Abstract: An efficient synthesis and assembly method for the large fragment DNA based on the programmable nuclease Argonaute, specifically includes: constructing and treating antibiotic resistance gene reconstructed vectors with linearization, dividing a target DNA into multiple small DNA fragments and then synthesizing the small DNA fragments, followed by loading the synthesized small DNA fragments to the antibiotic resistance gene reconstructed vectors; and the SLIC and resistance gene reconstruction are used to achieve assembly of the target DNA. The method combines the SLIC with a resistance gene reconstruction strategy, allowing for the assembly of 5-6 small fragments in a single resistance gene reconstruction, which is more efficient and time-saving. Moreover, the number of the resistance gene reconstructions can be flexibly chosen according to the length of the DNA fragments. Mutations are not introduced caused by PCR, and the reconstructed large fragment do not need a second sequencing, saving time and costs.

Abstract: A measurement method for the impedance of the converter at multiple operating points based on the secondary-side disturbance includes the following steps: superimposing the positive sequence current disturbance in the current sampling value; superimposing the positive sequence voltage disturbance in the voltage sampling value; determining whether measurement data of the converter at two or more current operating points are obtained, if so, based on the measurement data of the converter at two current operating points, calculating values of A(s) and B(s); further calculating values of coefficients at each frequency point, and obtaining the impedance value of the converter at multiple current operating points by calculation. The above-mentioned measurement method overcomes the disadvantages of high cost and complicated operation of the impedance measurement method based on the primary-side disturbance, and solves the problem of lacking the pre-judgment function.

Abstract: Embodiments of this application disclose a method for processing a word sequence using a sequence model and apparatus, to improve a task execution effect of a sequence model. The method includes: encoding a source sequence of words of a first language by using an encoder side of the sequence model, to obtain a first encoding result and a second encoding result; inputting a target sequence of words of the first language, the first encoding result, and the second encoding result into a decoder side of the sequence model to obtain a second sequence of words of a second language, and outputting the second sequence of words in the second language.

Abstract: Embodiments of this application disclose a sequence model processing method and apparatus, to improve a task execution effect of a sequence model. The method includes: inputting a source sequence into an encoder side of a sequence model, the encoder side including a self-attention encoder and a temporal encoder; encoding the source sequence by using the temporal encoder, to obtain a first encoding result, the first encoding result including time series information obtained by performing time series modeling on the source sequence; and encoding the source sequence by using the self-attention encoder, to obtain a second encoding result; inputting a target sequence, the first encoding result, and the second encoding result into a decoder side of the sequence model; and decoding the target sequence, the first encoding result, and the second encoding result by using the decoder side, and outputting a decoding result obtained after the decoding.

Abstract: A Pyrococcus furiosus Argonaute (PfAgo) mutant protein with mesophilic target nucleic acid cleavage activity and an application thereof are provided. The PfAgo mutant protein has an amino acid mutation on at least one of a 617th position and a 618th position relative to the sequence shown in SEQ ID NO: 1, specifically, K617E/G and/or L618Y/F/W/G. Compared with a wild-type PfAgo protein, the provided PfAgo mutant protein has activity in the temperature range of 30-95 Celsius degree (° C.), effectively expanding application range of prokaryotic Argonaute (pAgo) protein.

Abstract: A text translation method includes: obtaining a to-be-translated text sequence; encoding the to-be-translated text sequence, to obtain a first hidden state sequence; obtaining a first state vector; generating a second hidden state sequence according to the first state vector and the first hidden state sequence; generating a context vector corresponding to a current word according to the second hidden state sequence and the first state vector; determining a second target word according to the context vector, the first state vector, and a first target word. The first state vector corresponds to a predecessor word of a current word, the current word is a to-be-translated word in the source language text, the predecessor word is a word that has been translated in the source language text. The first target word is a translation result of a predecessor word, and the second target word is a translation result of the current word.

Abstract: A smart conference room system is provided. The smart conference room system includes an intelligent conference interactive panel including one or more processors configured to convert user input into a conference information, a signage, and a memory configured to store the conference information. The intelligent conference interactive panel is configured to transmit conference information to one or more terminal devices. The one or more terminal devices are configured to perform a controllable operation based on the conference information transmitted to the one or more terminal devices. The intelligent conference interactive panel is further configured to receive a status check signal reminding a user of a conference room that a scheduled conference period of time is expired or about to expire.

Abstract: A smart conference room system is provided. The smart conference room system includes an intelligent conference interactive panel including one or more processors configured to convert user input into a conference information, and a memory configured to store the conference information. The intelligent conference interactive panel is configured to transmit conference information to one or more terminal devices. The one or more terminal devices are configured to perform a controllable operation based on the conference information transmitted to the one or more terminal devices.

