Property Inference Attacks on Convolutional Neural Networks:Influence and Implications of Target Model's Complexity

Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (i.e., the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines

Dispersion mécanique de l'or dans les matériaux de surface : exemple du site aurifère de Piéla (Burkina-Faso)

A Piéla, l'or primaire est associé à des filons de quartz, dans une zone de cisaillement des formations volcano-sédimentaires birimiennes (Protérozoïque inférieur). La présence d'or a également été mise en évidence dans les matériaux superficiels latéritiques bien que les roches sous-jacentes et leurs altérites soient en général stériles. L'étude de la répartition de l'or sur les différentes surfaces aplanies montre que l'or est concentré par des transferts mécaniques dans les cailloutis et graviers de cuirasse ferrugineuse et de quartz de la plaine alluviale et dans des portions du moyen-glacis cuirassé assimilables à une ancienne terrasse. Ailleurs sur les surfaces cuirassées (haut-glacis, moyen-glacis), l'or est dispersé à faible teneur ou absent. Les caractères morphologiques et chimiques des particules d'or évoluent depuis la zone minéralisée jusqu'à la plaine alluviale distale ainsi que sur les glacis. Cette évolution se marque par une augmentation de l'émoussé, par des traits morphologiques spécifiques du transport mécanique (stries, bordures repliées, aplatissement) et par un lessivage de l'argent jusqu'au coeur des particules. Dans les matériaux superficiels du site de Piéla, les particules d'or présentent des degrés d'usure différents et ne montrent pas l'évolution progressive avec diminution de la taille des particules et lessivage de l'argent, souvent décrite de bas en haut des profils cuirassés sur roche mère minéralisée. (Résumé d'auteur

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Sawtooth period changes with mode conversion current drive on Alcator C-Mod

DEFC0299ER54512. Reproduction,  translation,  publication,  use and disposal,  in whole or in part,  by or for the United States government is permitted. Submitted for publication to Plasma Physics and Controlled Fusion. Sawtooth period changes with mode conversion current drive on Alcator C-Mo

OntoGene in BioCreative II

BACKGROUND: Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment. RESULTS: In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation. CONCLUSION: Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature