Abstract: Provided is a driving device for detecting mechanical characteristics and electrical characteristics of cells. A structure of the driving device includes a piezoelectric stack, a bridge-type flexible hinge mechanism, a parallel hinge mechanism, a lead screw guide rail, a stepping motor, a linear displacement sensor, a force sensor, a ceramic needle, a first electrode, a second electrode, a cell container, an XY axis displacement platform, a positioning hole, a first metal base, a second metal base, a first metal connecting plate, a second metal connecting plate, a first pre-tightening wedge, a second pre-tightening wedge, screws, and a pre-tightening screw. During the operation of the driving device, the piezoelectric stack is driven under an excitation effect of a driving electric field signal, such that the bridge-type flexible hinge mechanism stretches, and the ceramic needle is driven by the parallel flexible hinge mechanism to move downwards.

Abstract: This application provides a data processing method and an apparatus. The data processing method includes: acquiring a first frame of time-of-flight (TOF) data; processing the first frame of TOF data into a TOF image; and performing face recognition by using the first frame of TOF image to obtain a recognition result. When TOF data is used for face recognition, higher security can be achieved. In addition, the first frame of TOF image is used for face recognition immediately after the first frame of TOF image is obtained, so that the execution speed is high. Because the TOF data is less affected by light, a high-quality TOF image can be obtained even if the first frame of TOF data is used, and therefore, the recognition accuracy is high.

Abstract: A separator contains a first monomer and/or a first polymer. The first monomer has a structure shown in Formula (I) or Formula (II), R1 and R2 are each independently selected from H, —CnNR3, or —CnNR4R5, and n is an integer from 0 to 4; NR3 is a 4-7 membered heterocyclic ring, R3 contains 0 to 2 oxygen atoms and/or 0 to 2 nitrogen atoms, and the balance are carbon atoms. There is or there is no grafting group R6 on R3, R6 is —CmOH, and m is an integer from 0 to 2; and R4 and R5 are each independently a C1-C4 aliphatic chain.

Abstract: The present application disclose a device for preparing rock samples with different moisture contents, which comprises a sample container, wherein two ends of the rock sample are respectively connected with a first steam inlet and a first steam outlet of the sample container; the first steam outlet, a pump and a gas-liquid separator are connected in sequence, a gas outlet end of the gas-liquid separator, a steam compressor and the first steam inlet are connected in sequence, and a liquid outlet end of the gas-liquid separator, a steam generator and the steam compressor are connected in sequence. The water vapor in the sample container is pumped into the gas-liquid separator by the pump to separate low-temperature water vapor and condensate, and the low-temperature water vapor and condensate form high-temperature and high-pressure steam again and return to the inside of the sample container. The water in the steam generator always circulates inside the preparation device.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.

Abstract: Provided are a distributed energy storage planning method and system for a power distribution network. The method includes a step S1 of setting a mathematical indicator for quantitatively evaluating superiority and inferiority of construction positions of distributed energy storage assemblies as a location evaluation indicator in a distribution area of a distributed power supply, a step S2 of planning an optimal laying path of the distributed energy storage assemblies in a planning area based on the location evaluation indicator and a step S3 of constructing the distributed energy storage assemblies according to the optimal laying path and connecting the distributed energy storage assemblies to the power distribution network to perform an energy storage operation so that the distributed power supply performs orderly energy storage on the distributed energy storage assemblies in the power distribution network.

Abstract: A computing device can receive a query that identifies a set of data to be processed and determine that a portion of the set of data resides in an external data system. The query system can request data identifiers associated with data objects of the set of data from the external data system and communicate the data identifiers to a data queue. The computing device can instruct one or more search nodes to retrieve the identifiers from the data queue. The search nodes can use the data identifiers to retrieve data objects from the external data system and process the data objects according to instructions received from the computing device. The search nodes can provide results of the processing to the computing device.

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 processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.

Abstract: Provided in the present invention is an antibody or an antigen binding protein thereof. The antibody or the antigen binding protein thereof can specifically bind to human CD3 or human GPRC5D protein or MUC16, or specifically bind to human CD3 and a second antigen. Further provided in the present invention are a nucleic acid molecule encoding the antibody, an expression vector for expressing the antibody, a host cell and a preparation method therefor. Also provided in the present invention is a diagnosis and treatment method using the antibody of the invention.

Abstract: Embodiments of the present disclosure include obtaining a text prompt describing an element, layout information indicating a target region for the element, and a precision level corresponding to the element. Some embodiments generate a text feature pyramid based on the text prompt, the layout information, and the precision level, wherein the text feature pyramid comprises a plurality of text feature maps at a plurality of scales, respectively. Then, an image is generated based on the text feature pyramid. In some cases, the image includes an object corresponding to the element of the text prompt at the target region. Additionally, a shape of the object corresponds to a shape of the target region based on the precision level.

Abstract: A liquid crystal panel and a manufacturing method thereof, and a display apparatus are disclosed. The liquid crystal panel includes a liquid crystal cell, a first adhesive layer, a first polarizer, a second adhesive layer and an optical composite film; the first adhesive layer is located on a first side of the liquid crystal cell; the first polarizer is located on a side of the first adhesive layer away from the liquid crystal cell; the second adhesive layer is located on a side of the first polarizer away from the first adhesive layer; the optical composite film is located on a side of the second adhesive layer away from the first polarizer; the first adhesive layer adheres the first polarizer onto the liquid crystal cell, the second adhesive layer adheres the optical composite film onto the first polarizer, and the optical composite film includes diffusion particles.

Abstract: Embodiments of this application provide a face recognition method and apparatus, including: obtaining TOF data; processing the TOF data into a TOF image in a TEE; and performing face recognition by using the TOF image, to obtain a recognition result. Because the TOF data is used for face recognition, higher security is achieved. Implementation in the TEE can further improve the security.

Abstract: Provided is a display panel. The display panel includes: a driving backplane, including a base substrate; a light-emitting device on a side of the driving backplane; a package structure on a side, facing away from the driving backplane, of the light-emitting device, wherein the package structure includes an inorganic package structure and an organic package structure, the inorganic package structure being closer to the driving backplane than the organic package structure; and a color stop layer on a side, facing away from the driving backplane, of the organic package structure, wherein the color stop layer is attached to the inorganic package structure, and in a direction perpendicular to the base substrate, a maximum thickness of the organic package structure is less than a thickness of the inorganic package structure.

Abstract: An electrolyte including one or more nitrile benzoquinone compounds, and the nitrile benzoquinone compound is selected from the group consisting of the compounds represented by formula I, formula II, and formula III: The substituents R1 to R9 are each independently selected from the group consisting of hydrogen, a C2 to C12 ether group, a C1 to C12 alkoxy group, halogen, a C1 to C12 alkyl group, a C2 to C12 alkenyl group, a C2 to C12 alkynyl group, and a C6 to C26 aryl group. The electrolyte can form a stable protective film on a cathode, thereby increasing the cycle capacity retention rate and high temperature storage performance of an electrochemical device.

Abstract: Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.

Abstract: Systems and methods provide editing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. An eye-gaze network may produce a hotspot map of predicted focal points in a video frame. These predicted focal points may then be used by a gaze-to-mask network to determine objects in the image and generate an object mask for each of the detected objects. This process may then be repeated to effectively track the trajectory of objects and object focal points in videos. Based on the determined trajectory of an object in a video clip and editing parameters, the editing engine may produce editing effects relative to an object for the video clip.

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.