Abstract: A data acquisition apparatus includes a light source, a first beam splitter, a predetermined beam splitter, a first light deflector, a second light deflector, a first measuring unit, a second measuring unit, a second beam splitter, and a photodetector. A second measurement optical path is positioned in a first direction and a reference optical path is positioned in a second direction. The predetermined beam splitter is disposed in the second measurement optical path or the reference optical path. A first measurement optical path is positioned between the predetermined beam splitter and the photodetector. The first light deflector and the first measuring unit are disposed in the first measurement optical path, and the second light deflector and the second measuring unit are disposed in the second measurement optical path. The first measurement optical path and the second measurement optical path intersect.

Abstract: An object of the present invention is to achieve generation of a prediction model appropriate for each site without a necessity of transfer of data located at a plurality of sites to the outside of the sites. An analysis device capable of communicating with a plurality of learning devices includes a reception unit (301, 401, 1501) that receives transformed features obtained by transforming, in accordance with a predetermined rule, features contained in pieces of learning data individually retained in the plurality of learning devices, a distribution analysis unit (302) that analyzes distributions of a plurality of the features of the plurality of learning devices on the basis of the transformed features received by the reception unit (301, 401, 1501) for each of the learning devices, and an output unit (304, 1504) that outputs a distribution analysis result analyzed by the distribution analysis unit (302).

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

Abstract: An integration device performs a reception process of receiving a knowledge coefficient relating to first training data in a first prediction model of a first training device from the first training device, a transmission process of transmitting the first prediction model and data relating to the knowledge coefficients of the first training data received in the reception process respectively to a plurality of second training devices, and an integration process of generating an integrated prediction model by integrating a model parameter in a second prediction model generated by training the first prediction model with second training data and the data relating to the knowledge coefficients respectively by the plurality of second training devices, as a result of transmission in the transmission process

Abstract: Facilitation of an explanation about an object to be analyzed is realized with high accuracy and with efficiency. A data analysis apparatus is disclosed which uses a first neural network configured with an input layer, an output layer, and two or more intermediate layers provided between the input layer and the output layer. Each performs a calculation by giving data from a layer of a previous stage and a first learning parameter to a first activation function and outputs a calculation result to a layer of a subsequent stage. The data analysis apparatus includes a conversion section; a reallocation section; and an importance calculation section.

Abstract: A vehicle includes a motor and a controller configured to provide a traveling application of an own vehicle including the motor and to provide a non-traveling application of the own vehicle. The controller is further configured to: determine whether the own vehicle is providing the traveling application or the non-traveling application; generate usage information of an application being provided, based on a result of the determination of whether the own vehicle is providing the traveling application or the non-traveling application; and inform an outside of the usage information.

Abstract: A medical image processing apparatus and a medical image processing method that can improve extraction accuracy of a tumor region included in a diagnosis target image are provided. The medical image processing apparatus configured to extract a predetermined region from a diagnosis target image includes: an organ extraction unit configured to extract an organ region from the diagnosis target image; and a tumor extraction unit generated by executing machine learning using a known tumor region included in each medical image group as teacher data and using an organ region extracted from the medical image group and the medical image group as input data. The tumor extraction unit is configured to extract a tumor region from the diagnosis target image using the organ region extracted from the diagnosis target image by the organ extraction unit.

Abstract: A vehicle includes: a traveling driving system configured to execute traveling of an own vehicle; a non-traveling-application providing unit configured to provide a passenger with an application other than the traveling of the own vehicle; and a control unit. The control unit includes: inquiry means for inquiring a usage application of the own vehicle to the passenger when it is detected that the passenger gets into the own vehicle, the usage application including a traveling application and the application other than traveling of the own vehicle; non-traveling-application providing unit activating means for activating the non-traveling-application providing unit in a case where the application other than traveling is selected in response to the inquiry by the inquiry means; and traveling driving system activating means for activating the traveling driving system in a case where the traveling application is selected in response to the inquiry by the inquiry means.

Abstract: In performing aperture synthesis according to an ultrasound imaging apparatus, the amount of spatial change of amplification factors of phasing signals at respective receive phasing points are reduced, and a high-quality image is obtained. In a receive beamformer that generates an inter-transmission weight in accordance with a phasing range, and performs aperture synthesis processing, the inter-transmission weight being generated is applied to the receive phasing points within the phasing range obtained through transmission and reception, and the inter-transmission synthesis is performed. The inter-transmission weight is generated in such a manner that a variation form of the amplification factor between adjacent receive phasing points is smoothed and a difference of the amplification factor is reduced, as to each receive phasing point after the inter-transmission synthesis is performed.

