Abstract: A computer-implemented method for designing a 3D modeled object representing a transmission mechanism with a target 3D motion behavior. The method including obtaining a 3D finite element mesh and data associated to the mesh, performing a topology optimization based on the mesh and on the associated data, therefore obtaining a density field representing distribution of material quantity of the 3D modeled object. The method further includes computing a signed field based on the density field and the associated data, identifying one or more patterns of convergence and divergence in the signed field, each pattern forming a region of the signed field, and for each identified pattern, identifying a joint representative of the identified pattern and replacing a part of the density field corresponding to the respective region formed by the identified pattern by a material distribution representing the identified joint.

Abstract: A computer-implemented method of machine-learning. The method includes obtaining a training dataset of 3D models of real-world objects. The method further includes learning, based on the training dataset and on a patch-decomposition of the 3D models of the training dataset, a finite codebook of quantized vectors and a neural network. The neural network comprises a rotation-invariant encoder. The rotation-invariant encoder is configured for rotation-invariant encoding of a patch of a 3D model into a quantized latent vector of the codebook. The neural network further includes a decoder. The decoder is configured for decoding a sequence of quantized latent vectors of the codebook into a 3D model. The sequence corresponds to a patch-decomposition. This constitutes an improved solution for 3D model generation.

Abstract: A computed-implemented method for processing a computer-aided design 3D model of a mechanical part including a portion having a distribution of material. The method including obtaining the 3D model, the 3D model including a skin portion of the 3D model representing an outer surface of the portion of the mechanical part. The method further including processing the skin portion based on an extrusion-processing algorithm, where a transform of the skin portion is inputted to the algorithm. The transform represents an unfolding of the distribution of material of the portion.

Abstract: A computer-implemented method for profile detection in a discrete 3D model. The discrete 3D model represents a mechanical part. The method includes, for each 3D surface of at least one 3D surface of the 3D model, providing a candidate parameterization of the 3D surface and a candidate projection of the 3D surface into . The method further includes computing a function. The function penalizes, for each couple of points of the 3D surface having neighboring parameter values, a distortion. The distortion is between a disparity between the projection of the points, and a disparity between the parameterization of the points. The method further includes determining that the candidate parameterization and the candidate projection form a valid profile of the 3D surface if the computed function is smaller than a predefined threshold.

Abstract: A computer-implemented method of machine-learning. The method includes obtaining a dataset of 3D modeled objects representing real-world objects. The method further includes learning, based on the dataset, a generative neural network. The generative neural network is configured for generating a deformation basis of an input 3D modeled object. The learning includes an adversarial training.

Abstract: A computer-implemented method for predicting a cardiovascular behavior of a newborn to birth from his/her fetus physiological parameters. The method comprises obtaining physiological parameters of the fetus, obtaining a non-calibrated surrogate cardiovascular model modeling a cardiovascular system of the fetus and modelling at least one physiological change triggered by the birth, calibrating the non-calibrated surrogate cardiovascular model with a data assimilation algorithm using the obtained physiological parameters of the fetus on the non-calibrated surrogate cardiovascular model, thereby obtaining a calibrated surrogate cardiovascular model of the fetus, and predicting the cardiovascular behavior of the newborn by triggering the at least one physiological change of the calibrated surrogate cardiovascular model of the fetus, thereby obtaining a calibrated surrogate cardiovascular model of the newborn.

Abstract: A computer-implemented method for generating a summary of a graph database comprising a set of RDF tuples including obtaining the graph database and generating a summary having a set of probabilistic filters. Each probabilistic filter of the set determines if at least one RDF tuple existing in the graph database corresponds to a respective basic graph pattern of the probabilistic filter with a possibility of false positive.

Abstract: A computer-implemented method for generating by a query engine a graph of operators for a SPARQL query over an RDF graph. The method includes obtaining a graph of operators executable by the query engine, the graph comprising a plurality of basic operators, at least two of said operators being of a first type each configured to find RDF triples of the RDF graph that match a respective basic graph pattern. The method further comprises identifying a group of operators among the at least two basic operators of the graph which are of the first type. The respective basic graph patterns of the group of operators have same subject and/or predicate and/or object and the identified group of operators is replaced in the graph by an equivalent operator configured to find RDF triples of the RDF graph that match the respective basic graph patterns of the group of operators.

Abstract: A computer-implemented method of applying a machine-learning function preconfigured for taking an input 3D layout and a given noise level, and for predicting an output 3D layout. The function is preconfigured with a conditioning drop-out with respect to at least one layout parameter. The method further comprises obtaining a set of conditioning inputs and, for each conditioning input, determining one or more conditioning candidate 3D layouts and determining a plurality of perturbed conditioning candidate 3D layouts. The method further includes, applying the preconfigured function to each perturbed conditioning candidate, in which the one layout parameter is dropped out, computing reconstruction errors, and averaging the reconstruction errors, thereby obtaining a score. This forms an improved solution for predicting 3D layouts.

Abstract: A computer-implemented method of machine-learning for assembling mechanical parts, based on a mating score and a mating axis. The method includes providing a dataset of pairs of B-Reps, each pair comprising at least one B-Rep representing an assembly of mechanical parts, being labelled with mating compatibility data and, when the B-Reps of the pair are compatible according to the mating compatibility data, mating axis compatibility data. The method also comprises training a neural network based on the dataset, configured for taking as input a pair of B-Reps. The neural network also outputs a mating score of a pair of single embeddings, each single embedding representing a B-Rep, the mating score representing a score of mating compatibility between the mechanical parts represented by the pair, and if the B-Reps are compatible according to the mating score, data defining a mating axis.

