Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: A method includes receiving multimodal data collected using at least one wearable device during an assessment window. The method also includes extracting biomarker features from the multimodal data, based on changes in the extracted biomarker features. The method also includes detecting that a stress event occurred during the assessment window. The method also includes accessing a plurality of templates of patterns in biomarker features, wherein a first subset of the templates is associated with unhealthy response to stress and a second subset of the templates is associated with healthy response to stress. The method also includes determining whether the stress event corresponds to a healthy response or an unhealthy response based on similarities between a pattern in the extracted biomarker features and the plurality of templates. The method also includes responsive to the stress event corresponding to an unhealthy response, providing a stress management recommendation.

Abstract: Provided herein are methods of purifying liposomal formulations by washing with acidic aqueous solutions.

Abstract: In one embodiment, a method includes recording, by a camera of a client device, a video of a region of a first user's skin and estimating, from the recorded video, a current ratio of ratios (RoR) for the first user corresponding to the first user's current blood-oxygen saturation. The method further includes converting the first user's determined RoR to a transformed RoR for the first user based at least in part on a baseline user's RoR determined while generating a trained SpO2 prediction model. The trained SpO2 prediction model is trained to estimate the baseline user's SpO2 value based on an input RoR value from the baseline user. The method further includes determining, by the trained SpO2 prediction model and based on the transformed RoR for the first user, the first user's current estimated blood-oxygen saturation.

Abstract: Apparatuses, systems, and techniques to determine pixel-level labels of a synthetic image. In at least one embodiment, the synthetic image is generated by one or more generative networks and the pixel-level labels are generated using a combination of data output by a plurality of layers of the generative networks.

Abstract: A vehicle, the vehicle comprises a hybrid power system, and the hybrid power system comprises an engine, a longitudinally mounted first electric motor, a longitudinally mounted second electric motor and a first coupling apparatus. The hybrid power system is configured: when in a first state, to disconnect the engine from the longitudinally mounted first electric motor by means of the first coupling apparatus, and to control the longitudinally mounted first electric motor and the longitudinally mounted second electric motor to jointly drive the vehicle.

Abstract: There is described a system and method for location determination of a beacon tag using signal differential. A first beacon is received at multiple receiving devices. First received signal strength indicator measurements associated with the receiving devices are identified based on the first beacon. A reference point for the beacon tag is established based on the plurality of first received signal strength indicator measurements. A second beacon is received at the receiving devices. The second beacon is received subsequent to the first beacon. Second received signal strength indicator measurements are received at the receiving devices are identified based on the second beacon. A displacement of the beacon tag is determined based on a first order difference of the second received signal strength indicator measurements to the first received signal strength indicator measurements. A location of the beacon tag relative to the reference point is updated based on the displacement.

Abstract: A data write method includes: obtaining a first logical address of first data; determining whether second data is stored in a storage pool, where a logical address of the second data is the same as the first logical address; and when the second data is stored in the storage pool, writing the first data to a first logical block set of the storage pool, where the first logical block set is used for storing hot data.

Abstract: In various examples, a system can include one or more processors to segment, using at least one machine learning model, a first representation of an object into a plurality of parts according to a hierarchy, where the hierarchy indicates groups of datapoints for at least one part of the plurality of parts based on a value. The one or more processors generate at least one second representation of the object based on the at least one of the part of the plurality of parts.

Abstract: The present disclosure involves methods, apparatus, and systems for predicting performance of a content delivery network (CDN). An example method includes determining a topology of a content delivery network (CDN); determining a first proxy node to test; testing the first proxy node at a test data rate to determine a first tested capacity of the first proxy node by: distributing the test data rate across intermediate nodes connected to the first proxy node; determining the first tested capacity of the first proxy node based on whether a total available capacity of edge nodes exceeds a distributed test data rate at the intermediate node; determining a second proxy node to test; testing the second proxy node to determine a second tested capacity of the second proxy node; and determining a capacity of the graph model based on the first tested capacity and the second tested capacity.

Abstract: In various examples, systems and methods are disclosed relating to generating an output 3D latent representation by encoding, using a text encoder, a text prompt and encoding, using a 2D-3D encoder, a 2D image of an object or a 3D representation of the object. A 3D output is generated by applying the output 3D latent representation to a decoder. A reconstruction loss and a SDS loss are determined for the 3D output. At least one of the text encoder, the 2D-3D encoder, and the decoder is updated using the reconstruction loss and the SDS loss.

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: Apparatuses, systems, and techniques to generate a two-dimensional (2D) image. In at least one embodiment, one or more neural networks are used to generate the image and a three-dimensional (3D) mesh representation of the image, wherein the 3D mesh representation is to be used to apply one or more effects to the 2D image.

Abstract: A multi-sensor fusion method and system for intelligent driving vehicles are provided, which relate to the technical field of intelligent driving vehicles. The method includes: establishing an extended target tracker based on a GM-PHD algorithm and a rectangular target model of a detected object; processing detection information of a millimeter-wave radar by using the extended target tracker to obtain millimeter-wave radar track information of the detected object; processing detection information of a laser radar by using the established bounding-box detector and a JPDA tracker provided with an IMM-UKF to obtain laser-radar track information of the detected object; processing the millimeter-wave radar track information and the laser-radar track information by performing time-space conversion to obtain a central fusion node; and processing the central fusion node by using an IMF algorithm to obtain global track information.

Abstract: The present disclosure involves methods, apparatus, and systems for predicting performance of a content delivery network (CDN). An example method includes determining a topology of a content delivery network (CDN); determining a first proxy node to test; testing the first proxy node at a test data rate to determine a first tested capacity of the first proxy node by: distributing the test data rate across intermediate nodes connected to the first proxy node; determining the first tested capacity of the first proxy node based on whether a total available capacity of edge nodes exceeds a distributed test data rate at the intermediate node; determining a second proxy node to test; testing the second proxy node to determine a second tested capacity of the second proxy node; and determining a capacity of the graph model based on the first tested capacity and the second tested capacity.

Abstract: A method for positioning a terminal device using an apparatus is provided. The method comprises: performing a first positioning of the terminal device based on WiFi angle estimation received from the terminal device: rotating a mmW antenna array to a target direction based on the first positioning: transmitting a radar signal via the rotated mmW antenna array; and performing a second positioning of the terminal device based on an echo received from the terminal device.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks using stratified sampled training data parameters. In at least one embodiment, one or more stochastic training data parameters may be stratified sampled from one or more sampling ranges to compute a gradient for updating the one or more neural networks.

Abstract: In one embodiment, a method includes accessing a plurality of images of a region of interest of a person's skin and extracting, from the plurality of images, a color signal of the region of interest as a function of time. The method further includes determining, based at least on the color signal, a first quality associated with an estimated vital sign of the person, where the vital sign estimate is determined from the plurality of images.

Abstract: Systems and methods are disclosed related to a 3D grounded video foundation model. A video generation method and system provide 3D conditioning information to a video diffusion model to improve generated video quality (object and temporal consistency) that is grounded in three dimensions (3D). The video generation method and system also enable precise camera control, cinematic effects, and scene editing. Video output corresponding to a set of camera specifications is generated for a scene from input image(s) including one or more images of a static scene or a sequence of images (video) for a dynamic scene. The input image(s) are used to calculate a 3D cache representing the scene. The 3D cache is rendered according to the set of camera specifications to produce a frame sequence and a mask sequence that identifies missing pixels in each frame. The frame sequence is encoded and masked to generate the output video.