Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Abstract: The present application discloses an MV device, wherein a first gate structure of the MV device is formed by stacking a first gate dielectric layer and a first gate conductive material layer. The first gate dielectric layer is divided into a body gate dielectric layer and an edge gate dielectric layer. The body gate dielectric layer is located in a middle region, and the edge gate dielectric layer surrounds the periphery of the body gate dielectric layer. A channel region is located in a surface of the semiconductor substrate between the lightly doped drain regions on the two sides of the first gate structure. In a channel length direction, the top of the channel region is covered by the body gate dielectric layer. The present application also discloses a method for manufacturing the MV device.

Abstract: An MV device is disclosed. A gate conductive material layer is segmented into a body gate conductive material layer and two edge gate conductive material layers along a channel length direction. The two edge gate conductive material layers are located on two sides of the body gate conductive material layer and are spaced apart from the body gate conductive material layer by dielectric segmentation structures. The lightly doped drain regions extend under the first side face and the second side face of the gate conductive material layer, to reach under the body gate conductive material layer, such that the channel region becomes located under the body gate conductive material layer; and the edge gate conductive material layers and the dielectric segmentation structures become located above the lightly doped drain regions. The present disclosure also discloses a method for manufacturing an MV device.

Abstract: The present disclosure provides a fixed-position defect doping method for a micro-nanostructure based on a self-alignment process, including: S1, sequentially forming a sacrificial layer and a photoresist layer on a surface of a crystal substrate; S2, performing a lithography on the photoresist layer to form a mask hole according to a micro-nano pattern; S3, performing an isotropic etching on the sacrificial layer through the mask hole, and amplifying the micro-nano pattern to the sacrificial layer; S4, performing an ion implantation doping on an exposed crystal surface below the mask hole; S5, removing the photoresist layer, and depositing a mask material; S6, removing the sacrificial layer, and transferring a micro-nano amplified pattern in the sacrificial layer to a mask material pattern; and S7, etching an exposed crystal surface, and removing the mask material on the surface and forming a specific defect by annealing.

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.

Abstract: A track monitoring system (and a method thereof) for mounting to a vehicle on a track comprising at least one rail is provided.

Abstract: A method includes determining, based on an input data sample, a set of probabilities. Each probability of the set of probabilities is associated with a respective label of a set of labels. A particular probability associated with a particular label indicates an estimated likelihood that the input data sample is associated with the particular label. The method includes modifying the set of probabilities based on a set of adjustment factors to generate a modified set of probabilities. The set of adjustment factors is based on a first relative frequency distribution and a second relative frequency distribution. The first relative frequency distribution indicates for each label of the set of labels, a frequency of occurrence of the label among training data. The second relative frequency distribution indicates for each label of the set of labels, a frequency of occurrence of the label among post-training data provided to the trained classifier.

Abstract: A mechanism is provided to implement suggestion of new entity types with discriminative importance analysis. The mechanism obtains a list of predefined intents from a chatbot designer. The mechanism receives an input sentence having a target intent within the list of predefined intents. The mechanism performs intent-specific importance analysis on the input sentence to generate an importance score for each token in the input sentence. The mechanism ranks the tokens in the input sentence by importance score and outputs a token with a highest importance score as a candidate entity type.