Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Watersheds in digital spaces: an efficient algorithm based on immersion simulations

Abstract: A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided. >

Flatness and defect of non-linear systems: introductory theory and examples

Abstract: We introduce flat systems, which are equivalent to linear ones via a special type of feedback called endogenous. Their physical properties are subsumed by a linearizing output and they might be regarded as providing another nonlinear extension of Kalman's controllability. The distance to flatness is measured by a non-negative integer, the defect. We utilize differential algebra where flatness- and defect are best defined without distinguishing between input, state, output and other variables. Many realistic classes of examples are flat. We treat two popular ones: the crane and the car with n trailers, the motion planning of which is obtained via elementary properties of plane curves. The three non-flat examples, the simple, double and variable length pendulums, are borrowed from non-linear physics. A high frequency control strategy is proposed such that the averaged systems become flat.

Spatial partitioning of the regulatory landscape of the X-inactivation centre

Abstract: High-order chromatin folding in topologically associating domains has a critical role in proper long-range transcriptional control around the Xist locus, and presumably throughout the genome. The spatial organization of the genome is linked to biological function, and advances in genomic technologies are allowing the conformation of chromosomes to be assessed genome wide. Two groups present complementary papers on the subject. Bing Ren and colleagues use Hi-C, an adaption of the chromosome conformation capture (3C) technique, to investigate the three-dimensional organization of the human and mouse genomes in embryonic stem cells and terminally differentiated cell types. Large, megabase-sized chromatin interaction domains, termed topological domains, are found to be a pervasive and conserved feature of genome organization. Edith Heard and colleagues use chromosome conformation capture carbon-copy (5C) technology and high-resolution microscopy to obtain a high-resolution map of the chromosomal interactions over a large region of the mouse X chromosome, including the X-inactivation centre. A series of discrete topologically associating domains is revealed, as is a previously unknown long intergenic RNA with a potential regulatory role. In eukaryotes transcriptional regulation often involves multiple long-range elements and is influenced by the genomic environment1. A prime example of this concerns the mouse X-inactivation centre (Xic), which orchestrates the initiation of X-chromosome inactivation (XCI) by controlling the expression of the non-protein-coding Xist transcript. The extent of Xic sequences required for the proper regulation of Xist remains unknown. Here we use chromosome conformation capture carbon-copy (5C)2 and super-resolution microscopy to analyse the spatial organization of a 4.5-megabases (Mb) region including Xist. We discover a series of discrete 200-kilobase to 1 Mb topologically associating domains (TADs), present both before and after cell differentiation and on the active and inactive X. TADs align with, but do not rely on, several domain-wide features of the epigenome, such as H3K27me3 or H3K9me2 blocks and lamina-associated domains. TADs also align with coordinately regulated gene clusters. Disruption of a TAD boundary causes ectopic chromosomal contacts and long-range transcriptional misregulation. The Xist/Tsix sense/antisense unit illustrates how TADs enable the spatial segregation of oppositely regulated chromosomal neighbourhoods, with the respective promoters of Xist and Tsix lying in adjacent TADs, each containing their known positive regulators. We identify a novel distal regulatory region of Tsix within its TAD, which produces a long intervening RNA, Linx. In addition to uncovering a new principle of cis-regulatory architecture of mammalian chromosomes, our study sets the stage for the full genetic dissection of the X-inactivation centre.

KPConv: Flexible and Deformable Convolution for Point Clouds

Abstract: We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of kernel points gives KPConv more flexibility than fixed grid convolutions. Furthermore, these locations are continuous in space and can be learned by the network. Therefore, KPConv can be extended to deformable convolutions that learn to adapt kernel points to local geometry. Thanks to a regular subsampling strategy, KPConv is also efficient and robust to varying densities. Whether they use deformable KPConv for complex tasks, or rigid KPconv for simpler tasks, our networks outperform state-of-the-art classification and segmentation approaches on several datasets. We also offer ablation studies and visualizations to provide understanding of what has been learned by KPConv and to validate the descriptive power of deformable KPConv.