Nec Corporation

Konica Minolta, Inc.

Tohoku University

Konica Corporation

Konica Minolta Opto, Inc.

Spin Sensing Factory Corp.

Masaaki Tsuchida

Hironori Mizuguchi

Takashi Onishi

Kai Ishikawa

Dai Kusui

Koujirou Sekine

Junichi Jono

Daniel Georg Andrade Silva

Kosuke Yamamoto

Yasuo Ando

Mikihiko Oogane

Kosuke Fujiwara

Takuji Hatano

Hideki Kawai

Toshiyuki Kamiya

Akihiro Tamura

Seiichi Isoguchi

Tomoaki Tamura

Susumu Akamine

Yoshitaka Ohta

Kentarou Sasaki

Tsukasa Yagi

Daniel Andrade

Shumpei Kubosawa

Masahiro Imada

A non-destructive inspection method of inspecting an inspection target using multiple different types of non-destructive inspection means that include one non-destructive inspection means and at least one other non-destructive inspection means. The method includes determining a marking position on the inspection target in a detection result by the one non-destructive inspection means, causing a device to store the marking position, and fixedly forming a mark on the inspection target corresponding to the marking position. The mark is detectable by the other non-destructive inspection means. The method further includes causing the other non-destructive inspection means to inspect an inspection target including the mark. The method further includes contrasting detection results by the multiple different types of non-destructive inspection means in reference to the mark which is the marking position.

Non-destructive inspection method

A magnetic sensor includes a plurality of magnetoresistive element units. Each of the magnetoresistive element units includes a flat-surface-type first magnetoresistive element having a detection axis in a first direction and a flat-surface-type second magnetoresistive element having a detection axis in a second direction different from the first direction. The first magnetoresistive element and the second magnetoresistive element are arranged so as to face each other. The plurality of magnetoresistive element units are arrayed in a direction orthogonal to flat surfaces of the first magnetoresistive element and the second magnetoresistive element. The surfaces facing a measurement sample constitute a surface parallel to the direction in which the magnetoresistive element units are arrayed.

Magnetic sensor

A classification model with a high precision ratio at a high recall ratio is learned. A classification model learning system () includes a learning data storage unit () and a learning unit (). The learning data storage unit () stores pieces of learning data each of which has been classified as a positive example or a negative example. The learning unit () learns, by using the pieces of learning data, a classification model in such a way that a precision ratio of classification by the classification model is made larger under a constraint of a minimum value of a recall ratio of classification by the classification model.

Information processing system, information processing method, and recording medium for learning a classification model

Provided is a production method for a magnetoresistive element including treating a stacked layer into a predetermined shape. The stacked layer includes a magnetoresistive layer whose resistance changes depending on a magnetic field and a cap layer above the magnetoresistive layer and having a thickness in a range of 10 nm to 60 nm. The method further includes covering and protecting the stacked layer with an insulating layer, forming an opening in the insulating layer by reactive etching and exposing a surface of the cap layer at the opening, etching the cap layer in a range less than a total thickness of the cap layer by ion milling of the surface, and depositing an upper layer to be a part of the magnetoresistive element. The upper layer is in contact with the surface of the cap layer after the etching.

Magnetoresistive element, and production method for magnetoresistive element

A method for producing a tunnel magnetoresistive element includes a stacking step, then in-magnetic field heating, and then dry etching. The stacking includes stacking a B absorption layer which is in contact with an upper surface of a CoFeB layer. The dry etching includes removal of layers to the B absorption layer. An end of etching is set as an end point time detected by an analysis device when a final layer before the B absorption layer directly above the CoFeB layer is exposed has reduced to a prescribed level, or when the B absorption layer directly above the CoFeB layer has increased to the prescribed level. An amount of over-etching after the end point time is specified in advance, and the B absorption layer is stacked such that the thickness from the prescribed level to the upper surface of the CoFeB layer corresponds to the over-etching amount.

