• Publicationsclose
• 2017-2021close
• Conference object close
• CA close
• AT close
• Mémoires en Sciences de l'Information et de la Communication close

The beta decay of the free neutron provides several probes to test the Standard Model of particle physics as well as to search for extensions thereof. Hence, multiple experiments investigating the decay have already been performed, are under way or are being prepared. These measure the mean lifetime, angular correlation coefficients or various spectra of the charged decay products (proton and electron). NoMoS, the Neutron decay prOducts MOmentum Spectrometer, presents a novel method of momentum spectroscopy: it utilizes the $R \times B$ drift effect to disperse charged particles dependent on their momentum in an uniformly curved magnetic field. This spectrometer is designed to precisely measure momentum spectra and angular correlation coefficients in free neutron beta decay to test the Standard Model and to search for new physics beyond. With NoMoS, we aim to measure inter alia the electron-antineutrino correlation coefficient $a$ and the Fierz interference term $b$ with an ultimate precision of $\Delta a/a < 0.3\%$ and $\Delta b < 10^{-3}$ respectively. In this paper, we present the measurement principles, discuss measurement uncertainties and systematics, and give a status update. Comment: 7 pages, 4 figures, accepted to the Proceedings of the International Workshop on Particle Physics at Neutron Sources PPNS 2018, Grenoble, France, May 24-26, 2018

Renal compartment segmentation on CT images targets on extracting the 3D structure of renal compartments from abdominal CTA images and is of great significance to the diagnosis and treatment for kidney diseases. However, due to the unclear compartment boundary, thin compartment structure and large anatomy variation of 3D kidney CT images, deep-learning based renal compartment segmentation is a challenging task. We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation. It has three innovations: (1) A Cycle Prototype Learning (CPL) is proposed to learn consistency for generalization. It learns from pseudo labels through the forward process and learns consistency regularization through the reverse process. The two processes make the model robust to noise and label-efficient. (2) We propose a Bayes Weakly Supervised Module (BWSM) based on cross-period prior knowledge. It learns prior knowledge from cross-period unlabeled data and perform error correction automatically, thus generates accurate pseudo labels. (3) We present a Fine Decoding Feature Extractor (FDFE) for fine-grained feature extraction. It combines global morphology information and local detail information to obtain feature maps with sharp detail, so the model will achieve fine segmentation on thin structures. Our extensive experiments demonstrated our great performance. Our model achieves Dice of \(79.1\%\) and \(78.7\%\) with only four labeled images, achieving a significant improvement by about \(20\%\) than typical prototype model PANet [16].

Part 8: Pointing, Touch, Gesture and Speech-Based Interaction Techniques; International audience; Eyewear displays allow users to interact with virtual content displayed over real-world vision, in active situations like standing and walking. Pointing techniques for eyewear displays have been proposed, but their social acceptability, efficiency, and situation awareness remain to be assessed. Using a novel street-walking simulator, we conducted an empirical study of target acquisition while standing and walking under different levels of street crowdedness. We evaluated three phone-based eyewear pointing techniques: indirect touch on a touchscreen, and two in-air techniques using relative device rotations around forward and a downward axes. Direct touch on a phone, without eyewear, was used as a control condition. Results showed that indirect touch was the most efficient and socially acceptable technique, and that in-air pointing was inefficient when walking. Interestingly, the eyewear displays did not improve situation awareness compared to the control condition. We discuss implications for eyewear interaction design.

Part 7: Digital Governance; International audience; Infrastructural development is a significant determinant of economic growth. It remains an elusive pursuit for many developing economies suffering from public infrastructural project failures. Although the causes of these failures are identifiable, they remain persistent. Government corruption has been identified as the primary cause of project failures amidst a host of other causal factors, spurred by the ambiguity in public service administration. These factors heighten capital expenditures and hence, the need for more transparent systems in public infrastructural project planning and -delivery. This research uses a case-study methodology to examine the importance of public involvement in addressing the causes of failures in public infrastructural project planning and -delivery. Using Nigeria as a case, the findings from conducted interviews and a document review support the proposition of a technologically collaborative approach in addressing the causes of public infrastructural project failures. The institutionalization of transparency-enhancing blockchain systems are vital in government and public involvement in the processes of public infrastructural project planning and -delivery.

