Behaviour of cellular beams at elevated temperatures

Cellular beams are beams that present openings in the web. The insertion of this openings can cause a good architectural characteristic, but the main reason is to improve the mechanical performance, overcoming larger spans compared to the original solid beams, reducing its weight in addition to the passage of technical installations trough the openings. However, cellular beams are subject to specific failure modes, different from solid beams, such as the Vierendeel mechanism, the web-post buckling or the 2T plastic collapse, among others. This work aims to analyse the behaviour of cellular beams at ambient and elevated temperatures, representing the effect of a fire situation. A set of experimental tests were performed in IPE220 steel beams, class S275, with openings in their webs, considering different diameters and web post widths. The cantilever beams were fixed at one end and subjected to an incremental concentrated load at the free end until the collapse, as represented in Figure 1. During experimental tests the vertical displacement at the free end was measured using a potentiometric wire gauge, and the strains around the holes and at the web post were measured by extensometers. For tests at elevated temperatures an electro-ceramic resistances were used to increase the temperatures at a heating rate of 800 [ºc/h] until a steady state regime of a constant temperature equal to 600[ºc] was attained and the mechanical load start to be applied until the collapse. The experimental results were compared with numerical results obtained from the finite element method using the software Ansys, considering nonlinear geometric and material simulations. The model includes local geometric imperfections based on the first buckling mode. An incremental and iterative procedure was used, with the Newton-Raphson method Experimental and numerical load vs displacement curves are compared and the collapse loads obtained for each beam. The experimental tests allowed to calibrate the numerical model and expand it for other geometric configurations.info:eu-repo/semantics/publishedVersio

Provably expressive temporal graph networks

Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings. We establish fundamental results about the representational power and limits of the two main categories of TGNs: those that aggregate temporal walks (WA-TGNs), and those that augment local message passing with recurrent memory modules (MP-TGNs). Specifically, novel constructions reveal the inadequacy of MP-TGNs and WA-TGNs, proving that neither category subsumes the other. We extend the 1-WL (Weisfeiler-Leman) test to temporal graphs, and show that the most powerful MP-TGNs should use injective updates, as in this case they become as expressive as the temporal WL. Also, we show that sufficiently deep MP-TGNs cannot benefit from memory, and MP/WA-TGNs fail to compute graph properties such as girth. These theoretical insights lead us to PINT -- a novel architecture that leverages injective temporal message passing and relative positional features. Importantly, PINT is provably more expressive than both MP-TGNs and WA-TGNs. PINT significantly outperforms existing TGNs on several real-world benchmarks.Comment: Accepted to NeurIPS 202

Federated Stochastic Gradient Langevin Dynamics

Publisher Copyright: © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying communication causes the Markov chains to diverge from the true posterior even for very simple models. To alleviate both these problems, we propose conducive gradients, a simple mechanism that combines local likelihood approximations to correct gradient updates. Notably, conducive gradients are easy to compute, and since we only calculate the approximations once, they incur negligible overhead. We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD). We demonstrate that our approach can handle delayed communication rounds, converging to the target posterior in cases where DSGLD fails. We also show that FSGLD outperforms DSGLD for non-IID federated data with experiments on metric learning and neural networks.Peer reviewe

Precarity and gender: the trajectory of portuguese women towards a new class in training

A partir das características apontadas por Standing para definir o conceito de precariado, esta investigação foca-se no caso particular de mulheres portuguesas diplomadas, que acreditaram que o ensino superior lhes permitiria ter uma carreira e uma trajectória segura de trabalho. Os dados foram recolhidos através de entrevistas semiestruturadas, analisados de acordo com as técnicas de análise de conteúdo, e codificadas com o auxílio do software Atlas/TI. Os resultados evidenciam como estas experiências profissionais estão na base de sentimentos associados à frustração de estatuto, ausência de perspectivas seguras de futuro e por uma maior vulnerabilidade às armadilhas da precariedade.Based on the characteristics pointed out by Standing to define the concept of precariat, this research focuses on the specific case of Portuguese women graduates, who believed that higher education would allow them to have a career and a safe career path. Data were collected through semi-structured interviews, analyzed according to the techniques of content analysis, and coded with the aid of Atlas / TI software. The results show how these professional experiences are based on feelings associated with status frustration, lack of secure future prospects, and greater vulnerability to precariousness traps

Parallel MCMC Without Embarrassing Failures

Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub) posteriors defined on data partitions. Then, a server combines local results. While efficient, this framework is very sensitive to the quality of subposterior sampling. Common sampling problems such as missing modes or misrepresentation of low-density regions are amplified–instead of being corrected–in the combination phase, leading to catastrophic failures. In this work, we propose a novel combination strategy to mitigate this issue. Our strategy, Parallel Active Inference (PAI), leverages Gaussian Process (GP) surrogate modeling and active learning. After fitting GPs to subposteriors, PAI (i) shares information between GP surrogates to cover missing modes; and (ii) uses active sampling to individually refine subposterior approximations. We validate PAI in challenging benchmarks, including heavy-tailed and multi-modal posteriors and a real-world application to computational neuroscience. Empirical results show that PAI succeeds where previous methods catastrophically fail, with a small communication overhead.Peer reviewe

Distill n' Explain: explaining graph neural networks using simple surrogates

Explaining node predictions in graph neural networks (GNNs) often boils down to finding graph substructures that preserve predictions. Finding these structures usually implies back-propagating through the GNN, bonding the complexity (e.g., number of layers) of the GNN to the cost of explaining it. This naturally begs the question: Can we break this bond by explaining a simpler surrogate GNN? To answer the question, we propose Distill n' Explain (DnX). First, DnX learns a surrogate GNN via knowledge distillation. Then, DnX extracts node or edge-level explanations by solving a simple convex program. We also propose FastDnX, a faster version of DnX that leverages the linear decomposition of our surrogate model. Experiments show that DnX and FastDnX often outperform state-of-the-art GNN explainers while being orders of magnitude faster. Additionally, we support our empirical findings with theoretical results linking the quality of the surrogate model (i.e., distillation error) to the faithfulness of explanations.Comment: To appear in AISTATS 202

Avaliação da aprendizagem no ensino de Biologia

Esse estudo tem como objeto de análise a construção dos sentidos das práticas avaliativas de professores de Biologia de um contexto educacional. Sob a perspectiva da Teoria das Representações Sociais, são discutidas as ancoragens que fundamentam seus saberes e orientam suas práticas avaliativas. Pretende-se ampliar, com o olhar psicossocial, a interpretação das tensões construídas entre os conhecimentos profissionais e as representações do trabalho do professor. Os resultados anunciam uma rede de significados que destacam a força das normas institucionais sobre o trabalho docente, associada à metáfora de uma engrenagem, sugerindo que essa representação social está ancorada na imagem de um protótipo de escola, em sua representação sobre a cultura escolar e na centralidade dos conteúdos disciplinares em suas práticas pedagógicas