Abstract: A handheld power tool includes a motor, a handle having a first switch for starting and stopping the motor, an output assembly, and an adjustment device including a telescopic rod assembly. The length of the adjustment device changes so that the handheld power tool has at least first and second working states. The distance between the frontmost side of the handheld power tool and the rearmost side of the first switch is an effective length of the handheld power tool. When the handheld power tool is in the first working state, the handheld power tool has a first effective length T1. When the handheld power tool is in the second working state, the handheld power tool has a second effective length T2. The first effective length T1 is less than or equal to 1 m, and the second effective length T2 is greater than the first effective length T1.

Abstract: The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation.

Abstract: The present disclosure provides an information processing method, an apparatus, an electronic device, and a storage medium. By establishing a session group including a first identity user and a second identity user in response to the first identity user satisfying a preset first condition, pushing a first session message for the first identity user within the session group according to a preset process node, and displaying a first information editing interface for the first identity user, in response to a first preset operation of the first identity user for the first session message, the present disclosure can achieve automatic group creation, automatic reminder through group session, and entry into a relevant information editing interface through group session, thereby achieving efficient distribution and processing of information.

Abstract: The present disclosure relates to a method and system for simulating water-induced rock strength deterioration based on a discrete element method, and relates to the field of simulation of water-induced rock strength deterioration.

Abstract: A pole chain saw includes a connecting rod and a front end assembly disposed at the front end of the connecting rod. The front end assembly includes an electric motor; an output device including a guide plate and a chain wound around the guide plate, where the electric motor drives the chain to rotate; a transmission assembly connecting the electric motor to the output device; and a gearbox supporting and accommodating the transmission assembly, where at least part of the gearbox is made of metal. The chain saw can effectively enhance the heat dissipation effect of the gearbox and increase the service life of a transmission mechanism.

Abstract: In an example embodiment, a structured linear program is provided that is usable in recommender models. This structured linear program is able to produce real-time results for a structured recommendation problem with diversity constraints, even for large data sets. The structured linear program operates by first reducing a two-sided diversity constraint to a one-sided diversity constraint, and then introducing a dual variable for a constraint, in order to define a dual objective function. The dual objective function is then solved using a bisection method. A primal solution is then recovered using the solved dual objective function. The resultant primal solution reflects a set of recommended content items that satisfy the diversity constraint, as computed in real-time.

Abstract: A method for cultivating a Tung tree seedling through hypocotyle grafting includes selection of a sand storage time of a rootstock (Vernicia montana Lour.) seed, seedbed construction, scion collection, scion cutting, rootstock cutting, bandaging, transplantation and management, and the like. A grafted plant obtained by a traditional grafting method (bud grafting) for Tung tree is easy to break at a grafting interface; and when the stem grafting is conducted, because a scion is thick, a rootstock for grafting must be cultivated one year in advance. These grafting methods have production problems such as long cycle and high cost, which can be solved by the present disclosure. The present disclosure has a short rootstock cultivation time, simple grafting operations, a high grafting efficiency, and a high survival rate, greatly shortens a cycle of asexual propagation-based seedling cultivation for Tung tree.

Abstract: An artificial intelligence (AI) interaction method, system, and computer program product include selecting an artificial intelligence model to respond to a query to generating a response to the query using the selected artificial intelligence model, and receiving the response to the query from the selected artificial intelligence model.

Abstract: A handheld cutting tool includes a housing, a motor, a saw chain, a guide plate, and a blade sheath, where the motor is supported by the housing, the saw chain is driven by the motor to perform a cutting function, an end of the guide plate is supported by the housing and the guide plate is used for supporting and guiding the saw chain, and the blade sheath is formed with or connected to the first limiting portion, the housing or the guide plate is formed with a second limiting portion, and the first limiting portion and the second limiting portion are adaptable to each other so that the blade sheath is not separated from the handheld cutting tool at least under the action of gravity.

Abstract: The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation.

