Solving L1L_1-CTA in 3D tables by an interior-point method for primal block-angular problems

The purpose of the field of statistical disclosure control is to avoid that no confidential information can be derived from statistical data released by, mainly, national statistical agencies. Controlled tabular adjustment (CTA) is an emerging technique for the protection of statistical tabular data. Given a table to be protected, CTA looks for the closest safe table. In this work we focus on CTA for three-dimensional tables using the L1 norm for the distance between the original and protected tables. Three L1-CTA models are presented, giving rise to six different primal block-angular structures of the constraint matrices. The resulting linear programming problems are solved by a specialized interior-point algorithm for this constraints structure, which solves the normal equations by a combination of Cholesky factorization and preconditioned conjugate gradients (PCG). In the past this algorithm shown to be one of the most efficient approaches for some classes of block-angular problems. The effect of quadratic regularizations is also analyzed, showing that for three of the six primal block-angular structures the performance of PCG is guaranteed to improve. Computational results are reported for a set of large instances, which provide linear optimization problems of up to 50 millions of variables and 25 millions of constraints. The specialized interior-point algorithm is compared with the state-of-the-art barrier solver of the CPLEX 12.1 package, showing to be a more efficient choice for very large L1-CTA instances.Preprin

Quadratic regularizations in an interior-point method for primal block-angular problems

One of the most efficient interior-point methods for some classes of primal block-angular problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. Its efficiency depends on the spectral radius—in [0,1)— of a certain matrix in the definition of the preconditioner. Spectral radius close to 1 degrade the performance of the approach. The purpose of this work is twofold. First, to show that a separable quadratic regularization term in the objective reduces the spectral radius, significantly improving the overall performance in some classes of instances. Second, to consider a regularization term which decreases with the barrier function, thus with no need for an extra parameter. Computational experience with some primal block-angular problems confirms the efficiency of the regularized approach. In particular, for some difficult problems, the solution time is reduced by a factor of two to ten by the regularization term, outperforming state-of-the-art commercial solvers.Peer ReviewedPostprint (author’s final draft

Using Moodle platform for the skills-oriented evaluation in Vocational Training

[cat] Els autors expliquen en aquest article l'ús de la plataforma virtual Moodle, en el nivell educatiu de Formació Professional, com a instrument per a l'avaluació orientada a competències. Moodle s’utilitza generalment només com a suport a la docència. Els autors aporten una metodologia per avaluar competències, concretament els Resultats d’Aprenentatge (RA) curriculars, usant les possibilitats que ofereix Moodle, de forma que el professorat pugui implementar la seva programació curricular del mòdul, que inclou els RA, els criteris d’avaluació (CA) i els seus indicadors en cada tasca, directament en Moodle, i possibilitant que es generi automàticament un butlletí de qualificacions on es mostra la qualificació de cada RA. Els autors inclouen les seves experiències en les diferents assignatures i titulacions en les quals han impartit classe. De l'estudi i anàlisi es conclou que Moodle ofereix eines que han provat ser útils per a l’avaluació de les competències dels alumnes.[eng] In this article the authors explain the usage of the virtual platform Moodle as a tool for skillsoriented learning and evaluation. Moodle is generally used only as an on line teaching support, with little or no use of the platform skills-oriented learning capabilities. The authors provide a methodology to assess skills, particularly learning outcomes (named in the context of the authors’ Learning Outcomes, abbreviated, in Spanish, RA). They use the possibilities offered by Moodle, so that teachers can implement their curriculum module -that includes learning outcomes, the criteria assessment and also their indicators in each task-, directly into Moodle, allowing it to an automatically generate gradebook which shows the status of each Learning Outcome (RA). The authors include their experiences in different subjects and courses where they have applied these capabilities. The study and analysis concludes that Moodle offers tools that have proven to be useful for assessing the students’ skills

Genetic Study of SARS-CoV-2 Non Structural Protein 12 in COVID-19 Patients Non Responders to Remdesivir

