Abstract: A computer-implemented method of predicting energy use for a route including inputting map data of roads included in K trips in a geographical area, predictors of rate of energy use along the roads, and energy consumption data of the K trips. The method includes dividing each of the roads in the map data for all the trips into segments of length measure ?i; grouping the segments from the trips into a number N of clusters, using an algorithm to build a model predicting the weights Wj based on solving a system of equations, one per trip, assigning the predicted weight applied to the cluster in which the segment was grouped and storing a segment ID with the corresponding cluster ID or predicted rate of energy use Yi to allow prediction of energy use for a route in the geographical area incorporating one or more of the segments.

Abstract: A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.

Abstract: An apparatus receives, at a discriminator within a generative adversarial network, first generation data from a first generator within the generative adversarial network, where the first generator has performed learning using a first data group. The apparatus receives, at the discriminator, a second data group, and performs learning of a second generator based on the first generation data and the second data group where the first generation data is handled as false data by the discriminator.

Abstract: A computer-implemented method of training a deep neural network to classify data comprises: for a batch of N training data Xi, where i=1 to N and ci is the class of training data Xi, carrying out a clustering-based regularization process at at least one layer l of the DNN having neurons j, in which process a regularization activity penalty is added to a loss function for the batch of training data which is to be optimized during training, whereby the regularization activity penalty comprises components associated with respective neurons in the layer which are dependent on the respective classes of the training data.

Abstract: A computer-implemented method of predicting energy use for a route including inputting map data of roads included in K trips in a geographical area, predictors of rate of energy use along the roads, and energy consumption data of the K trips. The method includes dividing each of the roads in the map data for all the trips into segments of length measure ?i; grouping the segments from the trips into a number N of clusters, using an algorithm to build a model predicting the weights Wj based on solving a system of equations, one per trip, assigning the predicted weight applied to the cluster in which the segment was to grouped and storing a segment ID with the corresponding cluster ID or predicted rate of energy use Yi to allow prediction of energy use for a route in the geographical area incorporating one or more of the segments.

Abstract: A storage medium storing a program that causes a processor to execute for acquiring distribution data indicating a distribution of spots in an area to be searched, and area data indicating positions of divided areas obtained by dividing the area to be searched; generating an adjacency matrix indicating an adjacency relation between the divided areas from the area data; generating an evaluation function for evaluating selection of the divided areas using: a variable indicating selection of consecutive divided areas, the distribution data, and the adjacency matrix; calculating a gradient of a value of the evaluation function from a current value of the variable; executing a gradient method search for updating the value of the variable using the calculated gradient; and determining selection of the divided areas as an optimal area for event occurrence analysis based on a result of the gradient method search.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: In an inference verification method for verifying a trained first machine learning algorithm, a set of data samples are input to each of a plurality of at least three different trained machine learning algorithms and a set of outcomes are obtained from each algorithm. The plurality of trained machine learning algorithms are the same as the algorithm to be verified except that each of the plurality has been trained using training data samples where at least some of the outcomes are different as compared to training data samples used to train the first algorithm. For each sample in the data set input to the plurality, the method further comprises determining whether all of the outcomes from the plurality are the same. When all of the outcomes from the plurality are the same, the first algorithm is reported as being potentially defective for that sample in the input data set.

Abstract: A computer-implemented method of reconciling values of a feature, each value being provided by a different artificial intelligence (AI) system, by collecting logs from the different AI systems, each log including a value of the feature; identifying any discrepancy between the values. When there is any discrepancy, creating global information from the values, the global information taking into account some or all of the values. When the global information differs from the value of one of the AI systems, sending the global information to that AI system.

Abstract: An apparatus receives, at a discriminator within a generative adversarial network, first generation data from a first generator within the generative adversarial network, where the first generator has performed learning using a first data group. The apparatus receives, at the discriminator, a second data group, and performs learning of a second generator based on the first generation data and the second data group where the first generation data is handled as false data by the discriminator.

Abstract: A data processing method includes steps of; allowing establishment of a first flag or a second flag for each of a plurality of items where corresponding values are inputted sequentially; upon detecting that a value associated with an item where the first flag is established in an Nth place is inputted and that a value associated with an item where the second flag is established in an Mth place (M is equal to or larger than N) is inputted, executing a predetermined processing to values in a range from the value that is inputted by associating with the item where the first flag is established in the Nth place to the value that is inputted by associating with the item where the second flag is established in the Mth place; and executing a processing of outputting a processed result in order from the item in the Nth place.

