Abstract: A display substrate, a method for manufacturing the same, and a display device are provided.

Abstract: A USB protocol-based IP infringement identification method for USB devices, including the following steps: S1, connecting an infringement identification device at a peer side of the USB host to be tested; S2, the USB host to be tested entering compliance mode; S3, the infringement identification device sending an X.LFPS file to the USB host to be tested; S4, upon the USB host to be tested receiving the X.LFPS file, the USB host to be tested sending IP copyright information to the infringement identification device; S5, determining whether the USB host to be tested infringes the IP. The infringement identification of the USB device to be tested is performed by using the compliance mode specified in the USB protocol, which is more stable, reliable and can also save costs.

Abstract: An antenna device includes a plurality of dipole antennas and a port. Each of the dipole antennas is connected to the port. The dipole antennas are arranged around the port. Each of the dipole antennas comprises two ends. The device further includes a plurality of passive elements. The ends of the dipole antennas and the passive elements are interchangeably arranged around the port such that each of the passive elements is situated between ends of two different antennas from the plurality of dipole antennas. One or more switches are configured to switch between an omnidirectional state, in which the ends of the dipole antennas are not connected to the plurality of passive elements, and a directional state, in which at least one end of one of the passive elements is connected to at least one end of one of the antennas.

Abstract: The present disclosure relates to a socket with a fixing device and a charging cabinet for electric vehicles with the socket. The fixing device includes a connection mechanism, which is provided on one side of a mainbody of the socket and is adapted to releasably connect the socket to a mounting object to which the socket is to be mounted by means of a first fixing member, and a pivot mechanism, which is provided between the other side of the mainbody opposite to the connection mechanism and the mounting object, so that the socket is pivotable relative to the mounting object when released from the mounting object. Through the fixing device for the electric vehicle charging gun socket, the charging gun socket can be easily and quickly fixed on a mounting object, such as a charging cabinet, since the number of required fixing parts is reduced.

Abstract: A method and apparatus for generating a drivable 3D character, an electronic device and a storage medium are disclosed, which relate to the field of artificial intelligence, such as computer vision, deep learning, or the like, and may be applied to 3D vision and other scenarios. The method may include: acquiring a 3D human body mesh model corresponding to a to-be-processed 2D image; performing a skeleton embedding operation on the 3D human body mesh model; and performing a skin binding operation on the 3D human body mesh model after the skeleton embedding operation to obtain a drivable 3D human body mesh model.

Abstract: A heterogeneous computing terminal for task scheduling is provided, including a processing unit and multiple computing units. The heterogeneous computing terminal generates a task scheduling strategy. The target computing unit acquires a target upgrade program, is upgrade based on the target upgrade program, processes a target task and sends a processing result to the processing unit. The processing unit combines processing results sent by multiple target computing units to obtain a processing result of an initial task set. The heterogeneous computing terminal can upgrade the computing unit online, that is, dynamically update the computing unit, thereby greatly expanding the versatility of the computing unit of hardware. Scheduling strategy adapted to the task can be generated, and the computing unit can dynamically provide better task processing capabilities. The computing efficiency of the heterogeneous computing terminal is improved.

Abstract: Cardiac images such as cardiac magnetic resonance (CMR) images and tissue characterization maps (e.g., T1/T2 maps) may be analyzed automatically using machine learning (ML) techniques, and reports may be generated to summarize the analysis. The ML techniques may include training one or more of an image classification model, a heart segmentation model, or a cardiac pathology detection model to automate the image analysis and/or reporting process. The image classification model may be capable of grouping the cardiac images into different categories, the heart segmentation model may be capable of delineating different anatomical regions of the heart, and the pathology detection model may be capable of detecting a medical abnormality in one or more of the anatomical regions based on tissue patterns or parameters automatically recognized by the detection model. Image registration that compensates for the impact of motions or movements may also be conducted automatically using ML techniques.

Abstract: The present application disclosures a double production line and a rapid prefabrication process of a segmental beam. The double production line including two production machine and a track system provided on the construction ground; the production machine includes a fixed end mold, two side molds, a bottom mold trolley, a middle internal mold trolley and two side internal mold trolley; two side molds are positioned on two sides of the fixed end mold respectively, the fixed end mold and two side molds together define a pouring position with an end opening, two openings of the pouring position are arranged facing each other; the track system includes a transverse track and a longitudinal track communicated with each other, two pouring positions are both positioned in the extension path of the transverse track, and the longitudinal track is positioned between two pouring positions.

