Structure Learning in Deep Multi-Task Models

Multi-Task Learning (MTL) aims at improving the learning process by solving different tasks simultaneously. Two general approaches for neural MTL are hard and soft information sharing during training. Here we propose two new approaches to neural MTL. The first one uses a common model to enforce a soft sharing learning of the tasks considered. The second one adds a graph Laplacian term to a hard sharing neural model with the goal of detecting existing but a priori unknown task relations. We will test both tasks on real and synthetic datasets and show that either one can improve on other MTL neural models.The authors acknowledge support from the European Regional Development Fund and the Spanish State Research Agency of the Ministry of Economy, Industry, and Competitiveness under the project PID2019-106827GB-I00. They also thank the UAM–ADIC Chair for Data Science and Machine Learning and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM

Sparse methods for wind energy prediction

© 2012 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.International Joint Conference on Neural Networks (IJCNN), celebrado en 2012 en Brisbane, QLD, AustraliaIn this work we will analyze and apply to the prediction of wind energy some of the best known regularized linear regression algorithms, such as Ordinary Least Squares, Ridge Regression and, particularly, Lasso, Group Lasso and Elastic-Net that also seek to impose a certain degree of sparseness on the final models. To achieve this goal, some of them introduce a non-differentiable regularization term that requires special techniques to solve the corresponding optimization problem that will yield the final model. Proximal Algorithms have been recently introduced precisely to handle this kind of optimization problems, and so we will briefly review how to apply them in regularized linear regression. Moreover, the proximal method FISTA will be used when applying the non-differentiable models to the problem of predicting the global wind energy production in Spain, using as inputs numerical weather forecasts for the entire Iberian peninsula. Our results show how some of the studied sparsity-inducing models are able to produce a coherent selection of features, attaining similar performance to a baseline model using expert information, while making use of less data features.The authors of the paper acknowledge partial support from grant TIN2010-21575-C02-01 of the TIN Subprogram from Spain’s MICINN and of the C´atedra UAM-IIC en Modelado y Predicci´on. The first author is also supported by the FPU– MEC grant AP2008-00167. We also thank Red E´ectrica de Espa˜na, Spain’s TSO, for providing historic wind energy dat

Sparse Linear Wind Farm Energy Forecast

In this work we will apply sparse linear regression methods to forecast wind farm energy production using numerical weather prediction (NWP) features over several pressure levels, a problem where pattern dimension can become very large. We shall place sparse regression in the context of proximal optimization, which we shall briefly review, and we shall show how sparse methods outperform other models while at the same time shedding light on the most relevant NWP features and on their predictive structure.With partial support from grant TIN2010-21575-C02-01 of Spain's Ministerio de Econom a y Competitividad and the UAM{ADIC Chair for Machine Learning in Modelling and Prediction. The rst author is supported by the FPU{MEC grant AP2008-00167. We thank our colleague Alvaro Barbero for the software used in this work

Enforcing Group Structure through the Group Fused Lasso

We introduce the Group Total Variation (GTV) regularizer, a modification of Total Variation that uses the 2,1 norm instead of the 1 one to deal with multidimensional features. When used as the only regularizer, GTV can be applied jointly with iterative convex optimization algorithms such as FISTA. This requires to compute its proximal operator which we derive using a dual formulation. GTV can also be combined with a Group Lasso (GL) regularizer, leading to what we call Group Fused Lasso (GFL) whose proximal operator can now be computed combining the GTV and GL proximals through proximal Dykstra algorithm. We will illustrate how to apply GFL in strongly structured but ill-posed regression problems as well as the use of GTV to denoise colour images.Acknowledgements With partial support from Spain’s grant TIN2010-21575-C02-01 and the UAM–ADIC Chair for Machine Learning

Faster SVM training via conjugate SMO

We propose an improved version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure. This new approach only involves a modest increase on the com- putational cost of each iteration but, in turn, usually results in a substantial decrease in the number of iterations required to converge to a given precision. Besides, we prove convergence of the iterates of this new Conjugate SMO as well as a linear rate when the kernel matrix is positive definite. We have im- plemented Conjugate SMO within the LIBSVM library and show experimentally that it is faster for many hyper-parameter configurations, being often a better option than second order SMO when performing a grid-search for SVM tuning

