Abstract: The present disclosure discloses a liquid crystal display panel and a compensation method thereof. The compensation method firstly acquires a plurality of consecutive and alternating odd-numbered frame grayscale data and even-numbered frame grayscale data, then determines a minimum grayscale difference, and determines a gamma voltage difference corresponding to the grayscale difference according to a curve of a relation between grayscales and gamma voltages, followed by obtaining a correction value for a common voltage based on the gamma voltage difference. The flicker phenomena can be improved by adjusting the common voltage only once with the correction value.

Abstract: The present embodiments relate to a language identification system for predicting a language and text content of text lines in an image-based document. The language identification system uses a trainable neural network model that integrates multiple neural network models in a single unified end-to-end trainable architecture. A CNN and an RNN of the model can process text lines and derive visual and contextual features of the text lines. The derived features can be used to predict a language and text content for the text line. The CNN and the RNN can be jointly trained by determining losses based on the predicted language and content and corresponding language labels and text labels for each text line.

Abstract: Techniques are disclosed for optical character recognition of extensible markup language content. A method can include a system generating a first training data comprising extensible markup language (XML) content, the first training data comprising a first plurality of training instances, each training instance including a respective image comprising XML content and annotation information for the respective image. The system can train a plurality of machine learning models using the first training data to generate a plurality of trained machine learning models, to perform image-based XML content extraction. The system can generate a plurality of trained machine learning models based at least in part on the training.

Abstract: Provided are a composition, kit, and application for the detection of colorectal cancer. The provided composition contains reagents for detecting methylation status of ADHFE1 gene, PPP2R5C gene, and SDC2 gene. The composition can be used for detecting colorectal cancer with high sensitivity and specificity.

Abstract: Techniques are described for automatically, and substantially without human intervention, generating training data where the training data includes a set of training images containing text content and associated label data. Both the training images and the associated label data are automatically generated. The label data that is automatically generated for a training image includes one or more labels identifying locations of one or more text portions within the training image, and for each text portion, a label indicative of the text content in the text portion. By automating both the generation of training images and the generation of associated label data, the techniques described herein are very scalable and repeatable and can be used to generate large amounts of training data, which in turn enables building more reliable and accurate language models.

Abstract: The present embodiments relate to a language identification system for predicting a language and text content of text lines in an image-based document. The language identification system uses a trainable neural network model that integrates multiple neural network models in a single unified end-to-end trainable architecture. A CNN and an RNN of the model can process text lines and derive visual and contextual features of the text lines. The derived features can be used to predict a language and text content for the text line. The CNN and the RNN can be jointly trained by determining losses based on the predicted language and content and corresponding language labels and text labels for each text line.

Abstract: The present embodiments relate to identifying a native language of text included in an image-based document. A cloud infrastructure node (e.g., one or more interconnected computing devices implementing a cloud infrastructure) can utilize one or more deep learning models to identify a language of an image-based document (e.g., a scanned document) that is formed of pixels. The cloud infrastructure node can detect text lines that are bounded by bounding boxes in the document, determine a primary script classification of the text in the document, and derive a primary language for the document. Various document management tasks can be performed responsive to determining the language, such as perform optical character recognition (OCR) or derive insights into the text.

Abstract: A novel, in-situ plasma treatment method for eliminating or reducing striations caused by standing waves in a photoresist mask, is disclosed. The method includes providing a photoresist mask on a BARC (bottom anti-reflective coating) layer that is deposited on a feature layer to be etched, etching the BARC layer and the underlying feature layer according to the pattern defined by the photoresist mask, and subjecting the photoresist mask to a typically argon or hydrogen bromide plasma before, after, or both before and after etching of the BARC layer prior to etching of the feature layer. Preferably, the photoresist mask is subjected to the plasma both before and after etching of the BARC layer.

Abstract: A novel, in-situ plasma treatment method for eliminating or reducing striations caused by standing waves in a photoresist mask, is disclosed. The method includes providing a photoresist mask on a BARC (bottom anti-reflective coating) layer that is deposited on a feature layer to be etched, etching the BARC layer and the underlying feature layer according to the pattern defined by the photoresist mask, and subjecting the photoresist mask to a typically argon or hydrogen bromide plasma before, after, or both before and after etching of the BARC layer prior to etching of the feature layer. Preferably, the photoresist mask is subjected to the plasma both before and after etching of the BARC layer.

Abstract: A method for compensating for CD variations across a semiconductor process wafer surface in a plasma etching process including providing a semiconductor wafer having a process surface including photolithographically developed features imaged from a photomask; determining a first dimensional variation of the features with respect to corresponding photomask dimensions along at least one wafer surface direction to determine a first levelness of the process surface; determining gas flow parameters in a plasma reactor for a plasma etching process required to approach a level process surface by reference to an archive of previous plasma etching process parameters carried out in the plasma reactor; carrying out the plasma etching process in the plasma rector according to the determined gas flow parameters; and, determining a second dimensional variation of the features along the at least one wafer surface direction to determine a second levelness of the process surface.

Abstract: A method for compensating for CD variations across a semiconductor process wafer surface in a plasma etching process including providing a semiconductor wafer having a process surface including photolithographically developed features imaged from a photomask; determining a first dimensional variation of the features with respect to corresponding photomask dimensions along at least one wafer surface direction to determine a first levelness of the process surface; determining gas flow parameters in a plasma reactor for a plasma etching process required to approach a level process surface by reference to an archive of previous plasma etching process parameters carried out in the plasma reactor; carrying out the plasma etching process in the plasma rector according to the determined gas flow parameters; and, determining a second dimensional variation of the features along the at least one wafer surface direction to determine a second levelness of the process surface.