Abstract: A clutter reduction effect using adaptive beam forming is uniformly obtained with respect to the entire image even in an imaging condition in which the number of bundled signals is greatly distributed in an ultrasonic image. A received signal processing unit includes a summing unit that bundles the plurality of received signals for a predetermined imaging point or a plurality of signals obtained by processing the received signals, and a weighting unit that obtains a coherence value among the plurality of signals summed in the summing unit, and weights the plurality of signals before being summed in the summing unit or a signal obtained through summing in the summing unit, with a weight corresponding to the coherence value. The weighting unit weights the coherence value nonlinearly in a predetermined direction in the subject, and weights the plurality of signals before being summed in the summing unit or the signal obtained through summing in the summing unit by using the nonlinearly weighted coherence value.

Abstract: To provide an optimum hyper parameter for determining a learning model using a natural language as a target. An optimization apparatus including: a processor and a memory and performing learning of a document set by natural language processing has an optimization section configured to determine a hyper parameter satisfying a predetermined condition on the basis of previously set group data, generate a learning model by the determined hyper parameter, and acquire a high-dimensional vector from the learning model; and a high-dimensional visualization section configured to convert the high-dimensional vector of a word or document as an analysis target on the basis of the group data.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: A vehicle detection apparatus includes: a specific color region detection unit that detects a specific color region from an image photographed by a camera provided in a vehicle; a processing target range calculation unit that calculates a processing target range in which the other vehicle is detected in the image on the basis of the specific color region detected by the specific color region detection unit; and a vehicle region detection unit that detects a region of the other vehicle from the image within the processing target range calculated by the processing target range calculation unit.

Abstract: Reception beamforming that does not generate discontinuity in the vicinity of the depth of a transmit focus is executed even if reception scanning lines are disposed outside of the irradiation area of a focusing-type transmission beam. The degree of discontinuity showing the discontinuity of reception signals detected by plural ultrasound elements 105 or the degree of discontinuity regarding the discontinuity of the wave fronts of phased signals is detected by a discontinuity extracting unit 113. If there is an area where the degree of discontinuity is larger than a predefined value, a delay time generating unit 114 for discontinuity elimination changes the delay times in the area where the discontinuity is generated.

Abstract: A vehicle includes: a traveling driving system configured to execute traveling of an own vehicle; a non-traveling-application providing unit configured to provide a passenger with an application other than the traveling of the own vehicle; and a control unit. The control unit includes: inquiry means for inquiring a usage application of the own vehicle to the passenger when it is detected that the passenger gets into the own vehicle, the usage application including a traveling application and the application other than traveling of the own vehicle; non-traveling-application providing unit activating means for activating the non-traveling-application providing unit in a case where the application other than traveling is selected in response to the inquiry by the inquiry means; and traveling driving system activating means for activating the traveling driving system in a case where the traveling application is selected in response to the inquiry by the inquiry means.

Abstract: A standard view which satisfies recommendations defined in a guideline on acquisition of standard views is extracted at a time of using information about defined landmarks and a defined landmark geometry in the three-dimensional information. When 3D volume data on a subject is input (S301), an image processing apparatus extracts a landmark geometry including landmarks contained in the 3D volume data on the basis of a position relationship among the landmarks (S302), determines a standard view of the 3D volume data using the landmark geometry and a position relationship that satisfies recommendations in a guideline on the acquisition of the standard views (S303), and outputs the obtained standard view (S304) to display the standard view on a display.

Abstract: Reception beamforming is executed using delay times that complexly vary in accordance with differences between transmission conditions. An irradiation area 32 of a transmission beam is calculated, and the lengths of segments, using which delay times are calculated, are set in accordance with the positional relationships between the calculated irradiation area 32 and reception scanning lines 31. For example, the reception scanning lines 31 are divided into areas A to C, and the lengths of segments 40b in the outer area B located outside of the irradiation area 32 are set shorter than the lengths of segments 40a and 40c in the inner areas A and C.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

Abstract: A time series data analysis apparatus: generates first internal data, based on first feature data groups, first internal parameter, and first learning parameter; transforms first feature data's position in a feature space, based on the first internal data and second learning parameter; reallocates the first feature data, based on a first transform result and first feature data groups; calculates a first predicted value, based on a reallocation result and third learning parameter; optimizes the first-third learning parameters by statistical gradient, based on a response variable and first predicted value; generates second internal data, based on second feature data groups, second internal parameter, and optimized first learning parameter; transforms the second feature data's position in a feature space, based on the second internal data and optimized second learning parameter; and calculates importance data for the second feature data, based on a second transform result and optimized third learning parameter.