Abstract: There are provided a data analysis device, a method, and a program that are capable of improving the accuracy of predicting an output variable for an unknown input variable by making it possible to use input/output data in which the value of the output variable is given as an interval value. A data analysis device 10A includes: a data processing unit 12 that performs a process of acquiring data represented by a set of a plurality of first input/output data in which a value of an output variable is given and a plurality of second input/output data in which a value of an output variable is gives as an interval value representing a range; and a prediction unit 16 that, based on an input variable for which a value of an output variable is unknown and the data, predicts a value of an output variable for the unknown input variable using a Gaussian process.

Abstract: A movement information calculating device includes a positioning sensor, a velocity sensor, an attitude sensor and processing circuitry. The positioning sensor is configured to calculate a position of the positioning sensor on a movable body. The velocity sensor is configured to calculate a velocity of the movable body. The attitude sensor is configured to calculate an attitude of the movable body. The processing circuitry is configured to calculate a center-of-gravity position and a center-of-gravity velocity of the movable body by using the position, the velocity, and the attitude, and calculate one of a turning center position and a pivoting position of the movable body by using the center-of-gravity position and the center-of-gravity velocity.

Abstract: A personal remaining movement estimation unit (12) estimates information relating to a remaining movement of a user on the move based on a movement trajectory of the user on the move, a personal destination estimation unit (16) predicts a destination of the user on the move based on information estimated by the personal remaining movement estimation unit (12) and information indicating the number of people moving between areas of a plurality of users stored in a movement trend information storage unit (34), and it is thereby possible to predict a destination even for a user having no information on past movement trajectories.

Abstract: It is possible to construct an agent that can deal with even a complicated task. For a value function for obtaining a policy for an action of an agent that solves an overall task represented by a weighting sum of a plurality of part tasks, an overall value function is obtained, which is a weighting sum of a plurality of part value functions learned in advance to obtain a policy for an action of a part agent that solves the part tasks for each of the plurality of part tasks using a weight for each of the plurality of part tasks. The action of the agent corresponding to the overall task is determined using a policy obtained from the overall value function and the agent is caused to act.

Abstract: The present disclosure is to calculate an estimated position with high precision. A course estimating device 10 includes an angular velocity calculating part 30, a horizontal ground speed calculating part 70 and an estimated position calculating part 80. The angular velocity calculating part 30 measures or calculates an angular velocity of a movable body. The horizontal ground speed calculating part 70 calculates a horizontal ground speed based on an attitude angle, a ground course, and a ground ship speed of the movable body. The estimated position calculating part 80 calculates an estimated position, based on a period of time from a current time point to an estimation time point, the horizontal ground speed, and an integration operation of the angular velocity.

Abstract: In order to perform optimization of parameters at high speed, an evaluating unit (120) repetitively calculates an evaluated value of machine learning or a simulation while changing a value of a parameter, an optimizing unit (100) uses a model constructed by learning a pair of a value of a parameter of which an evaluated value had been previously calculated and the evaluated value to predict an evaluated value with respect to a value of at least one parameter included in a parameter space specified based on a value of a parameter of which an evaluated value had been last calculated and select a value of a parameter of which an evaluated value is to be calculated next by the evaluating unit (120) based on predicted data of a currently predicted evaluated value and predicted data of a previously predicted evaluated value, and an output unit (180) outputs an optimal value of a parameter based on an evaluated value calculated by the evaluating unit 120.

Abstract: Upper-order parameters and lower-order parameters can be optimized by performing evaluation a small number of times. An optimization apparatus 10 includes: an evaluation unit 300 that performs calculation based on evaluation data, an upper-order parameter z, and a lower-order parameter x, and outputs an evaluation value indicating an evaluation on the calculation result; an optimization unit 100 that optimizes the upper-order parameter z and the lower-order parameter x; and an output unit 400 that outputs the optimized upper-order parameter z and lower-order parameter x that are obtained by repeating processing in the evaluation unit 300 and processing in the evaluation unit 300.

Abstract: An object is to predict an event with high accuracy by efficiently incorporating external information into a point process model of events. In an event prediction device 10 that predicts an event, an operation unit 3 extracts event history information from an event history storage device 1 and extracts external information from an external information storage device 2, the event history information including a point in time and a place at which an event has occurred, the external information including an external factor that affects the occurrence of an event. A parameter estimation unit 5 estimates an optimum parameter for prediction of an event by supplying the extracted history information and the extracted external information to learning of a relationship between the point process model of events and external factors.

Abstract: Movement information is calculated with high accuracy, without being influenced by the number of GNSS signals receivable by each of a plurality of antennas. A movement information calculating device includes a plurality of antennas, a clock generator, a plurality of GNSS receivers, and an arithmetic logical unit. The plurality of antennas, each receives a GNSS signal. The clock generator generates a clock signal. The plurality of GNSS receivers are connected to the respective antennas, and share the clock signal from the clock generator and calculate GNSS observed values by using the shared clock signal and the GNSS signals, respectively. The arithmetic logical unit calculates movement information including a speed of a movable body based on the GNSS observed values from the plurality of GNSS receivers.

Abstract: An oscillation observation device includes a first receiver and processing circuitry. The first receiver is configured to measure carrier phases of positioning signal. The processing circuitry is configured to calculate a velocity of an object by using an amount of change in the carrier phases measured by the first receiver, and calculate an amount of oscillation of the object in a translational direction using the velocity.

