Abstract: A solid-state imaging device includes a pixel array that is quadrilateral and includes pixels arranged in rows and columns, where each of the pixels accumulates an electric charge resulting from photoelectric conversion. The pixel array includes: a first area including first pixels for obtaining a captured image; and a second area including a second pixel for individually identifying the solid-state imaging device. The second area is provided in the vicinity of at least one corner among four corners of the pixel array, where the vicinity is a range of a predetermined number of pixels away from the at least one corner. The second pixel includes circuit elements or optical elements different from circuit elements or optical elements in each of the first pixels.

Abstract: A target task attribute estimation unit 81 estimates an attribute vector of an existing predictor based on samples in a domain of a target task, and estimates an attribute vector of the target task based on a transformation method for transforming labeled samples into a space consisting of the estimated attribute vector based on a result of applying the labeled samples of the target task to the predictor. A prediction value calculation unit 82 calculates a prediction value of a prediction target sample to be transformed by the transformation method based on the attribute vector of the target task.

Abstract: The learning unit 81 learns an attribute viewpoint model in which attributes of a target are explanatory variables for each target person so as to minimize a difference between a prediction result by a predictor that predicts an evaluation result of each target person based on a feature vector of the target person and a prediction result by a prediction model that predicts an evaluation result learned for each target person, using the attributes of the target as explanatory variables. The attribute generation unit 82 generates an attribute so that an evaluation result obtained according to the attribute applied to the learned prediction model satisfies the specified objective.

Abstract: A text classification device includes an important word extraction portion that extracts important words from analysis target text data, a distributed representation creation portion that creates distributed representations of words from related document data, a keyword candidate creation portion that extracts words near the important words as synonyms in the distributed representations of the words, a clustering portion that clusters the distributed representations of the important words and synonyms and creates a term cluster, and a viewpoint word creation portion that extracts a hypernym that is a word having a generalized concept of a term in the term cluster using a knowledge base in which relationships between terms are accumulated and creates a viewpoint dictionary in which a viewpoint word selected from the hypernyms is set as a headword and the terms included in the term cluster are set as keywords for the headword.

Abstract: In order to supplement results of diagnosis of degradation of an object that has been implemented at set intervals using a degradation progression model for simulating the progression of degradation of the object, a degradation prediction apparatus 100 is provided with: a data generation unit 112 configured to generate, as supplement data, diagnosis results that would be obtained if the degradation diagnosis were performed at an interval shorter than the set interval; a prediction model generation unit 113 configured, using the supplement data, to generate a prediction model for predicting a degradation index indicating a degradation state of the object at a specific point in time; and a degradation index prediction unit 114 configured to predict the degradation index of the object based on the prediction model.

Abstract: A learning device 100 includes correspondence inference unit which calculates outputs of predictors, which have learned for seen tasks or seen classes, for test input data, and infers correspondences between the calculated outputs and attribute information corresponding to an unseen task or an unseen class, and prediction unit which calculates a prediction output for the attribute information corresponding to the unseen task or the unseen class, using the inferred correspondences.

Abstract: A query specification unit 81 specifies a query as a combination of a variable, on which an intervention operation is performed for a causal relation, and a value of the variable. An intervention data generating unit 82 generates intervention data including a value of a target variable, acquired with an intervention operation based on the query, and the query. A causal relation updating unit 83 updates the causal relation using the generated intervention data. On this occasion, the query specification unit 81 specifies a query that minimizes an expected loss by updating from among queries specified based on the expected loss representing an estimation error of the target variable by the query.

Abstract: In order to supplement results of diagnosis of degradation of an object that has been implemented at set intervals using a degradation progression model for simulating the progression of degradation of the object, a degradation prediction apparatus 100 is provided with: a data generation unit 112 configured to generate, as supplement data, diagnosis results that would be obtained if the degradation diagnosis were performed at an interval shorter than the set interval; a prediction model generation unit 113 configured, using the supplement data, to generate a prediction model for predicting a degradation index indicating a degradation state of the object at a specific point in time; and a degradation index prediction unit 114 configured to predict the degradation index of the object based on the prediction model.

Abstract: An accuracy estimation unit 91 estimates accuracy of a predictive model using an accuracy estimating model that is learned using, as an explanatory variable, all or part of one or more contexts each indicating a feature value representing an operation status of the predictive model at a first point of interest that is a past point in time of interest a learning period of the predictive model, and a parameter used to learn the predictive model and, as a response variable, an accuracy index in a period after the first point of interest. The accuracy estimation unit 91 calculates the context at a second point of interest that is a point in time after the first point of interest, and applies the calculated context to the accuracy estimating model to estimate the accuracy from the second point of interest onward.

Abstract: An information processing apparatus comprises: a processor configured to: estimate a soundness degree of a checkup-object structure from an inspection result of the checkup-object structure, based on a model generated by using an inspection result of a learning-object structure and a soundness degree of the learning-object structure; and present in a recognizable manner an erroneous determination possibility indicating a possibility that a soundness degree determined from the inspection result of the checkup-object structure is erroneous, based on the estimated soundness degree of the checkup-object structure.

Abstract: A feature-converting device that provides good features quickly. The device includes first and second feature construction units and first and second feature selection units. The first feature construction unit receives one or more first features and constructs one or more second features that represent the results of applying a unary function to the respective first features. The first feature selection unit computes relevance between the first and second features and a target variable that includes elements associated with elements included in the first features and selects one or more third features that represent highly relevant features. The second feature construction unit constructs one or more fourth features that represent the results of applying a multi-operand function to the third features. The second feature selection unit computes the relevance between the third and fourth features and the target variable and selects at least one fifth feature that represents highly relevant features.