Abstract: A data generation method for generating data for domain generalization in machine learning includes performing, with a computer, augmentation using training data as raw data usable to train a machine learning model, and extracting, with the computer, a dataset including the raw data and data generated through the augmentation as a dataset for the domain generalization.

Abstract: According to one embodiment, an information processing apparatus is allowed to access a storage device storing time-series data generated by a first device. The information processing apparatus includes a processor holding a first public key and a first private key. The processor is configured to acquire a program for correcting an error in first data on a first product from a first entity. The processor is configured to correct the correction target first data, using data in a predetermined range of the time-series data. The processor is configured to generate ground data indicating correction grounds for the corrected correction target first data, based on the data in the predetermined range, and add the ground data to the corrected correction target first data.

Abstract: A purpose of the present invention is to improve precision when estimating the solution to a task expressed in a graph. A model generation device according to one embodiment of the present invention performs machine learning of an estimation module provided with a feature-incorporated network and an estimator. The feature-incorporated network is provided with a plurality of feature-incorporated layers. An encoder for each feature-incorporated layer is configured so as to incorporate the feature amount of the outflow edge and inflow edge of each vertex after reflecting the relationship with other vertices. Moreover, the encoder for each feature-incorporated layer is configured so as to derive the relative feature amount for each edge based on the feature amount for all of the edges inputted for a target vertex, without calculating the weighted sum of the features of the edges adjacent to each vertex.

Abstract: An estimator generation apparatus may include a first estimator and a second estimator sharing a common encoder. The first estimator may be trained to determine a target person's state from face image data. The second estimator may be trained to reconstruct physiological data from face image data. The machine learning may allow the common encoder to have its parameters converging toward higher-accuracy local solutions for estimating the target person's state, thus generating the estimator that may estimate the target person's state more accurately.

Abstract: Provided are a composition for an acoustic wave probe which includes polysiloxane that has a vinyl group and a phenyl group, polysiloxane that has two or more Si—H groups in a molecular chain, a titanium oxide particle, and a silica particle, in which at least one of the titanium oxide particle (C) or the silica particle (D) is a particle subjected to surface treatment; a silicone resin for an acoustic wave probe; an acoustic wave probe; an acoustic wave measurement apparatus; an ultrasound diagnostic apparatus; an ultrasound probe; a photoacoustic wave measurement apparatus; and an ultrasound endoscope.

Abstract: A model generation apparatus according to one or more embodiments executes: a first training step of training an encoder, a first estimator, and a second estimator so that, when training data of each of learning data sets is given to the encoder, a result of the first estimator executing a first estimation task matches first correct answer data, and a result of the second estimator executing a second estimation task matches second correct answer data; and a second training step of training the encoder so that, for each of the learning data sets, a correlation between a first portion of feature amounts and the second correct answer data is lowered, and a correlation between a second portion of the feature amounts and the first correct answer data is lowered.

Abstract: An information processing apparatus according to the present application includes an acquiring unit and a deciding unit. The acquiring unit acquires attribute information on a user. The deciding unit decides, based on the attribute information acquired by the acquiring unit, a placement position in which a virtual riding point that corresponds to a riding point that is used for the user to ride in a moving vehicle and that is virtually set is placed.

Abstract: Accuracy of a model extracting a graph structure as an intermediate representation from input data is improved. An encoding unit (100) extracts a feature amount of each of a plurality of vertices included in a graph structure (Tr) from input data (10), and calculates a likelihood that an edge is connected to the vertex. A sampling unit (130) determines the graph structure (Tr) based on a conversion result of a Gumbel-Softmax function for the likelihood. A learning unit (150) optimizes a decoding unit (140) and the encoding unit (100) by back propagation using a loss function including an error (LP) between output data (20) generated from the graph structure (Tr) and correct data.

Abstract: A model generation apparatus according to one or more embodiments executes operations, with respect to each of learning data sets. The operations includes training a second estimator so that an estimation result obtained from a second estimator conforms to second correct answer data; training a coder so that an estimation result obtained from the second estimator does not conform to the second correct answer data; and training the coder and the first estimator so that an estimation result obtained from a first estimator conforms to first correct answer data. The model generation apparatus executes operation of the training the second estimator and the training the coder alternately and repeatedly.

Abstract: An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.

Abstract: An inference system includes: a learning unit that generates an inference model using a first dataset including a plurality of sample data whose ground truth is given and a second dataset including a plurality of sample data whose ground truth is not given; and an inference unit that determines an inference result. The inference model includes an encoder that calculates from sample data a first feature value independent of the first and second datasets and a second feature value dependent on the first or second dataset. The learning unit trains the encoder such that the same first feature value is calculated from any of first and second sample data for a pair of the first sample data among the first dataset and the second sample data which is among the second dataset and whose ground truth is to be identical to the ground truth of the first sample data.

