Abstract: A metal wire includes tungsten or a tungsten alloy. The metal wire has a diameter of at most 13 ?m, a tensile strength of at least 4.8 GPa, and a natural hanging length per 1000 mm of at least 800 mm.

Abstract: According to one embodiment, an information processing device includes a hardware processor configured to acquire operation cost information indicative of a relationship between a state of an operator and a period of time required for the operator to perform an operation from a storage that stores the operation cost information, acquire state information indicative of a state of a target operator, and calculate a period of time required for the target operator to perform a target operation based on the operation cost information and the state information.

Abstract: A recognition apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: extract first feature quantity data from sensor data; generate attention information based on a classification contribution of the first feature quantity data; generate a second feature quantity data by processing the first feature quantity data with the attention information; generate processed feature quantity data including the first feature quantity data and the second feature quantity data; and perform classification of a recognition object from the processed feature quantity data by using a classification network.

Abstract: According to one embodiment, a learning apparatus includes a processor. The processor acquires first training data. The processor inputs the first training data to a model, and generate a plurality of estimation vectors that are a processing result of the model. The processor generates an estimation distribution from the estimation vectors. The processor calculates a distribution loss between the estimation distribution and a target distribution that is a target in an inference using the model. The processor updates parameters of the model, based on the distribution loss.

Abstract: According to one embodiment, a defect management apparatus includes a processor. The processor acquires first information and second information, the first information including first defect positions relating to defects detected with a first device for an inspection target and corresponding first labels indicating classifications of the defects, the second information including second defect positions relating to defects detected with a second device for the inspection target. The processor determines a first defect position corresponding to a second defect position as a corresponding defect position. The processor diverts the first label corresponding to the corresponding defect position as a second label of the second defect position.

Abstract: According to one embodiment, a learning apparatus includes a first compressor, a generator, a second compressor, a discriminator and an updating unit. The first compressor generates a first latent variable from a sample using a first network. A generator generates a reconstruction sample from the first latent variable using a second network. The second compressor generates a second latent variable from the reconstruction sample using a third network. The calculator calculates a distance in a latent space between the first and second latent variables. The discriminator outputs a discrimination score using a fourth network. The updating unit trains the first to fourth networks based on the discrimination score and train the third network based on the distance.

Abstract: A recognition apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: extract first feature quantity data from sensor data; generate attention information based on a classification contribution of the first feature quantity data; generate a second feature quantity data by processing the first feature quantity data with the attention information; generate processed feature quantity data including the first feature quantity data and the second feature quantity data; and perform classification of a recognition object from the processed feature quantity data by using a classification network.

Abstract: According to one embodiment, an information processing device includes a hardware processor configured to acquire operation cost information indicative of a relationship between a state of an operator and a period of time required for the operator to perform an operation from a storage that stores the operation cost information, acquire state information indicative of a state of a target operator, and calculate a period of time required for the target operator to perform a target operation based on the operation cost information and the state information.

Abstract: According to an embodiment, an information processing apparatus includes a memory and processing circuitry. The processing circuitry configured to acquire a deterioration degree of a structure calculated based on an image including the structure captured by an imaging device. The processing circuitry configured to acquire a measurement time, which is a date and time when the image being a basis of calculation of the deterioration degree has been captured. The processing circuitry configured to calculate necessity of additional measurement of the deterioration degree based on a plurality of the deterioration degrees measured at the measurement times different from each other.

Abstract: According to an embodiment, an apparatus includes a calculator, an pixel evaluator, an accumulator, and an area evaluator. The calculator is configured to calculate a feature of an image for each pixel in image data. The pixel evaluator is configured to produce a score that evaluates the feature for each pixel. The accumulator is configured to calculate, for each pixel, a cumulative score obtained by accumulating all scores in an area including a minor angle formed by a half line in a first direction from the each pixel position and another half line in a second direction from the each pixel position. The area evaluator is configured to calculate an evaluation value that is a total of the scores in a quadrilateral area enclosed by two lines of the first direction and two lines of the second direction based on the cumulative scores at pixel positions at vertexes of the quadrilateral area.

Abstract: According to an embodiment, a computing apparatus includes a memory, and a processor. The memory stores N first vectors in a d-dimensional binary vector space consisting of binary values. The processor acquires a second vector in the d-dimensional binary vector space. The processor extracts M first vectors having a distance from the second vector satisfying a first condition out of the N first vectors, and calculate a distribution of distances of the M first vectors from the second vector. The processor acquires a first kernel function per a first distance between the M first vectors and the second vector in a first range. The processor generates a second kernel function based on the distribution and the first kernel functions. The processor calculates an occurrence probability of the second vector in the N first vectors based on the second kernel function.

Abstract: According to an embodiment, an apparatus includes a calculator, an pixel evaluator, an accumulator, and an area evaluator. The calculator is configured to calculate a feature of an image for each pixel in image data. The pixel evaluator is configured to produce a score that evaluates the feature for each pixel. The accumulator is configured to calculate, for each pixel, a cumulative score obtained by accumulating all scores in an area including a minor angle formed by a half line in a first direction from the each pixel position and another half line in a second direction from the each pixel position. The area evaluator is configured to calculate an evaluation value that is a total of the scores in a quadrilateral area enclosed by two lines of the first direction and two lines of the second direction based on the cumulative scores at pixel positions at vertexes of the quadrilateral area.

