Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen for HILN (H, I, and/or L, or N). The method includes capturing images of the specimen at multiple different viewpoints, processing the images to provide segmentation information for each viewpoint, generating a semantic map from the segmentation information, selecting a synthetic viewpoint, identifying front view semantic data and back view semantic data for the synthetic viewpoint, and determining HILN of the serum or plasma portion based on the front view semantic data with an HILN classifier, while taking into account back view semantic data. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.

Abstract: An encoding, a decoding method, a system for encoding and decoding, an encoder, and a decoder are provided. The encoding method includes the following. In a palette mode, if colors of pixels of a coding unit block are all represented by one or more major colors of the coding unit block, a flag is set as a first state value, and if the color of at least one pixel of the coding unit block is not represented by the one or more major colors of the coding unit block, the flag is set as a second state value. The encoding method further includes establishing a palette table corresponding to the coding unit block according to a state value of the flag and the one or more major colors.

Abstract: A model-based method of inspecting a specimen for presence of one or more interferent, such as Hemolysis, Icterus, and/or Lipemia (HI L) is provided. The method includes generating a pixelated image of the specimen in a first color space, determining color components (e.g., an a-value and a b-value) for pixels in the pixelated image, classifying of the pixels as being either liquid or non-liquid, defining one or more liquid regions based upon the pixels classified as liquid, and determining a presence of one or more interferent within the one or more liquid regions. The liquid classification is based upon a liquid classification model. Pixel classification may be based on a trained multiclass classifier. Interference level for the one or more interferent may be provided. Testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: Method for fabricating a semiconductor structure is provided. First features are formed in a first product region of each die area in a material layer through a first mask. Second features are formed in a second product region of each die area in the material layer through a second mask. Third features are formed in a third product region of each die area in the material layer through a third mask. Fourth features are formed in a fourth product region of each die area in the material layer through a fourth mask. Fifth features are formed in an alignment region of each die area in the material layer through the first through fourth masks. The first product region is adjacent to and in physical contact with the second and third product regions, and the first product region is free of the second, third, and fourth features.

Abstract: A Rowing Machine, which includes a Main Bracket and a Cushion. The Main Bracket is equipped with a Slide Rail, the Cushion is slidably installed on the Slide Rail, and the Slide Rail is gradually bent upwards from front to back. Thereby, when the user uses the Rowing Machine, the Cushion can rise higher along the bent Slide Rail, which can more effectively enhance the user's exercise effect.

Abstract: A method of decoding a bitstream by an electronic device includes determining a block unit having a block height and a block width from an image frame according to the bitstream. A mode list including a plurality of intra modes is determined for reconstructing the block unit. At least one of the intra modes is selected from the mode list based on a comparison between the block height and the block width, and removed from the mode list to generate an adjusted list including a plurality of unselected modes. The block unit included in the image frame is reconstructed based on the adjusted list.

Abstract: An example device includes a memory and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine a value of a first syntax element indicative of a number of subpictures in a picture of video data. The one or more processors are configured to determine, for each subpicture among the subpictures in the picture, a value of a respective second syntax element indicative of an identification of a respective subpicture. The one or more processors are also configured to code the respective subpicture identified by the respective second syntax element.

Abstract: A video coder may code, in a picture parameter set (PPS), a syntax element indicative of a luma coding tree block size of coding tree units (CTUs) of a picture of video data to which the PPS is applicable. The video coder may further code the picture of the video data to which the PPS is applicable in accordance with the syntax element in the PPS.

Abstract: A video decoder can be configured to decode a first syntax element indicating a size of a coding tree unit (CTU); after decoding the first syntax element indicating the size of the CTU, decode a second syntax element indicating a width of elements of a subpicture identifier grid; after decoding the first syntax element indicating the size of the CTU, decode a third syntax element indicating a height of the elements of the subpicture identifier grid; and determine a location of a subpicture within a picture based on the first syntax element, the second syntax element, and the third syntax element.