Pooled-DNA sequencing identifies novel causative variants in PSEN1, GRN and MAPT in a clinical early-onset and familial Alzheimer’s disease Ibero-American cohort

INTRODUCTION: Some familial Alzheimer's disease (AD) cases are caused by rare and highly-penetrant mutations in APP, PSEN1, and PSEN2. Mutations in GRN and MAPT, two genes associated with frontotemporal dementia (FTD), have been found in clinically diagnosed AD cases. Due to the dramatic developments in next-generation sequencing (NGS), high-throughput sequencing of targeted genomic regions of the human genome in many individuals in a single run is now cheap and feasible. Recent findings favor the rare variant-common disease hypothesis by which the combination effects of rare variants could explain a large proportion of the heritability. We utilized NGS to identify rare and pathogenic variants in APP, PSEN1, PSEN2, GRN, and MAPT in an Ibero-American cohort. METHODS: We performed pooled-DNA sequencing of each exon and flanking sequences in APP, PSEN1, PSEN2, MAPT and GRN in 167 clinical and 5 autopsy-confirmed AD cases (15 familial early-onset, 136 sporadic early-onset and 16 familial late-onset) from Spain and Uruguay using NGS. Follow-up genotyping was used to validate variants. After genotyping additional controls, we performed segregation and functional analyses to determine the pathogenicity of validated variants. RESULTS: We identified a novel G to T transition (g.38816G>T) in exon 6 of PSEN1 in a sporadic early-onset AD case, resulting in a previously described pathogenic p.L173F mutation. A pathogenic p.L392V mutation in exon 11 was found in one familial early-onset AD case. We also identified a novel CC insertion (g.10974_10975insCC) in exon 8 of GRN, which introduced a premature stop codon, resulting in nonsense-mediated mRNA decay. This GRN mutation was associated with lower GRN plasma levels, as previously reported for other GRN pathogenic mutations. We found two variants in MAPT (p.A152T, p.S318L) present only in three AD cases but not controls, suggesting that these variants could be risk factors for the disease. CONCLUSIONS: We found pathogenic mutations in PSEN1, GRN and MAPT in 2.33% of the screened cases. This study suggests that pathogenic mutations or risk variants in MAPT and in GRN are as frequent in clinical AD cases as mutations in APP, PSEN1 and PSEN2, highlighting that pleiotropy of MAPT or GRN mutations can influence both FTD and AD phenotypic traits

España. Mapas militares. (1902)

Sumario: Comprende el mapa general de España que se levantó con motivo de la división del territorio en zonas militaresCopia Digital. Real Academia de la Historia : 2010Forma de ingreso: Legado. Fuente de ingreso: Gonzalo Menéndez-Pidal y Goyri (Madrid). Fecha de ingreso: 29 de febrero de 2003Márgenes graduados. Dibujados meridianos y paralelos formando cuadrícula. Relieve por sombreado. Abundante toponimia. Vértices geodésicos y redes de comunicaciones. Bajo el título relación de signos convencionales y nota: Para no romper la unidad geográfica de la Península se ha hecho una ligera indicación de Portugal, tomando lo indispensable de los trabajos portuguesesCarlos Ibáñez e Ibáñez de Ibero, marqués de Mulhacén (Barcelona, 1825 - Niza, 1891). Militar y fundador de la moderna geodesia española, presidió la Comisión Internacional de Pesos y Medidas (1872 a 1891). Siendo director del Instituto Geográfico y Estadístico, inició la publicación del mapa de España a escala 1:50.000Publicado en 1884 y reproducido en 1902Ejemplar entelad

Post-mortem findings in Spanish patients with COVID-19; a special focus on superinfections

