Abstract: A recommendation system generates item recommendations for a user based on a distance between a user embedding and item embeddings. To train the item and user embeddings, the recommendation system user-item pairs as training data to focus on difficult items based on the positive and negative items with respect to individual users in the training set. In training, the weight of individual user-item pairs in affecting the user and item embeddings may be determined based on the distance of the particular user-item pair between user embedding and item embedding, as well as the comparative distance for other items of the same type for that user and for the distance of user-item pairs for other users, which may regulate the distances across types and across the training batch.

Abstract: Method and system for processing a bipartite graph that comprises a plurality of first nodes of a first node type, and a plurality of second nodes of a second type, comprising: generating a target first node embedding for a target first node based on features of second nodes and first nodes that are within a multi-hop first node neighbourhood of the target first node, the target first node being selected from the plurality of first nodes of the first node type; generating a target second node embedding for a target second node based on features of first nodes and second nodes that are within a multi-hop second node neighbourhood of the target second node, the target second node being selected from the plurality of second nodes of the second node type; and determining a relationship between the target first node and the target second node based on the target first node embedding and the target second node embedding.

Abstract: System and method for processing an observed bipartite graph that has a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes such that at least some nodes have node neighbourhoods comprising edge connections to one or more other nodes. A plurality of random graph topologies are derived that are realizations of the observed graph topology by replacing the node neighbourhoods of at least some nodes with the node neighbourhoods of other nodes. A non-linear function is trained using the plurality of user nodes, plurality of item nodes and plurality of random graph topologies to learn user node embeddings and item node embeddings for the plurality of user nodes and plurality of item nodes, respectively.

Abstract: A recommendation system generates recommendations for user-item pairs based on embeddings in hyperbolic space. Each user and item may be associated with a local hyperbolic embedding representing the user or item in hyperbolic space. The hyperbolic embedding may be modified by neighborhood information. Because the hyperbolic space may have no closed form for combining neighbor information, the local embedding may be converted to a tangent space for neighborhood aggregation information and converted back to hyperbolic space for a neighborhood-aware embedding to be used in the recommendation score.

Abstract: Systems and method for predicting performance of pipelines for object detection, pose estimation, and object tracking are provided. In one embodiment, a plurality of regressors are stored where each regressor may be trained for a specific object detection (OD), pose estimation (PE), and/or tracking pipeline, so that each regressor outputs a score equal to or greater than that of another regressor in response to a first feature vector as input, the first feature vector corresponding to a first object that is more suitable for the specific pipeline than other pipelines. A second feature vector is stored corresponding to a second object. The regressors are run with the second feature vector as input to derive respective scores, and a recommendation of at least one pipeline is indicated based on the derived scores with respect to the second object. OD/PE/tracking may then be performed using the at least one indicated pipeline.

Abstract: System and method for processing an observed bipartite graph that has a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes such that at least some nodes have node neighbourhoods comprising edge connections to one or more other nodes. A plurality of random graph topologies are derived that are realizations of the observed graph topology by replacing the node neighbourhoods of at least some nodes with the node neighbourhoods of other nodes. A non-linear function is trained using the plurality of user nodes, plurality of item nodes and plurality of random graph topologies to learn user node embeddings and item node embeddings for the plurality of user nodes and plurality of item nodes, respectively.

Abstract: A method includes steps of: (a) causing a display device to display an image of a real object based on captured image data of a real object; (b) causing the display device to display a 2D model obtained from a projection of a 3D model corresponding to the real object based at least on a view; and (c) storing appearance data of the real object and data of the 2D model into a memory device while associating the appearance data and the data of the 2D model with each other after the real object and the 2D model are displayed so as to be aligned with or substantially aligned with each other by the display device.

Abstract: Systems and method for predicting performance of pipelines for object detection, pose estimation, and object tracking are provided. In one embodiment, a plurality of regressors are stored where each regressor may be trained for a specific object detection (OD), pose estimation (PE), and/or tracking pipeline, so that each regressor outputs a score equal to or greater than that of another regressor in response to a first feature vector as input, the first feature vector corresponding to a first object that is more suitable for the specific pipeline than other pipelines. A second feature vector is stored corresponding to a second object. The regressors are run with the second feature vector as input to derive respective scores, and a recommendation of at least one pipeline is indicated based on the derived scores with respect to the second object. OD/PE/tracking may then be performed using the at least one indicated pipeline.

