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 instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a digital image displaying a plurality of objects, the plurality of objects comprising a plurality of different types of objects. The disclosed systems generate, utilizing a segmentation neural network and without user input, an object mask for objects of the plurality of objects. The disclosed systems determine, utilizing a distractor detection neural network, a classification for the objects of the plurality of objects. The disclosed systems remove at least one object from the digital image, based on classifying the at least one object as a distracting object, by deleting the object mask for the at least one object.

Abstract: Thioalcohols are useful for dissolving and removing aldehyde-containing deposits from conduit and/or volumes that are clogged or blocked by the aldehyde-containing deposits, and also to prevent or inhibit aldehydes from polymerizing or otherwise forming aldehyde-containing deposits within conduits and/or volumes.

Abstract: The present disclosure provides a binder composition comprising (a) at least one fluoropolymer comprising the residue of vinylidene fluoride; (b) one or more (meth)acrylic polymers; and (c) an organic medium comprising a trialkyl phosphate solvent. Also disclosed are slurry compositions, electrodes, and electrical storage devices.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image comprising a first region that includes content and a second region to be inpainted. Noise is then added to the image to obtain a noisy image, and a plurality of intermediate output images are generated based on the noisy image using a diffusion model trained using a perceptual loss. The intermediate output images predict a final output image based on a corresponding intermediate noise level of the diffusion model. The diffusion model then generates the final output image based on the intermediate output image. The final output image includes inpainted content in the second region that is consistent with the content in the first region.

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 instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.

Abstract: In some embodiments, a head-mounted, near-eye display system comprises a stack of waveguides having integral spacers separating the waveguides. The waveguides may each include diffractive optical elements that are formed simultaneously with the spacers by imprinting. The spacers are disposed on one major surface of each of the waveguides and indentations are provided on an opposite major surface of each of the waveguides. The indentations are sized and positioned to align with the spacers, thereby forming a self-aligned stack of waveguides. Tops of the spacers may be provided with light scattering features, anti-reflective coatings, and/or light absorbing adhesive to prevent light leakage between the waveguides. As seen in a top-down view, the spacers may be elongated along the same axis as the diffractive optical elements. The waveguides may include structures (e.g.

Abstract: A component for a vehicle interior may comprise a light guide to allow light transmission from a light source, and a cover to cover the light guide. The light guide may comprise an icon or indicator and the cover may comprise a depression or indentation in an inner surface of the cover. The icon/indicator of the light guide may comprise a light-transmissive resin material configured to fit within the depression/indentation in the cover. The icon/indicator may be presented when illuminated and hidden by the cover when not illuminated. The cover may comprise a polymer material coupled to the light guide. The cover may present a user interface. A surface of the cover may comprise an information region and/or a decorative region. The component may comprise a steering wheel, console, floor console, center console, instrument panel, door panel, dashboard, display, armrest, or cockpit.

Abstract: A transistor device may include a semiconductor structure including a channel layer and a barrier layer on the channel layer, wherein the barrier layer has a higher bandgap than the channel layer; a source contact and a drain contact on the barrier layer; a gate contact on the semiconductor structure between the source contact and the drain contact, the gate contact including a drain-side wing portion extending from a central portion of the gate contact; and a field plate on the semiconductor structure between the gate contact and the drain contact and laterally offset from the gate contact by a distance. The field plate may include a first wing portion extending from a central portion of the field plate.

Abstract: A user equipment (UE) can be a mobile terminating (MT) device that may receive data from another UE or other network element via a telecommunication network. When a small data payload is available for an MT UE, a base station of the telecommunication network can send a paging message to the UE that includes a Mobile Terminating Small Data Transmission (MT-SDT) paging cause. Based on the MT-SDT paging cause in the paging message, the UE can use Small Data Transmission (SDT) procedures to respond to the paging message. The SDT procedures can allow the UE to obtain the small data payload while the UE remains in a Radio Resource Control (RRC) Inactive state.

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: 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 method to upregulate CD46 cell surface expression and combination therapies for various cancers employing an anti-CD46 antibody and an immunomodulatory imide drug (IMiD) or a Signal Transducer And Activator of Transcription 3 (STAT3) inhibitor or both are provided.

Abstract: Embodiments of the present disclosure describe methods, apparatuses, and systems for hybrid low dropout regulator (LDO) architecture and realization to provide high power supply rejection ratio (PSRR) and high conversion efficiency (CE), and other benefits. The hybrid LDO may be coupled with dual rails for its analog LDO branch and digital LDO respectively to achieve high PSRR and high CE by utilizing the hybrid architecture with several feedback loops. Other embodiments may be described and claimed.

Abstract: The present disclosure relates to an engineered antibody that co-engages a cell type-selective (or specific) antigen (guide) and a signaling receptor (effector) on a target cell. In some instances, the engineered antibody is capable of modulating a signaling pathway on a target cell. In other embodiments, an engineered antibody of the present disclosure is administered to a subject for the treatment of a disease or condition.

Abstract: A system includes an acoustic lure and a sensor package disposed within an interior volume of an insect trapping container and a computing device in communication with the acoustic lure and the sensor package configured to instruct the acoustic lure to output an acoustic tone. The computer device is further configured to receive sensor data from the sensor package, the sensor data representative of insects within the interior volume. Responsive to receiving the sensor data, the computing device is configured to instruct an imaging device to capture image data representative of the insects within the interior volume or determine insect count data based on the sensor data. The computing device is configured to then transmit output data to a remote computing system, the output data including at least one of a first portion of the image data or a second portion of the insect count data.