Abstract: Provided is an ultrafine pulverization system for rhizome traditional Chinese medicine (TCM), relating to the technical field of TCM processing, and comprising a coarse pulverizer and a fine-ultrafine pulverizer, a feeding elevator is provided on one side of the coarse pulverizer, and a screw feeder is provided between the coarse pulverizer and the fine-ultrafine pulverizer; the coarse pulverizer includes a coarse pulverization base and a coarse pulverization upper housing, the coarse pulverization rotor is provided in a coarse pulverization blade assembly; the coarse pulverization rotor is connected to a first driving mechanism; and the fine-ultrafine pulverizer includes a fine pulverization device, a pneumatic classification device, and an ultrafine pulverization device being provided in turn from top to bottom, an annular air bag is provided outside the ultrafine pulverization device, the fine pulverization device and the ultrafine pulverization device are communicated with each other through a number of b

Abstract: A method of audio generation based on a large language model is disclosed, which involves the fields of artificial intelligence such as large language models, natural language processing, deep learning, and audio generation. The method of audio generation based on a large language model comprises: acquiring a text to be processed; parsing the text to be processed using the large language model to obtain role information and emotional information corresponding to the text to be processed; obtaining a target reference text and a target reference audio according to the role information and the emotional information; and generating a target audio corresponding to the text to be processed according to the text to be processed, the target reference text, and the target reference audio.

Abstract: The present application discloses a model training method, a short message auditing method and apparatuses, a device, and a storage medium, and relates to the field of artificial intelligence. A specific implementation solution of model training is: performing a sample reduction on first unlabeled samples to obtain second unlabeled samples; inputting the second unlabeled samples to a machine learning model for prediction, to obtain a probability corresponding to a result of predicting the second unlabeled samples; selecting a third unlabeled sample from the second unlabeled samples according to the probability; and training the machine learning model by using a third unlabeled sample after labeling. In embodiments of the present application, redundant samples are removed through a sample reduction, such that the selected samples have a certain degree of representativeness.

Abstract: An audio generation method, a method of training an audio generation model, an electronic device, and a storage medium, which relate to a field of an artificial intelligence technology, in particular to fields of deep learning, large model and audio synthesis technologies. The audio generation method includes: fusing a target phoneme feature of a target text and a target semantic feature of the target text to obtain a target fusion feature; obtaining an encoding feature according to the target fusion feature, a reference fusion feature and a reference audio feature, where the reference fusion feature is obtained by fusing a reference phoneme feature of a reference text and a reference semantic feature of the reference text, and the reference audio feature is determined according to a reference audio corresponding to the reference text; and decoding the encoding feature to obtain a target audio corresponding to the target text.

Abstract: The present disclosure provides a method of training a classification model, which relates to an active learning, neural network and natural language processing technology. A specific implementation scheme includes: selecting, from an original sample set, a plurality of original samples with a class prediction result meeting a preset condition as to-be-labeled samples according to a class prediction result for a plurality of original samples in the original sample set; labeling the to-be-labeled sample as belonging to a class by using the second classification model, so as to obtain a first labeled sample set; and training the first classification model by using the first labeled sample set. The present disclosure further provides a method of classifying a sample, an electronic device, and a storage medium.

Abstract: The present disclosure provides a model training method and apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, to the field of natural language processing and deep learning. A specific implementation solution includes: constructing initial training corpora; performing data enhancement on the initial training corpora based on an algorithm contained in a target algorithm set to obtain target training corpora, wherein the target algorithm set is determined from multiple algorithm sets, and different algorithm sets are used for performing data enhancement on corpora with different granularity in the initial training corpora; and performing training on a language model based on the target training corpora to obtain a sequence labeling model, herein the language model is pre-trained based on text corpora.

Abstract: A method and apparatus for detecting a table. The method includes: acquiring a to-be-processed image; inputting the to-be-processed image into a pre-trained deep learning model, and outputting a full table detection branch result, a column detection branch result and a header detection branch result through the deep learning model; where the full table detection branch result represents a detection result for a full table in the to-be-processed image, the column detection branch result represents a detection result for a column in the table in the to-be-processed image, and the header detection branch result represents a detection result for a header in the to-be-processed image; and obtaining a detection result of the table in the to-be-processed image, based on the full table detection branch result, the column detection branch result and the header detection branch result.

Abstract: A saliva-based testing method that bypasses the need for RNA isolation/purification is described herein. In experiments with inactivated SARS-CoV-2 virus spiked into saliva, this method has a limit of detection of 500-1000 viral particles per mL, rivalling the standard NP swab method. Initial studies showed excellent performance with 100 clinical samples. This saliva-based process is operationally simple, utilizes readily available materials, and can be easily implemented by existing testing sites thus allowing for high-throughput, rapid, and repeat testing of large populations.

Abstract: The present application discloses a model training method, a short message auditing method and apparatuses, a device, and a storage medium, and relates to the field of artificial intelligence. A specific implementation solution of model training is: performing a sample reduction on first unlabeled samples to obtain second unlabeled samples; inputting the second unlabeled samples to a machine learning model for prediction, to obtain a probability corresponding to a result of predicting the second unlabeled samples; selecting a third unlabeled sample from the second unlabeled samples according to the probability; and training the machine learning model by using a third unlabeled sample after labeling. In embodiments of the present application, redundant samples are removed through a sample reduction, such that the selected samples have a certain degree of representativeness.

