Multivariate Density Estimation with Deep Neural Mixture Models

Albeit worryingly underrated in the recent literature on machine learning in general (and, on deep learning in particular), multivariate density estimation is a fundamental task in many applications, at least implicitly, and still an open issue. With a few exceptions, deep neural networks (DNNs) have seldom been applied to density estimation, mostly due to the unsupervised nature of the estimation task, and (especially) due to the need for constrained training algorithms that ended up realizing proper probabilistic models that satisfy Kolmogorov's axioms. Moreover, in spite of the well-known improvement in terms of modeling capabilities yielded by mixture models over plain single-density statistical estimators, no proper mixtures of multivariate DNN-based component densities have been investigated so far. The paper fills this gap by extending our previous work on neural mixture densities (NMMs) to multivariate DNN mixtures. A maximum-likelihood (ML) algorithm for estimating Deep NMMs (DNMMs) is handed out, which satisfies numerically a combination of hard and soft constraints aimed at ensuring satisfaction of Kolmogorov's axioms. The class of probability density functions that can be modeled to any degree of precision via DNMMs is formally defined. A procedure for the automatic selection of the DNMM architecture, as well as of the hyperparameters for its ML training algorithm, is presented (exploiting the probabilistic nature of the DNMM). Experimental results on univariate and multivariate data are reported on, corroborating the effectiveness of the approach and its superiority to the most popular statistical estimation techniques

A benchmark study on identification of inelastic parameters based on deep drawing processes using pso – nelder mead hybrid approach

Optimization techniques have been increasingly used to identification of inelastic material parameters owing to their generality. Development of robust techniques to solving this class of inverse problems has been a challenge to researchers mainly due to the nonlinear character of the problem and behaviour of the objective function. Within this framework, this work discusses application of Particle Swarm Optimization (PSO) and a PSO – Nelder Mead hybrid approach to identification of inelastic parameters based on a benchmark solution of the deep drawing process

Produção de matéria seca dos componentes vegetais do milheto em diferentes níveis de disponibilidade hídrica e níveis de corte.

CONGREGA 2012

Produção de matéria seca de folhas de milheto em condição de estresse hídrico e níveis de corte.

Congrega 2012

Análise da altura das plantas de milheto (Pennisetum glaucum) quando submetidadas a diferentes níveis de cortes e estresse hídrico.

CONGREGA 2012. Título: submetidadas [i.e. submetidas]

Learning activation functions from data using cubic spline interpolation

Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is a crucial step, which remains an open problem in the research community. Despite a large amount of investigations, most current implementations simply select one fixed function from a small set of candidates, which is not adapted during training, and is shared among all neurons throughout the different layers. However, neither two of these assumptions can be supposed optimal in practice. In this paper, we present a principled way to have data-dependent adaptation of the activation functions, which is performed independently for each neuron. This is achieved by leveraging over past and present advances on cubic spline interpolation, allowing for local adaptation of the functions around their regions of use. The resulting algorithm is relatively cheap to implement, and overfitting is counterbalanced by the inclusion of a novel damping criterion, which penalizes unwanted oscillations from a predefined shape. Experimental results validate the proposal over two well-known benchmarks.Comment: Submitted to the 27th Italian Workshop on Neural Networks (WIRN 2017

Negative consequences of conflict-related sexual violence on survivors: a systematic review of qualitative evidence

BackgroundConflicts exacerbate dynamics of power and inequalities through violence normalization, which acts as a facilitator for conflict-related sexual violence. Literature addressing its negative outcomes on survivors is scant. The aim of this systematic review was to analyze the qualitative evidence reported in scientific literature and focusing on the negative consequences of conflict-related sexual violence on victims' physical, psychological, and social dimensions of health in a gender-inclusive and disaggregated form.MethodsA literature search was conducted on January 13, 2023 on Pubmed, Scopus, and PsychArticles. The search strings combined two blocks of terms related to sexual violence and conflict. A time filter was applied, limiting the search to studies published in the last ten years. Information regarding the main characteristics and design of the study, survivors and their experience, and about conflict-related sexual violence was collected. The negative consequences of conflict-related sexual violence on the physical, psychological, and social dimension of victims were extracted according to the Biopsychosocial model of health. The review followed the Joanna Briggs Institute methodology for systematic reviews and relied on the Preferred Reporting Items for Systematic reviews and Meta-Analyses.ResultsAfter full text review, 23 articles met the inclusion criteria, with 18 of them reporting negative repercussions on physical health, all of them highlighting adverse psychological outcomes, and 21 disclosing unfavorable social consequences. The negative outcomes described in multiple studies were sexual and reproductive health issues, the most mentioned being pregnancy, manifestations of symptoms attributable to post-traumatic stress disorder, and stigma. A number of barriers to access to care were presented as emerging findings.ConclusionsThis review provided an analysis of the negative consequences of conflict-related sexual violence on survivors, thus highlighting the importance of qualitative evidence in understanding these outcomes and addressing barriers to access to care. Conflict-related sexual violence is a sexual and reproductive health issue. Sexuality education is needed at individual, community, and provider level, challenging gender norms and roles and encompassing gender-based violence. Gender-inclusive protocols and services need to be implemented to address the specific needs of all victims. Governments should advocate for SRHRs and translate health policies into services targeting survivors of CRSV

Temperatura base e soma térmica do subperíodo semeadura emergência do capim sudão.

O capim sudão (Sorghum sudanense) é uma poácea que está em expansão da área cultivada no Rio Grande do Sul. A cultivar BRS Estribo foi lançada oficialmente em 2013 pela Embrapa Pecuária Sul e é uma nova alternativa de cultura de verão para pecuaristas do sul do Brasil, portanto, há uma grande demanda de informações sobre o desenvolvimento fenológico uma vez que o conhecimento das fases fenológicas das pastagens podem ajudar o produtor a planejar o pastejo e obter melhores rendimentos