Abstract: An electronic device is provided. The electronic device includes a communication circuit, a display, at least one sensor, and at least one processor. The at least one processor may be configured to obtain sound data regarding a surrounding environment from a wearable electronic device through the communication circuit, obtain sensor data through the at least one sensor, identify whether the surrounding environment matches one of a plurality of preset contexts, based on the sound data and the sensor data, and perform at least one of transmitting, to the wearable electronic device, a signal for controlling the wearable electronic device through the communication circuit or displaying a notification message on the display, based on the matching context, a state of the display, or whether the wearable electronic device is outputting a sound, in response to identifying that the surrounding environment matches one of the plurality of preset contexts.

Abstract: Provided are biomarkers for parenteral nutrition associated cholestasis (PNAC) in subjects, especially infants receiving parenteral nutrition (PN). Using such biomarkers provide methods of diagnosing and/or assessing risk of PNAC in a subject, including screening a sample from a subject believed to be at risk for PNAC for one or more biomarkers of PNAC. The biomarkers can include one or more fecal metabolites. Such biomarkers and associated methods provide neonatal intensive care unit clinicians with an additional tool for early identification of PNAC risk. Early identification of high-risk infants would enable clinicians to confidently optimize caloric nutrition with PN for infants at low risk of developing PNAC and enable proactive mitigation with alterations to the administered PN.

Abstract: A method including, in a training phase, training a gaze prediction model including a first model and a second model, the first model and the second model being configured in conjunction to predict segmentation data based on training data, training a third model together with the first model and the second model, the third model being configured to predict a training characteristic using an output of the first model based on the training data, and in an operational phase, receiving operational data and predicting an operational characteristic using the trained first model and the trained third model.

Abstract: Nonlinear peri-codec optimization for image and video coding includes obtaining a source image including pixel values expressed in a first defined image sample space, generating a neuralized image representing the source image, the neuralized image including pixel values that are expressed as neural latent space values, encoding the input image wherein the neural latent space values are used as pixel values in a second defined image sample space and the input image is in an operative image format of the encoder, such that a decoder decodes the encoded image to obtain a reconstructed image in the second defined image sample space, wherein the reconstructed image is a reconstructed neuralized image including reconstructed neural latent space values, such that a deneuralized reconstructed image corresponding to the source image is obtained by a nonlinear post-codec image processor in the first defined image sample space.

Abstract: For an improved management of an app (120), a computer-implemented method is suggested comprising: •capturing the user's intent to insert an additional workflow artifact (122a) into the workflow (124) in response to user interactions with the app development UI (116); •inserting the additional workflow artifact (122a) to the workflow (124) according into the captured user's intent; •determining an overlap (126) of at least two of the workflow artifacts (122) caused by the inserted workflow artifact (122a); •determining a flow direction (128) of the workflow (124) from the preceding workflow artifact (122p) to the following workflow artifact (122f), the preceding and the following workflow artifact (122p, 122f) preceding and following the inserted workflow artifact (122a), respectively; •determining at least one workflow artifact (122m) of the workflow (124) which needs to be moved along the determined flow direction (128) to avoid the determined overlap (126); •determining an updated workflow (124?) by moving

Abstract: A method including generating a first vector based on a first grid and a three-dimensional (3D) position associated with a first implicit representation (IR) of a 3D object, generating at least one second vector based on at least one second grid and an upsampled first grid, decoding the first vector to generate a second IR of the 3D object, decoding the at least one second vector to generate at least one third IR of the 3D object, generating a composite IR of the 3D object based on the second IR of the 3D object and the at least one third IR of the 3D object, and generating a reconstructed volume representing the 3D object based on the composite IR of the 3D object.

Abstract: A method comprises receiving a first sequence of images of a portion of a user, the first sequence of images being monocular images; generating an avatar based on the first sequence of images, the avatar being based on a model including a feature vector associated with a vertex; receiving a second sequence of images of the portion of the user; and based on the second sequence of images, modifying the avatar with a displacement of the vertex to represent a gesture of the avatar.

Abstract: A lighting stage includes a plurality of lights that project alternating spherical color gradient illumination patterns onto an object or human performer at a predetermined frequency. The lighting stage also includes a plurality of cameras that capture images of an object or human performer corresponding to the alternating spherical color gradient illumination patterns. The lighting stage also includes a plurality of depth sensors that capture depth maps of the object or human performer at the predetermined frequency. The lighting stage also includes (or is associated with) one or more processors that implement a machine learning algorithm to produce a three-dimensional (3D) model of the object or human performer. The 3D model includes relighting parameters used to relight the 3D model under different lighting conditions.

