Abstract: A conveyor controlling apparatus for controlling a conveyor in accordance with environment parameters with respect to the conveyor includes: a capturing device for capturing an environmental image related to the conveyor; an image-analysing module for determining, based on the environmental image captured by the capturing device, whether an operational condition of the conveyor is satisfied; and a conveyor-controlling module for controlling the conveyor based on a result of the determination carried out by the image-analysing module, wherein if the image-analysing module determines that the operational condition is not satisfied, then the conveyor-controlling module stops the conveyor.

Abstract: Methods, systems, and computer storage media for providing generative artificial intelligence (AI) code lookup management using a generative AI code lookup engine in an item listing system. A generative AI code lookup engine supports generative AI code lookup management based on an automotive code lookup platform including code lookup training operations, code lookup engine operations, and code lookup interfaces associated with a generative AI model and an automotive domain for improved personalization and presentation of code lookup data and interfaces. In operation, a request associated with a diagnosis code of a vehicle is accessed. Based on the request, code lookup data associated with a generative AI model is accessed. The generative AI model is associated with code lookup training operations and a code lookup data structure that support code lookup guidance in the item listing system. The code lookup data is communicated for display via an item listing system client.

Abstract: Technologies for the prevention and/or treatment of pneumococcal infections.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning to generate a sequence of transition frames for a gap in a clipped digital video. For example, the disclosed system receives a clipped digital video that includes a pre-cut frame prior to a gap in the clipped digital video and a post-cut frame following the gap in the clipped digital video. Moreover, the disclosed system utilizes a natural motion sequence model to generates a sequence of transition keypoint maps between the pre-cut frame and the post-cut frame. Furthermore, using a generative neural network, the disclosed system generates a sequence of transition frames for the gap in the clipped digital video from the sequence of transition keypoint maps.

Abstract: A sorbent composition with improved acid gas reactivity comprising calcium hydroxide particles is provided. In the calcium hydroxide composition, the amount of time it takes the calcium hydroxide particles to neutralize in citric acid of a mass greater than 10 times the mass of the calcium hydroxide particles is less than 10 seconds; about 90% of the calcium hydroxide particles are less than or equal to about 10 microns; and the calcium hydroxide particles have a BET surface area of about 18 m2/g or greater.

Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

Abstract: An electrical assembly including a converter for connection to an electrical network, the converter including at least one module having at least one switching element and at least one energy storage device, the switching element and the energy storage device arranged to be combinable to provide a voltage source, the electrical assembly including a controller configured to control the switching of the switching element, wherein the electrical assembly includes a sensor configured for measuring a current of the electrical network, wherein the controller and sensor are configured to carry out a characterization of an electrical parameter so that, in use the controller controls the switching of the switching element to modify an electrical parameter of the converter so as to modify the current of the electrical network, the sensor measures a resultant modified current, and the controller processes the measured resultant modified current to characterize the electrical parameter.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images depicting photorealistic scenes utilizing a digital image collaging neural network. For example, the disclosed systems utilize a digital image collaging neural network having a particular architecture for disentangling generation of scene layouts and pixel colors for different regions of a digital image. In some cases, the disclosed systems break down the process of generating a collage digital into generating images representing different regions such as a background and a foreground to be collaged into a final result. For example, utilizing the digital image collaging neural network, the disclosed systems determine scene layouts and pixel colors for both foreground digital images and background digital images to ultimately collage the foreground and background together into a collage digital image depicting a real-world scene.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize one or more stages of a two-stage image colorization neural network to colorize or re-colorize digital images. In one or more embodiments, the disclosed system generates a color digital image from a grayscale digital image by utilizing a colorization neural network. Additionally, the disclosed system receives one or more inputs indicating local hints comprising one or more color selections to apply to one or more objects of the color digital image. The disclosed system then utilizes a re-colorization neural network to generate a modified digital image from the color digital image by modifying one or more colors of the object(s) based on the luminance channel, color channels, and selected color(s).

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: Provided herein are novel anti-CLDN 18.2 antibodies and chimeric antigen receptors (CAR), cells or compositions comprising the same, vector or plasmid encoding anti-CLDN 18.2 CAR, anti-CLDN18.2 antibody-drug conjugates (ADCs), bispecific antibodies containing anti-CLDN 18.2 antibody, and methods for producing the same, or using the same for detecting or treating ovarian cancer or prostate cancer. Also provided herein are anti-CLDN 18.2 antibody, compositions comprising the same, nucleic acid sequence encoding the same, and a kit for detecting CLDN 18.2.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation include obtaining an input image having a first resolution, where the input image includes random noise, and generating a low-resolution image based on the input image, where the low-resolution image has the first resolution. The method, non-transitory computer readable medium, apparatus, and system further include generating a high-resolution image based on the low-resolution image, where the high-resolution image has a second resolution that is greater than the first resolution.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: Various disclosed embodiments are directed to changing parameters of an input image or multidimensional representation of the input image based on a user request to change such parameters. An input image is first received. A multidimensional image that represents the input image in multiple dimensions is generated via a model. A request to change at least a first parameter to a second parameter is received via user input at a user device. Such request is a request to edit or generate the multidimensional image in some way. For instance, the request may be to change the light source position or camera position from a first set of coordinates to a second set of coordinates.

Abstract: Systems and methods for image generation are provided. An aspect of the systems and methods includes obtaining a text prompt, generating a style vector based on the text prompt, generating an adaptive convolution filter based on the style vector, and generating an image corresponding to the text prompt based on the adaptive convolution filter.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a low-resolution image and a text description of the low-resolution image. A mapping network generates a style vector representing the text description of the low-resolution image. An adaptive convolution component generates an adaptive convolution filter based on the style vector. An image generation network generates a high-resolution image corresponding to the low-resolution image based on the adaptive convolution filter.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning to generate a sequence of transition frames for a gap in a clipped digital video. For example, the disclosed system receives a clipped digital video that includes a pre-cut frame prior to a gap in the clipped digital video and a post-cut frame following the gap in the clipped digital video. Moreover, the disclosed system utilizes a natural motion sequence model to generates a sequence of transition keypoint maps between the pre-cut frame and the post-cut frame. Furthermore, using a generative neural network, the disclosed system generates a sequence of transition frames for the gap in the clipped digital video from the sequence of transition keypoint maps.

Abstract: Provided herein are novel anti-CLDN 18.2 antibodies and chimeric antigen receptors (CAR), cells or compositions comprising the same, vector or plasmid encoding anti-CLDN 18.2 CAR, anti-CLDN 18.2 antibody-drug conjugates (ADCs), bispecific antibodies containing anti-CLDN 18.2 antibodies, and methods for producing the same, or using the same for detecting or treating ovarian cancer or prostate cancer. Also provided herein are anti-CLDN 18.2 antibodies, compositions comprising the same, nucleic acid sequence encoding the same, and a kit for detecting CLDN 18.2.

Abstract: Systems and methods for fine-tuning diffusion models are described. Embodiments of the present disclosure obtain an input text indicating an element to be included in an image; generate a synthetic image depicting the element based on the input text using a diffusion model trained by comparing synthetic images depicting the element to training images depicting elements similar to the element and updating selected parameters corresponding to an attention layer of the diffusion model based on the comparison.