A method including: receiving an input comprising natural language texts at an encoder; adding a token to the input; obtaining a last-layer hidden state as a natural language text representation; feeding the natural language text representation into a single-layer classification head; predicting a salience allocation based on the single-layer classification head; developing a salience-aware cross-attention (SACA) decoder to determine salience in the natural language text representation; mapping a plurality of salience degrees to a plurality of trainable salience embeddings; estimating an amount of signal to accept from the plurality of trainable salience embeddings; incorporating the salience allocation and the signal in a cross-attention layer model; and generating a summarization based on the SACA decoder and the cross-attention layer model.

A method including: receiving an input comprising natural language texts at an encoder; adding a token to the input; obtaining a last-layer hidden state as a natural language text representation; feeding the natural language text representation into a single-layer classification head; predicting a salience allocation based on the single-layer classification head; developing a salience-aware cross-attention (SACA) decoder to determine salience in the natural language text representation; mapping a plurality of salience degrees to a plurality of trainable salience embeddings; estimating an amount of signal to accept from the plurality of trainable salience embeddings; incorporating the salience allocation and the signal in a cross-attention layer model; and generating a summarization based on the SACA decoder and the cross-attention layer model.

A recognition system for recognizing multiple inputs of gestures, handwriting symbols and virtual keys on a touch screen includes a touch IC serves to convert a plurality of touch signals of the touch screen to a touch data frame. A processor set is connected to the touch IC and serves to perform a touch data processing on the touch data frame. The touch data processing is performed by using a processing directly executed by an OS (Operating System) and a processing of AI (artificial intelligence) recognizing. An AI recognition module is connected to the processor set. The AI recognition module is used for recognizing multiple key inputs, operation gestures and handwriting symbols. The key inputs and handwriting symbols are corrected by a grammar correction and a symbol correction respectively. The touch screen serves to display a virtual keyboard.

A recognition system for recognizing multiple inputs of gestures, handwriting symbols and virtual keys on a touch screen includes a touch IC serves to convert a plurality of touch signals of the touch screen to a touch data frame. A processor set is connected to the touch IC and serves to perform a touch data processing on the touch data frame. The touch data processing is performed by using a processing directly executed by an OS (Operating System) and a processing of AI (artificial intelligence) recognizing. An AI recognition module is connected to the processor set. The AI recognition module is used for recognizing multiple key inputs, operation gestures and handwriting symbols. The key inputs and handwriting symbols are corrected by a grammar correction and a symbol correction respectively. The touch screen serves to display a virtual keyboard.

The information processing apparatus according to the present disclosure synthesizes a handwriting image with a noise image to generate a synthesized image, generates a correct label indicative of handwriting pixels from the handwriting image, and applies the synthesized image and the correct label as learning data to generate a learning model.

The information processing apparatus according to the present disclosure synthesizes a handwriting image with a noise image to generate a synthesized image, generates a correct label indicative of handwriting pixels from the handwriting image, and applies the synthesized image and the correct label as learning data to generate a learning model.

Various systems and methods are discussed for characterizing well properties. As one of many non-limiting examples, a well property system is discussed that includes a sensor set, a logging system, an archive conversion system, and a well property prediction system. Each of the aforementioned components and systems may be configured to sense and/or operate on pressure, volume, and temperature data from at least one location at a well site.

Various systems and methods are discussed for characterizing well properties. As one of many non-limiting examples, a well property system is discussed that includes a sensor set, a logging system, an archive conversion system, and a well property prediction system. Each of the aforementioned components and systems may be configured to sense and/or operate on pressure, volume, and temperature data from at least one location at a well site.

A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.

A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.