Fast cross-validation for multi-penalty ridge regression

High-dimensional prediction with multiple data types needs to account for potentially strong differences in predictive signal. Ridge regression is a simple model for high-dimensional data that has challenged the predictive performance of many more complex models and learners, and that allows inclusion of data type specific penalties. The largest challenge for multi-penalty ridge is to optimize these penalties efficiently in a cross-validation (CV) setting, in particular for GLM and Cox ridge regression, which require an additional estimation loop by iterative weighted least squares (IWLS). Our main contribution is a computationally very efficient formula for the multi-penalty, sample-weighted hat-matrix, as used in the IWLS algorithm. As a result, nearly all computations are in low-dimensional space, rendering a speed-up of several orders of magnitude. We developed a flexible framework that facilitates multiple types of response, unpenalized covariates, several performance criteria and repeated CV. Extensions to paired and preferential data types are included and illustrated on several cancer genomics survival prediction problems. Moreover, we present similar computational shortcuts for maximum marginal likelihood and Bayesian probit regression. The corresponding R-package, multiridge, serves as a versatile standalone tool, but also as a fast benchmark for other more complex models and multi-view learners

Cross-Domain Polarity Models to Evaluate User eXperience in E-learning

[EN] Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user's emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction.Partially supported by the Spanish MINECO and FEDER founds under Project TIN2017-85854-C4-2-R. Work of J.A. Gonzalez is financed under Grant PAID-01-17Sanchis-Font, R.; Castro-Bleda, MJ.; González-Barba, JÁ.; Pla Santamaría, F.; Hurtado Oliver, LF. (2021). Cross-Domain Polarity Models to Evaluate User eXperience in E-learning. 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Author Correction: Gap junction protein Connexin-43 is a direct transcriptional regulator of N-cadherin in vivo

Correction to: Nature Communications (2018); https://doi.org/10.1038/s41467-018-06368-x, published online 21 September 2018. The original version of this Article contained an error in the spelling of the author Alexandra Schambony, which was incorrectly given as Alexandra Schambon. This has now been corrected in both the PDF and HTML versions of the Article

Gap junction protein Connexin-43 is a direct transcriptional regulator of N-cadherin in vivo

Connexins are the primary components of gap junctions, providing direct links between cells under many physiological processes. Here, we demonstrate that in addition to this canonical role, Connexins act as transcriptional regulators. We show that Connexin 43 (Cx43) controls neural crest cell migration in vivo by directly regulating N-cadherin transcription. This activity requires interaction between Cx43 carboxy tail and the basic transcription factor-3, which drives the translocation of Cx43 tail to the nucleus. Once in the nucleus they form a complex with PolII which directly binds to the N-cadherin promoter. We found that this mechanism is conserved between amphibian and mammalian cells. Given the strong evolutionary conservation of connexins across vertebrates, this may reflect a common mechanism of gene regulation by a protein whose function was previously ascribed only to gap junctional communication

Size Matters: Large Objects Capture Attention in Visual Search

Can objects or events ever capture one's attention in a purely stimulus-driven manner? A recent review of the literature set out the criteria required to find stimulus-driven attentional capture independent of goal-directed influences, and concluded that no published study has satisfied that criteria. Here visual search experiments assessed whether an irrelevantly large object can capture attention. Capture of attention by this static visual feature was found. The results suggest that a large object can indeed capture attention in a stimulus-driven manner and independent of displaywide features of the task that might encourage a goal-directed bias for large items. It is concluded that these results are either consistent with the stimulus-driven criteria published previously or alternatively consistent with a flexible, goal-directed mechanism of saliency detection

Multi-Jet Event Rates in Deep Inelastic Scattering and Determination of the Strong Coupling Constant

Jet event rates in deep inelastic ep scattering at HERA are investigated applying the modified JADE jet algorithm. The analysis uses data taken with the H1 detector in 1994 and 1995. The data are corrected for detector and hadronization effects and then compared with perturbative QCD predictions using next-to-leading order calculations. The strong coupling constant alpha_S(M_Z^2) is determined evaluating the jet event rates. Values of alpha_S(Q^2) are extracted in four different bins of the negative squared momentum transfer~\qq in the range from 40 GeV2 to 4000 GeV2. A combined fit of the renormalization group equation to these several alpha_S(Q^2) values results in alpha_S(M_Z^2) = 0.117+-0.003(stat)+0.009-0.013(syst)+0.006(jet algorithm).Comment: 17 pages, 4 figures, 3 tables, this version to appear in Eur. Phys. J.; it replaces first posted hep-ex/9807019 which had incorrect figure 4

Measurements of Transverse Energy Flow in Deep-Inelastic Scattering at HERA

Measurements of transverse energy flow are presented for neutral current deep-inelastic scattering events produced in positron-proton collisions at HERA. The kinematic range covers squared momentum transfers Q^2 from 3.2 to 2,200 GeV^2, the Bjorken scaling variable x from 8.10^{-5} to 0.11 and the hadronic mass W from 66 to 233 GeV. The transverse energy flow is measured in the hadronic centre of mass frame and is studied as a function of Q^2, x, W and pseudorapidity. A comparison is made with QCD based models. The behaviour of the mean transverse energy in the central pseudorapidity region and an interval corresponding to the photon fragmentation region are analysed as a function of Q^2 and W.Comment: 26 pages, 8 figures, submitted to Eur. Phys.

Measurement of D* Meson Cross Sections at HERA and Determination of the Gluon Density in the Proton using NLO QCD

With the H1 detector at the ep collider HERA, D* meson production cross sections have been measured in deep inelastic scattering with four-momentum transfers Q^2>2 GeV2 and in photoproduction at energies around W(gamma p)~ 88 GeV and 194 GeV. Next-to-Leading Order QCD calculations are found to describe the differential cross sections within theoretical and experimental uncertainties. Using these calculations, the NLO gluon momentum distribution in the proton, x_g g(x_g), has been extracted in the momentum fraction range 7.5x10^{-4}< x_g <4x10^{-2} at average scales mu^2 =25 to 50 GeV2. The gluon momentum fraction x_g has been obtained from the measured kinematics of the scattered electron and the D* meson in the final state. The results compare well with the gluon distribution obtained from the analysis of scaling violations of the proton structure function F_2.Comment: 27 pages, 9 figures, 2 tables, submitted to Nucl. Phys.

Searches at HERA for Squarks in R-Parity Violating Supersymmetry

A search for squarks in R-parity violating supersymmetry is performed in e^+p collisions at HERA at a centre of mass energy of 300 GeV, using H1 data corresponding to an integrated luminosity of 37 pb^(-1). The direct production of single squarks of any generation in positron-quark fusion via a Yukawa coupling lambda' is considered, taking into account R-parity violating and conserving decays of the squarks. No significant deviation from the Standard Model expectation is found. The results are interpreted in terms of constraints within the Minimal Supersymmetric Standard Model (MSSM), the constrained MSSM and the minimal Supergravity model, and their sensitivity to the model parameters is studied in detail. For a Yukawa coupling of electromagnetic strength, squark masses below 260 GeV are excluded at 95% confidence level in a large part of the parameter space. For a 100 times smaller coupling strength masses up to 182 GeV are excluded.Comment: 32 pages, 14 figures, 3 table