Abstract: A non-transitory computer-readable recording medium stores an output program for causing a computer to execute a process including: acquiring a plurality of solutions over a Pareto front; fitting a Bezier simplex to the plurality of solutions that has been acquired; calculating a gradient of the Bezier simplex at a plurality of data points over the Bezier simplex that has been fit; and for each of the plurality of data points that has been generated, outputting evaluation information on robustness of a solution corresponding to the data point based on the gradient that has been calculated.

Abstract: An information processing apparatus includes one or more memories; and one or more processors coupled to the one or more memories, the one or more processors being configured to decide a gain matrix based on an input metric, perform selection of first training data from a plurality of unlabeled training data, to be used for training a machine learning model, based on the gain matrix, and perform training of the machine learning model based on the first training data, a predicted label that is predicted from the first training data, and a loss function including the gain matrix.

Abstract: A determination apparatus generates an interval vector having a plurality of components that are adjacent occurrence intervals between a plurality of events that have occurred in chronological order. The determination apparatus generates a plurality of local variable points each of which includes specific components as one set of coordinates, using a predetermined number of consecutive interval vectors in the chronological order. The determination apparatus generates a Betti sequence by applying persistent homology transform to the plurality of local variable points for which the interval vectors serving as starting points are different. The determination apparatus determines a type of the plurality of events based on the Betti sequence.

Abstract: A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: setting each of scores to each of a plurality of sets of unlabeled data with regard to each of labels used in a plurality of sets of labeled data based on a distance of each of the plurality of sets of unlabeled data with respect to each of the labels; and causing a learning model to learn using a neural network by using the plurality of sets of labeled data respectively corresponding to the labels of the plurality of sets of labeled data, and the plurality of sets of unlabeled data respectively corresponding to the scores of the plurality of sets of unlabeled data with regard to the labels.

Abstract: A storage medium storing a training processing program that causes at least one computer to execute a process that includes acquiring a deviation degree of a feature in a training dataset, by using a determination model, the training dataset being unlabeled; selecting one or more pieces of data included in the training dataset based on the deviation degree; outputting the selected one or more pieces of data or related data related to the selected one or more pieces of data; receiving an input of a determination result by a user for the one or more pieces of data; and determining an adjustment standard used to adjust a feature of each piece of the data included in the training dataset based on the received determination result, wherein when determination target data is determined by the determination model, a feature of the determination target data is adjusted based on the adjustment standard.

Abstract: An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Abstract: A determination apparatus extracts a plurality of specific events that have values greater than an event determination threshold from among a plurality of events that have occurred in chronological order. The determination apparatus generates a feature amount related to adjacent occurrence intervals of the plurality of specific events, using the plurality of specific events. The determination apparatus generates array data corresponding to the plurality of events using points each having components of the event determination threshold and the feature amount, while changing the event determination threshold. The determination apparatus determines a type of the plurality of events using the array data.

Abstract: A deterioration suppression device generates a plurality of trained machine learning models having different characteristics on the basis of each training data included in a first training data set and assigned with a label indicating correct answer information. In a case where estimation accuracy of label estimation with respect to input data to be estimated by any trained machine learning model among the plurality of trained machine learning models becomes lower than a predetermined standard, the deterioration suppression device generates a second training data set including a plurality of pieces of training data using an estimation result by a trained machine learning model with the estimation accuracy equal to or higher than the predetermined standard. The deterioration suppression device executes re-learning of the trained machine learning model with the estimation accuracy lower than the predetermined standard using the second training data set.

Abstract: A non-transitory computer-readable storage medium storing an estimation program that causes a computer to execute a process includes specifying representative points of each of training clusters that corresponds to each of labels targeted for estimation; setting boundaries between each of input clusters under a condition that a number of the input clusters and a number of the representative points coincide with each other, the input clusters being generated by clustering in a feature space for input data; acquiring estimation results for the labels with respect to the input data based on a correspondence relationship between the input clusters and the training clusters based on the boundaries; and estimating determination accuracy for the labels by using the machine learning model with respect to the input data based on the estimation results.

Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing including: acquiring a plurality of points on a Pareto front of a multi-objective optimization problem; fitting a Bezier simplex defined using a plurality of control points to the plurality of points on the Pareto front; and determining whether the Pareto front is degenerated based on a positional relationship among the plurality of control points in the Bezier simplex after the fitting.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including determining numerical values indicating features at respective timings having a predetermined time interval with respect to time-series data to be analyzed, numbers of the numerical values at the respective timings being made same, and generating an attractor related to the time-series data based on the determined numerical values.

Abstract: A method includes: executing a first generation process that includes generating a Betti number series corresponding to a contribution rate by performing persistent homology processing on a first point set, the first point set being generated by using a plurality of pieces of time series data and the contribution rate of each of the plurality of pieces of time series data, each of points included in the first point set being represented by coordinates; executing a second generation process that includes generating a characteristic image from a plurality of the Betti number series, the plurality of Betti number series being generated by performing the first generation process on each of the plurality of contribution rates; and executing a third generation process that includes generating machine learning data in which the characteristic image and a classification corresponding to the plurality of pieces of time series data are associated with each other.

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 non-transitory computer-readable recording medium storing a program that causes a computer to execute a procedure, the procedure includes generating, for each of a plurality of wafers, extended coordinates including a position on the wafer and a value calculated from a distance from a center of the wafer and a contribution parameter, for each defect on the wafer by using information of a defect position on the wafer, generating a Betti number group by persistent homology processing for a plurality of extended coordinates generated for each of the plurality of wafers generating, for each of the plurality of wafers, a defect pattern image from a plurality of Betti number groups generated for the plurality of values of contribution parameter, and generating machine learning data associating a plurality of defect pattern images generated for the plurality of wafers with determination information associated with the plurality of wafers.

Abstract: A detection device generates plural attractors based on brainwave data. Subsequently, the detection device calculates a Betti number by subjecting the attractors to persistent homology transform. The detection device determines an onset of encephalopathy based on a first order component of a Betti sequence calculated based on the Betti number.

Abstract: A detection device adds, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data. The detection device generates a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added. The detection device generates a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation. The detection device detects a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.

Abstract: A method for extracting features, the method being implemented by a computer, the method includes generating attractors from time series data having a cyclic characteristic; generating a persistence diagram by performing persistent homology conversion for the attractors; changing a degree of influence with respect to individual items of data in the persistence diagram in accordance with a time of existence or an appearance time of a hole generated by performing the persistent homology conversion; and extracting features of the time series data from the changed persistence diagram in which the degree of influence has been changed.

Abstract: An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Abstract: A non-transitory computer-readable recording medium stores therein a visualization program that causes a computer to execute a process including: generating a plurality of conversion vectors, from a plurality of vectors generated from plural pieces of input data, by a dimensional compression in a positional relation between the plurality of vectors; and plotting the plurality of conversion vectors.

Abstract: A disclosed machine learning method includes: calculating a first output error between a label and an output in a case where dropout in which values are replaced with 0 is executed for a last layer of a first channel among plural channels in a parallel neural network; calculating a second output error between the label and an output in a case where the dropout is not executed for the last layer of the first channel; and identifying at least one channel from the plural channels based on a difference between the first output error and the second output error to update parameters of the identified channel.