An augmented reality system for makeup, includes a makeup objective unit including computation circuitry operably coupled to a graphical user interface configured to generate one or more instances of user selectable makeup objectives and to receive user-selected makeup objective information, a makeup palette unit operably coupled to the makeup objective unit, the makeup palette unit including computation circuitry configured to generate at least one digital makeup palette for a digital makeup product, and a makeup objective visualization unit including computation circuitry configured to analyze a user's face to determine one or more of face shape, facial landmarks, skin tone, hair color, eye color, lip shape, eyelid shape, hair style and lighting, and automatically create one or more instances of a custom virtual try-on for a user in accordance with the user-selected makeup objective information and the at least one digital makeup palette generated based on the analysis of the user's face.

Distributed fiber optic sensing (DFOS) systems, methods and structures for determining the proximity of vibration sources located perpendicular to a sensor fiber that is part of the DFOS system that may potentially threaten/damage or otherwise compromise the sensor fiber itself. Systems, methods, and structures according to aspects of the present disclosure employ Artificial Intelligence (AI) methodology(ies) that use as input a fundamental physical understanding of wave propagation and attenuation in the ground along with Bayesian inference and Maximum Likelihood Estimation (MLE) techniques for estimating/determining the proximity of potentially damaging vibration sources to the optical sensor fiber.

An apparatus for convolution operations is provided. The apparatus includes a PE array, a datastore, writing modules, reading modules, and a controlling module. The PE array performs MAC operations. The datastore includes databanks, each of which stores data to be used by a column of the PE array. The writing modules transfer data from a memory to the datastore. The reading modules transfer data from the datastore to the PE array. Each reading module may transfer data to a particular column of the PE array. The controlling module can determine the rounds of a convolution operation. Each round includes MAC operations based on a weight. The controlling module controls the writing modules and reading modules so that the same data in a databank can be reused in multiple rounds. For different rounds, the controlling module can provide a reading module accesses to different databanks.

A system and method for providing and managing a remote patient monitoring (RPM) system. The method is implemented by a central server, an RPM client, and a networked monitoring device. The RPM client is a software program that is executed by a computing device that is connected to the server via a network. The networked monitoring device is implemented as a locator or a smart mobile cart. More specifically, the RPM system can provide a tele-monitor with the ability to remotely monitor multiple patients, control remote cameras, and address abnormal patient situations. The RPM system can enhance tele-monitor effectiveness by detecting patient motion and tracking tele-monitor alertness.

Embodiments of this application disclose methods, systems, and devices for video synthesis. In one aspect, a method comprises obtaining a plurality of frames corresponding to source image information of a first to-be-synthesized video, each frame of the source image information. The method also comprises obtaining a plurality of frames corresponding to target image information of a second to-be-synthesized video. For each frame of the plurality of frames corresponding to the target image information of the second to-be-synthesized video, the method comprises fusing a respective source image from the first to-be-synthesized video, a corresponding source motion key point, and a respective target motion key point corresponding to the frame using a pre-trained video synthesis model, and generating a respective output image in accordance with the fusing. The method further comprises repeating the fusing and the generating steps for the second to-be-synthesized video to produce a synthesized video.

A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.

A process is operable using a computerized system for grading the colour of a diamond using a pre-trained neural network for determination of a colour grading. The computerized system includes an optical image acquisition device, a pre-trained neural network and an output module operably interconnected together via a communication link. The process includes: (i) acquiring via an optical image acquisition device one or more optical image of at least a portion of a diamond; and (ii) in a pre-trained neural network, providing a regressive value associated with the colour grade of the diamond.

Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Systems and methods for efficient implementation of a convolutional layer of a convolutional neural network are disclosed. In one aspect, weight values of kernels in a kernel stack of a convolutional layer can be reordered into a tile layout with tiles of runnels. Pixel values of input activation maps of the convolutional layer can be reordered into an interleaved layout comprising a plurality of clusters of input activation map pixels. The output activation maps can be determined using the clusters of the input activation map pixels and kernels tile by tile.

The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.