Abstract: An online concierge system identifies a set of attributes of one or more future time periods and accesses a machine learning model trained to predict a set of working hours for a picker during a future time period, in which the set of working hours describes an availability of the picker to service orders placed with the online concierge system. The online concierge system then applies the machine learning model to the set of attributes to predict the set of working hours for the picker during the future time periods and stores the predicted set of working hours for the picker during the future time periods.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: The application provides a solution for resource scheduling. A method comprises: monitoring the status of online nodes in an online node cluster; in response to the status of the online nodes meeting a hybrid-deployment triggering condition, determining a lending node from the online node cluster; sending a first instruction to the online transaction controller to indicate the online transaction controller to schedule a current online transaction on the lending node to other online node than the lending node in the online node cluster for processing; and in response to the current online transaction on the lending node being scheduled to the other online node, changing a first status tag value of the lending node to a second status tag value, the second status tag value indicating that the lending node is to be scheduled by an offline transaction controller to process offline transactions.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: An online concierge system compensates pickers who fulfill orders including one or more items based in part on weights of the items included in an order. Because the online concierge system does not physically possess the items that are obtained, the online concierge system cannot directly weigh the items and weights specified for items in a catalog from a retailer may be inaccurate. To more accurately determine weights of items, the online concierge system trains a weight prediction model to estimate an item's weight from attributes of the item and uses the output of the weight prediction model to determine compensation to a picker. The weight prediction model may output a predicted weight of an item or a classification of the item as heavy or light. Where discrepancies are found between a predicted weight and the catalog weight of an item, additional information about the item is obtained.

Abstract: This application discloses a clock synchronization system, including a quantum control processor (QCP) and N digital/analog mutual conversion devices, each digital/analog mutual conversion device including a frequency conversion module and a signal synchronization module that includes a D flip-flop (DFF). The QCP generates a global synchronization signal and reference clock signals; and transmits the global synchronization signal and a reference clock signal to the frequency conversion module and transmits the global synchronization signal to the signal synchronization module of each conversion device. The frequency conversion module performs frequency conversion processing on the reference clock signal to obtain a target clock signal, and generates a signal synchronization instruction according to the global synchronization signal; and transmits the signal synchronization instruction and the target clock signal to the signal synchronization module.

Abstract: A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

Abstract: The application provides a solution for resource scheduling. A method comprises: monitoring the status of online nodes in an online node cluster; in response to the status of the online nodes meeting a hybrid-deployment triggering condition, determining a lending node from the online node cluster; sending a first instruction to the online transaction controller to indicate the online transaction controller to schedule a current online transaction on the lending node to other online node than the lending node in the online node cluster for processing; and in response to the current online transaction on the lending node being scheduled to the other online node, changing a first status tag value of the lending node to a second status tag value, the second status tag value indicating that the lending node is to be scheduled by an offline transaction controller to process offline transactions.

Abstract: The present application provides a high-voltage connector and an electromagnetic shielding shell for the high-voltage connector, the electromagnetic shielding shell has an integrated barrel-shaped structure formed by a deep drawing process, and integrated barrel-shaped structure includes a barrel wall and a barrel bottom that are connected to each other.

Abstract: The present application provides a high-voltage connector and an electromagnetic shielding shell for a high-voltage connector, the electromagnetic shielding shell has a main body; a connecting body; and a plurality of elastic arms, arranged at intervals between the main body and the connecting body; an end of each of the elastic arms is connected to the main body, and the other end of each of the elastic arms is connected to the connecting body, each of the elastic arms is provided with a protrusion protruding in a radial direction, and the protrusion is configured for electrically contacting a shielding member of a mating connector.

Abstract: A quantum error correction (QEC) decoding system includes an error correction chip. The error correction chip is configured to: obtain error syndrome information of a quantum circuit; and decode the error syndrome information by running neural network decoders, to obtain error result information, a core operation of the neural network decoders being a multiply accumulate (MA) operation of unsigned fixed-point numbers obtained through numerical quantization. According to the present disclosure, for the system that uses the neural network decoders for QEC decoding, the core operation of the neural network decoders is the MA operation of unsigned fixed-point numbers obtained through numerical quantization, thereby minimizing the data volume and the calculation amount desirable by the neural network decoders, so as to better meet the requirement of real-time error correction.

Abstract: A signal control system for quantum computing includes a signal source, a waveform calibration circuit, a qubit control line, and a qubit module. The signal source is configured to generate an original control signal. The waveform calibration circuit includes at least one IIR digital filter. The IIR digital filter is configured to perform waveform calibration on the original control signal to obtain a calibrated control signal. The qubit control line is configured to guide the calibrated control signal to the qubit module. The qubit module is configured to generate a qubit. The calibrated control signal acts on the qubit after passing through the qubit control line, so as to control the qubit.

Abstract: This application discloses a clock synchronization system, including a quantum control processor (QCP) and N digital/analog mutual conversion devices, each digital/analog mutual conversion device including a frequency conversion module and a signal synchronization module that includes a D flip-flop (DFF). The QCP generates a global synchronization signal and reference clock signals; and transmits the global synchronization signal and a reference clock signal to the frequency conversion module and transmits the global synchronization signal to the signal synchronization module of each conversion device. The frequency conversion module performs frequency conversion processing on the reference clock signal to obtain a target clock signal, and generates a signal synchronization instruction according to the global synchronization signal; and transmits the signal synchronization instruction and the target clock signal to the signal synchronization module.

Abstract: This application discloses a quantum control system and an instruction execution method. The quantum control system includes: a scheduler, an instruction memory, a plurality of processing units, and corresponding private instruction caches (PICs). The scheduler is configured to determine k sub-circuits executed in parallel in a quantum circuit, k being an integer greater than 1 and not greater than n; obtain instructions respectively corresponding to the k sub-circuits from the instruction memory, and respectively store the instructions into PICs respectively corresponding to k processing units. A target processing unit of the k processing units is configured to obtain an instruction corresponding to a target sub-circuit of the k sub-circuits from a PIC corresponding to the target processing unit for execution, the k processing units executing respective corresponding instructions in parallel.

Abstract: A quantum error correction (QEC) decoding system includes an error correction chip. The error correction chip is configured to: obtain error syndrome information of a quantum circuit; and decode the error syndrome information by running neural network decoders, to obtain error result information, a core operation of the neural network decoders being a multiply accumulate (MA) operation of unsigned fixed-point numbers obtained through numerical quantization. According to the present disclosure, for the system that uses the neural network decoders for QEC decoding, the core operation of the neural network decoders is the MA operation of unsigned fixed-point numbers obtained through numerical quantization, thereby minimizing the data volume and the calculation amount desirable by the neural network decoders, so as to better meet the requirement of real-time error correction.

Abstract: The present application provides a high-voltage connector and an electromagnetic shielding shell for the high-voltage connector, the electromagnetic shielding shell has an integrated barrel-shaped structure formed by a deep drawing process, and integrated barrel-shaped structure includes a barrel wall and a barrel bottom that are connected to each other.

Abstract: The present application provides a high-voltage connector and an electromagnetic shielding shell for a high-voltage connector, the electromagnetic shielding shell has a main body; a connecting body; and a plurality of elastic arms, arranged at intervals between the main body and the connecting body; an end of each of the elastic arms is connected to the main body, and the other end of each of the elastic arms is connected to the connecting body, each of the elastic arms is provided with a protrusion protruding in a radial direction, and the protrusion is configured for electrically contacting a shielding member of a mating connector.