Abstract: The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.

Abstract: The present disclosure provides a model training method and apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, to the field of natural language processing and deep learning. A specific implementation solution includes: constructing initial training corpora; performing data enhancement on the initial training corpora based on an algorithm contained in a target algorithm set to obtain target training corpora, wherein the target algorithm set is determined from multiple algorithm sets, and different algorithm sets are used for performing data enhancement on corpora with different granularity in the initial training corpora; and performing training on a language model based on the target training corpora to obtain a sequence labeling model, herein the language model is pre-trained based on text corpora.

Abstract: A saliva-based testing method that bypasses the need for RNA isolation/purification is described herein. In experiments with inactivated SARS-CoV-2 virus spiked into saliva, this method has a limit of detection of 500-1000 viral particles per mL, rivalling the standard NP swab method. Initial studies showed excellent performance with 100 clinical samples. This saliva-based process is operationally simple, utilizes readily available materials, and can be easily implemented by existing testing sites thus allowing for high-throughput, rapid, and repeat testing of large populations.

Abstract: The present disclosure provides a method of training a classification model, which relates to an active learning, neural network and natural language processing technology. A specific implementation scheme includes: selecting, from an original sample set, a plurality of original samples with a class prediction result meeting a preset condition as to-be-labeled samples according to a class prediction result for a plurality of original samples in the original sample set; labeling the to-be-labeled sample as belonging to a class by using the second classification model, so as to obtain a first labeled sample set; and training the first classification model by using the first labeled sample set. The present disclosure further provides a method of classifying a sample, an electronic device, and a storage medium.

Abstract: A saliva-based testing method that bypasses the need for RNA isolation/purification is described herein. In experiments with inactivated SARS-CoV-2 virus spiked into saliva, this method has a limit of detection of 500-1000 viral particles per mL, rivalling the standard NP swab method. Initial studies showed excellent performance with 100 clinical samples. This saliva-based process is operationally simple, utilizes readily available materials, and can be easily implemented by existing testing sites thus allowing for high-throughput, rapid, and repeat testing of large populations.

Abstract: A method and apparatus for detecting a table. The method includes: acquiring a to-be-processed image; inputting the to-be-processed image into a pre-trained deep learning model, and outputting a full table detection branch result, a column detection branch result and a header detection branch result through the deep learning model; where the full table detection branch result represents a detection result for a full table in the to-be-processed image, the column detection branch result represents a detection result for a column in the table in the to-be-processed image, and the header detection branch result represents a detection result for a header in the to-be-processed image; and obtaining a detection result of the table in the to-be-processed image, based on the full table detection branch result, the column detection branch result and the header detection branch result.

Abstract: A saliva-based testing method that bypasses the need for RNA isolation/purification is described herein. In experiments with inactivated SARS-CoV-2 virus spiked into saliva, this method has a limit of detection of 500-1000 viral particles per mL, rivalling the standard NP swab method. Initial studies showed excellent performance with 100 clinical samples. This saliva-based process is operationally simple, utilizes readily available materials, and can be easily implemented by existing testing sites thus allowing for high-throughput, rapid, and repeat testing of large populations.

Abstract: The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.

Abstract: Genes for proteins which spontaneously form dimers and/or oligomers can be recombinantly linked together, which upon expression in E. coli produces stable dimeric fusion proteins that spontaneously self-assemble into enormous, polyvalent complexes having increased immunogenicity and functionality. Linear, network and agglomerate complexes with enormous sizes and polyvalences are constructed using glutathione S-transferase, Norovirus P domains (NoV P? and NoV+), the protruding (P) domain of hepatitis E virus (HEV P), the astrovirus P domain (AstV), a monomeric peptide epitope (M2e of influenza virus), and/or a protein antigen (VP8* of rotavirus) fused in different combinations. The resulting complexes can contain hundreds to thousands NoV P-protein, HEV, AstV, M2e and/or VP8* copies and exhibit higher immunogenicity than the individual proteins alone.

Abstract: The present invention relates to methods to identify molecules that binds in the neomycin binding pocket of a bacterial ribosome using structures of an intact bacterial ribosome that reveal how the ribosome binds tRNA in two functionally distinct states, determined by x-ray crystallography. One state positions tRNA in the peptidyl-tRNA binding site. The second, a fully rotated state, is stabilized by ribosome recycling factor (RRF) and binds tRNA in a highly bent conformation in a hybrid peptidyl/exit (P/E) site. Additionally, the invention relates to various assays, including single-molecule assay for ribosome recycling, and methods to identify compounds that interfere with ribosomal function by detecting newly identified intermediate FRET states using known and novel FRET pairs on the ribosome. The invention also provides vectors and compositions with an N-terminally tagged S13 protein.

Abstract: This invention relates to a method to rapidly find crystallization conditions for a biomolecule in a desired conformation using single-molecule, fluorescent resonance energy transfer (smFRET) imaging techniques. The method provides significant cost and time advantages over the empricial exploration for crystallization conditions.