Abstract: A graphics processing system configured to use a rendering space which is subdivided into a plurality of tiles, includes geometry processing logic having geometry transform and sub-primitive logic configured to receive graphics data of input graphics data items, and to determine transformed positions within the rendering space of one or more sub-primitives derived from the input graphics data items using a plurality of shader stages; and a tiling unit configured to generate control stream data including sub-primitive indications to indicate which of the sub-primitives are to be used for rendering each tile. The geometry processing logic is configured to write to a memory, for each instance of a pre-determined shader stage, shader stage output data comprising data output from each instance of the pre-determined shader stage used to process the received graphics data.

Abstract: Aspects of the disclosure relate to processing systems using improved model-based techniques for performing virtual device simulation. A computing platform may receive commands directing the launch of a visual model corresponding to a device identifier. The computing platform may send commands directing a database to provide a state machine model corresponding to the visual model. The computing platform may receive the state machine model and subsequently an indication of a virtual interaction with the visual model. Based on the indication, the computing platform may cause a state change of the visual model and may send, to the client device, state change information corresponding to the visual model. The computing platform may receive information verifying simulated operation parameters, and based on the information the computing platform may cause an implementation update to a device, corresponding to the visual model, based on the simulated operation parameters.

Abstract: A method includes receiving a first representation of an internal surface of at least a portion of a wall of an organ of a patient, and a second representation of an external surface of at least the portion of the wall of the organ. The first and second representations are registered with one another. An exploded representation is generated from the first and second representations, that shows both the internal surface and the external surface. The exploded representation is presented to a user.

Abstract: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.

Abstract: Split applications and virtual objects in a multi-user communication session may include presenting, for a first device, a first environmental representation of the multi-user communication session, wherein the first device and the second device are active in the multi-user communication session, and wherein the first environmental representation and a second environmental representation of the multi-user communication session for the second device comprise one or more shared virtual objects presented in a common spatial configuration; duplicating a particular shared virtual object that is located at an initial location in the common spatial configuration; determining a modified location for the duplicated virtual object in the first environmental representation; and presenting a modified first environmental representation comprising the duplicated virtual object at the modified location.

Abstract: A semiconductor device includes a stack structure including conductive patterns spaced apart from each other, a channel structure penetrating the stack structure, and a slit insulating layer penetrating the stack structure. Air gaps are defined between the conductive patterns. The slit insulating layer includes a first interposition part covering a sidewall of one of the conductive patterns and a second interposition part covering one of the air gaps from the side. A smallest width of the second interposition part is smaller than a smallest width of the first interposition part.

Abstract: A system includes a determination component that determines output for successively larger neural networks of a set; and a consensus component that determines consensus between a first neural network and a second neural network of the set. A linear chain of increasingly complex neural networks trained on progressively larger inputs is utilized (e.g., increasingly complex neural networks is generally representative of increased accuracy). Outputs of progressively networks are computed until a consensus point is reached—where two or more successive large networks yield a same inference output. At such point of consensus the larger neural network of the set reaching consensus can be deemed appropriately sized (or of sufficient complexity) for a classification task at hand.

Abstract: A treatment planning prediction method to predict a Dose-Volume Histogram (DVH) or Dose Distribution (DD) for patient data using a machine-learning computer framework is provided with the key inclusion of a Planning Target Volume (PTV) only treatment plan in the framework. A dosimetric parameter is used as an additional parameter to the framework and which is obtained from a prediction of the PTV-only treatment plan. The method outputs a Dose-Volume Histogram and/or a Dose Distribution for the patient including the prediction of the PTV-only treatment plan. The method alleviates the complicated process of quantifying anatomical features and harnesses directly the inherent correlation between the PTV-only plan and the clinical plan in the dose domain. The method provides a more robust and efficient solution to the important DVHs prediction problem in treatment planning and plan quality assurance.

Abstract: Systems, methods, articles of manufacture, and computer program products to: train a prediction model using a machine learning process, the prediction model configured to estimate whether further application of a hyperparameter tuning technique will cause an improvement in at least one of the hyperparameters; select the hyperparameters using the tuning technique; apply the prediction model to determine if further adjustment of the hyperparameters is likely to improve the success metric; and terminate the tuning technique when: accuracy of the prediction model in predicting improvement in a hyperparameter is above a predetermined accuracy threshold, and the prediction model predicts that further application of the tuning technique will not result in an improvement to the hyperparameter; or the accuracy of the prediction model in predicting improvement in the parameter is below the predetermined accuracy threshold, and an accuracy of hyperparameter adjustment is determined to be below a predetermined adjustment

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: A semiconductor package includes; a semiconductor chip including a top surface and an opposing bottom surface, a heat dissipation structure including a lower adhesive layer adhered to the top surface of the semiconductor chip, a heat dissipation layer disposed on the lower adhesive layer, and a conductive layer disposed on the heat dissipation layer, a core layer including a cavity and a lower surface, wherein a combination of the semiconductor chip and the heat dissipation structure is disposed within the cavity, and a bottom re-wiring layer including a bottom re-wiring line connected to the semiconductor chip.

