@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{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{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{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{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{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{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{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{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{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{\<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{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{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-Castrom\'{a}n 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},

urldate = {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 L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. 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},

urldate = {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{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{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}

}