Abstract: A storage medium storing a program that causes a processor to execute for acquiring distribution data indicating a distribution of spots in an area to be searched, and area data indicating positions of divided areas obtained by dividing the area to be searched; generating an adjacency matrix indicating an adjacency relation between the divided areas from the area data; generating an evaluation function for evaluating selection of the divided areas using: a variable indicating selection of consecutive divided areas, the distribution data, and the adjacency matrix; calculating a gradient of a value of the evaluation function from a current value of the variable; executing a gradient method search for updating the value of the variable using the calculated gradient; and determining selection of the divided areas as an optimal area for event occurrence analysis based on a result of the gradient method search.

Abstract: An apparatus acquires a first piece of trajectory information from among plural pieces of trajectory information, and acquires a first planar graph from among one or more planar graphs. The apparatus generates a second planar graph, based on the first planar graph and plural pieces of position information included in the first piece of trajectory information, and extracts, from among the plural pieces of trajectory information, second pieces of trajectory information indicating trajectories passing a difference portion between the first and second planar graphs. For each of candidate graphs each obtained by excluding a reduction set of edges from the second planar graph, the apparatus calculates optimality of the each candidate graph with which an addition set of trajectories indicated by the first and second pieces of trajectory information are associated, and outputs one of the candidate graphs determined based on the calculated optimality.

Abstract: An initializable array has a plurality of blocks each having an address word and a data word, a boundary indicative of a two-division position where the plurality of blocks is divided into two divided areas and an initial value for each element of the array is stored, the boundary is a position where a ratio for the number of unwritten blocks in a first area and the number of written blocks in a second area is an integer ratio. An array control program causes a computer to execute shifting the boundary to extend the first area and generating an initialized written block in the first area; in a case where a write destination block is an unwritten block in the second area, forming a link between the initialized written block in the first area and the write destination block; and writing a write value to the write destination block.

Abstract: A processor obtains a table that contains numerical values or character strings in its cells. The processor then replaces each numerical value with a first constant value, and each character string with a second constant value. The two constant values have opposite signs. The processor generates area datasets each including first to third rectangular areas. The right side of the second rectangular area coincides with the left side of the first rectangular area. The bottom side of the third rectangular area coincides with the top side of the first rectangular area. With respect to each generated area dataset, the processor compares a sum of first and second constant values in the first rectangular area with a sum of first and second constant values in the second and third rectangular areas. The processor outputs at least one of the area datasets according to the comparison result.

Abstract: A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.

Abstract: A planar graph generation device that includes a processor that executes a process. The process includes: computing a specific value, including components of a value representing complexity of a track of the given track data, and a value representing a non-nearness between the given track data and each of all the other track data; selecting the track data with the smallest specific value among the collection; a first portion of the first track or a second portion of the second track positioned within the specific distance of each other, or a combination of the first portion and the second portion, is approximated to a specific portion such that a track of the addition target track data after addition passes through the specific portion in cases in which there are portions positioned within the specific distance of each other in a combination of the first track with the second track.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: A data processing method executed by a computer, the data processing method including specifying a first region range among from a data table, a first region range including a plurality of numerical value regions which are continuously disposed in a first direction, a plurality of numerical values in the plurality of numerical value regions having a relationship with a specified numerical value in an adjacent region, specifying a second region range, the second region range being specified by shifting the first region range in a second direction, the second region range including at least one character string region and at least one blank region, associating a character string in the at least one character string region and the plurality of numerical values, and outputting data that indicates an association between the character string in the at least one character string region and the plurality of numerical values.

Abstract: A traffic flow rate calculation method includes, by using a road network produced by representing a road system with a plurality of nodes and a plurality of edges including a stationary sensor edge in which a stationary sensor measures the number of moving bodies; obtaining the first number of observations corresponding to the number of trajectories measured by mobile sensors for each path, the each path including the at least one edge, the each of trajectories corresponding to a movement trajectory of the moving body, and the second number of observations corresponding to the number of moving bodies measured by the stationary sensor; estimating an observation rate by using the first number of observations and the second number of observations; calculating a traffic flow rate for the each path by using the estimated observation rate and the first number of observations for each path.