Abstract: A protein inhibitor or degrading agent, a pharmaceutical composition comprising same and a pharmaceutical use. Provided are a compound represented by general formula (I), and a pharmaceutical composition containing same. Also disclosed are an application of the compound of the general formula as a protein inhibitor and/or a protein degrading agent and a pharmaceutical use.

Abstract: The present disclosure provides a method for visual question answering, which relates to fields of computer vision and natural language processing. The method includes: acquiring an input image and an input question; detecting visual information and position information of each of at least one text region in the input image; determining semantic information and attribute information of each of the at least one text region based on the visual information and the position information; determining a global feature of the input image based on the visual information, the position information, the semantic information, and the attribute information; determining a question feature based on the input question; and generating a predicted answer for the input image and the input question based on the global feature and the question feature. The present disclosure further provides a device for visual question answering, a computer device and a medium.

Abstract: The present invention relates to a dual-clutch gearbox which can switch output shafts of two gearboxes, including a box body in which a rotating shaft configured to receive torque from a drive motor is provided. Two gears, which are both in slip fit on the rotating shaft and respectively in linkage with the output shaft of one gearbox, are coaxially sleeved on the rotating shaft. A motion bar is fitted to and provided on the rotating shaft, a linkage piece in coaxial linkage with the rotating shaft is fixed at one end of the motion bar, and a driver is in linkage with the other end of the motion bar, so that the motion bar can move in its own axial direction under the action of the driver and allow the linkage of the linkage piece with one of the two gears.

Abstract: The present disclosure provides a method and apparatus for training a document information extraction model and method and apparatus for extracting document information, and relates to the field of artificial intelligence, and more particularly to the field of natural language processing. A specific implementation solution is: acquiring training data labeled with an answer corresponding to a preset question and a document information extraction model, the training data includes layout document training data and streaming document training data; extracting at least one feature from the training data; fusing at least one feature to obtain a fused feature; inputting the preset question, the fused feature and the training data into the document information extraction model to obtain a predicted result; and adjusting network parameters of the document information extraction model based on the predicted result and the answer.

Abstract: A neural network training method and a document image understanding method is provided. The neural network training method includes: acquiring text comprehensive features of a plurality of first texts in an original image; replacing at least one original region in the original image to obtain a sample image including a plurality of first regions and a ground truth label for indicating whether each first region is a replaced region; acquiring image comprehensive features of the plurality of first regions; inputting the text comprehensive features of the plurality of first texts and the image comprehensive features of the plurality of first regions into a neural network model together to obtain text representation features of the plurality of first texts; determining a predicted label based on the text representation features of the plurality of first texts; and training the neural network model based on the ground truth label and the predicted label.

Abstract: A method and an apparatus for processing a table are provided. The method includes: obtaining text information of cells in the table; obtaining structure information of the cells in the table; and inputting a query word, the text information, and the structure information of the table into a table information extraction model to obtain an answer output from the table information extraction model, wherein the output answer corresponds to the query word in the table.

Abstract: A data processing method is provided. The method includes: determining fusion information based on a text to be processed and a plurality of reference text fragments; executing the following matching operation for each of the plurality of reference text fragments: determining a first coefficient of each feature vector of the fusion information respectively; determining a second coefficient of each feature vector of the fusion information respectively; determining a result feature vector of the reference text fragment using each feature vector included in the fusion information and a weight of the feature vector; and determining a matching degree of the reference text fragment and the text to be processed based on the result feature vector.

Abstract: A method for extracting text information includes: acquiring a text to be extracted and a target field name; extracting candidate text information matching the target field name from the text to be extracted based on the text to be extracted and the target field name; and acquiring target text information matching fusion semantics of the text to be extracted, the target field name and the candidate text information by filtering the candidate text information based on the fusion semantics. Therefore, when the candidate text information matching the target field name is extracted from the text to be extracted, the candidate text information is filtered based on the fusion semantics of the text to be extracted, the target field name and the candidate text information, which improves the accuracy of extracting text information.

Abstract: A method for extracting text information includes: acquiring a text to be extracted and a target field name; extracting candidate text information matching the target field name from the text to be extracted based on the text to be extracted and the target field name; and acquiring target text information matching fusion semantics of the text to be extracted, the target field name and the candidate text information by filtering the candidate text information based on the fusion semantics. Therefore, when the candidate text information matching the target field name is extracted from the text to be extracted, the candidate text information is filtered based on the fusion semantics of the text to be extracted, the target field name and the candidate text information, which improves the accuracy of extracting text information.

