Abstract: The present disclosure provides a microfluidic substrate, a microfluidic chip and a manufacturing method thereof. The microfluidic substrate includes: a first substrate; a conductive layer on the first substrate; and a defining layer on a side of the conductive layer facing away from the first substrate, the defining layer defining a concave portion; wherein the conductive layer comprises a plurality of conductive patterns corresponding to the concave portion, the plurality of conductive patterns are arranged along a first direction, each conductive pattern extends along a second direction and comprises a first end and a second end, the first direction is perpendicular to the second direction, and each conductive pattern has a maximum local resistance value at the first end and the second end of the conductive pattern.

Abstract: The present disclosure provides an array substrate, a microfluidic device, a microfluidic system, and a fluorescence detection method. The array substrate includes at least one recess, the array substrate is located in a plane, and a ratio of an area of an orthographic projection of the at least one recess on the plane to an area of an orthographic projection of the array substrate on the plane is between 0.05 and 0.60.

Abstract: The present application discloses an oxide thin film transistor, a display panel, and a preparation method thereof. Each thickness of the first gate insulating layer of the present application corresponding to the first source doped region, the first drain doped region, the first diffusion region, and the second diffusion region is less than a thickness corresponding to the first channel region; and thicknesses of the first gate insulating layer corresponding to the first diffusion region and the second diffusion region are both different from a thickness corresponding to the first source doped region and the first drain doped region. The the first gate insulating layer effectively shields the first channel region laterally.

Abstract: A laser radar receiving system including a laser radar and a reflecting element. The laser radar includes a first reflector mirror, an emitting channel, an emission system and a basic receiving system. The laser beam emitted by the emission system is reflected by the first reflector mirror, reaches a target through the emitting channel, is reflected from the target and passes through the emitting channel, and is reflected by the first reflector mirror and received by the basic receiving system. The reflecting element is provided in an area outside the emitting channel that is unreachable by the laser beam reflected from the target, and is used for reflecting the laser beam from the area outside the emitting channel to the basic receiving system, thereby simultaneously avoiding blind area and stray light interference in short-range measurements, and improving measurement accuracy and expanding the short-range measurement range of radar.

Abstract: Neural image compression with adaptive intra-prediction is performed by at least one processor and includes receiving an optimal partition and a compressed representation of an input comprising a first set of blocks, for each block in the first set of blocks, receiving a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block, and based on the received block selection signal, performing one of a first recovery and a second recovery, and merging the currently recovered blocks to obtain a reconstructed image. The first recovery comprises compute the first recovered block based on a respective block in the first set of blocks directly. The second recovery comprises generating a recovered residual based on a computed residual, partitioning the first predicted block and adding the recovered residual to obtain the second recovered block.

Abstract: A display device includes a display region and a non-display region. The display device includes a substrate; a non-organic insulation layer disposed on a side of the substrate; wherein a first groove is defined in the non-organic insulation layer corresponding to the non-display region, an opening of the first groove is away from the substrate, and a metal retaining wall is disposed in the first groove; a light-emitting layer disposed on a side of the non-organic insulation layer away from the substrate; and an encapsulation layer disposed on a side of the light-emitting layer away from the substrate and extending from the display region to the non-display region. Wherein, the encapsulation layer is overlapped with the first groove in the non-display region, and a first gap is between an edge of the encapsulation layer and an edge of the non-organic insulation layer.

Abstract: The present disclosure relates to a microfluidic chip. The microfluidic chip includes a first substrate, and the first substrate includes a sample input hole and a reaction region located downstream of the sample input hole. The reaction region includes at least one groove, an orthographic projection of each groove on the first substrate is an axisymmetric pattern, a width of the axisymmetric pattern in a first direction is not less than a width of the axisymmetric pattern in a second direction, and the first direction is perpendicular to the second direction.

Abstract: A device and a driving method for driving a microfluidic chip are disclosed. The device for driving a microfluidic chip includes a carrying member configured to carry the microfluidic chip; a releasing member configured to electrically connected to the microfluidic chip, and control the release of the reagent of the microfluidic chip a valve control member configured to control the opening and closing of the flow channel in the valve control area of the microfluidic chip when the valve control area is within the valve control range of the valve control member a fluid driving member configured to drive the flow of fluid in the microfluidic chip, and a controller configured to control the driving process of the microfluidic chip.

Abstract: A method of multi-scale neural image compression with intra-prediction residuals is performed by at least one processor and includes downsampling an input image, generating a current predicted image, based on a previously-recovered predicted image, and generating a prediction residual based on a difference between the downsampled input image and the generated current predicted image. The method further includes encoding the generated prediction residual, decoding the encoded prediction residual, and generating a currently-recovered predicted image based on an addition of the current predicted image and the decoded prediction residual. The method further includes upsampling the currently-recovered predicted image, generating a scale residual based on a difference between the input image and the upsampled currently-recovered predicted image, and encoding the scale residual.

Abstract: The present disclosure provides a microfluidic chip including a plurality of microcavities, at least two of the plurality of microcavities have different volumes.