International audience; Sliced Inverse Regression (SIR) has been extensively used to reduce the dimension of the predictor space before performing regression. SIR is originally a model free method but it has been shown to actually correspond to the maximum likelihood of an inverse regression model with Gaussian errors. This intrinsic Gaussianity of standard SIR may explain its high sensitivity to outliers as observed in a number of studies. To improve robustness, the inverse regression formulation of SIR is therefore extended to non-Gaussian errors with heavy-tailed distributions. Considering Student distributed errors it is shown that the inverse regression remains tractable via an Expectation- Maximization (EM) algorithm. The algorithm is outlined and tested in the presence of outliers, both in simulated and real data, showing improved results in comparison to a number of other existing approaches.

International audience; Les communications sans contact sont omniprésentes dans notre quotidien, allant des badges de contrôle d'accès au passeport électronique. Ces systèmes sont sensibles aux attaques par relais, dans lesquelles un adversaire transfère simplement les messages entre le prouveur et le vérifieur pour usurper l'identité du prouveur. Les protocoles délimiteurs de distance (distance-bounding) ont été ont ntroduits pour contrer ces attaques en assurant une borne sur la distance entre le prouveur et le vérifieur grâcè a la mesure du temps des communications. Par la suite de nombreux travaux ont amélioré la sécurité de ces protocoles, mais ont aussi cherché à assurer le respect de la vie privée face à des adversaires actifs et également face à des vérifieurs malicieux. En particulier, une menace difficile à prévenir est la fraude terroriste, où un prouveur lointain coopère avec un complice proche pour tromper le vérifieur. La contre-mesure usuelle pour cette menace est de rendre impossible l'action du complice sans l'aide du prouveur lointain, à moins que le prouveur ne lui donne suffisamment d'information pour qu'il retrouve sa clef privée et puisse ainsi toujours se faire passer pour le prouveur. Dans cet article, nous proposons une nouvelle approche où le prouveur ne révèle pas sa clef privée mais utilise une clef de session avec une signature de groupe, la rendant ainsi utilisable plusieurs fois. Ceci permet à un adversaire d'usurper l'identité du prouveur sans même connaître sa clef de signature. Grâce à cette approche nous proposons SPADE le premier protocole de délimiteur de distance qui est anonyme, révocable et formellement prouvé sûr. Mots-clefs : Protocole délimiteur de distance (Distance Bounding), Sécurité, résitance à la fraude terroriste.

International audience; In many disaster scenarios, communication infrastructure fails to provide network services for both civilians and first responders. One solution is to have rapidly deployable mobile networks formed by interconnected base stations, that are easy to move, deploy, and configure. Such public safety-oriented networks are different from classical mobile networks in terms of scale, deployment, and architecture. In this context, we revisit the user association problem, for two main reasons. First, the backhaul, formed by the links interconnecting the base stations, must be accounted for when deciding on the association, since it may present a bottleneck with its limited bandwidth. Second, the mission-critical nature of the traffic imposes strict guaranteed bit rate constraints, that must be respected when associating users. Therefore, we propose a network-aware optimal association that minimizes the bandwidth consumption on the backhaul, while still respecting the stringent performance requirements.

International audience; Software-Defined Networking is a new approach to the design and management of networks. It decouples the software-based control plane from the hardware-based data plane while abstracting the underlying network infrastructure and moving the network intelligence to a centralized software-based controller where network services are deployed. The challenge is then to efficiently provision the service chain requests, while finding the best compromise between the bandwidth requirements, the number of locations for hosting Virtual Network Functions (VNFs), and the number of chain occurrences. We propose two ILP (Integer Linear Programming) models for routing service chain requests, one of them with a decomposition modeling. We conduct extensive numerical experiments, and show we can solve exactly the routing of service chain requests in a few minutes for networks with up to 50 nodes, and traffic requests between all pairs of nodes. We investigate the best compromise between the bandwidth requirements and the number of VNF nodes.

International audience

Documents such as presentations, instruction manuals, and research papers are disseminated using various file formats, many of which barely support the incorporation of interactive content. To address this lack of interactivity, we present Chameleon, a system-wide tool that combines computer vision algorithms used for image identification with an open database format to allow for the layering of dynamic content. Using Chameleon, static documents can be easily upgraded by layering user-generated interactive content on top of static images, all while preserving the original static document format and without modifying existing applications. We describe the development of Chameleon, including the design and evaluation of vision-based image replacement algorithms, the new document-creation pipeline as well as a user study evaluating Chameleon.