Abstract: Provided is an oral composition comprising (a) a high-intensity sweetener in an amount corresponding to a sweetness intensity X1, (b) an intrinsic phospholipid, and (c) a foreign phospholipid in an amount less than a taste recognition threshold, wherein the oral composition has a sweetness of a sweetness intensity X2 exhibited by the components (a) and (b), and a sweetness of a sweetness intensity X3 exhibited by the components (a) to (c), respectively, and 0.1<X2<X3 is satisfied.

Abstract: Provided is an oral composition comprising (a) a high-intensity sweetener in an amount corresponding to a sweetness intensity X1, and (b) a phospholipid in an amount less than a taste recognition threshold, wherein the oral composition has a sweetness of a sweetness intensity X2 exhibited by the components (a) to (b), respectively, and 0.1<X1<X2 is satisfied.

Abstract: An estimation device (10) includes a data acquisition unit (131), a first smoothing processing unit (132), a second smoothing processing unit (133), a calculation unit (134), and an estimation unit (135). The data acquisition unit (131) acquires time-series data of a pupil diameter during work of a subject of work performance estimation. The first smoothing processing unit (132) smooths the acquired time-series data of the pupil diameter of the subject by a first time window. The second smoothing processing unit (133) smooths the time-series data of the pupil diameter by a second time window that is a time window larger than the first time window. The calculation unit (134) calculates a difference between the time-series data of the pupil diameter smoothed by the first time window and the time-series data of the pupil diameter smoothed by the second time window.

Abstract: Provided is an oral composition comprising (a) a high-intensity sweetener in an amount corresponding to a sweetness intensity X1, (b) an intrinsic phospholipid, (c) 60 mg/100 mL or less of sodium, and (d) a foreign phospholipid in an amount less than a taste recognition threshold, wherein the oral composition has a sweetness of a sweetness intensity X2 exhibited by the components (a) and (b), and a sweetness of a sweetness intensity X3 exhibited by the components (a) to (d), respectively, and 0.1<X2<X3 is satisfied.

Abstract: Provided is an oral composition comprising (a) a high-intensity sweetener in an amount corresponding to a sweetness intensity X1, (b) 60 mg/100 mL or less of sodium, and (c) a phospholipid in an amount less than a taste recognition threshold, wherein the oral composition has a sweetness of a sweetness intensity X2 exhibited by the components (a) to (c), respectively, and 0.1<X1<X2 is satisfied.

Abstract: An estimation device includes processing circuitry configured to acquire a pupil diameter of a subject whose work performance is to be estimated at a time of work and a luminance of a gaze target of the subject, calculate a variation amount of the pupil diameter of the subject from time-series data of the pupil diameter of the subject, determine whether or not the luminance of the gaze target of the subject is equal to or higher than a predetermined value, and determine that a correlation between a magnitude of the variation amount of the pupil diameter of the subject and a deterioration in the work performance of the subject is low in a case where it is determined that the luminance of the gaze target of the subject is equal to or higher than a predetermined threshold.

Abstract: A distance estimation apparatus includes processing circuitry configured to acquire a sensor value output from a sensor configured to measure a relative motion of a head or eyeballs of a user who visually searches for a target on a plane and estimate a distance between the user and a search target plane using a maximum value of an amount of change when a rate of change in the sensor value acquired within a time period that is equal to or greater than a predetermined threshold value becomes a maximum.

Abstract: The clustering apparatus constructs, on assumption that sensor data is generated from a latent variable which is a consecutive random variable of the number of dimensions suitable for handling in the shallow method, a model for estimating the latent variable from the sensor data, based on a generative model for generating the sensor data from the latent variable. Next, the clustering apparatus calculates, from the sensor data, an estimated value of the latent variable from which the sensor data is generated, by using the constructed model. The clustering apparatus then clusters the calculated estimated values of the latent variable by the shallow method, and identifies the optimum number of clusters. Thereafter, the clustering apparatus performs clustering of the sensor data by a neural network having three or more layers, by using the hyperparameter information of the constructed model and the identified optimum number of clusters.

Abstract: A learning apparatus (10) acquires a label corresponding to a variance not selectively explained by a latent variable, out of variances in characteristic of data. The learning apparatus (10) receives, as input data, real data or generated data output by a generator that generates data, discriminates whether the input data is the generated data or the real data, and adds, to a first neural network constituting a discriminator that estimates the latent variable, a path having two or more layers for estimating the label. The learning apparatus (10) performs learning for a second neural network obtained by adding the path so that by multiplying, by a minus sign, a gradient for an error propagating backward to the first neural network in a first layer of the added path during learning based on backpropagation, the gradient is propagated to minimize an estimation error for the latent variable, but the gradient is propagated to maximize an estimation error for the label.

Abstract: An estimation method includes first acquiring information of an eyeball motion of a user on the basis of a measurement value of an eye potential of the user, second acquiring positional information of an interaction target on a screen that corresponds to an interaction performed on a device through an operation of the user, third acquiring information regarding a relative motion of sight of the user on the basis at least of the information of the eyeball motion of the user, and estimating a sight position of the user on the screen on the basis of the information regarding the relative motion of the sight of the user and the positional information of the interaction target on the screen, by processing circuitry.

Abstract: An acquisition unit (15a) acquires a sensor value output from a sensor that measures a relative motion of a head or eyeballs of a user who visually searches for a target on a plane. An estimation unit (15b) estimates a distance between the user and a search target plane using a maximum value of an amount of change when a rate of change in the sensor value acquired within a time period that is equal to or greater than a predetermined threshold value becomes a maximum.

Abstract: A preprocessing unit (130) of a training device (10) include: a pre-training data collection unit (131) configured to collect pre-training data including continuous input data measured in an environment simulating an estimation environment and output data corresponding to the continuous input data; and a conversion unit (132) configured to convert the continuous input data into continuous input data pieces of a plurality of sizes including a size larger than the input data, convert the output data corresponding to the continuous input data into output data pieces respectively corresponding to the continuous input data pieces of the plurality of sizes, and output the continuous input data pieces of the plurality of sizes and the output data pieces as training data.

Abstract: An estimation method includes first acquiring information of an eyeball motion of a user on the basis of a measurement value of an eye potential of the user, second acquiring positional information of an interaction target on a screen that corresponds to an interaction performed on a device through an operation of the user, third acquiring information regarding a relative motion of sight of the user on the basis at least of the information of the eyeball motion of the user, and estimating a sight position of the user on the screen on the basis of the information regarding the relative motion of the sight of the user and the positional information of the interaction target on the screen, by processing circuitry.

Abstract: When an observation target is enabled to be observed by a plurality of types of measuring methods having different principles, to make it possible switch an illuminating method in a plurality of ways to increase types of observation targets that can be observed. An observation target SP is illuminated by at least one of coaxial epi-illumination 24 and non-coaxial epi-illumination 25. A focus search is performed on the basis of an image acquired by a first light receiving element 50. The observation target SP is illuminated by a light source 26. The focus search is performed on the basis of a signal acquired by a second light receiving element 51.

Abstract: When an observation target is enabled to be observed by a plurality of types of measuring methods having different principles, to make it possible switch an illuminating method in a plurality of ways to increase types of observation targets that can be observed. An observation target SP is illuminated by at least one of coaxial epi-illumination 24 and non-coaxial epi-illumination 25. A focus search is performed on the basis of an image acquired by a first light receiving element 50. The observation target SP is illuminated by a light source 26. The focus search is performed on the basis of a signal acquired by a second light receiving element 51.