Find your Way by Observing the Sun and Other Semantic Cues

In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information

New insight on early oxidation stages of austenitic stainless steel from in situ XPS analysis on single-crystalline Fe–18Cr–13Ni

International audienceIn situ X-ray photoelectron spectroscopy real-time measurements and angular-dependent high resolution core level analysis were used for the first time to investigate the Cr enrichment and oxide growth mechanisms on a model 304 austenitic stainless steel surface in the very initial stages of oxidation leading to pre-passivation. The oxidation kinetics was followed for increasing oxygen exposure and temperature, revealing an early nucleation regime (for exposure < 10 L) leading to the formation of a strongly Cr-enriched Cr 3+ /Fe 3+ mixed layer followed by an oxide growth regime where preferential iron oxidation takes over and mitigate the initial chromium enrichment

3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation

Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks (F-CNNs), enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase our method's ability to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201

Effect of hydrogen bonding and complexation with metal ions on the fluorescence of luotonin A

Fluorescence characteristics of a biologically active natural alkaloid, luotonin A (LuA), were studied by steady-state and time-resolved spectroscopic methods. The rate constant of the radiationless deactivation from the singlet-excited state diminished by more than one order of magnitude when the solvent polarity was changed from toluene to water. Dual emission was found in polyfluorinated alcohols of large hydrogen bond donating ability due to photoinitiated proton displacement along the hydrogen bond. In CH 2Cl2, LuA produced both 1:1 and 1:2 hydrogen-bonded complexes with hexafluoro-2-propanol (HFIP) in the ground state. Photoexcitation of the 1:2 complex led to protonated LuA, whose fluorescence appeared at a long wavelength. LuA served as a bidentate ligand forming 1:1 complexes with metal ions in acetonitrile. The stability of the complexes diminished in the series of Cd2+ > Zn2+ > Ag+, and upon competitive binding of water to the metal cations. The effect of chelate formation on the fluorescent properties was revealed. © 2013 The Royal Society of Chemistry and Owner Societies

L’intégration des évaluations de l’apprentissage autorégulé dans les activités d’évaluation dans les professions de la santé : un appel à l’action

How well have healthcare professionals and trainees been prepared for the inevitable demands for new learning that will arise in their future? Given the rapidity with which ‘core healthcare knowledge’ changes, medical educators have a responsibility to audit whether trainees have developed the capacity to effectively self-regulate their learning. Trainees who engage in effective self-regulated learning (SRL) skilfully monitor and control their cognition, motivation, behaviour, and environment to adaptively meet demands for new learning. However, medical curricula rarely assess trainees’ capacity to engage in this strategic process. In this position paper, we argue for a paradigm shift toward assessing SRL more deliberately in undergraduate and postgraduate programs, as well as in associated licensing activities. Specifically, we explore evidence supporting an innovative blend of principles from the science on SRL, and on preparation for future learning (PFL) assessments. We propose recommendations for how program designers, curriculum developers, and assessment leads in undergraduate and postgraduate training programs, and in licensing bodies can work together to develop integrated assessments that measure how and how well trainees engage in SRL. Claims about lifelong learning in health professions education have gone unmatched by responsive curricular changes for far too long. Further neglecting these important competencies represents a disservice to medical trainees and a potential risk to the future patients they will care for.Dans quelle mesure les professionnels de la santé et les étudiants ont-ils été préparés aux exigences inévitables de nouveaux apprentissages qui se présenteront à eux à l’avenir? Étant donné la rapidité avec laquelle les « connaissances de base en matière de soins de santé » évoluent, les enseignants en médecine ont la responsabilité de vérifier si les étudiants ont développé la capacité d’autoréguler adéquatement leurs apprentissages. Ceux qui pratiquent efficacement l’apprentissage autorégulé (AAR) surveillent et contrôlent habilement leur cognition, leur motivation, leur comportement et leur environnement pour s’adapter à la nécessité de nouveaux apprentissages. Cependant, les programmes d’études médicales évaluent rarement la capacité des étudiants à s’engager dans ce processus stratégique. Dans cet exposé de position, nous plaidons en faveur d’un changement de paradigme vers une évaluation plus ciblée de l’AAR dans les formations doctorale et postdoctorale, ainsi que pour les activités d’évaluation. Plus précisément, nous explorons les résultats convaincants de l’emploi d’un mélange innovant de principes issus de la recherche en matière d’AAR et d’évaluations de la préparation à l’apprentissage futur. Nous proposons des recommandations pour une collaboration entre les responsables de la conception de programmes d’études, ceux de l’élaboration du cursus, ceux chargés de l’évaluation dans les programmes d’études prédoctorales et postdoctorales et les organismes responsables de l’octroi d’un titre de compétence en vue de créer des évaluations intégrées qui mesurent la méthode et la qualité de l’AAR chez les étudiants. Les programmes d’études tardent encore à traduire dans la pratique la reconnaissance de l’importance de l’apprentissage tout au long de la vie dans l’éducation médicale. Continuer à négliger ces compétences importantes ne ferait que nuire aux étudiants en médecine et potentiellement à leurs futurs patients

Measure of genuine multipartite entanglement with computable lower bounds

We introduce an intuitive measure of genuine multipartite entanglement which is based on the well-known concurrence. We show how lower bounds on this measure can be derived that also meet important characteristics of an entanglement measure. These lower bounds are experimentally implementable in a feasible way enabling quantification of multipartite entanglement in a broad variety of cases.Comment: 5 pages, 2 figure