Abstract: Apparatus and methods related to applying lighting models to images are provided. An example method includes receiving, via a computing device, an image comprising a subject. The method further includes relighting, via a neural network, a foreground of the image to maintain a consistent lighting of the foreground with a target illumination. The relighting is based on a per-pixel light representation indicative of a surface geometry of the foreground. The light representation includes a specular component, and a diffuse component, of surface reflection. The method additionally includes predicting, via the neural network, an output image comprising the subject in the relit foreground. One or more neural networks can be trained to perform one or more of the aforementioned aspects.

Abstract: Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.

Abstract: The invention is that of a system and method of generating an individual compliance score based on one or more background checks that is useful in determining the compliance levels of service providers based on selected criteria. The individual compliance scores are the result of a matrix comparison of a background check score against industry standards to provide greater weight to areas of specific interest depending on the industry. An application program interface may be installed on a user system to allow individual compliance scores and corresponding unique identifiers to be visible to users of the system. Access and security controls are provided, with users being in network communication with the system. The methods enabled by the system allow for rapid real-time service provider qualification without the need for time consuming background audits. A system of the present invention may be presented as a feature of an existing mobile application.

Abstract: An electronic device is provided to detect a connection of a first wireless audio device and a second wireless audio device, identify whether a wearer of the first wireless audio device and a wearer of the second wireless audio device are different, based on a first sensor signal received from the first wireless audio device and a second sensor signal received from the second wireless audio device, execute an audio sharing mode, based on the wearer of the first wireless audio device and the wearer of the second wireless audio device being different, control configuration information about the first and the second wireless audio device that are related to a function of the audio sharing mode, and transmit audio data according to the audio sharing mode to the first wireless audio device and the second wireless audio device, based on the configuration information.

Abstract: Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.

Abstract: Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.

Abstract: Techniques include introducing a neural generator configured to produce novel faces that can be rendered at free camera viewpoints (e.g., at any angle with respect to the camera) and relit under an arbitrary high dynamic range (HDR) light map. A neural implicit intrinsic field takes a randomly sampled latent vector as input and produces as output per-point albedo, volume density, and reflectance properties for any queried 3D location. These outputs are aggregated via a volumetric rendering to produce low resolution albedo, diffuse shading, specular shading, and neural feature maps. The low resolution maps are then upsampled to produce high resolution maps and input into a neural renderer to produce relit images.

Abstract: Techniques of tracking a user's gaze includes identifying a region of a display at which a gaze of a user is directed, the region including a plurality of pixels. By determining a region rather than a point, when the regions correspond to elements of a user interface, the improved technique enables a system to activate the element to which a determined region is selected. In some implementations, the system makes the determination using a classification engine including a convolutional neural network; such an engine takes as input images of the user's eye and outputs a list of probabilities that the gaze is directed to each of the regions.

Abstract: Example embodiments relate to techniques for volumetric performance capture with neural rendering. A technique may involve initially obtaining images that depict a subject from multiple viewpoints and under various lighting conditions using a light stage and depth data corresponding to the subject using infrared cameras. A neural network may extract features of the subject from the images based on the depth data and map the features into a texture space (e.g., the UV texture space). A neural renderer can be used to generate an output image depicting the subject from a target view such that illumination of the subject in the output image aligns with the target view. The neural render may resample the features of the subject from the texture space to an image space to generate the output image.

Abstract: Methods, systems, and apparatus for prevention of electronic content capture using an embedded key server module are described. A user device sends a request to a computer for media content segments. The user device receives a manifest file and the media content segments from the computer. The media content segments are encrypted using a media encryption key and initialization vector. The user device detects that the media content segments are encrypted, and generates a key request for decryption of the media content segments. A key server module embedded within a computer application creates a key response including the media encryption key and initialization vector. The user device decrypts the media content segments using the media encryption key and initialization vector in the key response. The user device generates a display of the media content segments on a screen of the user device.

Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. An example method includes applying a geometry model to an input image to determine a surface orientation map indicative of a distribution of lighting on an object based on a surface geometry. The method further includes applying an environmental light estimation model to the input image to determine a direction of synthetic lighting to be applied to the input image. The method also includes applying, based on the surface orientation map and the direction of synthetic lighting, a light energy model to determine a quotient image indicative of an amount of light energy to be applied to each pixel of the input image. The method additionally includes enhancing, based on the quotient image, a portion of the input image. One or more neural networks can be trained to perform one or more of the aforementioned aspects.

Abstract: According to an aspect, a real-time active stereo system includes a capture system configured to capture stereo data, where the stereo data includes a first input image and a second input image, and a depth sensing computing system configured to predict a depth map. The depth sensing computing system includes a feature extractor configured to extract features from the first and second images at a plurality of resolutions, an initialization engine configured to generate a plurality of depth estimations, where each of the plurality of depth estimations corresponds to a different resolution, and a propagation engine configured to iteratively refine the plurality of depth estimations based on image warping and spatial propagation.