Abstract: A recognition device includes a storage unit, an acquiring unit, a first calculator, a second calculator, a determining unit, and an output unit. The storage unit stores multiple training patterns each belonging to any one of multiple categories. The acquiring unit acquires a recognition target pattern to be recognized. The first calculator calculates, for each of the categories, a distance histogram representing distribution of the number of training patterns belonging to the category with respect to distances between the recognition target pattern and the training patterns belonging to the category. The second calculator analyzes the distance histogram of each of the categories to calculate confidence of the category. The determining unit determines a category of the recognition target pattern from the multiple categories by using the confidences. The output unit outputs the category of the recognition target pattern.

Abstract: According to an embodiment, a computing apparatus includes a memory, and a processor. The memory stores N first vectors in a d-dimensional binary vector space consisting of binary values. The processor acquires a second vector in the d-dimensional binary vector space. The processor extracts M first vectors having a distance from the second vector satisfying a first condition out of the N first vectors, and calculate a distribution of distances of the M first vectors from the second vector. The processor acquires a first kernel function per a first distance between the M first vectors and the second vector in a first range. The processor generates a second kernel function based on the distribution and the first kernel functions. The processor calculates an occurrence probability of the second vector in the N first vectors based on the second kernel function.

Abstract: According to an embodiment, a recognition device includes a memory to store therein learning patterns each belonging to one of categories; an obtaining unit to obtain a recognition target pattern; a first calculating unit to calculate, for each category, a distance histogram representing distribution of the number of learning patterns belonging to the categories with respect to distances between the recognition target pattern and the learning patterns belonging to the categories; a second calculating unit to analyze the distance histogram of each category, and calculate a feature value of the recognition target pattern; a third calculating unit to make use of the feature value and one or more classifiers, and calculate degrees of reliability of the recognition target categories; and a determining unit to make use of the degrees of reliability and, from among the one or more recognition target categories, determine a category of the recognition target pattern.

Abstract: According to an embodiment, a recognition device includes a generation unit to select, plural times, groups each including learning samples from a storage unit, learn a classification metric for classifying the groups selected in each selection, and generate an evaluation metric including the classification metrics; a transformation unit to transform a first feature value of an image including an object into a second feature value using the evaluation metric; a calculation unit to calculate similarities of the object to categories in a table using the second feature value and reference feature values; and a registration unit to register the second feature value as the reference feature value in the table associated with the category of the object and register the first feature value as the learning sample belonging to the category of the object in the storage unit. The generation unit performs the generation again.

Abstract: According to an embodiment, a recognition device includes a storage unit, an acquiring unit, a first calculator, a second calculator, a determining unit, and an output unit. The storage unit stores multiple training patterns each belonging to any one of multiple categories. The acquiring unit acquires a recognition target pattern to be recognized. The first calculator calculates, for each of the categories, a distance histogram representing distribution of the number of training patterns belonging to the category with respect to distances between the recognition target pattern and the training patterns belonging to the category. The second calculator analyzes the distance histogram of each of the categories to calculate confidence of the category. The determining unit determines a category of the recognition target pattern from the multiple categories by using the confidences. The output unit outputs the category of the recognition target pattern.

Abstract: According to an embodiment, a recognition device includes a generation unit to select, plural times, groups each including learning samples from a storage unit, learn a classification metric for classifying the groups selected in each selection, and generate an evaluation metric including the classification metrics; a transformation unit to transform a first feature value of an image including an object into a second feature value using the evaluation metric; a calculation unit to calculate similarities of the object to categories in a table using the second feature value and reference feature values; and a registration unit to register the second feature value as the reference feature value in the table associated with the category of the object and register the first feature value as the learning sample belonging to the category of the object in the storage unit. The generation unit performs the generation again.

Abstract: A document/image retrieval method for retrieving a document/image corresponding to a captured digital image from a database by comparing features calculated based on feature points of the captured digital image with features preliminarily calculated based on feature points of each of documents and/or images stored in the database, the method comprising: extracting the feature points from the captured digital image; defining a local set of feature points for each of the extracted feature points; selecting feature points from the defined local set to define a feature point subset of the local set; determining invariant values as values characterizing the defined subset for combinations of the feature points in the subset, the invariant values being invariant to a geometric transformation; calculating a feature by combining the determined invariant values; and performing a voting process on the documents and/or images in the database based on the preliminarily calculated features of the documents and/or images;

Abstract: A document/image retrieval method for retrieving a document/image corresponding to a captured digital image from a database by comparing features calculated based on feature points of the captured digital image with features preliminarily calculated based on feature points of each of documents and/or images stored in the database, the method comprising: extracting the feature points from the captured digital image; defining a local set of feature points for each of the extracted feature points; selecting feature points from the defined local set to define a feature point subset of the local set; determining invariant values as values characterizing the defined subset for combinations of the feature points in the subset, the invariant values being invariant to a geometric transformation; calculating a feature by combining the determined invariant values; and performing a voting process on the documents and/or images in the database based on the preliminarily calculated features of the documents and/or images;