Abstract: The present disclosure provides an information extraction method and apparatus, an electronic device and a readable storage medium, and relates to the field of natural language processing technologies. The information extraction method includes: acquiring a to-be-extracted text; acquiring a sample set, the sample set including a plurality of sample texts and labels of sample characters in the plurality of sample texts; determining a prediction label of each character in the to-be-extracted text according to a semantic feature vector of each character in the to-be-extracted text and a semantic feature vector of each sample character in the sample set; and extracting, according to the prediction label of each character, a character meeting a preset requirement from the to-be-extracted text as an extraction result of the to-be-extracted text. The present disclosure can simplify steps of information extraction, reduce costs of information extraction and improve flexibility and accuracy of information extraction.

Abstract: This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.

Abstract: The present disclosure provides a training method and apparatus for a document processing model, a device, a storage medium and a program, which relate to the field of artificial intelligence, and in particular, to technologies such as deep learning, natural language processing and text recognition. The specific implementation is: acquiring a first sample document; determining element features of a plurality of document elements in the first sample document and positions corresponding to M position types of each document element according to the first sample document; where the document element corresponds to a character or a document area in the first sample document; and performing training on a basic model according to the element features of the plurality of document elements and the positions corresponding to the M position types of each document element to obtain the document processing model.

Abstract: Disclosed are a method and an apparatus for controlling a multi-legged robot, and a storage medium. The method includes: acquiring current state parameters of the multi-legged robot; when types and/or quantities of the current state parameters meet a first preset condition, acquiring a first motion control policy by inputting the current state parameters into a first model generated by training; and controlling the multi-legged robot based on the first motion control policy.

Abstract: A method for training a power system scheduling model includes: generating a plurality of first scheduling sub-models based on a first initial scheduling model; acquiring a first matching degree of historical running state information and each of candidate actions, output by each of the plurality of first scheduling sub-models, by inputting the historical running state information into each of the plurality of first scheduling sub-models; generating a second initial scheduling model by correcting the first initial scheduling model based on first matching degrees corresponding to each of the plurality of first scheduling sub-models; and returning to the generating the plurality of first scheduling sub-models based on the second initial scheduling model, until the matching degree output by the second initial scheduling module meets the convergence condition, determining the second initial scheduling model as the power system scheduling model.

Abstract: The present disclosure provides a data processing method, apparatus, electronic device and readable storage medium and relates to the technical field of data processing and particularly to the technical field of big data. In the present disclosure, the data repetition judgment processing is performed on the raw data according to the address information of each object in the address information of at least two objects in the acquired raw data provided by the user, and then the POI information of the electronic map is obtained according to the data repetition judgment processing result of the data repetition judgment processing, so that the repeated data in the raw data can be output according to the POI information of the electronic map and the data repetition judgment processing result.

Abstract: The present disclosure provides a form information extracting method, apparatus, electronic device and storage medium and relates to the field of artificial intelligence such as natural language processing, computer vision and deep learning. The method includes: for a form to be processed, obtaining feature information of characters in the form, respectively; determining types of the characters respectively and determining a reading order of the characters according to the feature information; extracting a predetermined type of information content from the form according to the types and the reading order of the characters. The solution of the present disclosure may be applied to save the manpower and time costs, and improve the information-extracting efficiency and the accuracy of extraction results.

Abstract: Provided in the present application are an antibody against factor XIa in the activated form of coagulant factor XI, a preparation method therefor and the use thereof. The CDR of the heavy chain variable region of the antigen-binding fragment of the antibody comprises the amino acid sequence shown in SEQ ID NOs: 1-3, and the CDR of the light chain variable region thereof comprises the amino acid sequence shown in SEQ ID NOs: 4-6. The antibody can specifically bind to FXIa but not to FXI, and has the effect of inhibiting the endogenous pathway of human coagulation without affecting the exogenous pathway thereof, which can significantly inhibit the formation of arteriovenous shunt thrombosis, but does not increase the bleeding time and volume. Same has the potential to become an antithrombotic drug.

Abstract: A method of comparing documents, an electronic device, and a readable storage medium are provided, which relate to the field of data processing technology, and specifically to the field of big data technology. In the present disclosure, an area division is performed on each document of two documents to be compared, according to a document layout of each document, so as to obtain at least two sets of comparison units. Each set of comparison units comprises comparison units for the two documents respectively and the comparison units for the two documents correspond to each other. Thus, a content comparison may be performed on between comparison units of each of the at least two sets, so as to obtain a content comparison result for each set of comparison units as a comparison result for the two documents.

Abstract: The present disclosure provides a method for visual question answering, which relates to fields of computer vision and natural language processing. The method includes: acquiring an input image and an input question; detecting visual information and position information of each of at least one text region in the input image; determining semantic information and attribute information of each of the at least one text region based on the visual information and the position information; determining a global feature of the input image based on the visual information, the position information, the semantic information, and the attribute information; determining a question feature based on the input question; and generating a predicted answer for the input image and the input question based on the global feature and the question feature. The present disclosure further provides a device for visual question answering, a computer device and a medium.

Abstract: This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.