Abstract: The present application provides a nutrient pump control method, apparatus, device and storage medium. The method includes: obtaining a feeding scheme for a target object in response to a feeding instruction of the target object, wherein the feeding scheme comprises a plurality of feeding phases, and at least one feeding parameter is different in different feeding phases; and controlling the nutrient pump to output nutrients according to the feeding scheme. The method of the present application improves the accuracy and timeliness of the feeding via the nutrient infusion.

Abstract: The invention provides a method and device for controlling a nutrient pump. Feeding subject information is obtained and a target feeding amount is determined according to the feeding subject information. The target feeding amount comprises target energy content and/or target nutrient amount. The nutrient pump then outputs a target nutrient according to the target feeding amount. The invention combines the feeding subject information with respect to different categories to determine the target feeding amount, which can improve the accuracy of the target feeding amount, and thus improve the feeding accuracy. Moreover, invention can be executed by a control device, which can acquire the feeding subject information to realize intelligent feeding, and thus avoid being affected by the working hours of the nutrition team, thereby improving time flexibility.

Abstract: The invention provides a nutrient infusion control method, apparatus, device, and storage medium. The method includes: acquiring at least one of nutrient infusion demand information, subject information, and vital sign status parameter of a current subject; determining a first nutrient infusion parameter according to subject the acquired information or parameter; and performing a nutrient infusion according to the first nutrient infusion parameter. The first nutrient infusion parameter is quickly and automatically determined directly according to at least one of the nutrient infusion demand information, the subject information, and the vital sign status parameter. This approach reduces reliance on nutritionists, enhances data accuracy, and improves the precision of nutrient infusion control. Furthermore, this method allows for rapid and accurate determination of the first nutrient infusion parameters, and thus controlling the nutrient infusion device with higher practicality.

Abstract: A method for controlling a nutrient infusion apparatus, includes: acquiring a feeding parameter; automatically determining a first flushing parameter according to the feeding parameter and a preset flushing rule; and controlling the nutrient infusion apparatus to flush according to the first flushing parameter. Healthcare personnel, when setting parameters, thus only need to set the feeding parameter, and the corresponding flushing parameter can be determined automatically. Furthermore, relative to having to set the flushing parameter manually, the accuracy of automatically determining the flushing parameter according to the feeding parameter is also higher, which improves the accuracy of parameter setting, which in turn improves the accuracy of the flushing control.

Abstract: Provided are a video processing method and apparatus, an electronic device, a storage medium and a program product. The method includes steps described below. A to-be-processed video is acquired, and a first face key point of a first target object in the to-be-processed video is recognized, where the first face key point corresponds to a mask key point in a three-dimensional human face mask effect; and the three-dimensional human face mask effect is added to video frames of the to-be-processed video to obtain a target video, where the mask key point in the three-dimensional human face mask effect moves with the corresponding first face key point.

Abstract: Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.

Abstract: Methods, systems, apparatus, and tangible non-transitory carrier media encoded with one or more computer programs for classifying an input text block into a sequence of one or more classes in a multi-level hierarchical classification taxonomy. In accordance with particular embodiments, a source sequence of inputs corresponding to the input text block is processed, one at a time per time step, with an encoder recurrent neural network (RNN) to generate a respective encoder hidden state for each input, and the respective encoder hidden states are processed, one at a time per time step, with a decoder RNN to produce a sequence of outputs representing a directed classification path in a multi-level hierarchical classification taxonomy for the input text block.

Abstract: Methods, systems, apparatus, and tangible non-transitory carrier media encoded with one or more computer programs for classifying an input text block into a sequence of one or more classes in a multi-level hierarchical classification taxonomy. In accordance with particular embodiments, a source sequence of inputs corresponding to the input text block is processed, one at a time per time step, with an encoder recurrent neural network (RNN) to generate a respective encoder hidden state for each input, and the respective encoder hidden states are processed, one at a time per time step, with a decoder RNN to produce a sequence of outputs representing a directed classification path in a multi-level hierarchical classification taxonomy for the input text block.

Abstract: A unified learning approach for large-scale ride-hailing is described, the approach includes obtaining an offline state value network for predicting a value of a vehicle state, the offline state value network being trained based on a plurality of historical vehicle trajectories; initializing an online state value network and dispatching a plurality of vehicles according to the online state value network for a period of time; training the online state value network based on vehicle states of the plurality of vehicles before and after the dispatching and rewards associated with the dispatching; ensembling the trained online state value network and the offline state value network to obtain an ensembled online state value network; and dispatching the plurality of vehicles according to the ensembled online state value network.

Abstract: Provided are a video processing method and apparatus, an electronic device, a storage medium and a program product. The method includes steps described below. A to-be-processed video is acquired, and a first face key point of a first target object in the to-be-processed video is recognized, where the first face key point corresponds to a mask key point in a three-dimensional human face mask special effect; and the three-dimensional human face mask special effect is added to video frames of the to-be-processed video to obtain a target video, where the mask key point in the three-dimensional human face mask special effect moves with the corresponding first face key point.

