Abstract: Provided are a system and method for implementing an incremental data comparison. The system includes: a synchronization T environment, a simulation F environment, a simulation tool, a memory database, a comparison tool and a result database. The T environment includes a synchronization environment core application, a traditional commercial database, an incremental synchronization tool and a synchronization environment distributed database. The F environment includes a simulation environment core application and a simulation environment distributed database. The simulation tool is configured to play back a T environment service and an F environment service. The memory database is configured to store a mapping relationship between message values of the T and F environment. The comparison tool is configured to compare data in the T and F environment according to the mapping relationship between the message values.

Abstract: A load balancing method, apparatus and device for a parallel model training task, and a computer-readable storage medium, includes: acquiring data traffic and a theoretical computational amount of each network layer in a target model; determining a theoretical computing capability of each computing device, and obtaining an initial computational amount corresponding to each computing device according to the theoretical computing capability and the theoretical computational amount; performing a load balancing operation according to the initial computational amount by using multiple device critical layer position division rule, so as to obtain a plurality of initial balancing schemes; compiling statistics on time performance parameters corresponding to the initial balancing schemes, and determining an intermediate balancing scheme from the initial balancing schemes according to the time performance parameters; and adjusting the intermediate balancing scheme according to the data traffic, so as to obtain a final

Abstract: Disclosed are a person Re-identification (Re-ID) method, system, and device, and a computer-readable storage medium. The method includes: acquiring a sample set to be trained (S101); training a pre-constructed person Re-ID model by data re-sampling and cross-validation methods based on the sample set to be trained to obtain a trained person Re-ID model (S102); and performing person Re-ID based on the trained person Re-ID model, wherein persons in any two groups are of different classes after the sample set to be trained is grouped according to the cross-validation method (S103).

Abstract: An nGraph-based graphics processing unit (GPU) backend distributed training method and system, a computer-readable storage medium, and an electronic device. The method includes: receiving a training request, and obtaining corresponding training data; obtaining a Nvidia® Collective multi-GPU Communication Library (NCCL) file by means of a system path of the NCCL file linked to an nGraph framework; invoking an NCCL communication interface configuration according to the training request to obtain a training model, the NCCL communication interface is an NCCL file-based communication operation interface located at a GPU backend of the nGraph framework; and performing GPU backend training on the training data using the training model. The present application can satisfy an urgent need of a user for performing neural network distributed training on the basis of an nGraph GPU backend, thus further improving the performance of deep learning network training.

Abstract: Disclosed are a method for realizing an nGraph framework supporting an FPGA backend device, and a related apparatus. The method includes: integrating an OpenCL standard API library into an nGraph framework; creating, in the nGraph framework, an FPGA backend device creation module for registering an FPGA rear-end device, initializing an OpenCL environment and acquiring the FPGA backend device; creating, in the nGraph framework, an FPGA buffer space processing module for opening up an FPGA buffer space and for reading and writing an FPGA cache; creating, in the nGraph framework, an OP kernel implementation module for creating an OP kernel and compiling the OP kernel; and creating, in the nGraph framework, an FPGA compiling execution module for registering, scheduling and executing the OP kernel.

Abstract: Disclosed are a method for realizing an nGraph framework supporting an FPGA backend device, and a related apparatus. The method includes: integrating an OpenCL standard API library into an nGraph framework; creating, in the nGraph framework, an FPGA backend device creation module for registering an FPGA rear-end device, initializing an OpenCL environment and acquiring the FPGA backend device; creating, in the nGraph framework, an FPGA buffer space processing module for opening up an FPGA buffer space and for reading and writing an FPGA cache; creating, in the nGraph framework, an OP kernel implementation module for creating an OP kernel and compiling the OP kernel; and creating, in the nGraph framework, an FPGA compiling execution module for registering, scheduling and executing the OP kernel.

Abstract: A load balancing method, apparatus and device for a parallel model training task, and a computer-readable storage medium, includes: acquiring data traffic and a theoretical computational amount of each network layer in a target model; determining a theoretical computing capability of each computing device, and obtaining an initial computational amount corresponding to each computing device according to the theoretical computing capability and the theoretical computational amount; performing a load balancing operation according to the initial computational amount by using multiple device critical layer position division rule, so as to obtain a plurality of initial balancing schemes; compiling statistics on time performance parameters corresponding to the initial balancing schemes, and determining an intermediate balancing scheme from the initial balancing schemes according to the time performance parameters; and adjusting the intermediate balancing scheme according to the data traffic, so as to obtain a final

Abstract: An image processing method is provided, which is applied to a deep learning model. A cache queue is provided in front of each layer of the deep learning model; a plurality of computation tasks are preset for each layer of the deep learning model in advance, and are configured for computing weight parameters and corresponding to-be-processed data in a plurality of channels in each corresponding layer in parallel, and storing a computation result into a cache queue behind the corresponding layer thereof; in addition, as long as the cache queue in front of the layer includes the computation result stored in the previous layer, the layer can obtain the to-be-processed data from the computation result, subsequent computation is performed, and a parallel pipeline computation mode is also formed between the layers.

Abstract: Disclosed is an image segmentation method, including: obtaining an original image set; performing feature extraction on the original image set by using a backbone network to obtain a feature map set; performing channel extraction fusion processing on the feature map set by using a channel extraction fusion model to obtain an enhanced feature map set; and segmenting the enhanced feature map set by using a preset convolutional neural network to obtain an image segmentation result. In addition, the present application also provides an image segmentation system and device, and a readable storage medium, which have the beneficial effects above.

Abstract: Provided in the present invention are an image segmentation method and apparatus.