Abstract: Aspects of the disclosure provide a method, an apparatus, and non-transitory computer-readable storage medium for video decoding. The apparatus includes processing circuitry that reconstructs blocks of an image that is to be reconstructed from a coded video bitstream. The processing circuitry decodes first deblocking information in the coded video bitstream including a first deblocking parameter of a deep neural network (DNN) in a video decoder. The first deblocking parameter of the DNN is an updated parameter that has been previously determined by a content adaptive training process. The processing circuitry determines the DNN for a first boundary region comprising a subset of samples in the reconstructed blocks based on the first deblocking parameter included in the first deblocking information. The processing circuitry deblocks the first boundary region comprising the subset of samples in the reconstructed blocks based on the determined DNN corresponding to the first deblocking parameter.

Abstract: A method and apparatus for neural network based cross component prediction with scaling factors during encoding or decoding of an image frame or a video sequence, which may include training a deep neural network (DNN) cross component prediction (CCP) model with at least one or more scaling factors, wherein the at least one or more scaling factors are learned by optimizing a rate-distortion loss based on an input video sequence comprising a luma component, and reconstructing a chroma component based on the luma component using the trained DNN CCP model with the at least one or more scaling factors for chroma prediction. The trained DNN CCP may be updated for chroma prediction of the input video sequence using the one or more scaling factors, and performing chroma prediction of the input video sequence using the updated DNN CCP model with the one or more scaling factors.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to receive an image or video comprising one or more blocks. The processing circuitry can decode a first post-filtering parameter in the image or video corresponding to the one or more blocks to be reconstructed. The first post-filtering parameter applies to at least one of the one or more blocks and has been updated by a post-filtering module in a post-filtering neural network (NN) that is trained based on a training dataset. The processing circuitry can determine the post-filtering NN in a video decoder corresponding to the one or more blocks based on the first post-filtering parameter. The processing circuitry can decode the one or more blocks based on the determined post-filtering NN corresponding to the one or more blocks.

Abstract: Aspects of the disclosure provide a method and an apparatus for video encoding. The apparatus includes processing circuitry configured to perform an iterative update of sample values of a plurality of samples in an initial input image. The iterative update includes generating a coded representation of a final input image based on the final input image by an encoding neural network (NN) and at least one training module. The final input image has been updated from the initial input image by a number of iterations of the iterative update. The iterative update includes generating a reconstructed image of the final input image based on the coded representation of the final input image by a decoding NN. One of a rate-distortion loss for the final input image or the number of iterations of the iterative update satisfies a pre-determined condition. An encoded image corresponding to the final input image is generated.

Abstract: A method for encoding dot-matrix product information method includes identifying, via a processor, a dot-matrix grid size. The method further includes evaluating, via the processor, one or more dot pattern variation levels. In some aspects, the method includes retrieving, via the processor, an encoding structure indicative of a plurality of product information attributes. The method also includes determining, via the processor, whether an alpha-numeric digit at a dot pattern variation level can include a plurality of product information. The method further includes outputting, via the processor, a dot pattern code map. In some aspects, the dot pattern code map is indicative of a relationship between each of the product information attributes and the plurality of values for each of the product information attributes.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to reconstruct blocks of an image from a coded video bitstream. The processing circuitry can perform post-processing on one of a plurality of regions of first two neighboring reconstructed blocks of the reconstructed blocks with at least one post-processing neural network (NN). The first two neighboring reconstructed blocks have a first shared boundary and include a boundary region having samples on both sides of the first shared boundary. The plurality of regions of the first two neighboring reconstructed blocks includes the boundary region and non-boundary regions that are outside the boundary region. The one of the plurality of regions is replaced with the post-processed one of the plurality of regions of the first two neighboring reconstructed blocks.

Abstract: Aspects of the disclosure provide a method and an apparatus for video encoding. The apparatus includes processing circuitry configured to generate an initial feature representation from an input image to be encoded and perform an iterative update of values of a plurality of elements in the initial feature representation. The iterative update includes generate a coded representation corresponding to a final feature representation based on the final feature representation that has been updated from the initial feature representation by a number of iterations of the iterative update. A reconstructed image corresponding to the final feature representation is generated based on the coded representation. An encoded image corresponding to the final feature representation having updated values of the plurality of elements is generated. One of (i) a rate-distortion loss corresponding to the final feature representation or (ii) the number of iterations of the iterative update satisfies a pre-determined condition.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to decode first neural network update information in a coded bitstream for a first neural network in the video decoder. The first neural network is configured with first pretrained parameters. The first neural network update information corresponds to a first block in an image to be reconstructed and indicates a first replacement parameter corresponding to a first pretrained parameter in the first pretrained parameters. The processing circuitry is configured to update the first neural network in the video decoder based on the first replacement parameter. The processing circuitry can decode the first block based on the updated first neural network for the first block.

Abstract: Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to decode neural network update information in a coded bitstream for a neural network in a video decoder. The neural network is configured with pretrained parameters. The neural network update information corresponds to an encoded image to be reconstructed and indicates a replacement parameter corresponding to a pretrained parameter in the pretrained parameters. The processing circuitry is configured to update the neural network in the video decoder based on the replacement parameter. The processing circuitry is configured to decode the encoded image based on the updated neural network for the encoded image.

Abstract: Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable storage medium for video encoding and video decoding. An apparatus for video decoding can include processing circuitry. The processing circuitry is configured to decode neural network update information in a coded bitstream for at least one neural network in the video decoder. The at least one neural network is configured with a set of pretrained parameters, and the neural network update information indicates a first modification parameter. The processing circuitry is configured to update the set of pretrained parameters in the at least one neural network in the video decoder based on the first modification parameter. The processing circuitry is configured to decode an encoded image based on the updated at least one neural network.