Laboratoire Jean Kuntzmann View Homepage


Ontology type: grid:Facility  | foaf:Organization     


Organization Info

NAME

Laboratoire Jean Kuntzmann

HOMEPAGE

http://www-ljk.imag.fr/

ADDRESS

http://www.grid.ac/institutes/grid.464181.e/address-0

Publications in SciGraph latest 20 shown

  • 2019-04-08 Towards an improved access to psychiatric rehabilitation: availability and effectiveness at 1-year follow-up of psychoeducation, cognitive remediation therapy, cognitive behaviour therapy and social skills training in the FondaMental Advanced Centers of Expertise-Schizophrenia (FACE-SZ) national cohort. in EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE
  • 2019-03 Stochastic proximal-gradient algorithms for penalized mixed models in STATISTICS AND COMPUTING
  • 2019-03 Noise-free latent block model for high dimensional data in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2019-03 Learning to Segment Moving Objects in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2019-02 Asymptotic model for twisted bent ferromagnetic wires with electric current in ZEITSCHRIFT FÜR ANGEWANDTE MATHEMATIK UND PHYSIK
  • 2019-01-08 Shape optimization of a coupled thermal fluid–structure problem in a level set mesh evolution framework in SEMA JOURNAL
  • 2018-12 Geometrical shape optimization in fluid mechanics using FreeFem++ in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-11 Simulating aggregates of bivalents in 2n = 40 mouse meiotic spermatocytes through inhomogeneous site percolation processes in JOURNAL OF MATHEMATICAL BIOLOGY
  • 2018-10-09 How Good Is My GAN? in COMPUTER VISION – ECCV 2018
  • 2018-10-06 Modeling Visual Context Is Key to Augmenting Object Detection Datasets in COMPUTER VISION – ECCV 2018
  • 2018-10-06 Analyzing Clothing Layer Deformation Statistics of 3D Human Motions in COMPUTER VISION – ECCV 2018
  • 2018-10-06 BodyNet: Volumetric Inference of 3D Human Body Shapes in COMPUTER VISION – ECCV 2018
  • 2018-10-06 End-to-End Incremental Learning in COMPUTER VISION – ECCV 2018
  • 2018-10-05 Predicting Future Instance Segmentation by Forecasting Convolutional Features in COMPUTER VISION – ECCV 2018
  • 2018-10-05 Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency in COMPUTER VISION – ECCV 2018
  • 2018-09 Image-Based Synthesis for Deep 3D Human Pose Estimation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2018-09 Change-Point Estimation in the Multivariate Model Taking into Account the Dependence: Application to the Vegetative Development of Oilseed Rape in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2018-09 Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding in SIGNAL, IMAGE AND VIDEO PROCESSING
  • 2018-08 Generating conformational transition paths with low potential-energy barriers for proteins in JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
  • 2018-08 An Explicit Isometric Reduction of the Unit Sphere into an Arbitrarily Small Ball in FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
  • JSON-LD is the canonical representation for SciGraph data.

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        "id": "http://www.grid.ac/institutes/grid.464181.e/address-0", 
        "sdDataset": "grid", 
        "type": [
          "http://www.grid.ac/ontology/Address"
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    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://www.grid.ac/institutes/grid.464181.e'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://www.grid.ac/institutes/grid.464181.e'

    Turtle is a human-readable linked data format.

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    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://www.grid.ac/institutes/grid.464181.e'


     

    This table displays all metadata directly associated to this object as RDF triples.

    210 TRIPLES      11 PREDICATES      16 URIs      6 LITERALS

    Subject Predicate Object
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