Remdesivir (RDV) was the first antiviral drug approved by the FDA to treat severe coronavirus disease-2019 (COVID-19) patients. RDV inhibits SARS-CoV-2 replication by stalling the non structural protein 12 (nsp12) subunit of the RNA-dependent RNA polymerase (RdRp). No evidence of global widespread RDV-resistance mutations has been reported, however, defining genetic pathways to RDV resistance and determining emergent mutations prior and subsequent antiviral therapy in clinical settings is necessary. This study identified 57/149 (38.3%) patients who did not respond to one course (5-days) (n = 36/111, 32.4%) or prolonged (5 to 20 days) (n = 21/38, 55.3%) RDV therapy by subgenomic RNA detection. Genetic variants in the nsp12 gene were detected in 29/49 (59.2%) non responder patients by Illumina sequencing, including the de novo E83D mutation that emerged in an immunosuppressed patient after receiving 10 + 8 days of RDV, and the L838I detected at baseline and/or after prolonged RDV treatment in 9/49 (18.4%) non responder subjects. Although 3D protein modeling predicted no interference with RDV, the amino acid substitutions detected in the nsp12 involved changes on the electrostatic outer surface and in secondary structures that may alter antiviral response. It is important for health surveillance to study potential mutations associated with drug resistance as well as the benefit of RDV retreatment, especially in immunosuppressed patients and in those with persistent replication. IMPORTANCE This study provides clinical and microbiologic data of an extended population of hospitalized patients for COVID-19 pneumonia who experienced treatment failure, detected by the presence of subgenomic RNA (sgRNA). The genetic variants found in the nsp12 pharmacological target of RDV bring into focus the importance of monitoring emergent mutations, one of the objectives of the World Health Organization (WHO) for health surveillance. These mutations become even more crucial as RDV keeps being prescribed and new molecules are being repurposed for the treatment of COVID-19. The present article offers new perspectives for the clinical management of non responder patients treated and retreated with RDV and emphasizes the need of further research of the benefit of combinatorial therapies and RDV retreatment, especially in immunosuppressed patients with persistent replication after therapy.This work was financed by a Gilead Sciences grant (IN-ES-540-6089) and CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, España (CB21/13/00081). This work was financed by ad hoc patronage funds for research on COVID-19 from donations from citizens and organizations to the Hospital Clínic de Barcelona-Fundació Clínic per a la Recerca Biomèdica.S

The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III

The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg2 of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg2; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra. \ua9 2015. The American Astronomical Society

Contribucions als agorismes de punt interior en mètodes iteratius per a sistemes d'equacions usant regularitzacions quadràtiques

Els mètodes de punt interior per a programació lineal proporcionen algorismes de complexitat polinòmica, que els fa ser molt eficients en l’optimització a gran escala. Aquests algorismes utilitzen el mètode de Newton per a convertir les equacions d’òptim del problema, que són no lineals, en un sistema d’equacions lineals, que solen resoldre’s aplicant factorizacions de matrius esparses. En aquells casos particulars en els quals el problema té una estructura especial, com ara en els problemes d’optimització en xarxes multiarticle, es pot aprofitar per millorar l’eficiència de l’algorisme. Aquests problemes de xarxes pertanyen a la família més general de problemes primals bloc-angulars. El punt de partida d’aquesta tesi va ser un fet empíric: l’observació del millor comportament computacional d’un algorisme especialitzat de punt inferior per a problemes bloc-angulars quan en la funció objectiu figurem termes quadràtics. Aquest algorisme utilitza factoritzacions de matrius per resoldre la part de les equacions associades a la zarza i el mètode del gradient conjugat precondicional per resoldre les equacions asociadse a les restriccions d’acoblament. Llavors l’objectiu original va ser buscar alguna forma d’aproximar un problema lineal per un quadràtic de manera que s’explotés el fet experimental observat sense perjudicar la convergència del problema. Posteriorment el plantejament inicial es va amplificar amb el nou objectiu de demostrar la convergència del mètode, entre altres resultats teòrics. El marc teòric usat per poder formular matemàticament aquesta idea ha estat la regularització de la funció de barrera logarítmica associada al problema d’optimització, entenent com a tal la transformació de la funció de barrera original per una altra que inclou un terme quadràtic variable de pertorbació, que disminueix progressivament conforme l’algorisme s’atansa a l’òptim. Aqueste terme quadràtic converteix el problema lineal original en un de quadràtic, de forma que en les primeres iteracions aprofitem el comportament empíric abans esmentat i, a mesura que progressa l’algorisme, el terme quadràtic esdevé negligible, i el problema amb regularització quadràtica s’atansa al problema lineal original. La barrera regularitzada resulta ser auto-concordant, assegurant així la convergència del mètode de punt interior