Abstract: An information processing apparatus calculates an index for a plant growth state with a neural network using fewer images to achieve training of the neural network. The apparatus includes first to N-th image analyzers (N?2) each analyzing a cultivation area image of a plant cultivation area with a neural network to calculate a state index indicating a growth state of the plant in the cultivation area and including the neural network trained using cultivation area images each having a predetermined growth index classified into a corresponding class of first to N-th growth index classes, and a selector receiving an input of a cultivation area image for which the state index is calculated and causing one of the first to N-th image analyzers trained using cultivation area images classified into the same growth index class as the input cultivation area image to analyze the input cultivation area image.

Abstract: An information processing apparatus calculates an accurate index for the growth state of a plant without increasing a user load. The apparatus includes a receiver that receives an input of a captured image obtained by capturing an image of a plant cultivation area including a rectangular area in a longitudinal direction of the rectangular area, a conversion factor calculator that calculates conversion factors at positions in a depth direction in the captured image by determining a pixel count in a width direction of the rectangular area in the captured image input into the receiver at each position in the depth direction and dividing an actual width of the rectangular area by the determined pixel count, and an index calculator that calculates an index for a growth state of the plant by analyzing the captured image using the conversion factors at the positions calculated by the conversion factor calculator.

Abstract: Disclosed are a probe for photoacoustic measurement which can suppress generation of artifacts obstructive to signal observation in a photoacoustic measurement, and a photoacoustic measurement apparatus including the same. The probe for photoacoustic measurement includes a light emission unit which emits measurement light to a subject, and an acoustic wave detection unit which detects a photoacoustic wave generated in the subject by the emission of measurement light. An emission end surface of the light emission unit is positioned to a side where the acoustic wave detection unit is located, with respect to a contact plane of the probe, and an optical axis at the emission end surface is inclined to a side opposite to the side on which the acoustic wave detection unit is positioned with respect to a normal direction of a detection surface of the acoustic wave detection unit.

Abstract: A cultivation and management device for fruit vegetable plants and fruit trees includes environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated, planned cultivation evaluation index information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree, a calculation unit configured to determine and output a work including a shape change work for the fruit vegetable plant or the fruit tree with respect to inputs of the environment state information and the planned cultivation evaluation index information with a learning model, and an output unit configured to output the work including the shape change work for the fruit vegetable plant or the fruit tree.

Abstract: A superconducting magnet device includes a tubular cryostat defining a central cavity therein, a first superconducting coil set and a second superconducting coil set disposed outside the central cavity and inside the tubular cryostat, and a power supply system being capable of controlling a magnitude of a first exciting current to the first superconducting coil set and a magnitude of a second exciting current to the second superconducting coil set independently of each other. The first superconducting coil set generates a magnetic field distribution, which is convex downward on an X axis and convex upward on a Y axis when the first exciting current is supplied, in the central cavity. The second superconducting coil set generates a magnetic field distribution, which is convex upward on the X axis and convex downward on the Y axis when the second exciting current is supplied, in the central cavity.

Abstract: A photoacoustic measurement probe and a probe unit capable of preventing generation of artifacts in a photoacoustic measurement apparatus are obtained. In a photoacoustic measurement probe having a light emitting unit that emits measurement light toward a subject, an acoustic wave detection element that detects an acoustic wave emitted from a portion of the subject that has received the measurement light, and a housing which has a surface facing the subject at the time of use and in which the light emitting unit and the acoustic wave detection element are housed, at least one slit that is opened to the housing surface and that extends from the housing surface toward the inside of the housing is provided between the light emitting unit and the acoustic wave detection element.

Abstract: A model generation apparatus according to one aspect of the present invention acquires a plurality of learning datasets each constituted by a first sample of a first time of predetermined data obtained in time series and feature information included in a second sample of the predetermined data of a future second time relative to the first time, and trains a prediction model, by machine learning, to predict feature information of the second time from the first sample of the first time, for each learning dataset. In the model generation apparatus, a rarity degree for is set each learning dataset, and, in the machine learning, the model generation apparatus trains more preponderantly on learning datasets having a higher rarity degree.

Abstract: A learning apparatus according to one or more embodiments executes, with respect to each learning data set, a first training step of training a second encoder and a second metadata identifier such that the identification result by the second metadata identifier matches the metadata, a second training step of training encoders and an estimator such that the result of estimation performed by the estimator matches correct answer data, a third training step of training a first metadata identifier such that the result of identification performed by the first metadata identifier matches the metadata, and a fourth training step of training a first encoder such that the result of identification performed by the first metadata identifier does not match the metadata. The third training step and the fourth training step are alternatingly and repeatedly executed.

Abstract: [Object] To provide a method for producing an absorbent article comprising a fiber assembly layer of a cellulose acetate fiber, which can sufficiently apply a drug to the cellulose acetate fiber. [Solution] The present invention provides a method for producing an absorbent article, wherein the absorbent article comprises: a fiber assembly layer including a cellulose acetate fiber, and a member including a water absorbent fiber and directly disposed on the fiber assembly layer, the method comprises: a first step of opening a cellulose acetate tow and collecting a cellulose acetate fiber to form a fiber assembly layer; a second step of spreading a drug aqueous solution on the fiber assembly layer; and a third step of mounting the member on the surface where the drug aqueous solution has been spread of the fiber assembly layer, and the drug aqueous solution contains an antibacterial agent and/or a deodorizer, and a surfactant.