Abstract: A method of decoding a bistream by an electronic device is provided. A block unit is determined from an image frame according to the bitstream. A line index determined in the bitstream is compared with a first predefined value to determine whether a mode flag is included in the bitstream. A mode index is determined for directly selecting a prediction mode of the block unit from a most probable mode (MPM) list of the block unit when the mode flag is not included in the bitstream. The mode flag is compared to a second predefined value when the mode flag is included in the bitstream to determine whether the prediction mode is selected from the MPM list. One of a plurality of reference lines is selected based on the line index. The block unit is reconstructed based on the selected reference line and the prediction mode.

Abstract: An example device for coding video data codes, in a parameter set, a first syntax element indicative of a luma coding tree block size of coding tree units (CTUs) to which the parameter set is applicable minus 5. The device codes, in the parameter set, a second syntax element indicative of a minimum luma coding block size minus 2 of luma coding blocks to which the parameter set is applicable, wherein a value of the second syntax element is in a range of 0 to a value based on the first syntax element, inclusive. The device codes the luma coding blocks to which the parameter set is applicable in accordance with the first syntax element and the second syntax element in the parameter set.

Abstract: An example device includes a memory configured to store video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine whether a maximum number of merge candidates for a slice of the video data is equal to a first value. The one or more processors are configured to infer a value of a first syntax element to be equal to a second value based at least in part on the maximum number of merge candidates for the slice being equal to the first value, the first syntax element being indicative of a maximum number of merge candidates and a maximum number of merge candidates of a non-rectangular coding mode. The one or more processors are also configured to decode the slice based on the maximum number of merge candidates and the first syntax element.

Abstract: A device, structure, and method are provided whereby an insert layer is utilized to provide additional support for surrounding dielectric layers. The insert layer may be applied between two dielectric layers. Once formed, trenches and vias are formed within the composite layers, and the insert layer will help to provide support that will limit or eliminate undesired bending or other structural motions that could hamper subsequent process steps, such as filling the trenches and vias with conductive material.

Abstract: An encoding, a decoding method, a system for encoding and decoding, an encoder, and a decoder are provided. The encoding method includes the following. In a palette mode, if colors of pixels of a coding unit block are all represented by one or more major colors of the coding unit block, a flag is set as a first state value, and if the color of at least one pixel of the coding unit block is not represented by the one or more major colors of the coding unit block, the flag is set as a second state value. The encoding method further includes establishing a palette table corresponding to the coding unit block according to a state value of the flag and the one or more major colors.

Abstract: A coating method applied to perform coating with liquid metal thermal grease and a heat dissipation module are provided. The coating method includes: providing liquid metal thermal grease on a surface of an electronic element, and scraping the liquid metal thermal grease by a scraper, to coat the surface of the electronic element with the liquid metal thermal grease. A surface of the scraper is roughened. According to the coating method, the surface of the electronic element is evenly coated with the liquid metal thermal grease effectively.

Abstract: A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.

Abstract: Method for fabricating a semiconductor structure is provided. First features are formed in a first product region of each die area in a material layer through a first mask. Second features are formed in a second product region of each die area in the material layer through a second mask. Third features are formed in a third product region of each die area in the material layer through a third mask. Fourth features are formed in a fourth product region of each die area in the material layer through a fourth mask. Fifth features are formed in an alignment region of each die area in the material layer through the first through fourth masks. The first product region is adjacent to and in physical contact with the second and third product regions, and the first product region is free of the second, third, and fourth features.

Abstract: A method of decoding a bitstream by an electronic device is provided. A block unit is determined from an image frame according to the bitstream. A plurality of candidate modes included in a mode list are separated in a first mode group and a second mode group. The electronics device determines whether a specific one of the candidate modes in the first mode group is replaced by one of the candidate modes in the second mode group. When the specific candidate mode in the first mode group is replaced, a prediction mode is determined from the candidate modes in the second mode group. When the specific candidate mode in the first mode group remains unchanged, the specific candidate mode in the first mode group is determined as the prediction mode. Then, the block unit of the image frame is reconstructed based on the prediction mode.