Abstract: An assembling equipment is for attaching a component to a workpiece. The assembling equipment includes a platform, a conveying mechanism, a first attaching mechanism, a film removing mechanism, and a second attaching mechanism. The conveying mechanism transports a positioning assembly carrying a workpiece to a first station. The first attaching mechanism attaches a first component with a protective film to the workpiece at the first station. The conveying mechanism transports the positioning assembly to a second station, and the film removing mechanism at the second station removes the protective film from the workpiece. The conveying mechanism transports the positioning assembly to a third station. The second attaching mechanism at the third station attaches a second component to the first component on the workpiece to be connected together. The assembling equipment reduces manual labor intensity and labor costs and improves efficiency of production and the product quality.

Abstract: A joint assembly, a swing device and a robot are provided. The joint assembly includes a movable part defining a movable groove and a rotating part. The rotating part includes a rotating body being a spherical shape. The rotating body is rotatably received in the movable groove to adapt to the movable part. The rotating part defines an air hole with an air inlet and an air outlet. The air inlet is configured to receive introduced air, and the air outlet is configured to expel the air to the movable groove.

Abstract: The present disclosure provides a method and apparatus for training an item copy-writing generation network. The method includes: obtaining item description information of each item in an item set; carrying out data preprocessing on an item description information set corresponding to the item set, to obtain a processed item description information set; training an initial first item copy-writing generation network, to obtain a trained first item copy-writing generation network; and training an initial second item copy-writing generation network through a knowledge distillation method, to obtain a trained second item copy-writing generation network.

Abstract: Metal object detection may be performed in a wireless power system by monitoring the 5th or other order harmonic current of a dual-frequency power transmitter driven by a voltage-fed inverter with square-wave output. While power transfer is maintained by tuning at the fundamental frequency, the transmitter circuit is tuned simultaneously at the 5th order frequency or other harmonic frequencies to increase or decrease the flow of the harmonic current for when a metal object is present. The receiver circuit may include a notch filter to filter out the 5th order frequency. With the dual-frequency tuning configuration, the presence of a metal object causes a significant drop of the increase in the harmonic current at the transmitter side while the receiver behaves as an open circuit with no effect on power transfer. The harmonic tuning circuit may be switched off as necessary to leave only the fundamental frequency for power transfer.

Abstract: An assembling equipment for performing asynchronous process on a first workpiece and a second workpiece. The assembling equipment includes a base and a conveying mechanism on the base. The conveying mechanism transports a first positioning assembly carrying a first workpiece and a second positioning assembly carrying a second workpiece to a first station synchronously, and a first attaching mechanism attaches a first component to the first workpiece at the first station. Then the conveying mechanism transports the first positioning assembly and the second positioning assembly to a second station synchronously, a second attaching mechanism attaches a second component to the second workpiece, and a first processing mechanism processes first workpiece that has been attached with the first component according to a preset requirement at the same time. The assembling equipment improves efficiency of production.

Abstract: An assembling equipment is for attaching a component to a workpiece. The assembling equipment includes a platform, a conveying mechanism, a first attaching mechanism, a film removing mechanism, and a second attaching mechanism. The conveying mechanism transports a positioning assembly carrying a workpiece to a first station. The first attaching mechanism attaches a first component with a protective film to the workpiece at the first station. The conveying mechanism transports the positioning assembly to a second station, and the film removing mechanism at the second station removes the protective film from the workpiece. The conveying mechanism transports the positioning assembly to a third station. The second attaching mechanism at the third station attaches a second component to the first component on the workpiece to be connected together. The assembling equipment reduces manual labor intensity and labor costs and improves efficiency of production and the product quality.

Abstract: The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a training dataset containing a first set of records associated with a first set of identifier (ID) values and an evaluation dataset containing a second set of records associated with a second set of ID values. Next, the system selects a random subset of ID values from the second set of ID values. The system then generates a sampled evaluation dataset comprising a first subset of records associated with the random subset of ID values in the second set of records. The system also generates a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records. Finally, the system outputs the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a machine learning model.

Abstract: A surface processing device includes a machining mechanism and a driving mechanism driving the machining mechanism to polish a workpiece. The machining mechanism includes a machining assembly and a first transmission shaft eccentrically coupled with the machining assembly at a first end. The first transmission shaft rotates to bring the machining assembly into revolving around the first transmission shaft. The driving mechanism includes a motor connected to a second end of the first transmission shaft for outputting power to the machining mechanism. The surface processing device provides smooth machining without the noise of a pneumatically-powered tool.

Abstract: Methods, systems, and computer programs are presented for determining parameters of neural networks and selecting embedding dimensions for the feature fields. One method includes an operation for initializing parameters of a neural network and weights for embedding sizes for each feature associated with the neural network. The parameters of the neural network and the weights are iteratively optimized. Each optimization iteration comprises training the neural network with current parameters of the neural network to optimize a value of the weights, and training the neural network with current values of the weights to optimize the parameters of the neural network. Further, the method includes operations for selecting embedding sizes for the features based on the optimized values of the weights, and for training the neural network based on the selected embedding sizes for the features to obtain an estimator model. A prediction is generated utilizing the estimator model.

