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.

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: First internal conductors are separated from each other in a first direction. Each of the first internal conductors includes a coil portion and a pad portion having a width larger than a width of the coil portion. The pad portions adjacent to each other in the first direction are connected to each other via a through-hole conductor and overlap each other when viewed from the first direction. When viewed from the first direction, each of the coil portions includes a first portion not overlapping the pad portion adjacent in the first direction and a second portion overlapping a part of the pad portion adjacent in the first direction. A second internal conductor is disposed on the same layer as the second portion and is positioned to overlap a portion of the pad portion adjacent in the first direction not overlapping the second portion when viewed from the first direction.

Abstract: A method for producing a patch including a support layer, and an adhesive agent layer formed on the support layer and including sodium diacetate, a pressure-sensitive adhesive base agent, and asenapine and/or a pharmaceutically acceptable salt thereof. The sodium diacetate is generated from sodium acetate in the presence of the asenapine and/or salt thereof, a content of the asenapine and/or salt thereof in terms of free asenapine in the adhesive agent layer is in range of 3.0 to 20 mg, and when a content of the asenapine and/or salt thereof in terms of free asenapine in the adhesive agent layer is 6.4 mg and the patch is in contact with skin for 24 hours, Cmax of free asenapine is in range of 0.5 to 6.0 ng/mL and tmax of free asenapine is in range of 8 to 28 hr.

Abstract: A developing unit includes a body and a displacement member. The displacement member is to be held by the body in a first mode in a first mounting state, and is to be held by the body displaceably between the first mode and a second mode in a reversible fashion in a second mounting state. The first mounting state is a state in which a first toner cartridge is mounted on the developing unit. The second mounting state is a state in which a second toner cartridge is mounted on the developing unit.

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: An apparatus, method, and computer program product for adapting an acoustic model to a specific environment are defined. An adapted model obtained by adapting an original model to the specific environment using adaptation data, the original model being trained using training data and being used to calculate probabilities of context-dependent phones given an acoustic feature. Adapted probabilities obtained by adapting original probabilities using the training data and the adaptation data, the original probabilities being trained using the training data and being prior probabilities of context-dependent phones. An adapted acoustic model obtained from the adapted model and the adapted probabilities.

Abstract: An apparatus, method, and computer program product for adapting an acoustic model to a specific environment are defined. An adapted model obtained by adapting an original model to the specific environment using adaptation data, the original model being trained using training data and being used to calculate probabilities of context-dependent phones given an acoustic feature. Adapted probabilities obtained by adapting original probabilities using the training data and the adaptation data, the original probabilities being trained using the training data and being prior probabilities of context-dependent phones. An adapted acoustic model obtained from the adapted model and the adapted probabilities.