Abstract: The present disclosure provides a microfluidic chip, a box device adapted to the microfluidic chip, and a microfluidic device including the microfluidic chip and the box device. The microfluidic chip includes a first container for accommodating a first fluid, a second container for accommodating a second fluid, a delivery channel, a sorting channel and a collector. The delivery channel is shaped such that the first fluid and the second fluid merge at a confluence. The sorting channel includes a first sorting channel and a second sorting channel. The collector includes a first collector and a second collector.

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 of decoding an image with latent feature-domain intra-prediction is performed by at least one processor and includes receiving a set of latent blocks and for each of the blocks in the set of latent blocks: predicting a block, based on a set of previously recovered blocks; receiving a selection signal indicating a currently recovered block, based on the selection signal performing one of (1) and (2): (1) generating a compact residual, a set of residual context parameters, a decoded residual, and generating a first decoded block; (2) generating a second decoded block, based on a compact representation block and a set of context parameters. The method further includes generating a set of recovered blocks comprising each of the currently recovered blocks; generating a recovered latent image by merging all the blocks in the set of recovered blocks; and decoding the recovered latent image, to obtain a reconstructed image.

Abstract: The present application discloses a device for driving a cell processing chip and a method of driving a cell processing chip. The cell processing chip is configured to process cells. The device for driving the cell processing chip includes a bearing member configured to carry the cell processing chip, an accommodating member in fluid communication with the cell processing chip, a fluid driving member configured to drive flow of fluid in the device and in the cell processing chip, a signal generating and processing member configured to apply a signal to a fluid in the cell processing chip to generate a response signal associated with the fluid, and configured to issue a control instruction, and a power supply configured to supply power to the fluid driving member, and configured to apply a sorting signal to the cell processing chip in response to the control instruction.

Abstract: In some examples, processing circuitry decodes, from a coded bitstream that carries a compressed image, an index that points a value in a set of values of a parameter. A value change of the parameter adjusts a compression rate of the compressed image. The compressed image is generated by a neural network based encoder based on the parameter. The processing circuitry inputs the value of the parameter into a multi-rate compression domain computer vision task decoder. The multi-rate compression domain computer vision task decoder includes one or more neural networks for performing a computer vision task from compressed images according to corresponding values of the parameter that are used for generating the compressed images. The multi-rate compression domain computer vision task decoder generates a computer vision task result according to the compressed image in the coded bitstream and the value of the parameter.

Abstract: Processing circuitry performs, based on an input image and a guideline image resulting of an image based computer vision task on the input image, an online training of a compression domain computer vision task framework (CDCVTF). The CDCVTF includes an encoding portion and a decoding portion. The encoding portion compresses the input image into a compression domain, and the decoding portion performs the image based computer vision task in the compression domain. The online training determines at least an update to a neural network in the decoding portion that reduces a loss between a result of the image based computer vision task in the compression domain and the guideline image. The processing circuitry encodes the input image into an encoded image, and forms a bitstream that carries the encoded image and additional bits. The additional bits are indicative of the update to the neural network in the decoding portion.

Abstract: The disclosure provides a display device and a display panel. The display panel includes an array substrate, a driving circuit, a touch electrode layer. Each of the touch electrode blocks includes a touch electrode and a compensation electrode which are disposed to be insulated from each other. The touch signal line and the compensation electrode are disposed on a same layer. The orthographic projections of the touch electrode and the compensation electrode projected on the array substrate have an overlapping area, and the overlapping area on the touch electrode block close to the driving circuit is larger than the overlapping area on the touch electrode block away from the driving circuit.

Abstract: In some examples, an apparatus for image/video processing includes processing circuitry. The processing circuitry determines, from a coded bitstream that carries a compressed image, a value of a parameter for tuning a compression rate of the compressed image. The compressed image is generated by a neural network based encoder according to the value of the parameter. The processing circuitry inputs the value of the parameter to a multi-rate compression domain computer vision task decoder, the multi-rate compression domain computer vision task decoder includes one or more neural networks for performing a computer vision task from compressed images according to corresponding values of the parameter that are used for generating the compressed images. The multi-rate compression domain computer vision task decoder generates a computer vision task result according to the compressed image in the coded bitstream and the value of the parameter.

Abstract: Neural network based image compression using an optimized end-to-end (E2E) neural image compression (NIC) framework being performed by at least one processor and includes receiving an input image to an E2E NIC framework, processing the input image in its entirety using an encoder in the E2E NIC framework to obtain a first bitstream representation of the input image in its entirety, reconstructing an output image from the first bitstream representation of the input image in its entirety using a decoder in the E2E NIC framework, optimizing the encoder in the E2E NIC framework by reducing a distortion loss between the input image and the output image, and processing the input image using the optimized encoder in the E2E NIC framework to obtain a second bitstream representation of the input image. Further, the input may be partitioned into blocks for which encoder optimization is performed for each block and each block is encoded instead of the entire image.

Abstract: The embodiment of the present disclosure provides a touch panel, including a plurality of sets of touch induction units including a plurality of touch induction blocks, at least a touch integrated circuit, a plurality of touch signal lines, and a plurality of subtend electrodes; wherein at least the touch integrated circuit is connected to the plurality of touch induction blocks by the plurality of touch signal lines; wherein the plurality of touch induction blocks are arranged in sequence, and lengths of the plurality of touch signal lines corresponding to adjacent touch induction blocks are different; the plurality of touch induction blocks include a plurality of touch electrodes, and areas of the plurality of touch electrodes corresponding to the adjacent touch induction blocks are different.

