Abstract: A pressure reducing valve for gas is provided in which a connecting passage providing a connection between a passage and a pressure action chamber is formed within a valve body, a guide hole guiding movement of the valve body between a spring chamber and a high pressure chamber is formed within a body, a spring linked to a piston and urging the valve body in an opening direction is provided within the spring chamber, and a seal member providing a seal between the high pressure chamber and the spring chamber is disposed between the guide hole and the valve body. This provides a pressure reducing valve for gas that can suppress problems based on a decrease in the temperature of a pressure reducing valve.

Abstract: A student neural network may be trained by a computer-implemented method, including: selecting a teacher neural network among a plurality of teacher neural networks, inputting an input data to the selected teacher neural network to obtain a soft label output generated by the selected teacher neural network, and training a student neural network with at least the input data and the soft label output from the selected teacher neural network.

Abstract: A student neural network may be trained by a computer-implemented method, including: inputting common input data to each teacher neural network among a plurality of teacher neural networks to obtain a soft label output among a plurality of soft label outputs from each teacher neural network among the plurality of teacher neural networks, and training a student neural network with the input data and the plurality of soft label outputs.

Abstract: A method for processing a speech signal. The method comprises obtaining a logmel feature of a speech signal. The method further includes one or more processors processing the logmel feature so that the logmel feature is normalized under a constraint that a power level of the logmel feature is kept as originally obtained. The method further includes inputting the processed logmel feature into a speech-to-text system to generate corresponding text data.

Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.

Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.

Abstract: A method and system are provided for training word embedding of domain-specific words. The method includes training, by a processor, a first word embedding, using a general domain corpus, on one or more terms inputted by a user. The method further includes retraining, by the processor, the first word embedding, using a specific domain corpus, for a Neuro-Linguistic Programming task, to create a tuned word embedding. The method also includes training, by the processor, a Neural Network for the Neuro-Linguistic Programming task, using the specific domain corpus. The method additionally includes incorporating, by the processor, the trained Neural Network and tuned word embedding into a Neural Network-based Neuro-Linguistic Programming task. The retraining of the first word embedding and the training of the Neural Network are performed together, and the tuned word embedding is accelerated due to a change in a hyper parameter for domain-specific words.

Abstract: A method for producing a patch comprising a support layer and an adhesive agent layer comprises: a mixture preparation step of mixing asenapine or a pharmaceutically acceptable salt thereof with sodium acetate whose particle diameter D50 at a cumulative volume of 50% in a particle diameter distribution is 40 to 1000 ?m, in such a manner that the sodium acetate and sodium diacetate generated from the sodium acetate have a particle diameter D50 of 10 ?m or smaller, thereby obtaining a mixture containing the sodium diacetate and the asenapine or pharmaceutically acceptable salt; and an adhesive-agent-layer formation step of forming the adhesive agent layer comprising the sodium diacetate, the asenapine or pharmaceutically acceptable salt, and a pressure-sensitive adhesive base agent, by using an adhesive agent layer composition obtained by mixing the mixture with the pressure-sensitive adhesive base agent.

Abstract: A method for suppressing a plasma concentration of an asenapine metabolite includes applying to a subject a patch comprising a support layer and an adhesive agent layer, the adhesive agent layer includes asenapine and/or a pharmaceutically acceptable salt thereof, and an adhesive base agent.

Abstract: A computer-implemented method, computer program product, and apparatus are provided. The method includes generating a plurality of sequences of small unit tokens from a first language model that is trained with a small unit corpus including the small unit tokens, the small unit corpus having been derived by tokenization with a small unit. The method further includes tokenizing the plurality of sequences of small unit tokens by a large unit that is larger than the small unit, to create a derived large unit corpus including derived large unit tokens.

Abstract: A system and method for expanding a question and answer (Q&A) database. The method includes obtaining a set of Q&A documents and speech recognition results, each Q&A document in the set having an identifier, and each speech recognition result having an identifier common with the identifier of a relevant Q&A document, and adding one or more repetition parts extracted from the speech recognition results to a corresponding Q&A document in the set to generate an expanded set of Q&A documents for increasing Q&A document extraction accuracy.

Abstract: A system and method for expanding a question and answer (Q&A) database. The method includes preparing a set of Q&A documents and speech recognition results of an agent's utterances in conversations between an agent and a customer, each Q&A document in the set having an identifier, and each speech recognition result having an identifier common with the identifier of a relevant Q&A document, and adding one or more repetition parts extracted from the speech recognition results of the agent's utterances to a corresponding Q&A document in the set.

Abstract: A method, computer program product, and apparatus for adapting a trained neural network having one or more batch normalization layers are provided. The method includes adapting only the one or more batch normalization layers using adaptation data. The method also includes adapting the whole of the neural network having the one or more adapted batch normalization layers, using the adaptation data.

Abstract: Provided is a lead-free solder alloy that has excellent tensile strength and ductility, does not deform after heat cycles, and does not crack. The In and Bi content are optimized and the Sb and Ni content are adjusted. As a result, this solder alloy has an alloy composition including, by mass, 1.0 to 7.0% of In, 1.5 to 5.5% of Bi, 1.0 to 4.0% of Ag, 0.01 to 0.2% of Ni, and 0.01 to 0.15% of Sb, with the remainder made up by Sn.

Abstract: There is provided a semiconductor film, including: an aggregate of oxide microparticles including at least one type of metal selected from the group consisting of In, Zn, and Sn; and at least one type of a ligand which is selected from the group consisting of a ligand expressed by General Formula (A) below, a ligand expressed by General Formula (B) below, and a ligand expressed by General Formula (C) below and which is coordinated with the oxide microparticles: in which, in General Formula (A), each of X1 and X2 independently represents —SH, —NH2, —OH, or —COOH, and each of A1 and B1 independently represents a hydrogen atom or a substituent having an atomic number of 1 to 10, in which, in General Formula (B), each of X3 and X4 independently represents —SH, —NH2, —OH, or —COOH and each of A2 and B2 independently represents a hydrogen atom or a substituent having an atomic number of 1 to 10, and in which, in General Formula (C), X5 represents —SH, —NH2, or —OH, and A3 represents a hydrogen atom or a substit

Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.