• Publicationsclose
• Conference object close
• CA close
• Mémoires en Sciences de l'Information et de la Communication close
• Hal-Diderot close
• Hyper Article en Ligne close

EBEX is a NASA-funded balloon-borne experiment designed to measure the polarization of the cosmic microwave background (CMB). Observations will be made using 1432 transition edge sensor (TES) bolometric detectors read out with frequency multiplexed SQuIDs. EBEX will observe in three frequency bands centered at 150, 250, and 410 GHz, with 768, 384, and 280 detectors in each band, respectively. This broad frequency coverage is designed to provide valuable information about polarized foreground signals from dust. The polarized sky signals will be modulated with an achromatic half wave plate (AHWP) rotating on a superconducting magnetic bearing (SMB) and analyzed with a fixed wire grid polarizer. EBEX will observe a patch covering ~1% of the sky with 8' resolution, allowing for observation of the angular power spectrum from \ell = 20 to 1000. This will allow EBEX to search for both the primordial B-mode signal predicted by inflation and the anticipated lensing B-mode signal. Calculations to predict EBEX constraints on r using expected noise levels show that, for a likelihood centered around zero and with negligible foregrounds, 99% of the area falls below r = 0.035. This value increases by a factor of 1.6 after a process of foreground subtraction. This estimate does not include systematic uncertainties. An engineering flight was launched in June, 2009, from Ft. Sumner, NM, and the long duration science flight in Antarctica is planned for 2011. These proceedings describe the EBEX instrument and the North American engineering flight. 12 pages, 9 figures, Conference proceedings for SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy V (2010)

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.

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.

International audience; The purposes of this study were to: (a) identify emotion profiles of athletes involved in an intensive training setting across three measurement points (beginning, middle and end of the season); (b) explore the stability and change of those emotion profiles over the season; and (c) examine if self-determined motivation predicts membership to the emotion profiles. Method: Three hundred and forty-three adolescent athletes in intensive training settings filled out measures of emotions (sadness, anxiety, anger, happiness, confidence, love, harmony, and vitality) and self-determined motivation (autonomous and controlled). Data were analyzed using a latent profile transition analysis (LPTA) approach. Design: longitudinal three-wave design. Results: LPTA results revealed four emotion profiles: High positive emotions (PE) and low negative emotions (NE), moderately high PE and low NE, moderately high PE and NE, and moderate PE and NE. Individuals exhibited both changes and stability in their emotion profile over time. Membership of emotion profiles were predicted by autonomous and controlled motivation assessed at baseline. Conclusions: The emotion profile approach was proven useful in understanding emotions experienced over time by adolescent athletes involved in intensive training settings and has implications for psychological intervention.

International audience; We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suffer from the curse of dimensionality, at the dimension of the true underlying manifold. This observation suggests to explore non-local manifold learning algorithms which attempt to discover shared structure in the tangent planes at different positions. A criterion for such an algorithm is proposed and experiments estimating a tangent plane prediction function are presented, showing its advantages with respect to local manifold learning algorithms: it is able to generalize very far from training data (on learning handwritten character image rotations), where a local non-parametric method fails.

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.

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].

International audience; The 3D visibility skeleton is a data structure used to encode global visibility information about a set of objects. Previous theoretical results have shown that for $k$ convex polytopes with $n$ edges in total, the worst case size complexity of this data structure is $\Theta(n^2 k^2) $ [Brˆnnimann et al. 07]; whereas for $k$ uniformly distributed unit spheres, the expected size is $\Theta(k)$ [Devillers et al. 03]. In this paper, we study the size of the visibility skeleton experimentally. Our results indicate that the size of the 3D visibility skeleton, in our setting, is $ C\,k\sqrt{n\,k}$, where $C$ varies with the scene density but remains small. % This is the first experimentally determined asymptotic estimate of the size of the 3D visibility skeleton for reasonably large $n$ and expressed in terms of both $n$ and $k$. We suggest theoretical explanations for the experimental results we obtained. Our experiments also indicate that the running time of our implementation is $O(n^{3/2} k\log k)$, while its worst-case running time complexity is $O(n^2k^2 \log k)$.

We present preliminary results on a study of the 2--850 micron SEDs of a sample of 30 FIRBACK galaxies selected at 170 micron. These sources are representative of the brightest ~10% of the Cosmic Infrared Background. They are a mixture of mostly local (z<~0.3) starforming galaxies, and a tail of ULIGs that extend up to z~1, and are likely to be a similar population to faint SCUBA sources. We use archival Spitzer IRAC and MIPS data to extend the spectral coverage to the mid-IR regime, resulting in an unprecended (for this redshift range) census of their infrared SEDs. This allows us to study in far greater detail this important population linking the near-IR stellar emission with PAH and thermal dust emission. We do this using a Markov Chain Monte Carlo method, which easily allows for the inclusion of ~6 free parameters, as well as an estimate of parameter uncertainties and correlations. Comment: 5 pages, 3 figures. Proceeding for the conference "Starbursts: From 30 Doradus to Lyman Break Galaxies", held in Cambridge (UK) in September, 2004

International audience; We present results of photoacoustics and laser-ultrasonics experiments that were performed on a ZnO ceramic sample and that led to optical, thermal and thermo-mechanical characterizations of this material.