5,450 research outputs found
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 Feâ18Crâ13Ni
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
A modifier screen in the Drosophila eye reveals that aPKC interacts with Glued during central synapse formation
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
LâintĂ©gration des Ă©valuations de lâapprentissage autorĂ©gulĂ© dans les activitĂ©s dâĂ©valuation dans les professions de la santĂ© : un appel Ă lâaction
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 âcore healthcare knowledgeâ 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 traineesâ 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 santĂ© et les Ă©tudiants ont-ils Ă©tĂ© prĂ©parĂ©s aux exigences inĂ©vitables de nouveaux apprentissages qui se prĂ©senteront Ă eux Ă lâavenir? Ătant donnĂ© la rapiditĂ© avec laquelle les «âconnaissances de base en matiĂšre de soins de santĂ©â» Ă©voluent, les enseignants en mĂ©decine ont la responsabilitĂ© de vĂ©rifier si les Ă©tudiants ont dĂ©veloppĂ© la capacitĂ© dâautorĂ©guler adĂ©quatement leurs apprentissages. Ceux qui pratiquent efficacement lâapprentissage autorĂ©gulĂ© (AAR) surveillent et contrĂŽlent habilement leur cognition, leur motivation, leur comportement et leur environnement pour sâadapter Ă la nĂ©cessitĂ© de nouveaux apprentissages. Cependant, les programmes dâĂ©tudes mĂ©dicales Ă©valuent rarement la capacitĂ© des Ă©tudiants Ă sâengager dans ce processus stratĂ©gique. Dans cet exposĂ© de position, nous plaidons en faveur dâun changement de paradigme vers une Ă©valuation plus ciblĂ©e de lâAAR dans les formations doctorale et postdoctorale, ainsi que pour les activitĂ©s dâĂ©valuation. Plus prĂ©cisĂ©ment, nous explorons les rĂ©sultats convaincants de lâemploi dâun mĂ©lange innovant de principes issus de la recherche en matiĂšre dâAAR et dâĂ©valuations de la prĂ©paration Ă lâapprentissage futur. Nous proposons des recommandations pour une collaboration entre les responsables de la conception de programmes dâĂ©tudes, ceux de lâĂ©laboration du cursus, ceux chargĂ©s de lâĂ©valuation dans les programmes dâĂ©tudes prĂ©doctorales et postdoctorales et les organismes responsables de lâoctroi dâun titre de compĂ©tence en vue de crĂ©er des Ă©valuations intĂ©grĂ©es qui mesurent la mĂ©thode et la qualitĂ© de lâAAR chez les Ă©tudiants. Les programmes dâĂ©tudes tardent encore Ă traduire dans la pratique la reconnaissance de lâimportance de lâapprentissage tout au long de la vie dans lâĂ©ducation mĂ©dicale. Continuer Ă nĂ©gliger ces compĂ©tences importantes ne ferait que nuire aux Ă©tudiants en mĂ©decine et potentiellement Ă 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
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