Abstract: An application-specific integrated circuit for an artificial neural network includes: neurons organized in an array, each of the neurons including a register, a processing element, and at least one input; and synaptic circuits, each of the synaptic circuits including a memory for storing a synaptic weight, wherein each of the neurons is connected to at least one other of the neurons via at least one of the synaptic circuits, the processing elements of the neurons configured to: receive historical configurations of runways for an airport; receive weather forecasts for the airport over an extended forward prediction window; and predict future configurations of the runways for each of several prediction intervals within the forward prediction window and over an entirety of the forward prediction window.

Abstract: Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

Abstract: A communication method, apparatus, electronic device and storage medium for a computing power cluster are provided. An implementation of the method includes: during a process of communicating with a communication receiver using Remote Direct Memory Access (RDMA) protocol, obtaining a first packet loss rate corresponding to the RDMA protocol; in response to the first packet loss rate being higher than a first preset packet loss rate, initiating a first handshake request to the communication receiver for requesting to switch to Transmission Control Protocol (TCP) for communication; receiving a first handshake response returned by the communication receiver for the first handshake request, and determining a first starting transmission position of data according to a last data receiving position in the first handshake response; and communicating, by using the TCP, with the communication receiver starting from data corresponding to the first starting transmission position.

Abstract: A method and apparatus for cross-computing power cluster communication, an electronic device, and a computer readable storage medium, are provided. An implementation of the method includes: in response to a communication initiator and a communication receiver respectively belonging to different computing power clusters, increasing a RDMA connection count on the basis of an initial connection count until a detected actual bandwidth value no longer increases, to obtain a target connection count; increasing, with the RDMA connection count being maintained at the target connection count, a Buffer size on the basis of an initial size until a detected actual bandwidth value no longer increases, to obtain a target size; determining cross-cluster transmission parameters, based on the target connection count and the target size; and communicating, cross-cluster transmission parameters, with the communication receiver belonging to a different computing power cluster.

Abstract: A communication method, apparatus, electronic device and storage medium for a computing power cluster are provided. An implementation of the method includes: during a process of communicating with a communication receiver using Remote Direct Memory Access (RDMA) protocol, obtaining a first packet loss rate corresponding to the RDMA protocol; in response to the first packet loss rate being higher than a first preset packet loss rate, initiating a first handshake request to the communication receiver for requesting to switch to Transmission Control Protocol (TCP) for communication; receiving a first handshake response returned by the communication receiver for the first handshake request, and determining a first starting transmission position of data according to a last data receiving position in the first handshake response; and communicating, by using the TCP, with the communication receiver starting from data corresponding to the first starting transmission position.

Abstract: A method and apparatus for cross-computing power cluster communication, an electronic device, and a computer readable storage medium, are provided. An implementation of the method includes: in response to a communication initiator and a communication receiver respectively belonging to different computing power clusters, increasing a RDMA connection count on the basis of an initial connection count until a detected actual bandwidth value no longer increases, to obtain a target connection count; increasing, with the RDMA connection count being maintained at the target connection count, a Buffer size on the basis of an initial size until a detected actual bandwidth value no longer increases, to obtain a target size; determining cross-cluster transmission parameters, based on the target connection count and the target size; and communicating, cross-cluster transmission parameters, with the communication receiver belonging to a different computing power cluster.

Abstract: This invention discloses an electronic device, including a first housing; a second housing, where the second housing is slidably connected to the first housing; a first elastic piece, where a first end of the first elastic piece is connected to the second housing; a flexible display screen, where a first end of the flexible display screen is connected to a second end of the first elastic piece, a second end of the flexible display screen is connected to the first housing, and the first housing can drive the second end of the flexible display screen to move; a rotating shaft, where the rotating shaft is rotatably disposed in the second housing, the flexible display screen is in rolling contact with the rotating shaft, and the first housing can drive the flexible display screen to move around the rotating shaft.

