Abstract: The present disclosure belongs to the technical field of robots, in particularly relates to a robot control method, a device, a computer-readable storage medium and a robot. The method includes selecting each candidate navigation point from a map of the robot; determining a target navigation point from each candidate navigation point according to the relative positional relationship between each candidate navigation point and the robot; and controlling the robot to move to the target navigation point. Through the above method, the best target navigation point can be determined according to the relative positional relationship between each candidate navigation point and the robot, which realizes the regular selection of the target navigation point, improves the efficiency of robot exploration and has strong practicability and ease of use.

Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.

Abstract: Disclosed is a method for updating a node model that resists discrimination propagation in federated learning. The method includes: obtaining a node model corresponding to a data node; calculating a mean value of the distribution of class features and a quantity ratio corresponding to training data of the data node, calculating a distribution weighted aggregation model based on the node model, the mean value of the distribution of class features and the quantity ratio; calculating a regularization term corresponding to the data node based on the node model and the distribution weighted aggregation model; calculating a variance of the distribution of the class features corresponding to the data node, calculating a class balanced complementary term by using a cross-domain feature generator; and updating the node model based on the distribution weighted aggregation model, the regularization term, and the class balanced complementary term.

Abstract: A data processing system includes an array of reconfigurable units and a compiler configured to generate one or more configuration files for an application for execution on one or more reconfigurable processors. The data processing system further includes an execution flow logic which is configured to cause execution of the configuration files on the reconfigurable processors to be dependent upon one or more breakpoint conditions. The data processing further includes a runtime logic configured to execute the configuration files depending upon the breakpoint conditions. A corresponding method is also disclosed herein.

Abstract: Disclosed is a method for updating a node model that resists discrimination propagation in federated learning. The method includes: obtaining a node model corresponding to a data node; calculating a mean value of the distribution of class features and a quantity ratio corresponding to training data of the data node, calculating a distribution weighted aggregation model based on the node model, the mean value of the distribution of class features and the quantity ratio; calculating a regularization term corresponding to the data node based on the node model and the distribution weighted aggregation model; calculating a variance of the distribution of the class features corresponding to the data node, calculating a class balanced complementary term by using a cross-domain feature generator; and updating the node model based on the distribution weighted aggregation model, the regularization term, and the class balanced complementary term.

Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.

Abstract: According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

Abstract: A data processing method based on simultaneous interpretation, applied to a server in a simultaneous interpretation system, including: obtaining audio transmitted by a simultaneous interpretation device; processing the audio by using a simultaneous interpretation model to obtain an initial text; transmitting the initial text to a user terminal; receiving a modified text fed back by the user terminal, the modified text being obtained after the user terminal modifies the initial text; and updating the simultaneous interpretation model according to the initial text and the modified text.

Abstract: A bus bar subassembly adapted to connect a first circuit board and a second circuit board includes a first bus bar, a second bus bar and a first electrical connector. The first bus bar is electrically connected to the first circuit board. The second bus bar is electrically connected to the second circuit board. The first connector is electrically connected to the first bus bar and the second bus bar.

Abstract: A method, an apparatus and a medium for optimizing allocation of switching resources in the polymorphic network. The method selects the ASIC switching chip, FPGA and PPK software switching on the polymorphic network element based on machine learning, and specifically comprises the following steps: manually pre-configuring, formulating basic rules for polymorphic software and hardware co-processing; offline learning, designing training configuration in the offline learning stage to capture different switching resource usage variables, running experiments to generate the original data of a training classifier, and using the generated performance indices to train the model offline; and online reasoning, obtaining switching resource allocation advises, and updating modality codes according to the switching resource allocation advises.

Abstract: Sound signals are received by one or more microphones disposed at an ADV. The sound signals are analyzed to extract a feature of a road on which the ADV is driving. A road condition of the road is determined based on the extracted feature. A path planning and speed planning is performed to generate a trajectory based on the road condition. The ADV is controlled to drive autonomously according to the generated trajectory.

Abstract: A data flow classification method includes obtaining, by a packet forwarding device, a plurality of data flows, extracting, by the packet forwarding device, address information and time information of each of the data flows, selecting, by the packet forwarding device, a data flow set that is generated when a first client device accesses a plurality of services, determining, by the packet forwarding device, a service set that includes a first service and a second service and that is accessed by the first client device based on the address information of the data flows, determining, by the packet forwarding device, a correlation between services in the service set based on time information of each data flow in the data flow set, and determining, by the packet forwarding device, that the first service and the second service implement a first application.

Abstract: Sound signals that are not audible are generated by one or more speakers disposed in the vehicle. Reflected sound signals from a driver or a passenger of the vehicle that are not audible are received by one or more microphones disposed in the vehicle. A behavior-induced acoustic pattern is detected based on the reflected ultrasound signals. The behavior-induced acoustic pattern is analyzed to recognize a behavior of the driver or the passenger of the vehicle. A response or an alert is generated according to the recognized behavior of the driver or the passenger in the vehicle.

Abstract: This application discloses a method, apparatus and storage medium for application identification. A network device performs flow behavior feature analysis according to a flow table to obtain a plurality of services. Each service includes one IP address and one port identifier, and one application may usually include a group of services. Therefore, the network device clusters the plurality of services according to the flow table and a domain name table, to obtain a plurality of application types. Each application type includes a plurality of services. Each application type corresponds to one application. Further, the network device may determine a label corresponding to each of the plurality of application types, the label identifying an application to which a data flow belongs.

Abstract: A sensor package, a sensor system, and a method for fabricating the sensor package are described that include a sensing chip having dispense chemistry disposed over an array of conductive elements. In an implementation, the sensor package may include a sensing chip that may include at least one conductive element, wherein the at least one conductive element may be part of an array of conductive elements defining a M by N matrix, where M is a number of rows of the at least one conductive element and N is a number of columns of the at least one conductive element. The sensing chip may further include dispense chemistry that may be disposed on the at least one conductive element and at least one contact pad. The sensor package may further include a microfluidic cap that may be positioned over at least a portion of the sensing chip, wherein the microfluidic cap and the sensing chip may define a cavity that may be configured to receive a fluid sample.

Abstract: In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

Abstract: A method of navigating an autonomous driving vehicle (ADV) includes determining a target function for an open space model based on one or more obstacles and map information within a proximity of the ADV, then iteratively performing first and second quadratic programming (QP) optimizations on the target function. Then, generating a second trajectory based on results of the first and second QP optimizations to control the ADV autonomously using the second trajectory. The first QP optimization is based on fixing a first set of variables of the target function. The second QP optimization is based on maximizing a sum of the distances from the ADV to each of the obstacles over a plurality of points of the first trajectory, and minimizing a difference between a target end-state of the ADV and a determined final state of the ADV using the first trajectory.

Abstract: A simulation of an autonomous driving vehicle (ADV) includes capturing first data that includes a control command output by an autonomous vehicle controller of the ADV, and capturing second data that includes the control command being implemented at a control unit of the ADV. The control command, for example, a steering command, a braking command, or a throttle command, is implemented by the ADV to affect movement of the ADV. A latency model is determined based on comparing the first data with the second data, where the latency model defines time delay and/or amplitude difference between the first data and the second data. The latency model is applied in a virtual driving environment.

Abstract: In one embodiment, control of an autonomous driving vehicle (ADV) includes determining a current scenario of the ADV. Based on the scenario, a control algorithm is selected among a plurality of distinct control algorithms as the active control algorithm. One or more control commands are generated using the active control algorithm, based one or more target inputs. The control commands are applied to effect movement of the ADV.