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

Maankäytön ja luonnollisten tekijöiden vaikutus putkilokasvien vegetatiiviseen korkeuteen Pohjois-Suomen lehtometsissä

Tiivistelmä. Lehtometsät ovat kasvillisuuden monimuotoisuuskeskuksia ja ainutlaatuisia ekosysteemejä boreaalisella metsävyöhykkeellä. Lehtokasvillisuuden elinvoima on riippuvainen monista ympäristötekijöistä ja niiden tasapainosta. Muutokset maankäytössä- ja luonnollisissa tekijöissä aiheuttavat häiriöitä lehtokasvillisuuden tilassa. Häiriöt muuttavat lehtokasviyhteisöjen rakennetta ja toimintaa. Tilan muutoksia voidaan tutkia toiminnallisilla ominaisuuksilla kuten putkilokasvien vegetatiivisella korkeudella. Kasvien korkeus muuttuu ympäristöhäiriöiden mukaan, ja korkeuden muutoksen avulla saadaan arvokasta tietoa elinympäristön tilasta ja sen muutoksista. Tutkielman tavoitteena oli tarkastella, millainen kokonaisvaikutus ympäristötekijöillä on putkilokasvien vegetatiiviseen korkeuteen Pohjois-Suomen lehtokasvillisuusalueilla. Erityisesti tavoitteena oli tutkia lehtokasvillisuuden kannalta keskeisiä ympäristötekijöiden yhteyksiä putkilokasvien vegetatiiviseen korkeuteen. Tutkielmassa on käytetty Pohjois-Suomen lehtokasvillisuusalueilta kerättyä havaintoaineistoa vuosilta 2013–2019. Havaintoaineisto koostuu 235 lehtokasvillisuusalasta, ja yhden alan pinta-ala on 5 m x 5 m. Havaintoaineistoa on täydennetty muilla aineistoilla, joita on ladattu tietokannoista. Tutkielmassa on käytetty lisäksi erilasia aineistoja, kuten ilmasto-, porotiheys- ja kosteusaineistoja. Tutkimusaineistoa on analysoitu Spearmanin järjestyskorrelaatiokertoimien ja yleistetyn additiivisen mallinnusmenetelmän avulla. Spatiaalista autokorrelaatiota on testattu monimuuttujamallin jäännöstermien avulla ja monimuuttujamallin luotettavuutta on tarkasteltu monipuolisesti. Tutkielman keskeisimmät tulokset olivat, että ympäristötekijöistä putkilokasvien vegetatiivista korkeutta kasvattavat porotiheys, sadanta ja maaperän pH-arvo. Puuston latvustopeittävyyden kasvu puolestaan laskee putkilokasvien vegetatiivista korkeutta. Kosteusindeksi ja lehtokasvillisuuden tilaa kuvastava rakenne ja toiminta osoittautuivat epävarmoiksi selittäviksi tekijöiksi. Niiden yhteyttä putkilokasvien korkeuteen on vaikea havaita. Tutkielman tulosten mukaan putkilokasvien korkeuteen vaikuttaa selvimmin latvustopeittävyys, joka indikoi metsänhoidon intensiteettiä. Latvustopeittävyys vaikuttaa epäsuorasti putkilokasvien korkeuteen ja on avaintekijä määrittämään vegetatiivisen korkeuden. Metsänhoidon intensiteetti tulee ottaa huomioon, jotta voidaan turvata elinvoimainen lehtokasvillisuus esimerkiksi Pohjois-Suomessa

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

Dynamique de l'or et d'autres minéraux lourds dans un profil d'altération cuirassé du Burkina Faso, Afrique de l'Ouest : intérêt pour l'interprétation de la mise en place des matériaux constituant les cuirasses de haut glacis

Le profil d'altération cuirassé de haut glacis, développé dans l'environnement du site aurifère de Gangaol, a subi des phases d'altération suffisamment intenses pour que des minéraux résistants, comme le zircon et l'or, présentent des traces de dissolution importantes. Dans l'horizon supérieur de la cuirasse, ces minéraux altérés coexistent avec des particules d'or conservant des formes primaires intactes et avec des sulfures sains. Au sein de cet horizon, les teneurs en particules d'or sont plus élevées dans la matrice que dans les nodules fortement indurés. Cela implique qu'à ce niveau, le cuirassement a affecté un matériau contenant des éléments de nature et de degré d'altération variés. L'absence d'or dans la partie médiane du profil confirme un certain degré d'allochtonie des matériaux parentaux de l'horizon supérieur de la cuirasse. (Résumé d'auteur

Latent Patient Network Learning for Automatic Diagnosis

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is known and recommendations can be made accordingly. However, in challenging cases only symptoms are mentioned and doctors have to rely on their clinical experience. Medical experts and our analyses of a regional dataset of intoxication records provide evidence that this is challenging, since occurring symptoms may not always match the textbook description due to regional distinctions, inter-rater variance, and institutional workflow. Computer-aided diagnosis (CADx) can provide decision support, but approaches so far do not consider additional information of the reported cases like age or gender, despite their potential value towards a correct diagnosis. In this work, we propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We further propose a novel symptom matching method that allows the effective incorporation of prior knowledge into the learning process and evidently stabilizes the poison prediction. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich and show our method's superiority in performance for poison prediction.Comment: 10 pages, 3 figure

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

Comparison of models for the simulation of landslide generated Tsunamis

In this paper, we analyze the relevance of the use of the shallow water model and the Boussinesq model to simulate tsunamis generated by a landslide. In a first part, we determine if the two models are able to reproduce waves generated by a landslide. Each model has drawbacks but it seems that it is possible to use them together to improve the simulations. In a second part we try to recover the landslide displacement from the generated wave. This problem is formulated as a minimization problem and we limit the number of parameters to determine assuming that the bottom can be well described by an empirical law