Solving L1L_1-CTA in 3D tables by an interior-point method for primal block-angular problems

The purpose of the field of statistical disclosure control is to avoid that no confidential information can be derived from statistical data released by, mainly, national statistical agencies. Controlled tabular adjustment (CTA) is an emerging technique for the protection of statistical tabular data. Given a table to be protected, CTA looks for the closest safe table. In this work we focus on CTA for three-dimensional tables using the L1 norm for the distance between the original and protected tables. Three L1-CTA models are presented, giving rise to six different primal block-angular structures of the constraint matrices. The resulting linear programming problems are solved by a specialized interior-point algorithm for this constraints structure, which solves the normal equations by a combination of Cholesky factorization and preconditioned conjugate gradients (PCG). In the past this algorithm shown to be one of the most efficient approaches for some classes of block-angular problems. The effect of quadratic regularizations is also analyzed, showing that for three of the six primal block-angular structures the performance of PCG is guaranteed to improve. Computational results are reported for a set of large instances, which provide linear optimization problems of up to 50 millions of variables and 25 millions of constraints. The specialized interior-point algorithm is compared with the state-of-the-art barrier solver of the CPLEX 12.1 package, showing to be a more efficient choice for very large L1-CTA instances

Solving L1L_1-CTA in 3D tables by an interior-point method for primal block-angular problems

The purpose of the field of statistical disclosure control is to avoid that no confidential information can be derived from statistical data released by, mainly, national statistical agencies. Controlled tabular adjustment (CTA) is an emerging technique for the protection of statistical tabular data. Given a table to be protected, CTA looks for the closest safe table. In this work we focus on CTA for three-dimensional tables using the L1 norm for the distance between the original and protected tables. Three L1-CTA models are presented, giving rise to six different primal block-angular structures of the constraint matrices. The resulting linear programming problems are solved by a specialized interior-point algorithm for this constraints structure, which solves the normal equations by a combination of Cholesky factorization and preconditioned conjugate gradients (PCG). In the past this algorithm shown to be one of the most efficient approaches for some classes of block-angular problems. The effect of quadratic regularizations is also analyzed, showing that for three of the six primal block-angular structures the performance of PCG is guaranteed to improve. Computational results are reported for a set of large instances, which provide linear optimization problems of up to 50 millions of variables and 25 millions of constraints. The specialized interior-point algorithm is compared with the state-of-the-art barrier solver of the CPLEX 12.1 package, showing to be a more efficient choice for very large L1-CTA instances

Existence, uniqueness and convergence of the regularized primal-dual central path

In a recent work [J. Castro, J. Cuesta, Quadratic regularizations in an interior-point method for primal block-angular problems, Mathematical Programming, in press (doi:10.1007/s10107-010-0341-2)] the authors improved one of the most efficient interior-point approaches for some classes of block-angular problems. This was achieved by adding a quadratic regularization to the logarithmic barrier. This regularized barrier was shown to be self-concordant, thus fitting the general structural optimization interior-point framework. In practice, however, most codes implement primal dual path-following algorithms. This short paper shows that the primal-dual regularized central path is well defined, i.e., it exists, it is unique, and it converges to a strictly complementary primal dual solution