Fontecilla and R Garc\'{i}a-Nieto and Antonio Art\'{e}s-Rodr\'{i}guez and C Morant-Ginestar and Philippe Courtet and Carlos Blanco and Fuensanta Aroca and Enrique Baca-Garc\'{i}a},

doi = {https://doi.org/10.1038/s41598-019-46109-8},

year  = {2019},

date = {2019-07-08},

journal = {Scientific Reports},

volume = {9},

number = {9865},

keywords = {Schizoprenia diagnosis},

pubstate = {published},

tppubtype = {article}

}