Abstract: Provided is a tensor data calculation device that includes a matrix product calculation processor and decomposes N-th order (N is an integer of 2 or more) non-negative tensor data into N factor matrices by factorization. The tensor data calculation device includes: a factorization means that represents update formulae of the factor matrices for optimizing a prescribed objective function value in a format that includes a matrix product of a first matrix obtained by expanding other N?1 factor matrices other than the factor matrices by a Kronecker product and a second matrix defined by a tensor product of the non-negative tensor data and the N factor matrices and calculate the update formulae; and a matrix calculation means that calculates the matrix products included in the update formulae with the aid of the matrix product calculation processor. The factorization means calculates the update formulae using calculation results of the matrix products calculated by the matrix calculation means.

Abstract: An object is to make it possible to learn a prediction model for precisely predicting the number of passersby at a prediction time point. A learning unit 300 learns parameters of the prediction model with respect to each of a plurality of observation points based on learning data such that the number of passersby at the observation point at a prediction time point predicted using the prediction model matches the number of passersby at a time point corresponding to the prediction time point included in the learning data. The learning data includes, with respect to each observation point, a past time point of the observation point and the number of passersby at the observation point at the past time point.

Abstract: A parameter can be optimized with a small number of evaluations. For each of a plurality of candidate search points that are parameters used as candidates for search points which a candidate search point generation unit 120 has generated based on a plurality of parameters used for calculation, a search point determination unit 130 determines whether or not to set the candidate search point as a search point using a plurality of data points, each including a set of a parameter used for calculation by an evaluation unit 300 and an evaluation value that has been calculated by using the parameter used for calculation by the evaluation unit 300 as a search point.

Abstract: A movement state recognition multitask DNN model training section 46 trains a parameter of a DNN model based on an image data time series and a sensor data time series, and based on first annotation data, second annotation data, and third annotation data generated for the image data time series and the sensor data time series. Training is performed such that a movement state recognized by the DNN model in a case in which input with the image data time series and the sensor data time series matches movement states indicated by the first annotation data, the second annotation data, and the third annotation data. This thereby enables information to be efficiently extracted and combined from both video data and sensor data, and also enables movement state recognition to be implemented with high precision for a data set including data that does not fall in any movement state class.

Abstract: A movement means label can be applied with good precision to a movement locus of which movement means is unknown, using a trained classifier. Filtering is performed on movement loci, including coordinates at each time of day, and of which the movement means are unknown, using parameters relating to a Gaussian process and parameters relating to noise, learned beforehand for each movement means label, and a feature vector is extracted from filtering results of the movement locus for each movement means label, a probability representing which movement means label the movement locus is, is calculated using a classifier for identifying which movement means label the movement locus is, the classifier having been learned beforehand for each movement means label, and a prediction label for the movement locus is output on the basis of calculation results.

Abstract: A moving state analysis device improves accuracy of moving state recognition by including a detection unit configured to detect, from image data associated with a frame, an object and a region of the object, for each of frames that constitute first video data captured in a course of movement of a first moving body, and a learning unit configured to learn a DNN model that takes video data and sensor data as input and that outputs a probability of each moving state, based on the first video data, a feature of first sensor data measured in relation to the first moving body and corresponding to a capture of the first video data, a detection result of the object and the region of the object, and information that indicates a moving state associated with the first video data.

Abstract: A human migration number can be estimated as an integer value with high accuracy without depending on the size of an area. A human migration number between areas at each time is estimated on the basis of the population of each of the areas at the time under a constraint that the population of each of the areas at each time and a human migration number between the areas at the time are in a predetermined relation and that the human migration number between the areas at the time is an integer value in order to maximize a likelihood function expressed using the population of each of areas at each time, the human migration number between the areas at the time, and the migration probability between the areas, the migration probability between the areas is estimated on the basis of the estimated human migration number between the areas at the time in order to maximize the likelihood function, and the estimation processing is repeated until a predetermined condition is satisfied.

Abstract: A parameter estimation unit (16) estimates a set of parameters so as to optimize a likelihood function of a strength function expressing the event occurrence probability of a type m space-time event at a time t and a geospatial location s when the strength function is modelled with use of the occurrence probability of the type m space-time event at the time t and the geospatial location s, the function expressing the degree of influence of the event occurrence history, the value of the strength function representing the event occurrence probability in an observation section that includes the time t and the geospatial location s, and the relationship between the type m and the type of the event occurrence history included in the observation section, and here, the estimated parameters include the value of the strength function expressing the event occurrence probability in the observation sections, the relationship between types, and the function expressing the degree of influence of the event occurrence histor

Abstract: An interval-valued matrix including elements represented by interval values can be resolved into factor matrices with high accuracy. A parameter estimation unit 20 estimates a factor matrix A and a factor matrix B such that an objective function, represented by including a probability of an element xij taking a scalar value thereof, which is represented using an estimate of the element xij estimated from the factor matrix A and the factor matrix B, for each element xij that is a scalar value, and a probability of the element xij taking an interval value thereof, which is represented using the estimate of the element xij estimated from the factor matrix A and the factor matrix B, for each element xij that is an interval value, is optimized.

Abstract: It is possible to accurately predict data for a prediction target time. The operation unit 10 receives high-dimensional array data representing data at each time and external information data that is a tensor or matrix representing external information at each time. The parameter estimation unit 16 decomposes the high-dimensional array data into a weighted sum of products of a plurality of factor matrices for each rank and decomposes the external information data into a weighted sum of products of a plurality of factor matrices for each rank, under a sparse constraint of weighting parameters for each rank. The prediction unit 22 predicts the data for a prediction target time based on the weighting parameters for each rank and the plurality of factor matrices for each rank, obtained for the high-dimensional array data.