Abstract: A computer-implemented method for machine-learning a neural network having a Spike Neural Network for predicting a time occurrence of a clinical event. The method includes providing a training dataset of training examples. Each training example includes one or more time series of values of respective physiological measures of a patient over a past time period and a corresponding ground truth of a time occurrence of the clinical event. The method further includes training the neural network based on the training dataset. The neural network is trained for taking as input one or more time series of values of the respective physiological measures of a patient over a past time period and for outputting a prediction of probabilities over a time period of the occurrence of the clinical event. The method is an improved solution for predicting a time occurrence of a clinical event.

Abstract: A computer-implemented method for encoding a time series of values of a physiological measure of a patient. The method includes obtaining the time series of values of the physiological measure of the patient. The method further includes, for each time point of the time series of values, obtaining a slope coefficient of the time series of values at the time point. The obtaining a slope coefficient is based on the time point, a predetermined number of previous time points, and a predetermined number of next time points. The method further includes, for each time point of the time series of values, comparing the slope coefficient to a reference slope coefficient. The method is an improved solution for encoding a time series of values of a physiological measure of a patient.

Abstract: A computer implemented method for designing a 3D modeled object representing a manufacturing product. The method includes obtaining a base mesh representing the 3D modeled object, selecting one or more connected edges of the base mesh, subdividing the base mesh based on the selected edges by obtaining a bevel pattern area over the selected path. The method obtains, for at least one of the two endpoints of the path, a transition area by grouping all faces sharing the at least one of the two endpoints of the path, except those of the computed bevel pattern area. The method re-meshes the transition area by obtaining a transition vertex located in the transition area and computing an edge connecting each vertex of the pair of vertices with the obtained transition vertex. The method outputs the subdivided base mesh. This improves the design of a 3D modeled object.

Abstract: A method for generation of a sequence of synthetic ultrasound images of an organ. The method includes providing a video diffusion model trained to take as input a sequence of semantically labelled 2D representations of the organ and a corresponding sequence of noise images, to generate a corresponding denoised sequence of ultrasound images respecting the labels. The method includes providing several consecutive sequences of labelled 2D representations of the organ and corresponding consecutive sequences of noise images, iteratively applying the video diffusion model to each couple formed by one said consecutive sequence and one corresponding noise images sequence, including, at each iteration, using, for each denoising step of the backward process of the video diffusion model, a part of the denoised synthetic ultrasound images generated at the previous iteration for that denoising step in replacement of a part of images of the given couple for that denoising step.

Abstract: A computer-implemented method for predicting new occurrences of an event of a physical system. The method includes providing a first set of past events of the physical system, each past event comprising several attributes, providing a signature for each past event of the first set, providing a new event comprising several attributes, computing a signature of the new event, computing a similarity measure between the signature of the new event and each signature of each past event of the first set, determining the past events closest to the new event according to the similarity measures thereby forming a second set of past events, computing a score of relevance for each attribute of the second set, providing a set of attributes by selecting the attributes having the greater scores of relevance.

Abstract: A method for updating an exploded path within a 3D scene including displaying a 3D scene provided with a three-axis system and including an exploded path extending from a starting point to a finishing point, the exploded path being a connected series of at least three line segments each aligned with one of the axes (X, Y, Z) of the three-axis system according to a periodic direction pattern; and, upon instruction to apply a translation to a line segment: applying the translation to this line segment and to the following or preceding line segment to preserve the periodic direction pattern, and, if necessary, adding a linking line segment to relink the exploded path to the starting or finishing point.

Abstract: A computer-implemented method for designing at least one 3D model in a 3D scene including receiving a user's 2D sketch and displaying it on a plane, the 2D sketch representing a view of the 3D model to be designed, inferring a 3D primitive based on the 2D sketch, the 3D primitive being oriented and positioned in the 3D scene to match the view, performing a 2D projection of the 3D primitive on the plane, and fitting the 2D projection onto the 2D sketch.

Abstract: A computer-implemented method for updating a virtual RDF graph database, the virtual database comprising tuples including a file storage having a durability property of ACID property and guarantying consistent write, providing a virtual RDF graph database including a first RDF graph database updatable by streams and a second read-only RDF graph database stored on the file storage and updatable by batches, a catalog for storing metadata describing the second read-only RDF graph database on the file storage, the catalog being compliant with ACID and obtaining a stream of tuples and a batch of tuples, applying the stream of tuples on the first RDF graph database, applying the batch of tuples on the second RDF graph database by computing a snapshot of the second RDF graph database, including the batch of tuples, the computed snapshot and registering the computed snapshot in the catalog.