Abstract: Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

Abstract: Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

Abstract: A dance segment recognition method includes: extracting an audio segment from a video segment in response to the audio segment including a music segment with a time length greater than or equal to a pre-specified time length, using a video sub-segment that is included in the video segment and that corresponds to the music segment as a candidate dance segment and in response to a quantity of image frames showing a dance move in a plurality of image frames of the candidate dance segment being greater than a first pre-specified threshold, determining the candidate dance segment to be a dance segment. The dance segment recognition method can increase the speed of recognizing a dance segment in a video segment.

Abstract: A dance segment recognition method, a dance segment recognition apparatus, and a storage medium. The dance segment recognition method comprises: extracting an audio segment in a video segment; in response to the audio segment comprising a music segment having a duration greater than or equal to a preset duration, taking a video sub-segment in the video segment that corresponds to the music segment to serve as a candidate dance segment; in response to the number of image frames having a dance action among a plurality of image frames of the candidate dance segment being greater than a first preset threshold, determining the candidate dance segment as a dance segment. The present dance segment recognition method can improve the speed of recognizing a dance segment in a video segment. The present dance segment recognition method can improve the speed of recognizing a dance segment in a video segment.

Abstract: Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

Abstract: A method and apparatus for locating an express package. The method includes: receiving an express locating request, where the express locating request is used to locate an express waybill corresponding to a target express package from an express image presenting express packages, and the express locating request includes express information; searching for pre-stored location information of the express waybill corresponding to the express information; generating an express locating result image by means of the location information, where the express locating result image includes a locating identifier, and the locating identifier is used to indicate the express waybill corresponding to the target express package from the express image; and outputting the express locating result image.

Abstract: This invention discloses an electronic device, including a first housing; a second housing, where the second housing is slidably connected to the first housing; a first elastic piece, where a first end of the first elastic piece is connected to the second housing; a flexible display screen, where a first end of the flexible display screen is connected to a second end of the first elastic piece, a second end of the flexible display screen is connected to the first housing, and the first housing can drive the second end of the flexible display screen to move; a rotating shaft, where the rotating shaft is rotatably disposed in the second housing, the flexible display screen is in rolling contact with the rotating shaft, and the first housing can drive the flexible display screen to move around the rotating shaft.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.

Abstract: The disclosure herein describes creating and managing an editable rules-based model. A model is trained to generate output values for data sources based on input feature data of the data sources. Using the trained model, results are generated that include feature-output value pairs and associated confidence values for each data source. The confidence values are aggregated for each feature-output value pair of the generated results, and rules of a rules-based model are built using the feature-output value pairs and the respective aggregated confidence values of the generated results. A user is provided access to the rules of the rules-based model via a model management interface. The rules-based model is updated based on rule change instructions received from the user via the model management interface.

Abstract: Examples provide a system and method for retraining a machine learning (ML) algorithm associated with a trained model using root cause pattern recognition. The system analyzes the results of parsing unstructured data and identifies a root cause pattern causing the trained model to underperform when parsing data including the identified pattern. Examples of data including the pattern are created for use in retraining the model to correctly detect and parse data following the identified pattern. Once retrained, the model is able to parse unstructured data, including data having the identified pattern, in accordance with expected performance metrics. The system automatically identifies parsing errors, identifies the root cause patterns for these errors and retrains the models to correctly handle those patterns for more accurate and efficient handing of unstructured data by trained models.

Abstract: In an approach to limiting container CPU usage based on network traffic, a packet CPU usage for each packet type of one or more packet types is determined. A network CPU usage is calculated for a specific container based on the network CPU usage for each packet type. A container process CPU usage and a total container CPU usage are calculated for the specific container, where the total container CPU usage is the sum of the network CPU usage and the container process CPU usage. Responsive to determining that the total container CPU usage exceeds a threshold, the container CPU consumption quota and the container network bandwidth setting for the specific container are adjusted to reduce the total container CPU usage, using a set of pre-configured parameters.