Abstract: A method includes steps of: (a) causing a display device to display an image of a real object based on captured image data of a real object; (b) causing the display device to display a 2D model obtained from a projection of a 3D model corresponding to the real object based at least on a view; and (c) storing appearance data of the real object and data of the 2D model into a memory device while associating the appearance data and the data of the 2D model with each other after the real object and the 2D model are displayed so as to be aligned with or substantially aligned with each other by the display device.

Abstract: A general-purpose multiple-cutting edge drill includes: a drill point (1); a drill body (2); margins (3) located on an outer wall of the drill body (2); two lips, wherein, if viewed from an axial direction, from out to inside, each of the two lips successively includes an outer cutting edge (11), an arc cutting edge (12) and an inner cutting edge (13); a chisel edge (14); outer peaks (15) formed by intersection of the outer cutting edges (11) and the arc cutting edges (12); a center peak (16) formed by intersection of two inner flank surfaces and two split surfaces. Moreover, an included angle between the inner cutting edges (13) ranges from 108° to 128°, an included angle between the outer cutting edge (11) ranges from 130° to 140°, and an included angle between the inner cutting edge (13) and outer cutting edge (11) ranges from 28° to 40°.

Abstract: System for, and method of ellipsometric investigation of and analysis of samples which have, for instance, a non-random effectively “regular” textured surface, and/or a surface characterized by an irregular array of faceted structures.

Abstract: A general-purpose multiple-cutting edge drill includes: a drill point (1); a drill body (2); margins (3) located on an outer wall of the drill body (2); two lips, wherein, if viewed from an axial direction, from out to inside, each of the two lips successively includes an outer cutting edge (11), an arc cutting edge (12) and an inner cutting edge (13); a chisel edge (14); outer peaks (15) formed by intersection of the outer cutting edges (11) and the arc cutting edges (12); a center peak (16) formed by intersection of two inner flank surfaces and two split surfaces. Moreover, an included angle between the inner cutting edges (13) ranges from 108° to 128°, an included angle between the outer cutting edge (11) ranges from 130° to 140°, and an included angle between the inner cutting edge (13) and outer cutting edge (11) ranges from 28° to 40°.

Abstract: System for, and method of ellipsometric investigation of and analysis of samples which have, for instance, a non-random effectively “regular” textured surface, and/or a surface characterized by an irregular array of faceted structures.

Abstract: System for, and method of ellipsometric investigation of and analysis of samples which have, for instance, a non-random effectively “regular” textured surface, and/or a surface characterized by an irregular array of faceted structures.

Abstract: Porous dielectric layers are produced by introducing pores in pre-formed composite dielectric layers. The pores may be produced after the barrier material, the metal or other conductive material is deposited to form a metallization layer. In this manner, the conductive material is provided with a relatively smooth continuous surface on which to deposit.

Abstract: Porous dielectric layers are produced by introducing pores in pre-formed composite dielectric layers. The pores may be produced after the barrier material, the metal or other conductive material is deposited to form a metallization layer. In this manner, the conductive material is provided with a relatively smooth continuous surface on which to deposit.

Abstract: System for, and method of ellipsometric investigation of and analysis of samples which have, for instance, a non-random effectively “regular” textured surface, and/or a surface characterized by an irregular array of faceted structures.

Abstract: Methods of preparing a low-k dielectric material on a substrate are provided. The methods involve using plasma techniques to remove porogen from a precursor layer comprising porogen and a dielectric matrix and to protect the dielectric matrix with a silanol capping agent, resulting in a low-k dielectric matrix. Porogen removal and silanol capping can occur concurrently or sequentially. If performed sequentially, silanol capping is performed without first exposing the dielectric matrix to moisture or ambient conditions.

Abstract: Porous dielectric layers are produced by introducing pores in pre-formed composite dielectric layers. The pores may be produced after the barrier material, the metal or other conductive material is deposited to form a metallization layer. In this manner, the conductive material is provided with a relatively smooth continuous surface on which to deposit.