GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.Comment: 8 pages, 1 tables, 2 figure

Orthogonality and Boolean Algebras for Deduction Modulo

Originating from automated theorem proving, deduction modulo removes computational arguments from proofs by interleaving rewriting with the deduction process. From a proof-theoretic point of view, deduction modulo defines a generic notion of cut that applies to any first-order theory presented as a rewrite system. In such a setting, one can prove cut-elimination theorems that apply to many theories, provided they verify some generic criterion. Pre-Heyting algebras are a generalization of Heyting algebras which are used by Dowek to provide a semantic intuitionistic criterion called superconsistency for generic cut-elimination. This paper uses pre-Boolean algebras (generalizing Boolean algebras) and biorthogonality to prove a generic cut-elimination theorem for the classical sequent calculus modulo. It gives this way a novel application of reducibility candidates techniques, avoiding the use of proof-terms and simplifying the arguments

The disabled set sail

International audienceSailing is not an activity that can easily be done with a handicap. Sailors typically need mobility to steer a boat. An Arduino-compatible CAN architecture for sailing applications is set to change that

An Arduino compatible CAN Bus architecture for sailing applications

International audienceThis paper describes a Controller Area Network (CAN) Bus architecture based on Arduino compatible boards, to be used as an alternative communication system for robotic applications. This combines both, the robustness of CAN and the accessibility of Arduino software. The architecture is developed here to improve a Navigational Assistance System, which was initially created for disabled people. The system is composed of Arduino compatible boards, wired with various sensors and actuators, and communicating with an Human Machine Interface (HMI), directly accessible via a mobile phone or a tablet running on the open-source operating system Android. Information is transferred through the CAN bus architecture between multiple nodes (i.e. Arduino compatible boards) and the implementation of a CAN bootloader allows the reconfiguration of the nodes directly through the bus. The aim is to create a generic system able to work in various kinds of situations, adaptable to all kinds of users, including persons with all sorts of disabilities. This work will result in a demonstrator on a Miniji for the WRSC 2013 and an entirely joystick controlled boat for single handed sailing

IMPORTANCE DIDACTIQUE DES CONCEPTIONS DES ENSEIGNANTS TUNISIENS SUR L'ÉDUCATION À LA SEXUALITÉ DANS UNE PERSPECTIVE CITOYENNE

http://ensciencias.uab.esInternational audienceL'objectif de cette étude est d'analyser les conceptions d'enseignants et futurs enseignants tunisiens et d'identifier les valeurs qu'expriment ces conceptions. Les nombreuses conceptions exprimées sont étudiées en tant qu'interactions entre les connaissances scientifiques, les valeurs et les pratiques d'enseignement, au moyen d'une analyse en composante principale (ACP). L'échantillon est formé de 753 enseignants et futurs enseignants du primaire et du secondaire, qui ont répondu à un questionnaire, dans un contexte strictement contrôlé

Fault Injection on Embedded Neural Networks: Impact of a Single Instruction Skip

With the large-scale integration and use of neural network models, especially in critical embedded systems, their security assessment to guarantee their reliability is becoming an urgent need. More particularly, models deployed in embedded platforms, such as 32-bit microcontrollers, are physically accessible by adversaries and therefore vulnerable to hardware disturbances. We present the first set of experiments on the use of two fault injection means, electromagnetic and laser injections, applied on neural networks models embedded on a Cortex M4 32-bit microcontroller platform. Contrary to most of state-of-the-art works dedicated to the alteration of the internal parameters or input values, our goal is to simulate and experimentally demonstrate the impact of a specific fault model that is instruction skip. For that purpose, we assessed several modification attacks on the control flow of a neural network inference. We reveal integrity threats by targeting several steps in the inference program of typical convolutional neural network models, which may be exploited by an attacker to alter the predictions of the target models with different adversarial goals.Comment: Accepted at DSD 2023 for AHSA Special Sessio