Abstract: The present invention relates to a resin composition for preparing a thermoplastic polymer matrix, and a thermoplastic polymer matrix obtained therefrom, a thermoplastic composite comprising the thermoplastic polymer matrix, a preparation method and use thereof, and a mechanical or structured part or article comprising the thermoplastic composite, and use thereof. The resin composition comprises a polyisocyanate with an isocyanate group functionality of 2.10 to 2.84, an isocyanate-reactive component with an isocyanate-reactive group functionality of less than 2.2, and an additive; wherein the molar ratio of isocyanate groups to isocyanate-reactive groups of the resin composition is of 0.5:1 to 2:1. The resin composition of the present invention has good high temperature fluidity and adjustable softness/hardness.

Abstract: Systems and techniques are described herein for generating image data. For instance, a method for generating image data is provided. The method may include selecting enrollment images from among a plurality of enrollment images of a subject based on a target viewing angle, wherein each enrollment image of the plurality of enrollment images represents the subject as viewed from a different respective viewing angle; determining, from the selected enrollment images, appearance features representing at least one characteristic of the subject in the selected enrollment images; combining the appearance features based on a three-dimensional geometry of the subject to generate combined appearance features; and generating an image of the subject from the target viewing angle based on a texture image of the subject and the combined appearance features.

Abstract: Systems and techniques are described herein for generating three-dimensional (3D) representations. For instance, a method for generating three-dimensional (3D) representations is provided. The method may include generating a Gaussian-splat representation of a subject based on a point-cloud representation of the subject; and iteratively adjusting the Gaussian-splat representation and a mask indicative of Gaussian splats of the Gaussian-splat representation to render images by rendering images based on the Gaussian-splat representation and the mask, comparing the rendered images to input images of the subject, and adjusting parameters of the Gaussian-splat representation and values of the mask based on the comparison.

Abstract: Systems and methods are provided for machine learning models configured as zero-shot personalized text-to-speech models which comprise a feature extractor, a speaker encoder, and a text-to-speech module. The feature extractor is configured to extract acoustic features and prosodic features from new target reference speech associated with the new target speaker. The speaker encoder is configured to generate a speaker embedding corresponding to the new target speaker based on the acoustic features extracted from the new target reference speech. The text-to-speech module is configured to generate the personalized voice corresponding for the new target speaker based on the speaker embedding and the prosodic features extracted from the new target reference speech without applying the text-to-speech module on new labeled training data associated with the new target speaker.

Abstract: A SRE-PTPRZ1 inhibitor or a PTPRZ1 inhibitor in the preparation of a drug for treating squamous cell carcinoma are provided. The SRE-PTPRZl inhibitor is an inhibitor for SRE-PTPRZ1 structural variation, the SRE-PTPRZ1 structural variation being somatic simple repeat expansion at insertion site GRCh38 chr7: 121603263-121603506, the repeating pattern of an insertion region and a somatic cell simple repeat expansion sequence are (CTTT)n, in which 10?n<500. The PTPRZ1 inhibitor is one of the followings compounds, a pharmaceutically acceptable salt thereof, one or two derivatives among derivatives thereof, or a mixture of the above: NAZ2329, SCB4380, MY-33-3, or MY-10; and the HGNC number of the PTPRZ1 gene is 9685.

Abstract: Systems and techniques are described herein for generating image data. For instance, a method for generating image data is provided. The method may include selecting enrollment images from among a plurality of enrollment images of a subject based on a target viewing angle, wherein each enrollment image of the plurality of enrollment images represents the subject as viewed from a different respective viewing angle; determining, from the selected enrollment images, appearance features representing at least one characteristic of the subject in the selected enrollment images; combining the appearance features based on a three-dimensional geometry of the subject to generate combined appearance features; and generating an image of the subject from the target viewing angle based on a texture image of the subject and the combined appearance features.

Abstract: Techniques and systems are provided for image processing. For instance, a process can include obtaining, from one or more image sensors, a first image of an environment; determining semantic labels for a plurality of pixels of the first image based on whether each pixel of the plurality of pixels is associated with an object in the first image to generate a semantically segmented image; applying a two-dimensional line representation of the first image to the semantically segmented image to generate a labeled 2D line representation; back-projecting the labeled 2D line representation to three dimensions to generate a line representation model; fusing the line representation model and the semantically segmented image to generate a labeled line representation model; and outputting the labeled line representation model.

Abstract: Techniques and systems are provided for generating a representation of a face. For instance, a process can include obtaining a plurality of images of a face, extracting features for each image of the plurality of images to generate a plurality of feature maps and fusing the plurality of feature maps based on features of the plurality of feature maps along a common axis to generate an aligned feature map.

Abstract: A method for generating speech through multi-speaker neural text-to-speech (TTS) synthesis is provided. A text input may be received (1410). Speaker latent space information of a target speaker may be provided through at least one speaker model (1420). At least one acoustic feature may be predicted through an acoustic feature predictor based on the text input and the speaker latent space information (1430). A speech waveform corresponding to the text input may be generated through a neural vocoder based on the at least one acoustic feature and the speaker latent space information (1440).

Abstract: A cleaning device including a cleaning component (1) and a cleaning aid member (2) is provided. The cleaning component (1) includes a first annular cleaning belt (11), a first roller (12), and a second roller (13). The first roller (12) and the second roller (13) are spaced apart and the second roller (13) is disposed above the first roller (12). The first annular cleaning belt (11) rotatably surrounds the first roller (12) and the second roller (13). The first annular cleaning belt (11) is configured to clean a surface to be cleaned. The cleaning aid member (2) is disposed behind the cleaning component along a forward direction of the cleaning device, and the cleaning aid member (2) is configured to assist the first annular cleaning belt (11) in cleaning trash.