Abstract: A computing method and apparatus for a convolutional neural network model. The method comprises: acquiring a computing model of a training task of a convolutional neural network model (S101); then splitting multiply-accumulate operation in a computing model of a training task of the convolutional neural network model into a plurality of multiply-add operation tasks (S102); confirming a computing device corresponding to each multiply-add operation task according to the correlation between a preset computing model and the computing device (S103); and finally, respectively computing each multiply-add operation task by utilizing the computing device corresponding to each multiply-add operation task (S104). The purposes of improving the flexibility of migration of a CNN model training task on different computing devices or cooperative computing of different processors and improving the computing speed are achieved.

Abstract: An image processing method is provided, which is applied to a deep learning model. A cache queue is provided in front of each layer of the deep learning model; a plurality of computation tasks are preset for each layer of the deep learning model in advance, and are configured for computing weight parameters and corresponding to-be-processed data in a plurality of channels in each corresponding layer in parallel, and storing a computation result into a cache queue behind the corresponding layer thereof; in addition, as long as the cache queue in front of the layer includes the computation result stored in the previous layer, the layer can obtain the to-be-processed data from the computation result, subsequent computation is performed, and a parallel pipeline computation mode is also formed between the layers.

Abstract: The invention provides a physical simulation experimental device and method for water invasion and drainage gas recovery in gas reservoir, and the experimental device includes: a heterogeneous reservoir model having a first core holder, a second core holder, a third core holder and a fourth core holder, wherein the third core holder is connected between the first core holder and the second core holder, and the fourth core holder is connected between an outlet end of the first core holder and an outlet end of the second core holder; a gas injection mechanism having a gas injection bottle and a gas injection cylinder; a water body simulation mechanism having a water storage tank and a water injection pump. The invention can simulate and reveal different drainage gas recovery modes, timings, scales and their influences on the recovery ratio of the gas reservoir.

Abstract: The prism-coupling systems and methods include using a prism-coupling system to collect initial TM and TE mode spectra of a chemically strengthened article having a refractive index profile with a near-surface spike region and a deep region. The prism-coupling system has a light source configured to generate sequential measurement light beams or reflected light beams having different measurement wavelengths. The different measurement wavelengths generate different TM and TE mode spectra. The light source can include multiple light-emitting elements and optical filters or a broadband light source and optical filters. The optical filters can be sequentially inserted into either the input optical path or the output optical path of the prism-coupling system.

Abstract: One measurement method of pig two cell embryo volume. Pig is an important economic animal, the regulation of reproduction can not only provide reference for human reproductive physiology and pathology process research, but also can provide a theoretical basis for improving the reproductive performance of pig. However, the low fertility is a problem plaguing the industry to raise pig. One measurement method of pig two cell embryo volume, based on the figure of pig two cell embryo that was obtained by the microscope, and then measurement, making the regression curve and regression equation. Thus, simple camera and can get more accurate measurement, embryo volume. that is conducive for further research to improve the developmental potential of pig embryo, enhance the production efficiency, is a reliable method to identificatory high quality embryo. The invention is applied to the field of embryo engineering technology.

Abstract: The invention provides a physical simulation experimental device and method for water invasion and drainage gas recovery in gas reservoir, and the experimental device includes: a heterogeneous reservoir model having a first core holder, a second core holder, a third core holder and a fourth core holder, wherein the third core holder is connected between the first core holder and the second core holder, and the fourth core holder is connected between an outlet end of the first core holder and an outlet end of the second core holder; a gas injection mechanism having a gas injection bottle and a gas injection cylinder; a water body simulation mechanism having a water storage tank and a water injection pump. The invention can simulate and reveal different drainage gas recovery modes, timings, scales and their influences on the recovery ratio of the gas reservoir.

Abstract: Disclosed herein are devices comprising a receive (RX) sensor layer, a transmit (TX) sensor layer, a cover glass, a polarizer, and at least one conductive element disposed on at least one surface of the cover glass, at least one surface of the polarizer, or both. Also disclosed herein are methods for reducing mura in a touch-display device.

Abstract: The disclosure is related to a shard manager that manages assignment of shards (data partitions) to application servers. An application service (“app service”) provides a specific service to clients and can be executing on multiple application servers. The dataset managed by the app service can be divided into multiple shards and the shards can be assigned to different app servers. The shard manager can manage the assignment of shards to different app servers based on an assignment policy. The shard assignments can be published to a configuration service. A client can request the configuration service to provide identification information of the app server to which a particular shard the client intends to access is assigned. The shard manager can also provide dynamic load balancing solutions. The shard manager can poll the app servers in runtime to determine the load information and per-shard resource usage, and balance the load by reassigning the shards accordingly.

Abstract: The disclosure is directed to a failover mechanism for failing over an application service, e.g., a messaging service, from servers in a first region to servers in a second region. Data is stored as shards in which each shard contains data associated with a subset of the users. Data access requests are served by a primary region of the shard. A global shard manager manages failing over the application service from a current primary region of a shard to a secondary region of the shard. The current primary determines whether a criterion for failing over, e.g., a replication lag between the primary and the secondary regions is within a threshold, and if it is within the threshold, the failover process waits until the lag is zero. After the replication lag is zero, the application service is failed over to the second region, which then becomes the primary for the shard.

Abstract: A system for monitoring cleanliness of a material handling system is disclosed. The system includes a measuring device, a first signal device, a second signal device, and a measuring host. The measuring device installed in the cartridges conduct the cleanliness measurements and obtains the measured result. The first signal device installed in the cartridges transforms the measured results to wireless signals. The second signal device installed in a predetermined location outside of the cartridges receives the wireless signal. The measuring host transforms the received wireless signals back to the measured results. The cartridges of the material handling system are also disclosed. The disclosed cleanliness monitoring system and the cartridge can reduce the cost.