Abstract: A machine-learning method including obtaining a training dataset of B-rep graphs. Each B-rep graph represents a respective B-rep. Each B-rep graph includes graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features. Each B-rep graph further comprises graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge. The method further includes learning, based on the training dataset, a local Deep CAD neural network. The local Deep CAD neural network takes as input a B-rep graph and to output, for each graph node of the input B-rep graph, a local topological signature of the B-rep element represented by the graph node.

Abstract: The disclosure notably relates to a computer-implemented method for simulating together a plurality of physics simulation instances included in a global physics simulation. The method includes creating a database of local simulation instances. The creating includes providing a set of local simulations. The set of local simulations includes at least two local simulations. A local simulation is a physics simulation that is part of the global physics simulation and that can be computed alone and independently of the multi-physics simulation. Each local simulation of the set of local simulations is already computed. The creating further includes, for each local simulation of the set of local simulations, computing a respective reduced model of the local simulation. The creating further includes, for each local simulation of the set of local simulations, storing in the database a respective local simulation instance. The respective local simulation instance includes the respective computed reduced model.

Abstract: A computer-implemented method for applying, to a given curve, a CAD deformation operator. The given curve results from a conversion of a corresponding subdivision curve defined by a base mesh into a computer-aided design (CAD) curve format. The CAD deformation operator is configured to deform an input CAD object so as to create a contact geometrical relation with another CAD object. The method comprises converting the given curve into a format readable by the CAD deformation operator. The method further comprises applying the CAD deformation operator to the converted given curve. The method further comprises converting back the result of applying the CAD deformation operator into the CAD curve format.

Abstract: A computer-implemented method for converting a subdivision curve into a corresponding computer-aided design (CAD) curve format. The subdivision curve is controlled by a base mesh. The base mesh is formed by vertices connected by one or more edges. The subdivision curve defines at least a part of a product to be manufactured for designing the product. The method comprises transforming the subdivision curve into a G2-continous CAD curve. The CAD curve is formed by the union of one or more Bezier curves. Each Bezier curve corresponds to a respective edge of the base mesh. A control polygon of the CAD curve is computed from the base mesh. The transformation is based on a Catmull-Clark refinement process.

Abstract: A method for teletransmitting to a processing site a captured video stream of a remote intervention scene comprises modifying the captured video stream so as to erase defined zones, then transmitting this stream thus modified to the processing site. The teletransmitting method is employed to remotely assist a field operator with an intervention on a site that is sensitive in terms of confidentiality. The video stream may be captured directly by the operator or from a drone, for example.

Abstract: A computer implemented method for improving search engine queries including receiving a text corpus, determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, the number of said consecutive words within said series being an integer n superior or equal to two, modifying at least partially said text corpus based on said list of n-gram candidates, performing a machine learning embedding on the resulting text corpus, for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element, adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores.

Abstract: A computer-implemented method for determining characteristics of a patient that influence a progression of a disease of the patient. The method includes, while training a neural network with a provided dataset, for each transition of the multi-state model, and for each characteristic, determining a respective quantification of an impact of the characteristic on the results of the neural network. The method includes, for each transition, identifying a list of characteristics of the set of characteristics, and, for each given characteristic of the identified list, determining a relationship between the given characteristic and probabilities of transition. The method includes providing the identified lists and the determined relationships that influence the progression of the disease of the patient. Such a method forms an improved solution for determining patient's characteristics that influence patient disease progression.

Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: A computer-implemented method for correcting topological defects on a surface mesh representing an organ homeomorphic to a sphere and obtained from a medical image. The method comprises applying a transformation to the surface mesh distributing positions of vertices of the surface mesh into a spherical point cloud and maintaining vertices neighborhoods. The method also comprises meshing the points of the spherical point cloud according to a triangular surface mesh presenting: the union of the triangles of the triangular surface mesh forms the convex hull of the points of the spherical point cloud and the intersection between any first triangle of the triangular surface mesh and any second triangle intersecting the first triangle is a vertex of the first triangle or an edge of the first triangle. The method also comprises mapping back the meshing onto the vertices of the surface mesh by reversing the transformation.

Abstract: A method for training a Deep Material Network-based neural network configured to predict a macroscopical physical property of a multi-scale material. The multi-scale material comprises one or more components. The method includes obtaining a dataset, each entry of the dataset corresponding to a respective multi-scale material object. The entry includes a tensor describing the physical property of the object at a macroscopical level, one or more tensors each describing the physical property of a component of the object at a microscopical level, and one or more morphological parameters each describing a morphology of the object. The method further includes training, based on the dataset, the neural network to predict a tensor describing the physical property of a multi-scale material object at a macroscopical level based on the one or more tensors for the object and based on the one or more morphological parameters for the object.

