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Institution

ParisTech

EducationParis, France
About: ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Residual stress & Finite element method. The organization has 1888 authors who have published 1965 publications receiving 55532 citations. The organization is also known as: Paris Institute of Technology & ParisTech Développement.


Papers
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Journal ArticleDOI
TL;DR: Active gel physics as discussed by the authors is a field that has emerged in recent years to fill this gap and is underpinned by a theory that takes into account the transduction of chemical energy on the molecular scale.
Abstract: The mechanical behaviour of cells is largely controlled by a structure that is fundamentally out of thermodynamic equilibrium: a network of crosslinked filaments subjected to the action of energy-transducing molecular motors. The study of this kind of active system was absent from conventional physics and there was a need for both new theories and new experiments. The field that has emerged in recent years to fill this gap is underpinned by a theory that takes into account the transduction of chemical energy on the molecular scale. This formalism has advanced our understanding of living systems, but it has also had an impact on research in physics per se. Here, we describe this developing field, its relevance to biology, the novelty it conveys to other areas of physics and some of the challenges in store for the future of active gel physics. Equilibrium physics is ill-equipped to explain all of life’s subtleties, largely because living systems are out of equilibrium. Attempts to overcome this problem have given rise to a lively field of research—and some surprising biological findings.

611 citations

Journal ArticleDOI
TL;DR: A novel geometric approach for solving the stereo problem for an arbitrary number of images (= or >2) based upon the definition of a variational principle that must be satisfied by the surfaces of the objects in the scene and their images.
Abstract: We present a novel geometric approach for solving the stereo problem for an arbitrary number of images (/spl ges/2). It is based upon the definition of a variational principle that must be satisfied by the surfaces of the objects in the scene and their images. The Euler-Lagrange equations that are deduced from the variational principle provide a set of partial differential equations (PDEs) that are used to deform an initial set of surfaces which then move toward the objects to be detected. The level set implementation of these PDEs potentially provides an efficient and robust way of achieving the surface evolution and to deal automatically with changes in the surface topology during the deformations, i.e., to deal with multiple objects. Results of an implementation of our theory also dealing with occlusion and visibility are presented on synthetic and real images.

446 citations

Proceedings ArticleDOI
27 Jun 2016
TL;DR: It is demonstrated that the end-to-end trained ConvNet supervised by cycle-consistency outperforms state-of-the-art pairwise matching methods in correspondence-related tasks.
Abstract: Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and realto-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms stateof-the-art pairwise matching methods in correspondencerelated tasks.

387 citations

Journal ArticleDOI
TL;DR: This paper demonstrates that breakthrough performances in flow analysis can be reached using this concept of ultrafast compound Doppler, which allows faster acquisition frame rates for high-velocity flow imaging, or very high sensitivity for low-flow applications.
Abstract: Doppler-based flow analysis methods require acquisition of ultrasound data at high spatio-temporal sampling rates. These rates represent a major technical challenge for ultrasound systems because a compromise between spatial and temporal resolution must be made in conventional approaches. Consequently, ultrasound scanners can either provide full quantitative Doppler information on a limited sample volume (spectral Doppler), or averaged Doppler velocity and/or power estimation on a large region of interest (Doppler flow imaging). In this work, we investigate a different strategy for acquiring Doppler information that can overcome the limitations of the existing Doppler modes by significantly reducing the required acquisition time. This technique is called ultrafast compound Doppler imaging and is based on the following concept: instead of successively insonifying the medium with focused beams, several tilted plane waves are sent into the medium and the backscattered signals are coherently summed to produce high-resolution ultrasound images. We demonstrate that this strategy allows reduction of the acquisition time by a factor of up to of 16 while keeping the same Doppler performance. Depending on the application, different directions to increase performance of Doppler analysis are proposed and the improvement is quantified: the ultrafast compound Doppler method allows faster acquisition frame rates for high-velocity flow imaging, or very high sensitivity for low-flow applications. Full quantitative Doppler flow analysis can be performed on a large region of interest, leading to much more information and improved functionality for the physician. By leveraging the recent emergence of ultrafast parallel beamforming systems, this paper demonstrates that breakthrough performances in flow analysis can be reached using this concept of ultrafast compound Doppler.

386 citations

Journal ArticleDOI
TL;DR: A new fabrication method to produce homogeneously fluorescent nanodiamonds with high yields is described, and the whole fabrication yield of colloidal quasi-spherical nanod diamonds was several orders of magnitude higher than those previously reported starting from microdiamonds.
Abstract: A new fabrication method to produce homogeneously fluorescent nanodiamonds with high yields is described. The powder obtained by high energy ball milling of fluorescent high pressure, high temperature diamond microcrystals was converted in a pure concentrated aqueous colloidal dispersion of highly crystalline ultrasmall nanoparticles with a mean size less than or equal to 10 nm. The whole fabrication yield of colloidal quasi-spherical nanodiamonds was several orders of magnitude higher than those previously reported starting from microdiamonds. The results open up avenues for the industrial cost-effective production of fluorescent nanodiamonds with well-controlled properties.

366 citations


Authors

Showing all 1899 results

NameH-indexPapersCitations
Mathias Fink11690051759
George G. Malliaras9438228533
Mickael Tanter8558329452
Gerard Mourou8265334147
Catherine Lapierre7922718286
Carlo Adamo7544436092
Jean-François Joanny7229420700
Marie-Paule Lefranc7238121087
Paul B. Rainey7022217930
Vincent Lepetit7026826207
Bernard Asselain6940923648
Michael J. Baker6939420834
Jacques Prost6819819064
Jean-Philippe Vert6723517593
Jacques Mairesse6631020539
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20239
202212
202174
202093
2019127
2018145