Abstract: A method for recognizing emotion, a training method, apparatuses, a device, a storage medium and a product. The method for recognizing emotion includes: acquiring to-be-recognized spiking sequences corresponding to video information; and recognizing the to-be-recognized spiking sequences by using a spiking neural network emotion recognition model, so as to obtain a corresponding emotion category.

Abstract: Disclosed is a method for accelerated computation of data. The method includes: acquiring, by an accelerating device, computation acceleration control information from a memory of a host, and the computation acceleration control information includes input parameter address information and computation configuration information; acquiring, based on the input parameter address information, parameters to be computed from the memory of the host; and controlling, based on the computation configuration information, a computation unit to perform a computation operation on the parameters to be computed, and obtaining a computation result.

Abstract: A difference description statement generation method includes: performing feature concatenation on an image encoding feature and a text encoding feature; inputting a concatenated encoding feature to a preset image-text alignment unit constructed based on a preset self-attention mechanism to obtain a concatenated alignment feature; processing, by using a preset noise monitoring unit constructed based on the preset self-attention mechanism and a preset cross-attention mechanism, the text encoding feature, and an image alignment feature and a text alignment feature that are obtained by splitting the concatenated alignment feature respectively, to extract a difference signal; and generating a difference description statement based on the difference signal by using a preset difference description generation algorithm.

Abstract: A method and apparatus for allocating a computing task of a neural network in heterogeneous resources, a computer device, and a storage medium. The method includes: acquiring task information of the computing task of the neural network and resource information of the heterogeneous resources; determining, according to the task information and the resource information, an allocation mode for allocating each subtask to the heterogeneous resources for execution and a task processing cost corresponding to each allocation mode; constructing a directed acyclic graph according to each allocation mode and each task processing cost; obtaining a value of a loss function corresponding to each allocation path according to the task processing cost corresponding to each subtask in an allocation path of the directed acyclic graph; and selecting a target allocation path according to the value of each loss function.

Abstract: Provided are a communication method and system for a distributed heterogeneous acceleration platform, a device and a medium. The method includes: after starting a collaborative acceleration task, determining, by a first target heterogeneous acceleration card in a distributed heterogeneous acceleration platform, a second target heterogeneous acceleration card from the distributed heterogeneous acceleration platform by querying an information table corresponding to the collaborative acceleration task; generating, by the first target heterogeneous acceleration card, a target data packet according to a predefined data packet format, and sending the target data packet to the second target heterogeneous acceleration card via a PCIE interface; and parsing, by the second target heterogeneous acceleration card, the target data packet according to the data packet format, and executing a corresponding read operation or write operation according to a parsing result, so as to complete the collaborative acceleration task.

Abstract: A high-resolution image matching method and system are provided. The method includes performing regional fidelity down-sampling on an initial high-resolution image to obtain a multi-level low-resolution image, performing local matching on the obtained multi-level low-resolution images using a method with global probes to obtain a matching result of the low-resolution images, and performing reverse refinement on the obtained matching result of the low-resolution image using overall consistency of the image matching, to obtain the matching results of the high-resolution images at all levels until the matching results of the initial resolution images are obtained, so as to reduce the computational complexity of the image matching process and improve the accuracy of the matching result, and then the matching result of the high-resolution image is obtained through reverse refinement based on the overall consistency.

Abstract: The present application discloses a password protection method.

Abstract: Provided is a wireless communication method, including: monitoring, by a device node, a wake-up frame using a monitoring duration at a first wake-up interval, wherein the wake-up frame is a wake-up frame that is continuously sent by a collection node; in response to that the device node has detected the wake-up frame, monitoring, by the device node, the wake-up frame using the monitoring duration at a second wake-up interval, wherein the first wake-up interval is greater than the second wake-up interval, and a duration of the wake-up frame is greater than the monitoring duration; and in response to that the device node fails to detect the wake-up frame within a preset duration when monitoring the wake-up frame at the second wake-up interval, entering, by the device node, a receiving state, so as to perform wireless communication between the collection node and the device node.

Abstract: A remote mapping method, apparatus and device for computing resources, and a storage medium, which are applied to a server. Said method comprises: identifying each FPGA heterogeneous accelerator card in an FPGA BOX; establishing a network communication connection with each FPGA heterogeneous accelerator card via a network interface of each FPGA heterogeneous accelerator card in the FPGA BOX, and establishing a network communication connection between FPGA heterogeneous accelerator cards; mapping each FPGA heterogeneous accelerator card to the server; establishing network transmission for the established network communication connections, and migrating a control flow and a data flow that are performed by the PCIE to the network transmission; and deploying a target application in the FPGA BOX through the established network transmission, and when running the target application, performing data exchange with the FPGA BOX via the network transmission.

Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: The optical device includes: a first coupler having an adjustable beam splitting ratio; a sensing arm and a programmable modulation arm which are connected to the first coupler; and a second coupler having an input port connected to the sensing arm and the programmable modulation arm and an output port connected to a photodetector. The sensing arm is used for generating, by means of a slot waveguide, a first signal from a first light wave beam outputted by the first coupler. The programmable modulation arm is used for obtaining, by utilizing a grating, a second signal according to a second light wave beam outputted by the first coupler, and the grating is a nano grating generated under a pre-programmed voltage parameter of a programmable piezoelectric transducer of the programmable modulation arm. An electronic device and a programmable photonic integrated circuit are also disclosed herein.

Abstract: A remote mapping method, apparatus and device for computing resources, and a storage medium, which are applied to a server. Said method comprises: identifying each FPGA heterogeneous accelerator card in an FPGA BOX; establishing a network communication connection with each FPGA heterogeneous accelerator card via a network interface of each FPGA heterogeneous accelerator card in the FPGA BOX, and establishing a network communication connection between FPGA heterogeneous accelerator cards; mapping each FPGA heterogeneous accelerator card to the server; establishing network transmission for the established network communication connections, and migrating a control flow and a data flow that are performed by the PCIE to the network transmission; and deploying a target application in the FPGA BOX through the established network transmission, and when running the target application, performing data exchange with the FPGA BOX via the network transmission.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: A three-dimensional reconstruction method, a system, and a non-transitory computer readable storage medium are disclosed herein. The method includes: performing local pose optimization by using a target image frame to obtain a local pose error; performing neural network prediction on the target image frame to obtain an initial reconstruction error; performing three-dimensional reconstruction according to the local pose error and the initial reconstruction error to obtain an initial reconstruction model; performing global pose optimization by using historical image frames to obtain a global optimization result and a global pose error; performing neural network completion on the global optimization result to obtain a final reconstruction error; and optimizing the initial reconstruction model according to the global pose error and the final reconstruction error to obtain a final reconstruction model.

Abstract: An image recognition method and apparatus based on a systolic array, and a medium are disclosed. The method includes: converting obtained image feature information into a one-dimensional feature vector; converting an obtained weight matrix into a one-dimensional weight vector, and allocating a corresponding weight group to each node in a trained three-dimensional systolic array model; performing multiply-accumulate of the feature vector and a weight value on the one-dimensional feature vector in parallel by using the three-dimensional systolic array model, to obtain a feature value corresponding to each node, the feature values with different values reflecting article categories contained in an image; and determining an article category contained in the image according to the feature value corresponding to each node and a pre-established corresponding relationship between the feature value and the article category.

Abstract: An automatic driving method includes following steps: S101: acquiring real-time traffic environment information in a travel process of an autonomous vehicle at a current moment; S102: mapping the real-time traffic environment information based on a preset mapping relationship to obtain mapped traffic environment information; S103: adjusting a target deep reinforcement learning model based on a pre-stored existing deep reinforcement learning model and the mapped traffic environment information; and S104: judging whether automatic driving is finished, and in response to the automatic driving is not finished, returning to perform the step of acquiring the real-time traffic environment information in the travel process of the autonomous vehicle at the current moment. An automatic driving system, an automatic driving device and a computer medium storing the automatic driving method are further provided.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: The present invention provides a method and apparatus for data caching. The method comprises: output matrixes are acquired one by one, a plurality of acquired output matrixes are written alternately into two queue sets of a first cache unit according to a sequence in which the output matrixes are acquired, and the output matrixes stored line by line in a first cache unit are written into a second cache unit one by one, according to the sequence in which the output matrixes are written into the second cache unit, valid data of each output matrix of the second cache unit is determined one by one according to preset parameters, and the valid data of each output matrix is written into a third cache unit, and the valid data of the output matrixes stored in the third cache unit are configured to be sequentially written into a memory according to a sequence in which the valid data are written into the third cache unit.

Abstract: An image recognition method and apparatus based on a systolic array, and a medium are disclosed. The method includes: converting obtained image feature information into a one-dimensional feature vector; converting an obtained weight matrix into a one-dimensional weight vector, and allocating a corresponding weight group to each node in a trained three-dimensional systolic array model; performing multiply-accumulate of the feature vector and a weight value on the one-dimensional feature vector in parallel by using the three-dimensional systolic array model, to obtain a feature value corresponding to each node, the feature values with different values reflecting article categories contained in an image; and determining an article category contained in the image according to the feature value corresponding to each node and a pre-established corresponding relationship between the feature value and the article category.