Abstract: Systems and methods for the placement of an endoluminal device are provided. A delivery system for endoluminal devices includes a delivery catheter comprising an imaging sensor and a position sensor. The position sensor is located at a known distance from the imaging sensor and configured to sense relative position to an element with a position code that is readable by the position sensor. The delivery catheter is configured for mounting an endoluminal device on the delivery catheter at a known distance from the imaging sensor or position sensor. The imaging sensor, the position sensor, or both the imaging sensor and the position sensor can be used for intravascular imaging of an anatomical location, guidance for deployment of the endoluminal device, confirmation of the placement of the endoluminal device at the anatomical location, or any combination thereof.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.

Abstract: Implementations of the present specification provide methods and apparatuses for training a behavior prediction model. In the implementations of the present specification, a sequence (a behavior sequence of a user) that includes multiple behavior types is split according to behavior types to obtain multiple single-behavior sequences. Time coding is performed on all time points of each of the multiple single-behavior sequences, to obtain multiple single-behavior time sequences corresponding to the multiple single-behavior sequences. Each single-behavior time sequence in the multiple single-behavior time sequences is modeled by using a behavior prediction model, and attention is paid to all time points of each single-behavior time sequence in the multiple single-behavior time sequences, so as to obtain a distribution situation of behaviors corresponding to the multiple single-behavior time sequences in terms of time as predicted by the behavior prediction model.

Abstract: An analysis apparatus analyses a video image signal comprising successive frames of imaging an endoscopy procedure. A machine learning block analyses the video image signal using a machine learning technique that classifies regions of the frames as belonging to one of plural classes corresponding to respective types of image artefact. The classes include a motion blur class corresponding to motion blur of the image, at least one erroneous exposure class corresponding to a type of erroneous exposure of the image, and at least one noise artefact class corresponding to a type of image artefact that is noise. A quality score block derives quality scores representing image quality of the successive frames based on the classified regions.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.

Abstract: The present invention provides a high-strength and high-plasticity titanium matrix composite and a preparation method thereof. The preparation method includes: preparing high-oxygen hydride-dehydride titanium powder using a high-temperature rotary ball grinding treatment process, in which the prepared hydride-dehydride titanium powder has a particle size of 10-40 ?m, and has an oxygen content of 0.8-1.5 wt. %; preparing high-purity ultra-fine oxygen adsorbent powder using a wet grinding method of high-energy vibration ball grinding treatment process; in which a purity of the oxygen adsorbent powder is ?99.9%, and a particle size of the oxygen adsorbent powder is ?8 ?m; mixing the high-oxygen hydride-dehydride titanium powder with the oxygen adsorbent powder in a protective atmosphere, and then press-forming the powder obtained after mixing to obtain a raw material blank; and performing atmosphere protective sintering treatment on the raw material blank to obtain a titanium matrix composite.

Abstract: In implementations of systems for efficient object segmentation, a computing device implements a segment system to receive a user input specifying coordinates of a digital image. The segment system computes receptive fields of a machine learning model based on the coordinates of the digital image. The machine learning model is trained on training data to generate segment masks for objects depicted in digital images. The segment system processes a portion of a feature map of the digital image using the machine learning model based on the receptive fields. A segment mask is generated for an object depicted in the digital image based on processing the portion of the feature map of the digital image using the machine learning model.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.

Abstract: A projection method is provided. The method includes acquiring an image of a real scene using the camera. A target object is identified from the image of the real scene, and a target area is determined according to the target object. In response to a projection command, a projection content of a virtual scene is acquired. The projection content or a part of the projection content is projected outside the target area using a projection device.

Abstract: A treatment method for EVA waste materials includes the following steps: step 1: crushing waste EVA shoe soles, waste EVA sheets, and waste EVA trimmings to obtain block-shaped crushed materials with a length of less than 7 cm and a width of less than 5 cm; then, putting the block-shaped crushed materials into a waste crusher to obtain strip-shaped crushed materials; step 2: putting the strip-shaped crushed materials obtained in step 1, Surlyn resin, and PMP into an internal mixer in a weight ratio of 85:(4-10):(4-8), wherein an internal mixing temperature gradually rises, a highest temperature of the internal mixing temperature is 150° C., and an internal mixing time is 18 to 25 minutes, so as to obtain block-shaped mixed materials; and step 3: processing the block-shaped mixed materials, which have been subjected to internal mixing by a screw extruder and an open mill to obtain an EVA recycled film.