Abstract: A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: Systems and methods for generating embeddings for nodes of a corpus graph are presented. The embeddings correspond to aggregated embedding vectors for nodes of the corpus graph. Without processing the entire corpus graph to generate all aggregated embedding vectors, a relevant neighborhood of nodes within the corpus graph are identified for a target node of the corpus graph. Based on embedding information of the target node's immediate neighbors, and also upon neighborhood embedding information from the target node's relevant neighborhood, an aggregated embedding vector can be generated for the target node that comprises both an embedding vector portion corresponding to the target node, as well as a neighborhood embedding vector portion, corresponding to embedding information of the relevant neighborhood of the target node. Utilizing both portions of the aggregated embedding vector leads to improved content recommendation to a user in response to a query.

Abstract: Methods, devices, media, and other embodiments are described for a state-space system for pseudorandom animation. In one embodiment animation elements within a computer model are identified, and for each animation element motion patterns and speed harmonics are identified. A set of motion data values comprising a state-space description of the motion patterns and the speed harmonics are generated, and a probability assigned to each value of the set of motion data values for the state-space description. The probability can then be used to select and update a particular motion used in an animation generated from the computer model.

Abstract: Various implementations disclosed herein include a method performed at an electronic device including one or more processors, a non-transitory memory, an image sensor, and a display device. The method includes obtaining, via the image sensor, an input image that includes an object. The method includes obtaining depth information characterizing the object, wherein the depth information characterizes a first distance between the image sensor and a portion of the object. The method includes determining a distance warp map for the input image based on a function of the depth information and a first offset value characterizing an estimated distance between eyes of a user and the display device. The method includes setting an operational parameter for the electronic device based on the distance warp map and generating, by the electronic device set to the operational parameter, a warped image from the input image.

Abstract: Various systems, devices, and methods for operating on a data sequence. A system includes a set of circuits that form an input layer to receive a data sequence; first hardware computing units to transform the data sequence, the first hardware computing units connected using a set of randomly selected weights, a first hardware computing unit to: receive an input from a second hardware computing unit, determine a weight of a connection between the first and second hardware computing units using an identifier of the second hardware computing unit and a fixed random weight generator, and operate on the input using the weight to determine a state of the first hardware computing unit; and second hardware computing units to operate on states of the first computing units to generate an output based on the data sequence.

Abstract: The subject technology receives a selection of a selectable graphical item from a plurality of selectable graphical items, the selectable graphical item comprising an augmented reality content generator for applying a 3D effect, the 3D effect including at least one beautification operation. The subject technology captures image data and depth data using a camera. The subject technology applies, to the image data and the depth data, the 3D effect including the at least one beautification operation based at least in part on the augmented reality content generator, the beautification operation being performed as part of applying the 3D effect. The subject technology generates a 3D message based at least in part on the applied 3D effect including the at least one beautification operation. The subject technology renders a view of the 3D message based at least in part on the applied 3D effect including the at least one beautification operation.

Abstract: Systems, methods, logic, and devices may support shoe design through 2.5-dimensional (2.5D) shoe models. In some examples, a system may include a 2D shoe shell pattern engine configured to access a 2D shoe shell pattern, the 2D shoe shell pattern generated for shoe design. The system may also include a 2.5D shoe model engine configured to generate a 2.5D shoe model by placing shoe design elements provided by a user onto the 2D shoe shell pattern of the shoe, including by adding 2.5D layering data for each shoe design element placed on the 2D shoe shell pattern to form the 2.5D shoe model, and wrap the 2.5D shoe model into a 3D shoe model for construction of a physical shoe from the 3D shoe model.

Abstract: Aspects of data modification for neural networks are described herein. The aspects may include a data modifier configured to receive input data and weight values of a neural network. The data modifier may include an input data configured to modify the received input data and a weight modifier configured to modify the received weight values. The aspects may further include a computing unit configured to calculate one or more groups of output data based on the modified input data and the modifier weight values.

Abstract: An artificial neural network (ANN) system includes a processor, a virtual overflow detection circuit and a data format controller. The processor performs node operations with respect to a plurality of nodes included in each layer of an ANN to obtain a plurality of result values of the node operations and performs a quantization operation on the obtained plurality of result values based on a k-th fixed-point format for a current quantization of the each layer to obtain a plurality of quantization values. The virtual overflow detection circuit generates a virtual overflow information indicating a distribution of valid bit numbers of the obtained plurality of quantization values. The data format controller determines a (k+1)-th fixed-point format for a next quantization of the each layer based on the generated virtual overflow information. An overflow and/or an underflow are prevented efficiently by controlling the fixed-point format using the virtual overflow.