Abstract: A docker image is received. The docker image is for a container. The container contains files that allow for virtualization of applications that run within the container. The docker image is parsed to identify layer files in the docker image. Installed software components (e.g., installed files) and/or hardware components in the layer files are identified. Software application index calls are made to generate information that identifies relationships between the installed software components and/or hardware components. The relationships between the installed software components and/or hardware components are then displayed to a user.

Abstract: The invention discloses a method for step parameter identification based on an electrochemical model, including: identifying a first type of electrochemical parameters through a reduced-order electrochemical model by utilizing different preset charge-discharge rate data; determining a first type of target electrochemical parameters identified by the reduced-order electrochemical model through extrapolation; the first type of target electrochemical parameters including an approximate value of the first type of electrochemical parameters or the first type of electrochemical parameters at a specific rate; identifying the first type of electrochemical parameters, which includes solid-phase lithium concentration and exchange current, through a full-order electrochemical model by taking the first type of target electrochemical parameters as actual values of the first type of electrochemical parameters of the full-order electrochemical model; and identifying a second type of electrochemical parameters, which include

Abstract: The invention provides a method and system for decoupling electric field of electrochemical model based on a parallel targeting method. The method includes selecting a negative or positive electrode region as a calculation region; selecting a solid or liquid phase current as an observed quantity, and a solid and liquid phase potential as a costate variable; inserting nodes between two endpoints of the calculation region, and determining a target value of the observed quantity of each node; constructing N calculation units; respectively performing a target shooting on the N calculation units; and determining whether a distance between the target shooting value of the observed quantity of the end point of each calculation unit and the target value of the observed quantity of the node corresponding to the end point of the calculation unit is within a preset range.

Abstract: A computer-implemented method dynamically switches access plans for a query during concurrent query execution. The method includes receiving a first query configured to be processed by a database system. The method also includes generating, for the first query, an access plan for each of identified resource sets. The method includes determining a first set of available resources that represent an available capacity for the database system. The method further includes selecting a first resource set of the one or more resource sets, where the selecting is based on the first set of available resources being closest to the first resource set. The method also includes selecting, based on the first set of available resources, a first access plan of the one or more access plans. The method includes executing the first query and returning results of the first query to a source of the first query.

Abstract: Disclosed is a selective field-assisted machining system. The system includes a micron-level high-speed identification module, an in-situ laser assisted module, an ultrasonic vibration module, an energy field loading high-speed control module, and a diamond tool. The micron-level high-speed identification module is used to quickly identify the type of a material substrate of a workpiece to be processed, process the identification information into a corresponding control signal, and send same to the energy field loading high-speed control module to implement selective processing of the workpiece to be processed, i.e. to process brittle particles using in-situ laser assisted machining and to process a soft metal substrate using ultrasonic vibration processing. In the present invention, ultra-precision cutting of brittle particles and a soft metal substrate can be completed at the same time in a single processing process.

Abstract: Described herein are MAT2A inhibitors and pharmaceutical compositions comprising said inhibitors. The subject compounds and compositions are useful for the treatment of a disease or disorder associated with MAT2A.

Abstract: In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.

Abstract: Described herein are systems, methods, and instrumentalities associated with landmark detection. The detection may be accomplished by determining a graph representation of a plurality of hypothetical landmarks detected in one or more medical images. The graph representation may include nodes that represent the hypothetical landmarks and edges that represent the relationships between paired hypothetical landmarks. The graph representation may be processed using a graph neural network such a message passing graph neural network, by which the landmark detection problem may be converted and solved as a graph node labeling problem.

Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.

Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.

Abstract: Methods and apparatuses of boundary refinement for instance segmentation. The methods for instance segmentation include receiving an image and an instance mask identifying an instance in the image; extracting a set of image patches from the image based on a boundary of the instance mask; generating a refined mask patch for each of the set of image patches based on at least a part of the instance mask corresponding to the each of the set of image patches; and refining the boundary of the instance mask based on the refined mask patch for each of the set of image patches.

Abstract: A computer-implemented method is provided for model training performed by a processing system. The method comprises determining a set of first weights based on a first matrix associated with a source model, determining a set of second weights based on the set of first weights, forming a second matrix associated with a target model based on the set of first weights and the set of second weights, initializing the target model based on the second matrix, and training the target model.