Abstract: A system for generating a nearest neighboring vertices index. The system includes a memory and one or more processors. The one or more processors receive a base figure asset and an item asset, determine nearest neighbor vertices between the base figure asset and the item asset using at least one of a k-dimensional tree algorithm and a geodesic algorithm, and generate the nearest neighboring vertices index based on the determined nearest neighbor vertices between the base figure asset and the item asset.

Abstract: A photovoltaic device including a photovoltaic cell and method of use is disclosed. The photovoltaic cell includes at least a first photovoltaic layer and a second photovoltaic layer arranged in a stack. The first photovoltaic layer has a first thickness and receives light at its top surface. A second photovoltaic layer has a second thickness and is disposed beneath the first photovoltaic layer and receives light passing through the first photovoltaic layer. The first thickness and the second thickness are selected so that a first light absorption at the first photovoltaic layer is equal to a second light absorption at the second photovoltaic layer. The photovoltaic cell is irradiated at its top surface with monochromatic light to generate a current.

Abstract: A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Abstract: A virtual reality display method, device, apparatus and storage medium are provided. The method includes: acquiring multimedia data to be displayed and a visible region of a viewer on a curved display surface, wherein the visible region is obtained by projecting a visible range of the viewer to the curved display surface, and is not larger than a display area of the curved display surface; determining target curvatures of at least two positions in the visible region of the viewer, wherein in the target curvatures, target curvatures of different positions are related to a distance to a center of the visible region of the viewer; adjusting, based on the target curvatures of the at least two positions in the visible region, a curvature of a corresponding position on the curved display surface; and mapping the multimedia data to be displayed to the curved display surface having the adjusted curvature.

Abstract: Various embodiments of the present disclosure are directed towards a FinFET MOS capacitor. In some embodiments, the FinFET MOS capacitor comprises a substrate and a capacitor fin structure extending upwardly from an upper surface of the substrate. The capacitor fin structure comprises a pair of dummy source/drain regions separated by a dummy channel region and a capacitor gate structure straddling on the capacitor fin structure. The capacitor gate structure is separated from the capacitor fin structure by a capacitor gate dielectric.

Abstract: A method of communicating information, comprising modeling a stream of sensor data, to produce parameters of a predictive statistical model; communicating information defining the predictive statistical model from a transmitter to a receiver; and after communicating the information defining the predictive statistical model to the receiver, communicating information characterizing subsequent sensor data from the transmitter to the receiver, dependent on an error of the subsequent sensor data with respect to a prediction of the subsequent sensor data by the statistical model. A corresponding method is also encompassed.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users.

Abstract: A light-emitting device may include a first electrode, a second electrode, and a light-emitting layer therebetween. The first electrode may include a reflection layer and a metal oxide layer provided on the reflection layer. The metal oxide layer may be provided between the reflection layer and the light-emitting layer. The metal oxide layer may include molybdenum dioxide and an oxide of a group-V element, and a content of the group-V element to a total amount of the metal oxide layer may range from 2 at % to 10 at.

Abstract: A package structure includes a circuit substrate, a semiconductor package, a lid structure, a passive device and a barrier structure. The semiconductor package is disposed on and electrically connected to the circuit substrate. The lid structure is disposed on the circuit substrate covering the semiconductor package. The lid structure is attached to the circuit substrate through an adhesive material. The passive device is disposed on the circuit substrate in between the semiconductor package and the lid structure. The barrier structure is separating the passive device from the lid structure and the adhesive material, and the barrier structure is in contact with the adhesive material.

Abstract: A method and apparatus of open set recognition, and a computer-readable storage medium are disclosed. The method comprises acquiring auxiliary data and training data of known categories for open set recognition, training a neural network alternately using the auxiliary data and the training data, until convergence; extracting a feature of data to be recognized for open set recognition, using the trained neural network; and recognizing a category of data to be recognized, based on the feature of the data to be recognized.

Abstract: Markov random field parameters are identified to use for covariance modeling of correlation between gradient terms of a loss function of the classifier. A subset of images are sampled, from a dataset of images, according to a normal distribution to estimate the gradient terms. Black-box gradient estimation is used to infer values of the parameters of the Markov random field according to the sampling. Fourier basis vectors are generated from the inferred values. An original image is perturbed using the Fourier basis vectors to obtain loss function values. An estimate of a gradient is obtained from the loss function values. An image perturbation is created using the estimated gradient. The image perturbation is added to an original input to generate a candidate adversarial input that maximizes loss in identifying the image by the classifier. The neural network classifier is queried to determine a classifier prediction for the candidate adversarial input.

