Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

Abstract: The present application discloses a method and apparatus for training a retrieval model, device and computer storage medium that relate to intelligent search and natural language processing technologies. An implementation includes: acquiring initial training data; performing a training operation using the initial training data to obtain an initial retrieval model; selecting texts with the correlation degrees with a query in the training data meeting a preset first requirement from candidate texts using the initial retrieval model; performing a training operation using the updated training data to obtain a first retrieval model; and selecting texts with the correlation degrees with the query in the training data meeting a preset second requirement from the candidate texts using the first retrieval model; and/or selecting texts with the correlation degrees with the query meeting a preset third requirement; and performing a training operation using the expanded training data to obtain a second retrieval model.

Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

Abstract: A method for processing questions and answers includes: in a process of determining an answer to a question to be answered, determining the semantic representation on the question to be answered respectively with a first semantic representation model of question and a second semantic representation model of question. Semantic representation vectors obtained through the first semantic representation model of question and the second semantic representation model of question are spliced. A spliced semantic vector is determined as a semantic representation vector of the question to be answered. An answer semantic vector matching the semantic representation vector of the question to be answered is acquired from a vector index library of answer, and an answer corresponding to the answer semantic vector is determined as a target answer to the question to be answered.

Abstract: The invention relates to a nano-biohybrid organism (or nanorg) comprising one of at least seven different core-shell quantum dots (QDs) or gold nanoparticle clusters, with excitations ranging from ultraviolet to near-infrared energies, couple with targeted enzyme sites in bacteria. When illuminated by light, these QDs drive the renewable production of biofuel molecules and chemicals using carbon-dioxide (CO2), water, and nitrogen (from air) as substrates. Nanorgs catalyze light-induced air-water-CO2 reduction with a high turnover number (TON) of approximately 106-108 (mols of product per mol of cells) to biofuels such as isopropanol (IPA), butane diol, gasoline additives, gasoline substitutes, 2,3-butanediol (BDO), C11-C15 methyl ketones (MKs), and hydrogen (H2); Sand chemicals such as formic acid (FA), ammonia (NH3), ethylene (C2H4), and degradable bioplastics, e.g. polyhydroxybutyrate (PHB). These nanorg cells function as nano-microbial factories powered by light.

Abstract: The present application discloses a method and apparatus for training a retrieval model, device and computer storage medium that relate to intelligent search and natural language processing technologies. An implementation includes: acquiring initial training data; performing a training operation using the initial training data to obtain an initial retrieval model; selecting texts with the correlation degrees with a query in the training data meeting a preset first requirement from candidate texts using the initial retrieval model; performing a training operation using the updated training data to obtain a first retrieval model; and selecting texts with the correlation degrees with the query in the training data meeting a preset second requirement from the candidate texts using the first retrieval model; and/or selecting texts with the correlation degrees with the query meeting a preset third requirement; and performing a training operation using the expanded training data to obtain a second retrieval model.

Abstract: The present disclosure discloses a method and an apparatus for processing questions and answers, an electronic device and a storage medium. The implementation solution includes: in a process of determining an answer to a question to be answered, determining the semantic representation on the question to be answered respectively with a first semantic representation model of question and a second semantic representation model of question, splicing semantic representation vectors obtained through the first semantic representation model of question and the second semantic representation model of question, determining a spliced semantic vector as a semantic representation vector of the question to be answered, acquiring an answer semantic vector matching the semantic representation vector of the question to be answered from a vector index library of answer, and determining an answer corresponding to the answer semantic vector as a target answer to the question to be answered.

Abstract: Photocatalysts for reduction of carbon dioxide and water are provided that can be tuned to produce certain reaction products, including hydrogen, alcohol, aldehyde, and/or hydrocarbon products. These photocatalysts can form artificial photosystems and can be incorporated into devices that reduce carbon dioxide and water for production of various fuels. Doped wide-bandgap semiconductor nanotubes are provided along with synthesis methods. A variety of optical, electronic and magnetic dopants (substitutional and interstitial, energetically shallow and deep) are incorporated into hollow nanotubes, ranging from a few dopants to heavily-doped semiconductors. The resulting wide-bandgap nanotubes, with desired electronic (p- or n-doped), optical (ultraviolet bandgap to infrared absorption in co-doped nanotubes), and magnetic (from paramagnetic to ferromagnetic) properties, can be used in photovoltaics, display technologies, photocatalysis, and spintronic applications.

Abstract: Photocatalysts for reduction of carbon dioxide and water are provided that can be tuned to produce certain reaction products, including hydrogen, alcohol, aldehyde, and/or hydrocarbon products. These photocatalysts can form artificial photosystems and can be incorporated into devices that reduce carbon dioxide and water for production of various fuels. Doped wide-bandgap semiconductor nanotubes are provided along with synthesis methods. A variety of optical, electronic and magnetic dopants (substitutional and interstitial, energetically shallow and deep) are incorporated into hollow nanotubes, ranging from a few dopants to heavily-doped semiconductors. The resulting wide-bandgap nanotubes, with desired electronic (p- or n-doped), optical (ultraviolet bandgap to infrared absorption in co-doped nanotubes), and magnetic (from paramagnetic to ferromagnetic) properties, can be used in photovoltaics, display technologies, photocatalysis, and spintronic applications.

Abstract: Photocatalysts for reduction of carbon dioxide and water are provided that can be tuned to produce certain reaction products, including hydrogen, alcohol, aldehyde, and/or hydrocarbon products. These photocatalysts can form artificial photosystems and can be incorporated into devices that reduce carbon dioxide and water for production of various fuels. Doped wide-bandgap semiconductor nanotubes are provided along with synthesis methods. A variety of optical, electronic and magnetic dopants (substitutional and interstitial, energetically shallow and deep) are incorporated into hollow nanotubes, ranging from a few dopants to heavily-doped semiconductors. The resulting wide-bandgap nanotubes, with desired electronic (p- or n-doped), optical (ultraviolet bandgap to infrared absorption in co-doped nanotubes), and magnetic (from paramagnetic to ferromagnetic) properties, can be used in photovoltaics, display technologies, photocatalysis, and spintronic applications.

Abstract: Photocatalysts for reduction of carbon dioxide and water are provided that can be tuned to produce certain reaction products, including hydrogen, alcohol, aldehyde, and/or hydrocarbon products. These photocatalysts can form artificial photosystems and can be incorporated into devices that reduce carbon dioxide and water for production of various fuels. Doped wide-bandgap semiconductor nanotubes are provided along with synthesis methods. A variety of optical, electronic and magnetic dopants (substitutional and interstitial, energetically shallow and deep) are incorporated into hollow nanotubes, ranging from a few dopants to heavily-doped semiconductors. The resulting wide-bandgap nanotubes, with desired electronic (p- or n-doped), optical (ultraviolet bandgap to infrared absorption in co-doped nanotubes), and magnetic (from paramagnetic to ferromagnetic) properties, can be used in photovoltaics, display technologies, photocatalysis, and spintronic applications.