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: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

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: This application provides a method for enabling an intent and an apparatus. The method includes: A first network element obtains first information, where the first information indicates at least one intent, and the at least one intent is all intents that conflict with a first intent. When the first network element determines that a status of the at least one intent changes, the first network element initiates a procedure of enabling the first intent. According to the solution of this application, when the first intent is in a conflict state, the first network element may initiate the procedure of enabling the first intent after determining that statuses of all the intents that conflict with the first intent change. The solution in this application can be used to re-initiate, in a timely, accurate, and efficient manner, the procedure of enabling the first intent.

Abstract: An expansion anchor having an anchor bolt, an expansion sleeve surrounding the anchor bolt, and an expansion body located in a front region of the anchor bolt, wherein the expansion body has a converging zone for expanding the expansion sleeve. The expansion body has at least one expansion sleeve abutment wall facing the expansion sleeve. The invention also relates to a method for using such an expansion anchor.

Abstract: A neural network optimization method includes receiving a model file of a to-be-optimized neural network; obtaining a search space of a target neural network architecture based on the model file of the to-be-optimized neural network, where the search space includes a value range of each attribute of each neuron in the target neural network architecture; obtaining the target neural network architecture based on the search space; training the target neural network architecture based on the model file of the to-be-optimized neural network, to obtain a model file of a target neural network; and providing the model file of the target neural network to a user.

Abstract: The present invention provides a B7-H3 nanobody, the preparation method and use thereof. The B7-H3 nanobody comprises framework regions 1-4 (FR 1-4) and complementarity determining regions 1-3 (CDR 1-3), can specifically bind to B7-H3, and can be used for detecting B7-H3 molecules, and be used for the treatment of various malignant tumors with abnormal expression of B7-H3 molecule.

Abstract: In a group chat-based instant messaging method, a message is received from a first messaging application of a first user in a first messaging group. The first messaging application is associated with a first messaging service. When the message is to be announced to all group members of the first messaging group, whether the first user is authorized to send the message is determined based on identity information of the first user. Based on the first user being authorized, protocol conversion on the message is performed based on a second messaging protocol of a second messaging service to output the message in a first preset announcement style for messages announced to all group members. The converted message is transmitted, via a second messaging server, to a second messaging application corresponding to one of the plurality of group members in the first messaging group.

Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that utilize channel pruning and knowledge distillation to generate a compact noise-to-image GAN. For example, the disclosed systems prune less informative channels via outgoing channel weights of the GAN. In some implementations, the disclosed systems further utilize content-aware pruning by utilizing a differentiable loss between an image generated by the GAN and a modified version of the image to identify sensitive channels within the GAN during channel pruning. In some embodiments, the disclosed systems utilize knowledge distillation to learn parameters for the pruned GAN to mimic a full-size GAN.

Abstract: Embodiments include semiconductor processing methods to form low-K films on semiconductor substrates are described. The processing methods may include flowing one or more deposition precursors to a semiconductor processing system, wherein the one or more deposition precursors include a silicon-containing precursor. The silicon-containing precursor may include a carbon chain. The methods may include generating a deposition plasma from the one or more deposition precursors. The methods may include depositing a silicon-and-carbon-containing material on the substrate from plasma effluents of the deposition plasma. The silicon-and-carbon-containing material as-deposited may be characterized by a dielectric constant less than or about 3.0.

Abstract: The present disclosure provides a resource utilization method of crude sodium sulfate. The method comprises the following step: reducing the crude sodium sulfate to form a sodium sulfide solution; making the sodium sulfide solution perform a first reaction with chlorine to obtain sulfur and a sodium chloride solution; and electrolyzing the sodium chloride solution to obtain a sodium hydroxide solution and chlorine, and supplying the generated chlorine to the sodium sulfide solution to perform the first reaction.

Abstract: Exemplary semiconductor processing methods may include providing one or more deposition precursors to a processing region of a semiconductor processing chamber. A semiconductor substrate may be positioned within the processing region. The methods may include forming a layer of low dielectric constant material on the semiconductor substrate. The methods may include purging the processing region of the one or more deposition precursors. A plasma power may be maintained at less than or about 750 W while purging the processing region. The methods may include forming an interface layer on the layer of low dielectric constant material. The methods may include forming a cap layer on the interface layer.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.

Abstract: One example method involves operations for receiving a request to transform an input image into a target image. Operations further include providing the input image to a machine learning model trained to adapt images. Training the machine learning model includes accessing training data having a source domain of images and a target domain of images with a target style. Training further includes using a pre-trained generative model to generate an adapted source domain of adapted images having the target style. The adapted source domain is generated by determining a rate of change for parameters of the target style, generating weighted parameters by applying a weight to each of the parameters based on their respective rate of change, and applying the weighted parameters to the source domain. Additionally, operations include using the machine learning model to generate the target image by modifying parameters of the input image using the target style.

Abstract: Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects.

Abstract: An expansion anchor includes an anchor bolt and an expansion sleeve which encompasses the anchor bolt. The anchor bolt has an expansion-sleeve expansion region and an expansion-element expansion region. The expansion sleeve has an expansion element which is disposed on the expansion sleeve. The expansion sleeve is radially displaceable by the expansion-sleeve expansion region and the expansion element is radially displaceable by the expansion-element expansion region. The expansion element is disposed on the expansion sleeve on a side of the expansion sleeve that radially faces the anchor bolt.

Abstract: A cached information updating method includes receiving an update request, determining an update processing manner according to a number of pieces of cached information to be updated indicated in the update request, and updating the cached information according to the update processing manner, to update differently according to different numbers of pieces of cached information.