TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networks

Deep learning models have been developed for a variety of tasks and are deployed every day to work in real conditions. Some of these tasks are critical and models need to be trusted and safe, e.g. military communications or cancer diagnosis. These models are given real data, simulated data or combination of both and are trained to be highly predictive on them. However, gathering enough real data or simulating them to be representative of all the real conditions is: costly, sometimes impossible due to confidentiality and most of the time impossible. Indeed, real conditions are constantly changing and sometimes are intractable. A solution is to deploy machine learning models that are able to give predictions when they are confident enough otherwise raise a flag or abstain. One issue is that standard models easily fail at detecting out-of-distribution samples where their predictions are unreliable. We present here TrustGAN, a generative adversarial network pipeline targeting trustness. It is a deep learning pipeline which improves a target model estimation of the confidence without impacting its predictive power. The pipeline can accept any given deep learning model which outputs a prediction and a confidence on this prediction. Moreover, the pipeline does not need to modify this target model. It can thus be easily deployed in a MLOps (Machine Learning Operations) setting. The pipeline is applied here to a target classification model trained on MNIST data to recognise numbers based on images. We compare such a model when trained in the standard way and with TrustGAN. We show that on out-of-distribution samples, here FashionMNIST and CIFAR10, the estimated confidence is largely reduced. We observe similar conclusions for a classification model trained on 1D radio signals from AugMod, tested on RML2016.04C. We also publicly release the code.Comment: 8 pages, 6 figures, 1 table, presented at CAID 2022: Conference on Artificial Intelligence for Defenc

Compression of Recurrent Neural Networks using Matrix Factorization

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training rank-selection method called Rank-Tuning that selects a different rank for each matrix. Used in combination with training adaptations, our method achieves high compression rates with no or little performance degradation. Our numerical experiments on signal processing tasks show that we can compress recurrent neural networks up to 14x with at most 1.4% relative performance reduction

Protótipo de uma plataforma móvel baseada em Android para monitoramento de parâmetros de qualidade da água do Lago Paranoá

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2015.A água do planeta sofre constantes transformações, se renova e é reutilizada. Uma das principais transformações que a água sofreu no último século é a crescente contaminação, problema que afeta especialmente as grandes áreas urbanas e zonas litorâneas. Em razão do aumento da importância da água para a segurança de populações e devido ao risco de contaminação decorrente das atividades humanas surge a necessidade de um controle mais rígido para o abastecimento público. Neste cenário, destaca-se a importância do monitoramento da qualidade da água para a gestão dos recursos hídricos. O presente trabalho apresenta uma proposta de desenvolvimento de um protótipo de uma plataforma móvel aquática controlada remotamente com o objetivo de possibilitar a obtenção de parâmetros relevantes na estimação da qualidade da água de forma remota. Como prova de conceito foi escolhido escolhido apenas um desses parâmetros, em particular, foram aferidas medições da temperatura da água. No desenvolvimento do trabalho são investigados os parâmetros para estimar a qualidade da água, princípios de um ASV (Autonomous Surface Vehicle), utilização de sensores embarcados em um dispositivo móvel com sistema operacional Android, filtragem de sinais através do filtro media móvel, microcontrolador Arduino ADK e integração de aplicações utilizando um webservice. Como resultados foram obtidos um aplicativo para plataforma Android que acessa alguns sensores do aparelho smartphone, além de comunicar-se com um microcontrolador via USB o aplicativo também comunica-se com um webservice implementado para este trabalho. Além do aplicativo e do webservice foi implementado um programa controlador interfaciado, escrito em linguagem C#, que se comunica com o aplicativo através do webservice e por fim foi obtido um protótipo para plataforma móvel. Finalmente, foram realizados testes da plataforma no lago Paranoá, verificando o comportamento do algoritmo de navegação, coletando amostras reais da temperatura da água do lago e validando os dados coletados pelo aplicativo Android, assim como dos dados armazenados na solução webservice.Water of planet undergoes constant transformation, being renewed and reused. At last century, one of the major transformations that water has suffered is the increasing contamination, problem that especially affects large urban areas and coastal areas. A hard control of water is more necessary in reason of importance increasing of water for the safety of people and risk of contamination from human activity. In this scenario, it highlights the importance of water quality monitoring for management of water resources. This work presents a proposal to develop a prototype of an aquatic mobile platform remotely controlled in order that can obtain water parameters, propolsing as proof of concept, the measurement of one water parameter. On development of work, parameters are investigated to estimate water quality, principles of ASV (Autonomous Surface Vehicle), use of embedded sensors at mobile device with Android operating system, signal filtering through mobile average filter, Arduino ADK microcontroller and application’s integration using webservices. As results, were obtained an application for Android Plataform that reads some sensors of smartphone device, gets communication with Arduino ADK microcontroller through USB connection and gets communication with a webservice developed for this work. In addition, were obtained a graphical user interface writed in C# language (using .NET Plataform) that communicates to Android application through webservice. Finally, were obtained a aquatic mobile plataform prototype