Abstract: Systems and techniques are described herein for generating one or more three-dimensional models. For instance, a method for generating one or more three-dimensional models is provided. The method may include obtaining a plurality of images of an object; obtaining a plurality of segmentation masks associated with the plurality of images, each segmentation mask of the plurality of segmentation masks including at least one label indicative of at least one segment of the object in a respective image of the plurality of images; training, using the plurality of images and the plurality of segmentation masks, a machine-learning model to generate one or more semantically-labeled three-dimensional models of the object; and generating using the trained machine-learning model, a semantically-labeled three-dimensional model of the object, the semantically-labeled three-dimensional model of the object including at least one label indicative of the at least one segment of the object.

Abstract: The present invention relates to polyisocyanate compositions comprising two different types of polyisocyanate, their use in the manufacture of prepregs and polyisocyanurate composited made from said prepregs.

Abstract: Techniques and systems are provided for image processing. For instance, a process can include obtaining, from one or more image sensors, a first image of an environment; determining semantic labels for a plurality of pixels of the first image based on whether each pixel of the plurality of pixels is associated with an object in the first image to generate a semantically segmented image; applying a two-dimensional line representation of the first image to the semantically segmented image to generate a labeled 2D line representation; back-projecting the labeled 2D line representation to three dimensions to generate a line representation model; fusing the line representation model and the semantically segmented image to generate a labeled line representation model; and outputting the labeled line representation model.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include generating a three-dimensional (3D) model of a first portion of an object based on one or more frames depicting the object. The process can also include generating a mask for the one or more frames, the mask including an indication of one or more regions of the object. The process can further include generating a 3D base model based on the 3D model of the first portion of the object and the mask, the 3D base model representing the first portion of the object and a second portion of the object. The process can include generating, based on the mask and the 3D base model, a 3D model of the second portion of the object.

Abstract: Provided is a molding method of a sealing cover, realized by setting up a sealing cover mold, the mold includes a cavity and a core group arranged in the cavity, including setting up a first core and a second core, the mold opening direction of the first core and the second core are not parallel. The present application does not need a process hole to perform the injection molding of the sealing cover with a fastening structure, whose sealing is also better.

Abstract: Provided is a molding method of a sealing cover, realized by setting up a sealing cover mold, the mold includes a cavity and a core group arranged in the cavity, including setting up a first core and a second core, the mold opening direction of the first core and the second core are not parallel. The present application does not need a process hole to perform the injection molding of the sealing cover with a fastening structure, whose sealing is also better.

Abstract: The invention relates to a polyurethane composition for preparing composites, a polyurethane composite obtained by the preparation and a method for preparing the polyurethane composite. The polyurethane composition comprises: a) an isocyanate component; b) an isocyanate-reactive component; c) a radical reaction initiator and d) an organometallic catalyst; wherein the hydroxyl value of the component b) isocyanate-reactive component is 200 mgKOH/g to 700 mgKOH/g, and the molar ratio of isocyanate groups to hydroxyl groups of the composition is 0.6 to 1.5. The polyurethane composition of the present invention has the advantages of long pot life and simple operation process. The polyurethane composite comprising the polyurethane resin matrix prepared by the polyurethane composition of the present invention has both excellent weather resistance and mechanical strength.

Abstract: The present disclosure generally relates to systems and techniques for constructing three-dimensional (3D) models. Certain aspects of the present disclosure provide an apparatus for model generation. The apparatus generally includes a memory, and one or more processors coupled to the memory. The one or more processors and the memory may be configured to receive one or more images depicting an object to be modeled, determine a category associated with the object to be modeled, select a shape of a space based on the category, and generate a 3D model of the object at least in part by carving one or more points associated with the space based on the one or more images depicting the object.

Abstract: The invention relates to a composition for preparing a flexible foam and its application. The composition comprises the following components: a. an isocyanate mixture; b. a polymer polyol mixture; c. an isocyanate-reactive group-containing compound having a number average molecular weight of 32-400 g/mol; d. an aqueous alkali metal salt and/or ammonium bicarbonate solution, having a concentration of greater than or equal to 1.5 wt % and less than 30 wt %, and a pH value of less than 9.5; and e. a metal catalyst; wherein the amount of the tertiary amine catalyst in the composition is not more than 0.1 wt %, and the isocyanate index of the composition is 70-120. The composition has a high reaction efficiency and can meet the quantitative production requirements of the textile industry.

Abstract: Embodiments and examples are disclosed for intelligent vehicle diagnostics using driving behavior modeling and monitoring. For one example, a data processing system for a vehicle includes a plurality of sensors and a vehicle control unit (VCU). The VCU may sample the output from each of the plurality of sensors and assemble a dataset which may be transmitted to a cloud computing center. The cloud computing center may apply statistical machine learning algorithms to the dataset and training data to develop a model of a user's expected driving behavior. The cloud computing center may transmit the model to the vehicle, wherein the VCU may utilize the model to monitor the user's driving behavior. In response to detecting driving behavior that is anomalous to the expected driving behavior, the VCU may diagnose the cause of the anomalous behavior and take one or more preventative actions based on the diagnosis.