Abstract: A computer-implemented method for designing a 3D model, which includes providing an initial 3D model in a 3D scene including at least one extruded section, being defined by a set of parameters, receiving a user sketch on the plane perpendicular to the sight of view direction, at each iteration: modifying at least one of said parameters, thereby obtaining a modified 3D model, performing a perspective projection, on a plane perpendicular to the sight of view direction, of the modified 3D mode, thereby obtaining a 2D visible wireframe including the visible inner and outer edges of the modified 3D model, computing an energy including a first term which penalizes an inconsistency between the modified and the initial 3D model, and a second term which penalizes a mismatch between the 2D visible wireframe and the user sketch, said parameters being modified to minimize said energy, and outputting the modified 3D model.

Abstract: A computer-implemented method for designing a 3D model in an AR/VR environment including obtaining a 3D model in a 3D scene, the 3D model including at least one extruded section which results from the extrusion of a planar section, said extruded section being defined by a set of parameters, receiving a 3D user sketch in the 3D scene, at each iteration of a plurality of iterations: modifying at least one of said parameters, thereby obtaining a modified 3D model, performing a discretization of the modified 3D model, thereby obtaining a 3D point cloud, computing an energy which comprises a first term which penalizes an inconsistency between the modified 3D model and the initial 3D model, and a second term which penalizes a mismatch between the 3D point cloud and the 3D user sketch, said parameters being modified so as to minimize said energy, and outputting the modified 3D model.

Abstract: A computer-based method includes enabling a user to create or select a geometric entity in a design in a computer-aided design program, predicting a location and orientation in the design for a copy of the geometric entity, and displaying, as a suggestion to the user, a visual representation of the copy of the geometric entity in the predicted location and orientation in the design.

Abstract: A computer-implemented method for determining a machine-learning function configured for taking an input 3D scene and for outputting one or more camera viewpoints each for generating a respective 2D rendering of the 3D scene. The method includes obtaining a library having 3D scenes. The method includes, based on the library, forming a first dataset for training a first neural network configured for outputting a camera position and forming a second dataset for training a second neural network configured for outputting a camera orientation. The method includes training the first neural network based on the first dataset and training the second neural network based on the second dataset. Each camera viewpoint outputted by the machine-learning function includes a camera position and a camera orientation. Such a method forms an improved solution for outputting one or more camera viewpoints of a 3D scene.

Abstract: A method for designing a mechanism including rigid bodies and mechanical joints including obtaining input parameter values which represent the mechanism in an input state. The method also includes determining output parameter values which represent the mechanism in an output state. The determining includes minimizing an objective function under constraints. The objective function penalizes a distance between the output dimensional values and the input dimensional values. The constraints include a first constraint representing verification of the closure equation by the output parameter values. The constraints further include a second constraint representing mobility of the mechanism in the output state. This forms an improved solution for designing a mechanism comprising rigid bodies and mechanical joints.

Abstract: A computer-implemented method for mapping a texture on one or more points in a 3D scene. The one or more points in the 3D scene are obtained from a user-input with an input device. The method includes determining, from the user-input performed with the input device, the one or more points in the 3D scene to be textured. The method includes computing a 3D support comprising the determined one or more points to be textured. The method includes computing a texture based on the determined one or more points. The method includes rendering the computed texture on the computed 3D support. The method forms an improved solution for rendering a 3D scene.

Abstract: A computer-implemented method for rendering two overlapping textures in a 3D scene. The rendering method includes obtaining a first 3D support comprising a first rendered texture. The rendering method includes obtaining a second 3D support comprising a second rendered texture. The rendering method includes detecting that the second support intersects with the first support. The rendering method includes computing a third 3D support by merging the first 3D support and the second 3D support. The rendering method includes computing a third texture by mixing the first texture and the second texture. The rendering method includes rendering the computed third texture on the computed third 3D support. The rendering method includes displaying the rendered third texture on the third 3D support. The method forms an improved solution for rendering a 3D scene.

Abstract: A method for projecting onto a real workpiece an image calculated on the basis of a digital mockup recorded on a digital information server associated with the real workpiece, for the viewing of the workpiece under augmented reality, comprises the following steps: capture by a camera of the image of the real workpiece, real-time alignment of the reference frame associated with the digital mockup with the reference frame of the video capture system and the reference frame of the real workpiece, comprising a step of reprocessing of the image calculated as a function of the topology of the digital mockup and as a function of the orientation of the projection means with respect to the real workpiece.

Abstract: A computer-implemented method for automatically positioning an input 3D object representing a real object in an input 3D scene representing a room. The method includes obtaining a dataset having information about objects of a plurality of rooms. The method includes executing computer program instructions that cause attempting to identify first, second and/or third pairs. The method includes outputting one or more pairs among the set consisting of each identified pair and the counts of the one or more identified pairs. The method includes, for each outputted pair, determining a respective position of the input 3D object in the input 3D scene. The method includes positioning the input 3D object according to the respective position determined for one of the one or more outputted pairs. The method improves the positioning of an input 3D object in an input 3D scene.