Abstract: A computer-implemented method of machine-learning for learning a generative function configured to generate a B-rep given a conditioning signal representing a geometry. The generative function includes a vertex neural network, an edge neural network, and a face neural network. The edge and face neural network each further include a topological model and a geometrical model. Each of the neural networks is configured to generate the respective parts of the B-rep (vertices, edges, and faces) respecting the conditioning signal. This provides an improved solution for designing B-reps given a conditioning signal representing a geometry.

Abstract: A computer-implemented method estimates a mass distribution of a physical product comprising a plurality of product parts. The method: a) creates a data structure comprising, for each part, data representing position and orientation of the part, its mass of the part, preferably its tolerance associated to each mass of the part, and its geometry in voxel format; b) decomposes a digital model of the physical product into a plurality of parallel slices (SL1); c) for each slice, uses the data structure for identifying a set of product parts of the data structure overlapping with the slice and determining a respective overlap rate; and d) attributes to each slice a mass value corresponding to a sum of the masses of all product parts overlapping with the slice, weighted by the respective overlap rates. A computer program product, non-transitory computer-readable data-storage medium, a computer system and a Computer Aided Design (CAD) Systems carry out such a method.

Abstract: A computer-implemented method of designing a 3D modeled object representing a mechanical product. The 3D modeled object has a feature tree and an original value of a set of semantic parameters. The method includes by a user graphically interacting with the 3D representation graphically selecting a semantic point of an outer surface of the 3D modeled object and graphically selecting one or more target 3D positions of the semantic point. The method further includes, automatically, iteratively modifying the value of the set of semantic parameters. Thereby the method obtains an updated value of the set of semantic parameters. The method further includes, automatically by the CAD system, updating the 3D representation of the 3D modeled object based on the updated value of the set of semantic parameters. This provides an improved solution for updating 3D modeled objects based on graphical interactions.

Abstract: A machine-learning method for learning a function for predicting a next feature in a feature tree. The method includes obtaining a dataset of examples each including a graph representing at least a part of a feature tree. The graph includes nodes each representing a feature and each labeled with a label of a set of labels each indicating a feature type of a predetermined set. The graph includes edges each connecting nodes and represents a parent-child relationship between the features represented by the nodes. The graph comprises ground truth data indicating a next feature according to one or more selected features each corresponding to a graph node. The method further includes training the function to take as input a graph representing at least a part of a feature tree and to output a prediction of one or more next features in the feature tree.

Abstract: A computer-implemented method for forming a gap on a surface. The surface represents a surface portion of a product to be manufactured. The method includes providing specifications of the gap. The specifications include a radius of a connection between the surface and flanges of the gap. The specifications further include a profile of the gap. The specifications further include a shape defining the gap on the surface. The method further includes sweeping the profile along the shape. The method further includes connecting the result of the sweep to the surface by applying a fillet operation. The fillet operation has as radius the provided radius.

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 computer-implemented data structure representing an adjacency matrix associated with a predicate of a set of quads of an RDF graph database, the adjacency matrix being partitioned into a grid of rectangular partitions, the partitions having a same dimension, the adjacency matrix comprising a set of adjacency sub-matrices, each adjacency sub-matrix corresponding to a respective partition of the adjacency matrix; each adjacency sub-matrix being identified by its respective position in the grid.

Abstract: A method for executing linear recursive queries. The method comprises obtaining a first part of the query that defines as input one or more initial conditions of query elements in a second part of the query on the RDF graph database. The method also comprises obtaining at least two clauses specifying that a second part of the query will be executed recursively. The first clause defines how an output of an execution of the second part of the query is used as an input of a next execution of the second part of the query. The second clause defines the second part of the query and comprises the query elements describing how the output of an execution is queried using (i) the input of the one or more initial conditions for an initial execution or (ii) the output of a previous execution for the next execution.

Abstract: A computer-implemented data structure for storing RDF dataset including a set of RDF quads. The data structure includes a first data structure. The first data structure includes a first non-graph database with ACID properties for temporarily storing one or more modifications to be applied to the RDF dataset. The data structure also includes a second data structure. The second data structure includes a second non-graph database with ACID properties for storing a state of the RDF dataset as a binary representation. The binary representation is stored as binary data in a native structure of the second non-graph database.

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 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 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.