Abstract: A computer-implemented method to estimate an execution time for large query batches. The method includes identifying a batch of queries, where the batch of queries where each query is configured to retrieve data from a database. Selecting a first query and generating an access path for the first query including one or more mini plans. Building a learning model for the one or more mini plans. Estimating an estimated execution time for each of the one or more mini plans. Adding each of the estimated execution times for each of the one or more mini plans to determine a first total estimated time for the first query. Displaying the first total estimated time.

Abstract: A text processing model training method, and a text processing method and apparatus in the natural language processing field in the artificial intelligence field are disclosed. The training method includes: obtaining training text; separately inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model; the sample data includes a sample semantic feature and a sample label; the prediction data includes a prediction semantic feature and a prediction label; and the teacher model is a pre-trained language model used for text classification; and training a model parameter of the student model based on the sample data and the prediction data, to obtain a target student model. The method enables the student model to effectively perform knowledge transfer, thereby improving accuracy of a text processing result of the student model.

Abstract: For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.

Abstract: Machine training a network for and use of the machine-trained network are provided for tissue parameter estimation for a magnetic scanner using magnetic resonance fingerprinting. The machine-trained network is trained to both reconstruct a fingerprint image or fingerprint and to estimate values for multiple tissue parameters in magnetic resonance fingerprinting. The reconstruction of the fingerprint image or fingerprint may reduce noise, such as aliasing, allowing for more accurate estimation of the values of the multiple tissue parameters from the under sampled magnetic resonance fingerprinting information.

Abstract: Data processing techniques comprise, in response to determining that a storage unit storing first data blocks is damaged, determining storage units associated with the storage unit, and obtaining second data blocks from the storage units, where the second data blocks and the first data blocks are generated by applying to data an error correction code in a first format. The method further comprises recovering the first data blocks based on the second data blocks. In addition, the method comprises generating error correction blocks by applying an error correction code in a second format to the recovered first data blocks, where the second format is different from the first format. The above techniques can reduce input/output operations and shortens the recovery time of data blocks, and further can reduce the risk of data loss even in an extremely bad situation.

Abstract: A method and system for predicting a response time for a workload prior to making a hardware upgrade to a computing system. Data related to operation of the system is collected. Then a workload model of a plurality of workloads and CPU utilization for the plurality of workloads and a transaction model for each transaction within a workload of the plurality of workloads are built. Next the process determines that a characteristic of at least one workload in the plurality of workloads will change due to the hardware upgrade. As a result of the change, a new workload model for the changed workload is built based on the changed characteristic, and the response time for the workload based on the new workload model is calculated.

Abstract: Functional verification of a new database feature is provided. A set of SQL query seeds are evolved using information contained in a knowledge map that was generated based on a real SQL query workload of an entity corresponding to a database. Real SQL queries are identified in the database that are similar to the evolved set of sample SQL query seeds. A new feature to be implemented in the database is injected into the real SQL queries that are similar to the evolved set of sample SQL query seeds. Multiple runtime access paths are generated for each of the real SQL queries injected with the new feature for output cross validation. Each of the real SQL queries injected with the new feature is run in a test environment using each of the multiple runtime access paths corresponding to a real SQL query injected with the new feature for functional verification.

Abstract: This application relates to the field of artificial intelligence, and provides a text processing method, a model training method, and an apparatus. The method includes: obtaining target knowledge data; processing the target knowledge data to obtain a target knowledge vector; processing to-be-processed text to obtain a target text vector; fusing the target text vector and the target knowledge vector based on a target fusion model, to obtain a fused target text vector and a fused target knowledge vector; and processing the fused target text vector and/or the fused target knowledge vector based on a target processing model, to obtain a processing result corresponding to a target task. The foregoing technical solution can improve accuracy of a result of processing a target task by the target processing model.

Abstract: Embodiments are disclosed for a method for dynamic access paths. The method includes generating real-time statistics (RTS) estimates based on a log of a database. Further, the method includes generating access paths based on a structured query language command and the RTS estimates. The method also includes training a machine learning model to map the RTS estimates to the access paths.

Abstract: The present invention relates to the technical field of silk scaffolds, and in particular, to a silk/pet mix-woven scaffold and a preparation method and use thereof. The silk/PET mix-woven scaffold is formed by weaving silk and PET fibers. Sericin of the silk is removed. The silk and the PET fibers are mixed and knitted. The PET fibers provide reliable fixation in an early stage to maintain the stability of mechanical properties, and the silk degrades gradually in a later stage to promote the growth of new tissues to achieve the integration of the scaffold and the body. When the scaffold is used for artificial tendon/ligament recovery, its overall performance is better than that of pure silk or pure PET fiber scaffolds, and the scaffold has excellent clinical transformation potential.