Abstract: An apparatus for image/video encoding includes processing circuitry. The processing circuitry performs, based on one or more input images, respective online training based encoder tunings on a plurality of neural image compression (NIC) frameworks. An online training based encoder tuning on an NIC framework in the plurality of NIC frameworks determines an update to an encoder of the NIC framework with a decoder of the NIC framework having fixed parameters. The processing circuitry selects a first NIC framework based on respective performances of the plurality of NIC frameworks with updated encoders from the online training based encoder tunings. The first NIC framework has a first updated encoder from the online training based encoder tunings. The processing circuitry encodes, by the first updated encoder, the one or more input images, into a coded bitstream and includes a signal indicative of the first NIC framework in the coded bitstream.

Abstract: An apparatus for image/video encoding includes processing circuitry. The processing circuitry performs, based on one or more input images, an online training of a neural image compression (NIC) framework. The NIC framework is an end-to-end framework that comprises one or more first neural networks in an encoding portion and one or more second neural networks in a decoding portion. The online training determines an update to one or more tunable parameters in the one or more first neural networks with the one or more second neural networks having fixed parameters. The processing circuitry updates the one or more tunable parameters in the one or more first neural networks according to the update, and encodes, by the encoding portion of the NIC framework with the one or more tunable parameters in the one or more first neural networks being updated, the one or more input images into a bitstream.

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.

Abstract: The present disclosure provides a microfluidic chip and a droplet separation method, and belongs to the field of biological chip technology. The microfluidic chip includes a first liquid tank and a second liquid tank opposite to each other and a channel layer therebetween. The channel layer includes a plurality of microfluidic channels separated from each other, first ends of the microfluidic channels are communicated with the first liquid tank, and second ends are communicated with the second liquid tank. The first liquid tank contains sample solution to be detected, and the second liquid tank contains encapsulating liquid. The sample solution to be detected entering the first liquid tank may be separated into a plurality of sample droplets through the microfluidic channels, the separated sample droplets enter the second liquid tank, so that the encapsulating liquid is encapsulated on a surface of each of the plurality of sample droplets.

Abstract: A method and apparatus for reducing artifacts in a compressed image using a neural-network based deblocking filter. The method may include receiving at least one reconstructed image, wherein each reconstructed image comprises one or more reconstructed blocks and extracting boundary areas associated with boundaries of the one or more reconstructed blocks in the at least one reconstructed image. The extracted boundary areas may be input in a trained deblocking model to generate boundary areas having reduced artifacts and the trained deblocking mode is trained on training data based on estimated compression by a neural image compression (NIC) network. The edge areas associated with the generated boundary areas may be removed; and at least one reconstructed image with reduced artifacts may be generated based on the generated boundary areas.

Abstract: A method of video coding at a video coding device includes performing a deformable convolution through a deformable convolutional deep neural network (DNN) to generate one or more first feature maps based on a set of one or more previously reconstructed reference frames, generating a predicted frame based on the one or more first feature maps, and reconstructing a current frame based on the predicted frame. In an embodiment, a set of one or more second feature maps corresponding to the one or more previously reconstructed reference frames can be generated based on a feature extraction DNN. One or more offset maps corresponding to the one or more second feature maps can be generated, respectively, using an offset generation DNN.

Abstract: A method of video decoding at a video decoder can include receiving one or more syntax elements associated with a current first block that belongs to a plurality of first blocks partitioned from a picture, the one or more syntax elements indicating an optimal partition indicating how the current first block is partitioned into second blocks for intra-prediction, a set of block selection signals, wherein the current first block is re-partitioned into third blocks, each block selection signal corresponds to one of the third blocks and indicates whether the respective third block is coded using a first coding method or a second coding method, and a set of compressed representations each corresponding to one of the third blocks. The current first block can be reconstructed based on the one or more syntax elements to generate a reconstructed current first block.

Abstract: Iterative content-adaptive online training for end-to-end (E2E) neural image compression (NIC) using a neural network performed by at least one processor, is provided, including receiving an input image, to an E2E NIC framework, fine-tuning the E2E NIC framework, based on the input image, computing parameter updates using a first neural network of the fine-tuned E2E NIC framework, enhancing the fine-tuned E2E NIC framework based on a second neural network, the second neural network being a post-enhancement network, and generating an updated E2E NIC framework, based on the enhanced E2E NIC framework and the parameter updates.

Abstract: Content-adaptive online training for end-to-end (E2E) neural image compression (NIC) using a neural network performed by at least one processor, is provided, including receiving an input image, to an E2E NIC framework, including one or more blocks, preprocessing a first neural network of the E2E NIC framework, based on the one or more blocks, computing updated parameters using the preprocessed first neural network, encoding the one or more blocks and the updated parameters, updating the first neural network based on the encoded updated parameters, and generating a compressed representation of the encoded one or more blocks using the updated first neural network.

Abstract: Content-adaptive online training for end-to-end (E2E) neural image compression (NIC) using a neural network performed by at least one processor, is provided, including receiving an input image, to an E2E NIC framework, splitting the input image into a plurality of blocks, selecting a subset of blocks from the plurality of blocks, the subset of blocks sharing a same pattern, preprocessing a neural network of the E2E NIC framework, wherein the preprocessed neural network is applied to the selected subset of blocks, computing updated parameters using the preprocessed neural network, and generating an updated E2E NIC framework, based on the updated parameters.