Abstract: A computer-implemented method of machine-learning. The method includes obtaining a dataset of ground truth 3D layouts. The machine-learning method further comprises obtaining a probability distribution of noise levels. The machine-learning method also comprises, for each ground truth 3D layout, obtaining a respective perturbed 3D layout. The machine-learning method moreover comprises training a function. The function is configured for taking an input 3D layout and a given noise level, and for predicting an output 3D layout. The training is performed over the dataset based on a loss which penalizes a dissimilarity metric between each ground truth 3D layout and a respective predicted 3D layout.

Abstract: A computer-implemented method for storing a database state. The method comprises providing a database, receiving by the database one or more write events, logging each write event, each logged write event thus forming a new state on the database, buffering pages modified or created by the write events, and creating a patch by flushing to a database storage the buffered pages if a threshold has been met.

Abstract: A computer-implemented method for genetic data processing. The processing method including obtaining a dataset comprising microbial gene expression data for a plurality of patients. The method comprises clustering the plurality of patients into a set of clusters based on the microbial gene expression data. The method comprises determining genes of which expression explains the clustering by selecting genes included in a set of metabolic pathways and/or by identifying genes exhibiting significantly different expressions across the clusters. The method forms an improved solution for patient stratification based on metagenomic data.

Abstract: A technical data detection method in a technical drawing image. The technical drawing includes a view of a technical object and a technical annotation. The method includes identifying one or more views in the technical drawing. The method includes identifying one or more technical annotations in each view. The method includes identifying characters in each technical annotation. The method includes determining a graph representation of each view. The graph representation includes nodes each corresponding to a classification of pixels in the view into a semantic class and edges each connects two nodes either if the two nodes represent neighboring pixels or if the two nodes represent pixels distant from each other below a threshold. The method includes, for each identified view, using the graph topology and the identified characters to associate nodes corresponding to the dimension-related symbol or dimension classes to nodes corresponding to the geometry class.

Abstract: A graph processing method where the graph represents an image of a technical drawing including a view and a technical annotation. The method includes, for each view, providing the graph. The graph including nodes and edges. Each node corresponds to a classification of pixels into a semantic class of a predetermined set. Each edge connects two nodes either if the two nodes represent neighboring pixels or if the two nodes represent pixels distant from each other below a predetermined threshold. The set includes geometry, dimension, dimension-related symbol. The method includes clustering, based on the graph topology: nodes corresponding to the geometry class, to reconstruct the geometries in the view, and nodes corresponding to the dimension and dimension-related symbol classes, to reconstruct the annotations of the view. The method includes, associating reconstructed annotations to reconstructed geometries based on a detected position of the annotations and on the graph topology.

Abstract: A computer-implemented method for machine-learning a function configured to take as input a plurality of aligned images of a same patient and each of a different modality among a predetermined set of medical-imaging modalities, and to calculate a fused image. The method includes obtaining a dataset including, for each patient of a plurality of patients and for each modality of a respective at least part of the predetermined set, a respective image, the respective images for a patient being aligned; and training the function based on the dataset. This forms an improved solution for medical imaging.

Abstract: A computer-implemented method for parceling grey matter of a human brain of a human patient comprising obtaining a tractogram including tractogram streamlines, each having a first extremity located in a first portion of grey matter of a second extremity located in a second portion of grey matter. The parceling method also comprises using a predetermined clustering algorithm to obtain tractogram streamline clusters, and, for each cluster of at least a part of the clusters, identifying a respective first region of grey matter including for each streamline of the cluster its first extremity, and a respective second region of grey matter including for each streamline of the cluster its second extremity. The parceling method also comprises determining a parcellation based on the identified regions, including an iterative merging process which includes merging pairs of regions based on a metric quantifying an overlap.

Abstract: A computer-implemented method for generating a 2D image of a 3D scene. The method comprises obtaining arrangement data comprising a layout of the 3D scene and at least one conditioning signal. Each conditioning signal has a type among a predetermined set of at least two types. The method comprises applying a machine-learning function to the obtained arrangement data and viewpoint. The function comprises a scene encoder and a generative image model. The scene encoder takes as input the obtained arrangement data and viewpoint and outputting a scene encoding tensor. The generative image model takes as input the scene encoding tensor outputted by the scene encoder and outputting the generated 2D image. Such a generating method forms an improved solution for controllably generating a 2D image of a 3D scene.

Abstract: A computer-implemented method for annotating a 3D modeled object representing a CAD mechanical part or assembly of parts, two or more views of the 3D modeled object being displayed and at least one of the views being compliant with a view of a technical drawing. The method includes obtaining a 3D scene and displaying an orientation-free view of the 3D modeled object, at least one fixed view of the 3D modeled object. The method also includes creating, upon user action, an annotation by selecting at least one geometrical element on at least one of the views of the 3D modeled object and an annotation type. The method further includes displaying the created annotation on at least the orientation-free view.

Abstract: A computer-implemented method for generating a plurality of 2D images of a 3D scene. The method comprises obtaining arrangement data and a machine-learning model configured for generating a 2D image. The method comprises generating a plurality of first 2D images of the 3D scene each having a respective viewpoint. The method comprises, for each generated first 2D image, computing a first latent vector. The method comprises, for each generated first 2D image, computing a second latent vector as a weighted combination of the computed first latent vectors. The method comprises generating a plurality of second 2D images of the 3D scene by applying, for each given viewpoint of the first 2D images, the model to the computed second latent vector. Such a generating method forms an improved solution for generating a plurality of 2D images of a given 3D scene.