AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs

Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.Comment: Project page: https://despoisj.github.io/AgingMapGAN

Learning Long-Term Style-Preserving Blind Video Temporal Consistency

When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a postprocessing model, agnostic to the transformation applied to videos (e.g. style transfer, image manipulation using GANs, etc.), in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation, as well as a novel style preserving perceptual loss. The former improves long-term temporal consistency learning, while the latter fosters style preservation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal, and better keeps the overall style of the videos than previous approaches

CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer

While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style transfer task, with the objective to learn a color controllable makeup style synthesis. We introduce CA-GAN, a generative model that learns to modify the color of specific objects (e.g. lips or eyes) in the image to an arbitrary target color while preserving background. Since color labels are rare and costly to acquire, our method leverages weakly supervised learning for conditional GANs. This enables to learn a controllable synthesis of complex objects, and only requires a weak proxy of the image attribute that we desire to modify. Finally, we present for the first time a quantitative analysis of makeup style transfer and color control performance

Bayesian Modelling of PWR Vessels Flaw Distributions

We present a full Bayesian method for estimating the density and size distribution of subclad-flaws in French Pressurized Water Reactor (PWR) vessels. This model takes into account in service inspection (ISI) data, a flaw size-dependent probability of detection function (different function types are possible) with a threshold of detection, and a flaw sizing error distribution (different distribution types are possible). It is identified through a Markov Chain Monte Carlo (MCMC) algorithm. The article includes discussion for choosing the prior distribution parameters and an illustrative application is presented highlighting its ability to provide good parameter estimates even when a small number of flaws is observed

Scikit-learn: Machine Learning in Python

International audienceScikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net

Exome sequencing identifies germline variants in DIS3 in familial multiple myeloma

[Excerpt] Multiple myeloma (MM) is the third most common hematological malignancy, after Non-Hodgkin Lymphoma and Leukemia. MM is generally preceded by Monoclonal Gammopathy of Undetermined Significance (MGUS) [1], and epidemiological studies have identified older age, male gender, family history, and MGUS as risk factors for developing MM [2]. The somatic mutational landscape of sporadic MM has been increasingly investigated, aiming to identify recurrent genetic events involved in myelomagenesis. Whole exome and whole genome sequencing studies have shown that MM is a genetically heterogeneous disease that evolves through accumulation of both clonal and subclonal driver mutations [3] and identified recurrently somatically mutated genes, including KRAS, NRAS, FAM46C, TP53, DIS3, BRAF, TRAF3, CYLD, RB1 and PRDM1 [3,4,5]. Despite the fact that family-based studies have provided data consistent with an inherited genetic susceptibility to MM compatible with Mendelian transmission [6], the molecular basis of inherited MM predisposition is only partly understood. Genome-Wide Association (GWAS) studies have identified and validated 23 loci significantly associated with an increased risk of developing MM that explain ~16% of heritability [7] and only a subset of familial cases are thought to have a polygenic background [8]. Recent studies have identified rare germline variants predisposing to MM in KDM1A [9], ARID1A and USP45 [10], and the implementation of next-generation sequencing technology will allow the characterization of more such rare variants. [...]French National Cancer Institute (INCA) and the Fondation Française pour la Recherche contre le Myélome et les Gammapathies (FFMRG), the Intergroupe Francophone du Myélome (IFM), NCI R01 NCI CA167824 and a generous donation from Matthew Bell. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Association des Malades du Myélome Multiple (AF3M) for their continued support and participation. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organizatio

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Automatic recognition of cortical sulci

The determination of specic biomarkers of brain pathologies at population scale is extremely dicult because of the huge inter-individual variability of the sulco-gyral topography. This thesis addresses this issue by automatically identifying 125 sulcal structures and pairing them through individuals, thanks to a manually labeled database of 62 subjects. Relying on the sulcal roots theory, cortical folds are split into elementary segments to be labeled. In a rst time, the structural approach proposed earlier by Jean-François Mangin and Denis Rivière has been revisited to manage the increasing amount of morphometric features involved in the identication process. In a second time, this model has been fully reviewed in favor of a Bayesian framework based on localized information (positions or directions) previously neglected, thus allowing eective optimization schemes. In this context, data normalization is essential ; this issue has been considered through global or sulciwise local ane registration techniques, jointly to the sulcal identication. In order to introduce more structural informations, a Markovian model has been successfully introduced to reect the local neighbored cortical folds organization. Finally, the overall recognition rate has reached 86 % for each hemisphere. From now on, only atypical patterns or the most variable anatomical structures remain a real issue.La mise en évidence de biomarqueurs spéciques de pathologies cérébrales à l'échelle d'une population reste extrêmement dicile compte tenu de la variabilité inter-individuelle de la topographie sulco-gyrale. Cette thèse propose de répondre à cette diculté par l'identication automatique de 125 structures sulcales et leur mise en correspondance au travers des individus, à partir d'une base de 62 sujets labélisés manuellement. En s'appuyant sur la théorie des racines sulcales, les plissements corticaux sont découpés en entités élémentaires à labéliser. Dans une première phase, l'approche structurelle proposée précédemment par Jean-François Mangin et Denis Rivière a été revisitée pour faire face aux nombreux descripteurs morphomé- triques impliqués dans le processus d'identication. Dans une deuxième phase, cette modélisation a été reconsidérée intégralement au prot d'un cadre Bayésien exploitant des informations localisées (positions ou directions) négligées jusqu'alors, autorisant ainsi des schémas d'optimisation ecace. Dans ce cadre, la normalisation des données est essentielle ; cette question a été traitée sous la forme d'un processus de recalage ane global ou local à chaque sillon, de façon couplée au probl ème d'identication des sillons. Dans l'optique d'introduire plus d'information structurelle, une modélisation Markovienne traduisant une vue localisée de l'agencement entre plissements corticaux voisins a été introduite avec succès pour atteindre un taux de reconnaissance de plus de 86% pour chaque hémisphère. Seules les congurations atypiques ou les structures anatomiques les plus variables présentent encore de réelles dicultés