Abstract: This application relates to a machine translation method performed at a computer device. The method includes: obtaining an original source text and a reconstructed source text; performing semantic encoding on the original source text, to obtain a source vector sequence; sequentially decoding the source vector sequence to obtain target vectors by performing decoding on the source vector sequence at a current time according to a word vector of a candidate target word determined at a previous time, determining a candidate target word at the current time according to a target vector at the current time, and forming a target vector sequence accordingly; performing reconstruction assessment on the source vector sequence and the target vector sequence using the reconstructed source text, to obtain reconstruction scores corresponding to the candidate target words; and generating a target text according to the reconstruction scores and the candidate target words.

Abstract: A smart conference room system is provided. The smart conference room system includes an intelligent conference interactive panel including one or more processors configured to convert user input into a conference information, and a memory configured to store the conference information. The intelligent conference interactive panel is configured to transmit conference information to one or more terminal devices. The one or more terminal devices are configured to perform a controllable operation based on the conference information transmitted to the one or more terminal devices.

Abstract: Embodiments of the present invention provide a translation method and apparatus, and relate to the field of machine translation. The method includes: obtaining a to-be-translated sentence, where the to-be-translated sentence is a sentence expressed in a first language; determining a first named entity set in the to-be-translated sentence, and an entity type of each first named entity in the first named entity set; determining, based on the first named entity set and the entity type of each first named entity, a second named entity set expressed in a second language; determining a source semantic template of the to-be-translated sentence, and obtaining a target semantic template corresponding to the source semantic template from a semantic template correspondence; and determining a target translation sentence based on the second named entity set and the target semantic template.

Abstract: Embodiments of this application disclose a neural network model training method, a machine translation method, a computer device, and a storage medium. The method includes: obtaining a training sample set including a training sample and a standard tag vector corresponding to the training sample; inputting the training sample into a neural network model including a plurality of attention networks to obtain a feature fusion vector; obtaining a predicted tag vector according to the feature fusion vector, and performing adjustment on a model parameter of the neural network model until a convergence condition is met to obtain a target neural network model.

Abstract: A text translation method includes: obtaining a to-be-translated text sequence; encoding the to-be-translated text sequence, to obtain a first hidden state sequence; obtaining a first state vector; generating a second hidden state sequence according to the first state vector and the first hidden state sequence; generating a context vector corresponding to a current word according to the second hidden state sequence and the first state vector; determining a second target word according to the context vector, the first state vector, and a first target word. The first state vector corresponds to a predecessor word of a current word, the current word is a to-be-translated word in the source language text, the predecessor word is a word that has been translated in the source language text. The first target word is a translation result of a predecessor word, and the second target word is a translation result of the current word.

Abstract: Embodiments of this application disclose a sequence model processing method and apparatus, to improve a task execution effect of a sequence model. The method includes: inputting a source sequence into an encoder side of a sequence model, the encoder side including a self-attention encoder and a temporal encoder; encoding the source sequence by using the temporal encoder, to obtain a first encoding result, the first encoding result including time series information obtained by performing time series modeling on the source sequence; and encoding the source sequence by using the self-attention encoder, to obtain a second encoding result; inputting a target sequence, the first encoding result, and the second encoding result into a decoder side of the sequence model; and decoding the target sequence, the first encoding result, and the second encoding result by using the decoder side, and outputting a decoding result obtained after the decoding.

Abstract: This application relates to a machine translation method performed at a computer device. The method includes: obtaining an original source text and a reconstructed source text; performing semantic encoding on the original source text, to obtain a source vector sequence; sequentially decoding the source vector sequence to obtain target vectors by performing decoding on the source vector sequence at a current time according to a word vector of a candidate target word determined at a previous time, determining a candidate target word at the current time according to a target vector at the current time, and forming a target vector sequence accordingly; performing reconstruction assessment on the source vector sequence and the target vector sequence using the reconstructed source text, to obtain reconstruction scores corresponding to the candidate target words; and generating a target text according to the reconstruction scores and the candidate target words.

Abstract: Embodiments of the present invention provide a translation method and apparatus, and relate to the field of machine translation. The method includes: obtaining a to-be-translated sentence, where the to-be-translated sentence is a sentence expressed in a first language; determining a first named entity set in the to-be-translated sentence, and an entity type of each first named entity in the first named entity set; determining, based on the first named entity set and the entity type of each first named entity, a second named entity set expressed in a second language; determining a source semantic template of the to-be-translated sentence, and obtaining a target semantic template corresponding to the source semantic template from a semantic template correspondence; and determining a target translation sentence based on the second named entity set and the target semantic template.