The Alcock–Paczyński effect from Lyman-α forest correlations: analysis validation with synthetic data

The three-dimensional distribution of the Ly α forest has been extensively used to constrain cosmology through measurements of the baryon acoustic oscillations (BAO) scale. However, more cosmological information could be extracted from the full shapes of the Ly α forest correlations through the Alcock–Paczyński (AP) effect. In this work, we prepare for a cosmological analysis of the full shape of the Ly α forest correlations by studying synthetic data of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We use a set of 100 eBOSS synthetic data sets in order to validate such an analysis. These mocks undergo the same analysis process as the real data. We perform a full-shape analysis on the mean of the correlation functions measured from the 100 eBOSS realizations, and find that our model of the Ly α correlations performs well on current data sets. We show that we are able to obtain an unbiased full-shape measurement of DM/DH(zeff), where DM is the transverse comoving distance, DH is the Hubble distance, and zeff is the effective redshift of the measurement. We test the fit over a range of scales, and decide to use a minimum separation of rₘᵢₙ = 25 h−¹Mpc. We also study and discuss the impact of the main contaminants affecting Ly α forest correlations, and give recommendations on how to perform such analysis with real data. While the final eBOSS Ly α BAO analysis measured DM/DH(zeff = 2.33) with 4 per cent statistical precision, a full-shape fit of the same correlations could provide an ∼2 per cent measurement

Tourismes 2 - Moments de lieux

Tourismes 2 prolonge la réflexion entreprise dans Tourismes 1 en proposant une lecture originale des lieux qui ont fait le tourisme tel qu\u27il fonctionne aujourd\u27hui de Bath à Marrakech en passant par Saint-Tropez Benidorm Yellowstone Venise Waikiki ou la Floride Ce livre est un voyage à travers une collection de lieux touristiques qui ont été choisis parce que chacun d\u27eux exprime un moment fort dans l\u27histoire du tourisme en relation avec l\u27évolution du Monde Pourquoi à un moment donné s\u27est-on mis à fréquenter des lieux qui auparavant étaient ignorés ou fuis? Où et comment est-on passé du bain thérapeutique au bain plaisir du bain dans les mers froides au bain dans les mers chaudes? Si les hautes vallées ont d\u27abord été fréquentées en été par les touristes où et comment est née la saison d\u27hiver en montagne ? Sans vouloir constituer une histoire du tourisme cet ouvrage est une invitation à lire autrement le fil du temps le fil des événements à l\u27aide du concept de « moment de lieu » son ambition est de saisir les processus qui ont conduit à l\u27émergence sur quelques décennies tout au plus et dans des lieux identifiés de nouveaux systèmes d\u27acteurs et de nouvelles pratiques qui pour la plupart fonctionnent toujours aujourd\u27hui et ont été reproduits par millier