Abstract: The subject application relates to bonded abrasive with low wetting bond material. An abrasive article including a bonded abrasive body having a particular MOR/EMOD ratio associated with a particular bond vol %. The body also can include a) an average Bond Post Area (BPA) of not greater than microns2; b) an average Bond Post Count (BPC) of at least 140 per 1536 mm2; c) an average bond wetting radius of at least 30/microns; or d) a combination of a) and b).

Abstract: The subject application relates to a bonded abrasive for gear power honing. An abrasive article including a bonded abrasive body having abrasive particles and a bond material. The abrasive body can include an inner annular surface with at least 1 tooth. The abrasive article can provide an average Ff? of less than 2.0 according to a Gear Power Honing Test.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: A vacuum battery structural assembly and a vacuum multi-cell battery module composed thereof are provided and include a first repeating unit including a first frame plate and a second frame plate with respect to the first frame plate; and an electrolyte channel defined within the first frame plate and the second frame plate to accommodate a liquid electrolyte, wherein both a surface of the first frame plate and a surface of the second frame plate include a vacuum suction area, the vacuum suction area includes a vacuum aperture and a vacuum channel, wherein the vacuum aperture is formed on at least one surface of the first frame plate and the second frame plate, the vacuum channel is positioned inside the first frame plate and the second frame plate, and is configured to generate a longitudinal pressing suction force and seal the first frame plate and the second frame plate.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: A computer-implemented method for data augmentation is provided according an embodiment of the present disclosure. In the method, a first feature vector for input data may be obtained based on a first model. The input data may be clustered to a plurality of clusters. For each of the clusters, a second feature vector may be obtained based on the first model. Then, a similarity between the first feature vector and the second feature vector may be estimated for each of the clusters. At least one cluster of the plurality of clusters for which the similarity is lower than a threshold may be determined. Moreover, data augmentation may be performed to the at least one cluster.

Abstract: The present disclosure relates to a micron silver particle-reinforced 316L stainless steel matrix composite, including a 316L stainless steel matrix and silver particles uniformly distributed in the 316L stainless steel matrix. The silver particles have a weight 1% to 5% of the total weight of the composite; and the composite has a density of 7.9 g/cm3 to 8.2 g/cm3 and a relative density of more than 98%. The composite is prepared by the following method: mixing raw materials of a spherical silver powder and a spherical 316L stainless steel powder; subjecting a resulting mixture to mechanical ball milling to obtain a mixed powder; sieving the mixed powder and adding a resulting powder to a powder cylinder of an SLM forming machine; and charging an inert protective gas for printing to obtain the composite.

Abstract: The present disclosure provides a method and device for intelligent control of heating furnace combustion based on a big data cloud platform, which relates to the technical field of artificial intelligence control. The method includes: construction of big data cloud platform based on production and operation parameters of the heating furnace; identification of key factors in the production process of the heating furnace by using big data mining technology; independent deployment of traditional heating furnace combustion control systems based on the mechanism model; and integration of cloud platform big data expert knowledge base and the heating furnace combustion intelligent control system.

Abstract: Resource optimized forwarding of classified requests to different instances of a microservice is provided. The process includes obtaining classified requests to a microservice. The classified requests are classified based on different microservice resource consumption types. Further, the method includes forwarding the classified requests to instances of the microservice with different assigned resource configurations. The forwarding is based on the different microservice resource consumption types of the classified requests.

Abstract: A two-step door opening mechanism for an oven is provided. A mechanical button and an electronic button are provided, the oven defining an oven cavity openable and closable by an oven door. A controller is provided. A digital valve, comprising a shaft configured to slide laterally between a first, unlocked position in which the shaft allows inward movement of the mechanical button and a second, locked position in which the shaft blocks the inward movement of the mechanical button. The electronic button, once actuated, is operable to cause the controller to direct the digital valve to move the shaft from the second, locked position into the first, unlocked position to unlock the mechanical button, and the mechanical button, once actuated, is operable to open the door when the shaft is in the first, unlocked position.

Abstract: A system and method includes soft-segment based rules optimization that can mitigate the overall false positives while maintaining 100% true positive detection. The soft clustering allows real-time re-assignment of an account to a dominate archetype behavior, as well as rule optimization based on a logical order with more relaxation on thresholds for the most inefficient rules is performed within each archetype. The rule optimization provides false positive reduction compared to a baseline rule system. The method can be used to reduce false positives for any rule-based detection system in which the same true positive detection is required.

Abstract: A tissue factor (TF)-targeted antibody-drug conjugate (ADC) and a method for preparing the ADC. The ADC is capable of binding to TF antigen with high specificity, and has high affinity, low immunogenicity, high cytotoxicity, and significant anti-tumor activity.