Abstract: A self-cleaning dust collection seat and a dust collection system are provided, and the self-cleaning dust collection seat includes a bottom plate of the self-cleaning dust collection seat and a body of the self-cleaning dust collection seat, the bottom plate being connected to the body; the bottom plate includes a bottom plate body, a dust collection opening, and a lifting structure, the lifting structure being disposed on the upper surface of the bottom plate, and configured to press a cleaning module located on a bottom surface of an automatic cleaning apparatus, in a process of the automatic cleaning apparatus moving backward to the bottom plate along a first direction, so that the cleaning module moves toward the interior of the automatic cleaning apparatus.

Abstract: A driving circuit, a driving method and a display device are provided. The driving circuit includes a first control node control circuit, a second control node control circuit, a first node control circuit and a second node control circuit, wherein, the first control node control circuit is configured to control a potential of the first control node; the second control node control circuit is configured to control a potential of the second control node; the first node control circuit is configured to control a potential of the first node; the second node control circuit is electrically connected to the second control node, a first clock signal terminal and a second node respectively, and is configured to control to connect the first clock signal terminal and the second node under the control of the potential of the second control node.

Abstract: A housing assembly includes at least two housings, a hinge, a linkage assembly, a locking assembly, a locking key, and a driving assembly. The at least two housings are connected through the hinge, and the at least two housings are able to rotate relative to the hinge. A first end of the linkage assembly is connected to the hinge, and a second end of the linkage assembly extends into the first housing and is movably connected to the first housing. In a case that the locking assembly is in a first state, the locking assembly is separated from the second end; and in a case that the locking assembly is in a second state, the locking assembly abuts against the second end. The driving assembly is connected to the locking key to drive, through the locking key, the locking assembly to switch between the first state and the second state.

Abstract: A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes the characters based on the second attention score. The system trains the neural network based model by adjusting the neural network parameters to minimize a multi-class gradient harmonizing mechanism (GHM) loss. The multi-class GHM loss varies based on a level of difficulty of the sample.

Abstract: The present application provides a cap assembly and a bottle assembly. The cap assembly is configured to lock a bottle. The bottle includes a bottle head having at least one lock accommodating portion and comprises a bottle body. The cap assembly includes a cap, an elastic arm and at least one lock portion. The cap defines an annular receiving portion having an opening facing downward, and the bottle head can enter the receiving portion through the opening. The elastic arm is annular and is arranged in the receiving portion. The at least one lock portion is arranged on the cap and is configured to be capable of cooperating with the at least one lock accommodating portion of the bottle head, so as to lock the cap assembly to the bottle head.

Abstract: The present application provides a cap assembly and a bottle assembly. The cap assembly includes a cap, an inner cylinder assembly, an elastic member and a blocking member. The cap includes a cap top and a cap side portion. The cap side portion is formed by extending downwardly from an outer edge of the cap top, and the cap top and the cap side portion define a cap cavity. The inner cylinder assembly is connected to the cap top, is arranged in the cap cavity, and forms an annular receiving portion with the cap side portion. The elastic member is arranged in the receiving portion. The blocking member is arranged in the receiving portion below the elastic member. The cap is provided with at least one locking portion for preventing the elastic member and the blocking member from falling from the receiving portion.

Abstract: Aspects concern a method for training a neural network, comprising forming an autoencoder comprising the neural network as encoder and comprising a decoder, for each training image of multiple training images, generating a latent representation of the training image by the encoder, transforming the training image and supplying information about the transformation and at least a part of the latent representation to the decoder to generate a decoder output for the training image and adjusting the encoder and the decoder to reduce a loss between the transformed training images and the decoder outputs.

Abstract: The present invention provides a data loading method. The method comprises: reading data from an initial address in a memory; obtaining a frame header identifier, loading a valid data segment comprising the frame header identifier; verifying the valid data segment; when it is determined that the verification is successful, sending at least part of information within the valid data segment to a cache unit; updating a register according to at least part of the information by the cache unit. Using a specific frame header identifier, loading data in the memory starts only from the specific frame header identifier, which skips the data before the frame header identifier and avoids loading invalid data in the cache unit, thus saving time, preventing lengthy program, and being more scientific and convenient.

Abstract: The present invention discloses a control method, system, electronic device and readable storage medium for a capsule endoscope. The method includes: providing a working apparatus, comprising a capsule endoscope, and an external data recorder for cooperating with and controlling the capsule endoscope; monitoring the received ambient power by the external data recorder before wireless transmission of the capsule endoscope or during an intermittence between two transmissions, and/or monitoring the output power of the capsule endoscope by the external data recorder as data is transmitted during wireless transmission; adjusting the operating state of the working apparatus according to the ambient power and/or output power.

Abstract: Embodiments described herein provide a contrastive learning mechanism with self-labeling refinement, which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning. Specifically, the contrastive learning framework includes a self-labeling refinery module to explicitly generate accurate labels, and a momentum mix-up module to increase similarity between a query and its positive, which in turn implicitly improves label accuracy.

Abstract: A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes the characters based on the second attention score. The system trains the neural network based model by adjusting the neural network parameters to minimize a multi-class gradient harmonizing mechanism (GHM) loss. The multi-class GHM loss varies based on a level of difficulty of the sample.

Abstract: The present invention discloses a control method, system, electronic device and readable storage medium for a capsule endoscope. The method includes: providing a working apparatus, comprising a capsule endoscope, and an external data recorder for cooperating with and controlling the capsule endoscope; monitoring the received ambient power by the external data recorder before wireless transmission of the capsule endoscope or during an intermittence between two transmissions, and/or monitoring the output power of the capsule endoscope by the external data recorder as data is transmitted during wireless transmission; adjusting the operating state of the working apparatus according to the ambient power and/or output power.

Abstract: A method for generating a neural network, including initializing the neural network including a plurality of cells, each cell corresponding to a graph including one or more nodes, each node corresponding to a latent representation of a dataset. A plurality of gates are generated, wherein each gate independently determines whether an operation between two nodes is used. A first regularization is performed using the plurality of gates. The first regularization is one of a group-structured sparsity regularization and a path-depth-wised regularization. An optimization is performed on the neural network by adjusting its network parameters and gate parameters based on the regularization of the sparsity.