Abstract: A seat includes a seating portion, a backrest, two pairs of vibrators and a vibration absorbing member, and constitutes a driver’s seat of a vehicle. The vibration absorbing member is arranged at a boundary portion between a first region and a second region of the seating portion, and is arranged at an intermediate portion between the pair of vibrators. The vibration absorbing member suppresses vibration of the vibrator from being propagated to the second region and vibration of the vibrator from being propagated to the first region. The vibration absorbing member is arranged at a boundary portion between a first region and a second region of the backrest, and is arranged at an intermediate portion between the pair of vibrators. The vibration absorbing member suppresses vibration of the vibrator from being propagated to the second region and vibration of the vibrator from being propagated to the first region.

Abstract: An image capture device is mounted in a vehicle. The image capture device includes: an image capture unit; and a setting unit that sets an image capture condition for each region of the image capture unit each having a plurality of pixels, or for each pixel, based upon at least one of a state exterior to the vehicle and a state of the vehicle.

Abstract: A computer-implemented method for training a neural transducer for speech recognition is provided. The method includes initializing the neural transducer having a prediction network and an encoder network and a joint network. The method further includes expanding the prediction network by changing the prediction network to a plurality of prediction-net branches. Each of the prediction-net branches is a prediction network for a respective specific sub-task from among a plurality of specific sub-tasks. The method also includes training, by a hardware processor, an entirety of the neural transducer by using training data sets for all of the plurality of specific sub-tasks. The method additionally includes obtaining a trained neural transducer by fusing the plurality of prediction-net branches.

Abstract: A computer-implemented method for preparing training data for a speech recognition model is provided including obtaining a plurality of audio data sets, each audio data set having a different acoustic feature and sorting sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely, while imposing a weak constraint on audio length, to train the speech recognition model.

Abstract: There is provided an adhesive capable of improving the visibility and controlling gaps with high accuracy. The adhesive according to the present invention is an adhesive containing a curable component and a gap material, in which the 10% K value of the gap material is 10000 N/mm2 or less, and the absolute value of the difference between the refractive index of a cured product obtained by curing the curable component at 23° C. for 1 hour and the refractive index of the gap material is 0.1 or less.

Abstract: A light-emitting device including a solid-state light source that emits light having a peak wavelength in the range of 480 nm or less and a fluorescent film that covers the solid-state light source and includes at least one kind of phosphor, wherein the fluorescent film includes at least one kind of near-infrared phosphor that is excited by light from the solid-state light source, has a peak wavelength in the range exceeding 700 nm, and has an emission spectrum with a full width at half maximum of 100 nm or more in a range including the peak wavelength.

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: The present invention provides a reflective polarized-light separating diffraction-element usable in a wide wavelength region including an ultraviolet region, and an optical measurement device comprising the same. The reflective polarized-light separating diffraction-element comprises: a substrate (1); a reflection surface (2) formed on a surface of the substrate (1); and a lattice structured body assembly (3) that is provided on the reflection surface (2) and shows a form birefringence (?n*). The lattice structured body assembly (3) consists of lattice structured bodies (3A, 3B, 3C and 3D) of four patterns having lattice structures of different azimuths. The lattice structured bodies (3A, 3B, 3C and 3D) of a plurality of patterns are aligned on the reflection surface 2 in a predetermined direction such that the azimuths of the lattice structures change in a structurally periodic manner.

Abstract: Fusion of neural networks is performed by obtaining a first neural network and a second neural network. The first and the second neural networks are the result of a parent neural network subjected to different training. A similarity score is calculated of a first component of the first neural network and a corresponding second component of the second neural network. An interpolation weight is determined for the first and the second components by using the similarity score. A neural network parameter of the first component is updated based on the interpolation weight and a corresponding neural network parameter of the second component to obtain a fused neural network.

Abstract: Methods and systems for training a neural network include training language-specific teacher models using different respective source language datasets. A student model is trained, using the different respective source language datasets and soft labels generated by the language-specific teacher models, including shuffling the source language datasets and shuffling weights of language-dependent layers in language-specific parts of the student model. Weights of language-independent layers of the student model are copied to a language-independent layers of a target model to initialize language-independent layers of the target model. The target model is trained with a target language dataset.