IntroductionWhole-body autopsies may be crucial to understand coronavirus disease 2019 (COVID-19) pathophysiology. We aimed to analyze pathological findings in a large series of full-body autopsies, with a special focus on superinfections. MethodsThis was a prospective multicenter study that included 70 COVID-19 autopsies performed between April 2020 and February 2021. Epidemiological, clinical and pathological information was collected using a standardized case report form. ResultsMedian (IQR) age was 70 (range 63.75-74.25) years and 76% of cases were males. Most patients (90%,) had at least one comorbidity prior to COVID-19 diagnosis, with vascular risk factors being the most frequent. Infectious complications were developed by 65.71% of the patients during their follow-up. Mechanical ventilation was required in most patients (75.71%) and was mainly invasive. In multivariate analyses, length of hospital stay and invasive mechanical ventilation were significantly associated with infections (p = 0.036 and p = 0.013, respectively). Necropsy findings revealed diffuse alveolar damage in the lungs, left ventricular hypertrophy in the heart, liver steatosis and pre-infection arteriosclerosis in the heart and kidneys. ConclusionOur study confirms the main necropsy histopathological findings attributed to COVID-19 in a large patient series, while underlining the importance of both comorbid conditions and superinfections in the pathology

Socio-Demographic Health Determinants Are Associated with Poor Prognosis in Spanish Patients Hospitalized with COVID-19

Introduction Social vulnerability is a known determinant of health in respiratory diseases. Our aim was to identify whether there are socio-demographic factors among COVID-19 patients hospitalized in Spain and their potential impact on health outcomes during the hospitalization. Methods A multicentric retrospective case series study based on administrative databases that included all COVID-19 cases admitted in 19 Spanish hospitals from 1 March to 15 April 2020. Socio-demographic data were collected. Outcomes were critical care admission and in-hospital mortality. Results We included 10,110 COVID-19 patients admitted to 18 Spanish hospitals (median age 68 (IQR 54–80) years old; 44.5% female; 14.8% were not born in Spain). Among these, 779 (7.7%) cases were admitted to critical care units and 1678 (16.6%) patients died during the hospitalization. Age, male gender, being immigrant, and low hospital saturation were independently associated with being admitted to an intensive care unit. Age, male gender, being immigrant, percentile of average per capita income, and hospital experience were independently associated with in-hospital mortality. Conclusions Social determinants such as residence in low-income areas and being born in Latin American countries were associated with increased odds of being admitted to an intensive care unit and of in-hospital mortality. There was considerable variation in outcomes between different Spanish centers.JPA is under contract within the Ramón y Cajal Program (RYC-2016-20155, Ministerio de Economía, Industria y Competitividad, Spain). Investigators of Spanish Social-Environmental COVID-19 Register: Steering Committee: F. Javier Martín-Sánchez, Adrián Valls Carbó, Carmen Martínez Valero, Juan de D. Miranda, Juan Pedro Arrebola, Marta Esteban López, Annika Parviainen, Òscar Miró, Pere Llorens, Sònia Jiménez, Pascual Piñera, Guillermo Burillo, Alfonso Martín, Jorge García Lamberechts, Javier Jacob, Aitor Alquézar, Juan González del Castillo, Amanda López Picado and Iván Núñez. Participating centers: Oscar Miró y Sonia Jimenez. Hospital Clinic de Barcelona. José María Ferreras Amez. Hospital Clínico Universitario Lozano Blesa. Rafael Rubio Díaz. Complejo Hospitalario de Toledo. Julio Javier Gamazo del Rio. Hospital Universitario de Galdakao. Héctor Alonso. Hospital Universitario Miguel de Valdecilla. Pablo Herrero. Hospital Universitario Central de Asturias. Noemí Ruiz de Lobera. Hospital San Pedro de Logroño. Carlos Ibero. Complejo Hospitalario de Navarra. Plácido Mayan. Hospital Clínico Universitario de Santiago. Rosario Peinado. Complejo Hospitalario Universitario de Badajoz. Carmen Navarro Bustos. Hospital Universitario Virgen de la Macarena. Jesús Álvarez Manzanares. Hospital Universitario Rio Hortega. Francisco Román. Hospital Universitario General de Alicante. Pascual Piñera. Hospital Universitario Reina Sofia de Murcia. Guillermo Burillo. Hospital Universitario de Canarias de Tenerife. Javier Jacob. Hospital Universitario de Bellvitge. Carlos Bibiano. Hospital Universitario Infanta Leonor.Peer reviewe