Abstract: A method includes: obtaining a plurality of first signals corresponding to a vehicle and a plurality of second signals corresponding to a plurality of candidate carpool combinations each comprising one or more unassigned transportation orders, wherein: the plurality of first signals comprise a current time, a location of the vehicle at the current time, and one or more static features corresponding to the vehicle, the plurality of second signals comprise timestamps, origins, and destinations of the unassigned transportation orders, and the vehicle has an on-going transportation order at the current time; inputting the plurality of first and second signals to a trained machine learning model; and obtaining, from an output of the trained machine learning model, a utility score of each of the plurality of candidate carpool combinations.

Abstract: Systems and methods are provided for ride order dispatching and vehicle repositioning. A method for ride order dispatching and vehicle repositioning, comprises: obtaining information comprising a location of a vehicle, current orders, and a current time; inputting the obtained information to a trained model; and determining action information for the vehicle based on an output of the trained model, the action information comprising: re-positioning the vehicle or accepting a ride order. The model is configured with: receiving information of drivers and information of orders as inputs; obtaining a global state based on the information of drivers, the information of orders, and a global time; and querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine the action information as the output.

Abstract: Systems, methods, and non-transitory computer-readable media can construct a simulation framework for a ride sharing service. The simulation framework comprises a simulation environment and an agent comprising one or more algorithms including an order dispatching algorithm and a driver reposition algorithm. One or more states of the simulation environment include information about a plurality of drivers and a plurality of trip order requests, and can be provided to the agent. One or more actions from the agent can be obtained. The one or more actions comprises at least one of: a plurality of matches between the plurality of drivers and the plurality of trip order requests, or a plurality of reposition destinations for a subset of the plurality of drivers. The one or more states of the simulation environment can be updated based on the one or more actions.

Abstract: Systems and methods are provided for ride order dispatching. Such method may comprise obtaining information on a location of a vehicle and a time to input into a trained neural network algorithm; and based on a policy generated from the trained neural network algorithm, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order.

Abstract: A ride order dispatching system comprises a processor, and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to perform a method. The method comprises: obtaining, from a computing device, a current location of a vehicle; inputting the current location of the vehicle and a time to a trained neural network model to obtain action information for the vehicle, the action information comprising: staying at the current location of the vehicle, re-positioning the vehicle, or accepting a ride order; and transmitting the action information to the computing device to cause the vehicle to stay at the current location, re-position to another location, or accept the ride order by proceeding to a pick-up location of the ride order.

Abstract: Systems and methods are provided for ride order dispatching and vehicle repositioning. A method for ride order dispatching and vehicle repositioning, comprises: obtaining information comprising a location of a vehicle, current orders, and a current time; inputting the obtained information to a trained model; and determining action information for the vehicle based on an output of the trained model, the action information comprising: re-positioning the vehicle or accepting a ride order. The model is configured with: receiving information of drivers and information of orders as inputs; obtaining a global state based on the information of drivers, the information of orders, and a global time; and querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine the action information as the output.

Abstract: Dynamic pricing may be applied in an online ride-hailing platform. Information may be obtained. The information may include a set of pricing candidates and an initial status of a ride-hailing platform. The set of pricing candidates may be updated based on the initial status of the ride-hailing platform to minimize a cross-entropy between the set of pricing candidates and a target pricing policy that maximizes a total income of the ride-hailing platform. A price for at least one current trip request on the ride-hailing platform may be generated based on the updated set of pricing candidates.

Abstract: This disclosure describes systems and methods for repositioning vehicles. An exemplary method includes obtaining a plurality of current features associated with a vehicle located in one of the plurality of grid cells; inputting the plurality current features associated with the vehicle into a neural network; obtaining, from the neural network, a plurality of conditional action values for repositioning the vehicle to a plurality of target grid cells conditioned upon the plurality current features associated with the vehicle, wherein the plurality of target grid cells comprise the one grid cell that the vehicle is currently located in and other grid cells in the plurality of grid cells that are within two or more layers surrounding the one grid cell; and sending one or more of the plurality of target grid cells with highest conditional action values to the vehicle for repositioning.

Abstract: A unified learning approach for large-scale ride-hailing is described, the approach includes obtaining an offline state value network for predicting a value of a vehicle state, the offline state value network being trained based on a plurality of historical vehicle trajectories; initializing an online state value network and dispatching a plurality of vehicles according to the online state value network for a period of time; training the online state value network based on vehicle states of the plurality of vehicles before and after the dispatching and rewards associated with the dispatching; ensembling the trained online state value network and the offline state value network to obtain an ensembled online state value network; and dispatching the plurality of vehicles according to the ensembled online state value network.

Abstract: A system for evaluating order dispatching policy includes a first computing device, at least one processor, and a memory. The first computing device is configured to generate historical driver data associated with a driver. The at least one processor is configured to store instructions. When executed by the at least one processor, the instructions cause the at least one processor to perform operations. The operations performed by the at least one processor includes obtaining the generated historical driver data associated with the driver. Based at least in part on the obtained historical driver data, a value function is estimated. The value function is associated with a plurality of order dispatching policies. An optimal order dispatching policy is then determined. The optimal order dispatching policy is associated with an estimated maximum value of the value function.