Abstract: Described is a system (and method) for training and using machine learning models to identify potential discrepancies between predicted odds and actual odds for a future event. The system may create a predicted odds machine learning model using an ensemble training algorithm. To create the risk management machine learning model, the system may determine a set of past odds differences between predicted odds outputted by the predicted odds machine learning model for past events and the actual historical odds for those past events. Once the predicted odds machine learning model and risk management machine learning model are trained, the system may use current (or real-time) event information to determine potential opportunities to leverage based on discrepancies between predicted odds of upcoming events and the actual odds for those events.

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.

Abstract: In various embodiments, a generative design application iteratively generates designs via a generative design process. In operation, the generative design application performs one or more layout operations on virtual objects based on a first set of design constraints to generate a first design. The generative design application then modifies the first set of design constraints based on feedback associated with a mid-air representation of the first design displayed in a virtual reality environment to generate a second set of design constraints. Subsequently, the generative design application performs one or more layout operations on the virtual objects based on the second set of design constraints to generate a second design that achieves design goal(s). Advantageously, enabling a designer to incrementally indicate design goal(s) as constraints via a virtual reality environment instead of as a predetermined objective function reduces both the time and effort required to generate designs.

Abstract: A method is provided, including: receiving, from a client device, a request to spectate a live event by a remote spectator; assigning the remote spectator to a seat in a physical venue; streaming a first video feed, captured by a first camera in the physical venue, to the client device for rendering to a first display for viewing by the remote spectator, wherein the first video feed provides a field of view of the physical venue; rendering a second video feed, from a second camera that captures the seat in the physical venue, to a display device in the physical venue; wherein when the field of view provided by the first video feed includes the display device in the physical venue, then the rendering of the second video feed on the display device that is shown in the field of view is altered, showing the remote spectator in the seat.

Abstract: A chromosome recognition method based on deep learning includes the following steps: step 1, obtaining an independent chromosome image; step 2, calculating a manual feature of a chromosome; step 3, performing basic image processing on the chromosome; step 4, building a deep learning model; and step 5, predicting a type of the chromosome based on the deep learning model. By adopting a deep learning method, the chromosome recognition method can be used for recognizing the chromosome type accurately and efficiently. Compared with an existing recognition technology, the chromosome recognition method based on deep learning of the present invention has the advantages that the chromosome karyotype analysis efficiency can be effectively improved, the recognition sequencing time can be shortened, automatic classification and sequencing of chromosomes can be completely with high accuracy.

Abstract: Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain an anomaly score.

Abstract: A semiconductor device includes a semiconductor part; first and second electrodes respectively on back and front surfaces of the semiconductor part; third and fourth electrodes inside a trench of the semiconductor part, the fourth electrode being provided between the first electrode and the third electrode; a first insulating portion electrically insulating the third electrode from the semiconductor part; a second insulating portion electrically insulating the third electrode from the second electrode; a third insulating portion electrically insulating the fourth electrode from the semiconductor part; a fourth insulating portion electrically insulating the fourth electrode from the third electrode; and a fifth insulating portion including a first portion and a second portion, the first portion being provided inside the fourth electrode, the second portion extending outward of the fourth electrode.

Abstract: This disclosure involves mapping body movements to graphical manipulations for real-time human interaction with graphics. Certain aspects involve importing graphical elements and mapping input actions, such as gestures, to output graphical effects, such as moving, resizing, changing opacity, and/or deforming a graphic, by using nodes of a reference skeleton and edges (e.g., links) between the nodes of the reference skeleton and the pins. The mapping is used to trigger and interact with the graphical elements with body position and/or movement.

Abstract: A package structure includes a circuit substrate and a semiconductor device. The semiconductor device is disposed on and electrically connected to the circuit substrate. The semiconductor device includes an interconnection structure, a semiconductor die, an insulating encapsulant, a protection layer and electrical connectors. The interconnection structure has a first surface and a second surface. The semiconductor die is disposed on the first surface and electrically connected to the interconnection structure. The insulating encapsulant is encapsulating the semiconductor die and partially covering sidewalls of the interconnection structure. The protection layer is disposed on the second surface of the interconnection structure and partially covering the sidewalls of the interconnection structure, wherein the protection layer is in contact with the insulating encapsulant.