Abstract: Metal object detection may be performed in a wireless power system by monitoring the 5th or other order harmonic current of a dual-frequency power transmitter driven by a voltage-fed inverter with square-wave output. While power transfer is maintained by tuning at the fundamental frequency, the transmitter circuit is tuned simultaneously at the 5th order frequency or other harmonic frequencies to increase or decrease the flow of the harmonic current for when a metal object is present. The receiver circuit may include a notch filter to filter out the 5th order frequency. With the dual-frequency tuning configuration, the presence of a metal object causes a significant drop of the increase in the harmonic current at the transmitter side while the receiver behaves as an open circuit with no effect on power transfer. The harmonic tuning circuit may be switched off as necessary to leave only the fundamental frequency for power transfer.

Abstract: Computer-implemented techniques for learning composite machine learned models are disclosed. Benefits to implementors of the disclosed techniques include allowing non-machine learning experts to use the techniques for learning a composite machine learned model based on a learning dataset, reducing or eliminating the explorative trial and error process of manually tuning architectural parameters and hyperparameters, and reducing the computing resource requirements and model learning time for learning composite machine learned models. The techniques improve the operation of distributed learning computing systems by reducing or eliminating straggler effects and by reducing or minimizing synchronization latency when executing a composite model search algorithm for learning a composite machine learned model.

Abstract: Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

Abstract: Embodiments of the disclosed technologies provide tree-based transfer learning of hyperparameters of a machine learning model or tunable parameters of a black box system. A similar reference task tree is selected from a set of reference task trees. Data is transferred from the similar reference task tree to a target task tree.

Abstract: Techniques for training a machine-learned model based on feedback are provided. In one technique, reformulation data that comprises a plurality of sequence pairs is stored. Also, feedback data that comprises a plurality of sequence triples is stored. Based on the reformulation data and the feedback data, one or more machine learning techniques are used to train a sequence-to-sequence model. Training the sequence-to-sequence model involves using a loss function that comprises (1) a first term that takes, as input, sequence pairs from the reformulation data and (2) a second term that takes, as input, sequence triples from the feedback data. After training the sequence-to-sequence, a search query is received from a computing device. In response to receiving the search query, a set of embeddings is retrieved, each corresponding to a token in the search query. The set of embeddings is input into the sequence-to-sequence model, which generates one or more query suggestions.

Abstract: Techniques for using machine learning to predict document types for incomplete queries are provided. In one technique, one or more characters from input are identified. For each character, an embedding that corresponds to that character is retrieved. The embedding was machine-learned while training a neural network that outputs multiple classifications, each corresponding to a different document type. One or more embeddings, each corresponding to one of the characters, are input into the neural network. Based on the inputting, the neural network generates an output that comprises multiple values that includes (1) a first value that reflects a first probability that the input is associated with a first document type and (2) a second value that reflects a second probability that the input is associated with a second document type. Based on the first and second probabilities, a set of query completions is identified and presented on the computing device.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for generating personalized smart responses. A messaging system receives a message transmitted as part of a communication session between a first user and a second user and determines a set of candidate responses for replying to the message. The messaging system filters the set of candidate responses based on a personalized vocabulary for the first user, yielding a set of personalized candidate responses. The personalized vocabulary for the first user is determined based on member profile data associated with the first user. The messaging system determines, based on a set of personalized candidate selection rules, a subset of the candidate responses, yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.

Abstract: A polishing system for polishing a workpiece includes a sensor group detecting pressure information of the workpiece and generating a pressure sequence; a processor coupled to the sensor group and configured to: receive the pressure sequence; generate indication information including a predetermined track of a polishing head to polish the workpiece; based on the pressure sequence and the indication information, generate a deviation sequence of the pressure sequence; and based on the deviation sequence, generate an adjustment instruction, to adjust a position of the polishing head. A polishing method, an assistant polishing device, an assistant polishing system, and an assistant polishing method are also disclosed.

Abstract: The disclosed technologies include a navigation agent for a search interface. In an embodiment, the navigation agent uses reinforcement learning to dynamically generate and select navigation options for presentation to a user during a search session. The navigation agent selects navigation options based on reward scores, which are computed using implicit and/or explicit user feedback received in response to presentations of navigation options.

Abstract: A neural related query generation approach in a search system uses a neural encoder that reads through a source query to build a query intent vector. The approach then processes the query intent vector through a neural decoder to emit a related query. By doing so, the approach gathers information from the entire source query before generating the related query. As a result, the neural encoder-decoder approach captures long-range dependencies in the source query such as, for example, structural ordering of query keywords. The approach can be used to generate related queries for long-tail source queries, including long-tail source queries never before or not recently submitted to the search system.