Abstract: The present invention relates generally to the manufacture of conductive scaffolds of micro and/or nanofibers with the help of different printing techniques (e.g., near-field electrostatic printing, inkjet printing), such scaffolds enabling the formation of two-dimensional (2D) or three-dimensional (3D) neural networks to mimic the native counterparts. Applications of such patterned conductive scaffolds include, but are not limited to, an engineered conduit for guiding the differentiation and outgrowth of neural cells in peripheral nerve damage or in large-volume spinal cord injury under the electrical stimulation. Meanwhile, the scaffolds could also locally deliver various biomolecules in conjunction with electrical stimulation for facilitated nervous system regeneration (FIG. 1).

Abstract: A management method applied to the goods-to-person system includes: in response to a task of putting products on shelves, calculating popularity of a product to be put on shelf according to historical sales order data of the product to be put on shelf, and matching the popularity of the product to be put on shelf with popularity of a shelf to determine a shelf area; selecting a goods location with space randomly in the determined shelf area, Wherein the goods location is used for storing the product to be put on shelf; and controlling a mobile robot to transport a shelf where the goods location is located to a work station.

Abstract: A deep learning module classifies messages received from a plurality of entities into one or more conversation threads. In response to receiving a subsequent message, the deep learning module determines which of the one or more conversation threads and a new conversation thread is contextually a best fit for the subsequent message. The subsequent message is added to the determined conversation thread.

Abstract: A computer-implemented method includes using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The computer-implemented method also includes using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain and using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The computer-implemented method also includes comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values and comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The computer-implemented method also includes modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network.

Abstract: A computing device accesses a machine learning model trained on training data of first bonding operations (e.g., a ball and/or stitch bond). The first bonding operations comprise operations to bond a first set of multiple wires to a first set of surfaces. The machine learning model is trained by supervised learning. The device receives input data indicating process data generated from measurements of second bonding operations. The second bonding operations comprise operations to bond a second set of multiple wires to a second set of surfaces. The device weights the input data according to the machine learning model. The device generates an anomaly predictor indicating a risk for an anomaly occurrence in the second bonding operations based on weighting the input data according to the machine learning model. The device outputs the anomaly predictor to control the second bonding operations.

Abstract: Domain specific model compression by providing a weighting parameter for a candidate operation of a neural network, applying the weighting parameter to an output vector of the candidate operation, performing a regularization of the weighting parameter output vector combination, compressing the neural network model according to the results of the regularization, and providing the neural network model after compression.

Abstract: A computing device accesses a machine learning model trained on training data of first bonding operations (e.g., a ball and/or stitch bond). The first bonding operations comprise operations to bond a first set of multiple wires to a first set of surfaces. The machine learning model is trained by supervised learning. The device receives input data indicating process data generated from measurements of second bonding operations. The second bonding operations comprise operations to bond a second set of multiple wires to a second set of surfaces. The device weights the input data according to the machine learning model. The device generates an anomaly predictor indicating a risk for an anomaly occurrence in the second bonding operations based on weighting the input data according to the machine learning model. The device outputs the anomaly predictor to control the second bonding operations.

Abstract: Provided are a list flow implementation method and apparatus, an electronic device, and a storage medium. The method is applied to a client and includes displaying a first list page in the screen display range, where the first list page includes at least two list items; and in response to detecting a first trigger action of a user on one of the at least two list items, switching, in the screen display range, to display a second list page corresponding to the triggered list item. The content recommendation strategy adopted by the second list page is a recommendation strategy determined based on the associated content of the triggered list item.

Abstract: A computing device accesses a machine learning model trained on training data of first bonding operations (e.g., a ball and/or stitch bond). The first bonding operations comprise operations to bond a first set of multiple wires to a first set of surfaces. The machine learning model is trained by supervised learning. The device receives input data indicating process data generated from measurements of second bonding operations. The second bonding operations comprise operations to bond a second set of multiple wires to a second set of surfaces. The device weights the input data according to the machine learning model. The device generates an anomaly predictor indicating a risk for an anomaly occurrence in the second bonding operations based on weighting the input data according to the machine learning model. The device outputs the anomaly predictor to control the second bonding operations.