Abstract: In a method for improving generation and relevancy of search results, a processor receives a search query comprising a search term. A processor generates a document group based on the search query and at least one synonym related to the search term in a synonym dictionary. The synonym dictionary may include search document attributes for base words and synonyms of the base words. A processor extracts, from the document group, an extracted document having a document attribute matching a search document attribute of the at least one synonym. A processor lists the extracted document as a search result.

Abstract: A method, computer system, and a computer program product for audio data augmentation are provided. Sets of audio data from different sources may be obtained. A respective normalization factor for at least two sources of the different sources may be calculated. The normalization factors from the at least two sources may be mixed to determine a mixed normalization factor. A first set of the sets may be normalized by using the mixed normalization factor and to obtain training data for training an acoustic model.

Abstract: A first user request which specifies a target document set wherein a first subset of the documents is flagged by a user. A primary flag table is created for the target document set. A first document subset is created matching the first user request. It is determined whether a number of flagged documents exceeds a first threshold. If so, a secondary flag table is created for the first document subset and flag data corresponding to the first document subset is stored in the secondary flag table. The flag data in the secondary flag table is merged into the primary flag table.

Abstract: A technique for constructing a model supporting a plurality of domains is disclosed. In the technique, a plurality of teacher models, each of which is specialized for different one of the plurality of the domains, is prepared. A plurality of training data collections, each of which is collected for different one of the plurality of the domains, is obtained. A plurality of soft label sets is generated by inputting each training data in the plurality of the training data collections into corresponding one of the plurality of the teacher models. A student model is trained using the plurality of the soft label sets.

Abstract: A technique for generating soft labels for training is disclosed. A teacher model having a teacher side class set is prepared. A collection of class pairs for respective data units is obtained. Class pairs includes classes labelled to corresponding data units from the teacher side class set and a student side class set different from the teacher side class set. A training input is fed into the teacher model to obtain a set of outputs for the teacher side class set. A set of soft labels for the student side class set is calculated from the set of the outputs by using at least an output obtained for a class within a subset of the teacher side class set having relevance to the member of the student side class set, based at least in part on observations in the collection of the class pairs.

Abstract: In a hydrogen production apparatus, a front end of a housing is closed by an opening/closing door. In a closed state of the opening/closing door, the upper end of the upper rod and the lower end of the lower rod in the opening/closing switching mechanism are disposed behind the first front frame piece and the second front frame piece of the housing, respectively. The axial distance between the upper end of the lower collar member and the second stopper is greater than the axial distance between the upper end of the upper collar member and the first stopper.

Abstract: A method of training a student neural network is provided. The method includes feeding a data set including a plurality of input vectors into a teacher neural network to generate a plurality of output values, and converting two of the plurality of output values from the teacher neural network for two corresponding input vectors into two corresponding soft labels. The method further includes combining the two corresponding input vectors to form a synthesized data vector, and forming a masked soft label vector from the two corresponding soft labels. The method further includes feeding the synthesized data vector into the student neural network, using the masked soft label vector to determine an error for modifying weights of the student neural network, and modifying the weights of the student neural network.

Abstract: An approach to identifying alternate soft labels for training a student model may be provided. A teaching model may generate a soft label for a labeled training data. The training data can be an acoustic file for speech or a spoken natural language. A pool of soft labels previously generated by teacher models can be searched at the label level to identify soft labels that are similar to the generated soft label. The similar soft labels can have similar length or sequence at the word phoneme, and/or state level. The identified similar soft labels can be used in conjunction with the generated soft label to train a student model.

Abstract: Methods and systems for training a neural network include clustering a full set of training data samples into specialized training clusters. Specialized teacher neural networks are trained using respective specialized training clusters of the specialized training clusters. Soft labels are generated for the full set of training data samples using the specialized teacher neural networks. A student model is trained using the full set of training data samples, the specialized training clusters, and the soft labels.

Abstract: Provided are (i) a solution for forming an organic semiconductor layer which solution has an excellent coating property, (ii) an organic semiconductor which is produced with use of the solution and which has high heat resistance, (iii) a layer which contains the organic semiconductor, and (iv) an organic thin film transistor which exhibits high electrical properties. A composition containing: an organic semiconductor; and a polymer (1) having at least one unit selected from the group consisting of units represented by formulae (1-a), (1-b), and (1-c). A composition containing the organic semiconductor, the polymer (1), and an organic solvent can be suitably used as a solution for forming an organic semiconductor layer.