Multiple Instance Learning for Emotion Recognition using Physiological Signals

The problem of continuous emotion recognition has been the subject of several studies. The proposed affective computing approaches employ sequential machine learning algorithms for improving the classification stage, accounting for the time ambiguity of emotional responses. Modeling and predicting the affective state over time is not a trivial problem because continuous data labeling is costly and not always feasible. This is a crucial issue in real-life applications, where data labeling is sparse and possibly captures only the most important events rather than the typical continuous subtle affective changes that occur. In this work, we introduce a framework from the machine learning literature called Multiple Instance Learning, which is able to model time intervals by capturing the presence or absence of relevant states, without the need to label the affective responses continuously (as required by standard sequential learning approaches). This choice offers a viable and natural solution for learning in a weakly supervised setting, taking into account the ambiguity of affective responses. We demonstrate the reliability of the proposed approach in a gold-standard scenario and towards real-world usage by employing an existing dataset (DEAP) and a purposely built one (Consumer). We also outline the advantages of this method with respect to standard supervised machine learning algorithms

Roles of binding elements, FOXL2 domains, and interactions with cJUN and SMADs in regulation of FSHβ.

We previously identified FOXL2 as a critical component in FSHβ gene transcription. Here, we show that mice deficient in FOXL2 have lower levels of gonadotropin gene expression and fewer LH- and FSH-containing cells, but the same level of other pituitary hormones compared to wild-type littermates, highlighting a role of FOXL2 in the pituitary gonadotrope. Further, we investigate the function of FOXL2 in the gonadotrope cell and determine which domains of the FOXL2 protein are necessary for induction of FSHβ transcription. There is a stronger induction of FSHβ reporter transcription by truncated FOXL2 proteins, but no induction with the mutant lacking the forkhead domain. Specifically, FOXL2 plays a role in activin induction of FSHβ, functioning in concert with activin-induced SMAD proteins. Activin acts through multiple promoter elements to induce FSHβ expression, some of which bind FOXL2. Each of these FOXL2-binding sites is either juxtaposed or overlapping with a SMAD-binding element. We determined that FOXL2 and SMAD4 proteins form a higher order complex on the most proximal FOXL2 site. Surprisingly, two other sites important for activin induction bind neither SMADs nor FOXL2, suggesting additional factors at work. Furthermore, we show that FOXL2 plays a role in synergistic induction of FSHβ by GnRH and activin through interactions with the cJUN component of the AP1 complex that is necessary for GnRH responsiveness. Collectively, our results demonstrate the necessity of FOXL2 for proper FSH production in mice and implicate FOXL2 in integration of transcription factors at the level of the FSHβ promoter

A cost-sensitive constrained Lasso

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance constraints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization problem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered

Respuesta a la fertilización postergada con fósforo de pasturas perennes con base de alfalfa

Tanto la secuencia de cultivos anuales como el cultivo de alfalfa reducen extraordinariamente el nivel de fertilidad de los suelos con respecto al elemento fósforo. La fertilización de pasturas perennes con base de alfalfa en su implantación es una práctica muy común, no así la fertilización postergada. Por lo tanto, se establecieron ensayos de fertilización fosforada en suelos haplustoles enticos con contenidos de P Bray entre 12o 13 ppm sobre pasturas de alfalfa y gramíneas de más de 2 años de implantación. Los objetivos fueron: l. Evaluar la respuesta a la fertilización postergada en cuanto a la producción total de materia seca (MS) y a la distribución de ésta en los distintos cortes, 2. Evaluar económicamente esta práctica en un sistema de producción de carne. Se efectuaron análisis de suelo con determinaciones de nitrógeno como NO3-1)'· materia orgánica (MO), fósforo disponible Bray (P), azufre como sulfatos (SO4-2), equivalente de humedad y pH. No se hicieron determinaciones de nutrientes en planta. La fertilización se realizó en noviembre de 1999 con 152 kg. de fosfato diamónico por hectárea (70 kg. de P/ha) con fertilización, sin incorporarlo al suelo.Director: Ing. Agr. Elke NoelIemeyer, Cátedra de Edafología y Manejo de Suelos