Abstract: Disclosed are a method, an apparatus, and a device for predicting adverse drug-drug interactions, and a readable storage medium, and relates to the technical field of pharmaceutical research and development. An objective of the present application is to provide the method, apparatus, and device for predicting adverse drug-drug interactions, and the readable storage medium for solving the above problems. To achieve the above objective, a technical solution adopted by the present application is as follows: acquiring first information; constructing an adverse drug-drug interaction prediction model according to the first information; acquiring second information, where the second information is molecular structure information of two drugs to be predicted; and outputting predicted adverse drug-drug interactions with the second information as an input into the adverse drug-drug interaction prediction model.

Abstract: Systems and methods for document processing are provided. One aspect of the systems and methods includes obtaining a prompt including a document description describing a plurality of elements. A plurality of image assets are generated based on the prompt using a generative neural network. In some cases, the plurality of image assets correspond to the plurality of elements of the document description. A structured document is then generated that matches the document description. In some cases the structured document includes the plurality of image assets and metadata describing a relationship between the plurality of image assets.

Abstract: This disclosure describes a novel “kick and kill” strategy as an effective cancer therapy for treating virus-associated cancers. In particular, this disclosure provides a method of reactivating a latent Epstein-Barr virus (EBV) in a cell infected with the EBV. Also provided are a method of eliciting or enhancing an immune response against an EBV-positive cancer cell in a subject infected with the EBV and a method of treating a subject having cancer associated with EBV infection.

Abstract: In various examples, a machine learning model is converted for execution by a computing device. For example, a computing graph is generated based on the machine learning model and sub-graphs within the computing graph that match sub-structures that are detected and combined into a vertex to generate an optimized computing graph. A net-list object and weight object are then generated based on the optimized computing graph and provided to the computing device to enable inferencing operations.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.

Abstract: The present invention relates to a method for moderately increasing the content of dimethyl disulfide in casein-containing processed cheese. The method includes the following steps: taking a cheese powder, adding chymosin for enzymolysis, and subjecting a material obtained after the enzymolysis to puffing and drying to obtain a modified cheese powder; preparing materials; taking the modified cheese powder, heating the modified cheese powder under stirring until the cheese powder is melt, adding sodium bicarbonate to adjust the pH to 6-7, and adding papain for enzymolysis; taking a material obtained after the enzymolysis, adding butter, a skimmed milk powder, fructo-oligosaccharide and water for mixing by stirring, and then adding an emulsifying salt, glutamine transaminase and salt for uniform heating and stirring to obtain a mixture; and subjecting the mixture to heat treatment, hot filling and cooling in sequence to obtain processed cheese.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.

Abstract: A digital mirroring method includes creating a digital model for each physical device of a plurality of physical devices located in a physical space. Once a related data of each physical device is obtained, the related data of each physical device is mapped to corresponding digital model.

Abstract: Provided are a stable lentivirus packaging cell line and a preparation method therefor. The stable lentivirus packaging cell line contains a packaging plasmid group consisting of a pPuro.coTetR plasmid, a pVSVG plasmid, and a pGagPol-RRE-NES-cINT plasmid, wherein the pPuro.coTetR plasmid contains a CoTetR gene, the pVSVG plasnid contains a VSVG gene, and the pGagPol-RRE-NES-cINT plasmid contains GagPol and Rev genes. The stable lentivirus packaging cell line is relatively stable in terms of passage, virus production, and a genetic gene copy number, and the lentivirus produced by using the stable lentivirus packaging cell line has a high titer and a low impurity content.

Abstract: A method for transmitting an application programming interface API request includes receiving, by a first API gateway, a first API request; obtaining, by the first API gateway, a first forwarding label corresponding to the first API request, where the first forwarding label includes a first target security domain identifier, and a security domain identifier of the first API gateway is different from the first target security domain identifier. The method also includes determining an address of a second API gateway according to a mapping relationship between the first target security domain identifier and the address of the second API gateway. The method additionally includes sending the first API request to the second API gateway based on the address which is a next-hop API gateway of the first API gateway that sends the first API request to an API gateway corresponding to the first target security domain identifier.