Abstract: Cardiac features captured via an MRI scan may be tracked and analyzed using a system described herein. The system may receive a plurality of MR slices derived via the MRI scan and present the MR slices in a manner that allows a user to navigate through the MR slices. Responsive to the user selecting one of the MR slices, contextual and global cardiac information associated with the selected slice may be determined and displayed. The contextual information may correspond to the selected slice and the global information may encompass information gathered across the plurality of MR slices. A user may have the ability to navigate between the different display areas and evaluate the health of the heart with both local and global perspectives.

Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.

Abstract: An electrode assembly formed by winding a first electrode plate, a separator and a second electrode plate. The first electrode plate comprises a first current collector and a first active material layer arranged on a surface of the first current collector. A winding terminating end of the first electrode plate is positioned on an outer side of a winding terminating end of the second electrode plate facing away from a winding core. The first active material layer on the winding terminating end of the first electrode plate covers one surface of the first current collector facing toward the second electrode plate, The electrode assembly further comprises a first adhesive member arranged on an outer surface of the electrode assembly. The electrode assembly takes the winding terminating end of the first electrode plate as the tail end. The application further provides a battery having the same.

Abstract: This application discloses a decentralization processing method, which are applied to a microservice system. A first communication proxy in the microservice system receives a first microservice request sent by a first microservice application, the first communication proxy determines, based on a target control plane rule, a microservice access address corresponding to the first microservice request, and sends the first microservice request to the second communication proxy having the microservice access address, where the second communication proxy is used to forward the first microservice request to the second microservice application. The first communication proxy can send the microservice request to the corresponding second communication proxy, and a control plane does not need to route the microservice request, so that traffic for routing the microservice request by the control plane is effectively reduced, and impact of a burst of microservice requests on the control plane is effectively avoided.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed using a feature pyramid and/or a motion pyramid that correspond to multiple image scales. The motion estimation may be performed using neural networks and parameters that are learned via a training process involving a student network and a teacher network pre-pretrained with abilities to apply progressive motion compensation.

Abstract: A job is received. The job indicates moving one or more data to a pool storage group (PSG). The job includes resource requirements and the resource requirements include a size requirement to store the one or more data and a volume requirement to store the one or more data. A resource availability of the PSG is received. The resource availability of the PSG includes an available space on the PSG and one or more available volume on the PSG. Whether the resource availability of the PSG meets the resource requirements of the job is determined. Whether the PSG has a dynamic pool storage group (DPSG) flag indication on is determined.

Abstract: Metadata is replicated. For instance, in response to receiving a request for replicating metadata to a target node, information of an index structure associated with the metadata is obtained. The index structure may include terminal nodes for storing the metadata and index nodes for storing index information of the terminal nodes. Next, the metadata from the terminal nodes is extracted based on the information of the index structure. Further, a sequence is generated to be replicated of the metadata. The sequence to be replicated includes items of the metadata, and locations of the items in the sequence to be replicated are determined based on key information corresponding to the items. In addition, the items of the metadata, in the sequence to be replicated, are replicated to the target node. In this way, only the metadata in the index structure is replicated, thereby reducing the data amount of replication.

Abstract: This application provides a video labeling method performed by a server, and the method includes: receiving a video extraction instruction transmitted by a terminal, and obtaining a to-be-extracted video according to the video extraction instruction; extracting event information from video frames of the to-be-extracted video; forming at least one event information flow by using the event information; capturing, based on the at least one event information flow, at least one first clip that meets a plot trigger condition from the to-be-extracted video and obtaining a plot labeling tag of the at least one first clip; and transmitting the at least one first clip and the corresponding plot labeling tag to the terminal, wherein the terminal displays the at least one first clip and the corresponding plot labeling tag in a preset display region of a display interface in which the to-be-extracted video is displayed.

Abstract: For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.

Abstract: One or more implementations of the present specification disclose a database analysis information generation method, including: reading data in a to-be-merged data level and data in a merging data level; generating a new merging data level by merging the data in the to-be-merged data level and the data in the merging data level in a merging process; and performing an operation of generating new database analysis information based on data in the new merging data level and data in existing database analysis information in the process of generating the new merging data level.