Method for producing tunnel magnetoresistive element

A text visualization system allows a user to ascertain a relationship of viewpoints between categories in clustering of documents including texts of a plurality of categories. Each of a plurality of documents includes texts of each of a plurality of categories. A text and an element text that entails the text among texts included in the plurality of documents are set with respect to each of the categories. A text display displays a plurality of texts of each of one or more categories among the plurality of categories. The text display, in response to receiving a designation of a text of a specific category by a reception unit, extracts, from a plurality of texts of another category, a text that entails an element text of the another category included in a document including an element text that entails the text of the specific category, and displays the extracted text.

Text visualization system, text visualization method, and recording medium

The present invention is a text mining system comprising a synonym cluster acquiring section configured to acquire synonym clusters from texts in text data to be analyzed, the synonym clusters each being a collection of synonymous texts, an implication relationship acquiring section configured to acquire implication relationships among the synonym clusters, and an implication graph generating section configured to generate an implication graph including vertices of synonym clusters and directed edges each indicating a direction from an implied synonym cluster to an implying synonym cluster from the implication relationships among the synonym clusters.

Text mining system, text mining method, and program

An information processing system to increase weights of words that are related to a text, but that do not explicitly occur in the text, in a weight vector representing the text, is provided. An adjusting system () includes a distance storing unit () and an adjusting unit (). The distance storing unit () stores distances between any two terms of a plurality of terms. The distance between two terms becomes smaller as the two terms are semantically more similar. The adjusting unit () adjusts a weight of each term of the plurality of terms in a weight vector including weights of the plurality of terms and representing a text, on the basis of a distance between each term and other term in the weight vector and a weight of the other term.

Information processing system, an information processing method and a computer readable storage medium

A text processing system that is able to appropriately determine textual entailment between sentences with high coverage is provided. The text processing system is configured to execute: processing of extracting a common substructure that is a partial structure of a same type, the partial structure being common to a first sentence and a second sentence and, based on the a structure representing the first sentence and a structure representing the second sentence; processing of extracting at least one of a feature amount representing a dependency relationship between the at least one common substructure in the first and second sentences and a feature amount representing a dependency relationship between the common substructure in the first and second sentences and a substructure different from the common substructure; and processing of determining an entailment relationship between the first sentence and the second sentence by using the extracted feature amount.

Text processing system, text processing method and storage medium storing computer program

A text mining device () is used in which data composed of a set of records including an attribute value and text data is used as analysis target data. The text mining device () includes an analysis perspective candidate generation unit () that extracts an attribute value from the analysis target data and generates an analysis perspective candidate using the extracted attribute value, and a characteristic degree calculation unit () that compares text data in a record including the attribute value extracted as the analysis perspective candidate with text data in a record set that includes at least a record other than the record including the attribute value in the analysis target data, and calculates a characteristic degree indicating a relationship between the analysis perspective candidate and the analysis target data based on a result of the comparison.

Text mining device, text mining method, and computer-readable recording medium

Provided is a text processing system capable of classifying a plurality of texts into groups whose overviews are able to be grasped and classifying texts semantically having entailment relation into the same group even if the texts are not determined to have the entailment relation. Entailment recognition meansperforms entailment recognition between texts on given texts. Group generation meansselects an individual text and generates a group including texts entailing the selected text as members. Group integration meansintegrates groups in the case where groups satisfy a predetermined condition based on the degree of overlap of members between groups.

Text processing using entailment recognition, group generation, and group integration

An entailment evaluation device includes: a generation unit which generates first information indicating at least the order of occurrence of events of first and second simple sentences included in the hypothesis text and generates second information indicating at least the order of occurrence of events of third and fourth simple sentences included in a target text, the third simple sentence being related to the first simple sentence, the fourth simple sentence being related to the second simple sentence; a calculation unit which obtains a calculation result by comparing, based on the first and second information, the order of occurrence of events of first and second simple sentences and order of occurrence of events of third and fourth simple sentences; and a determination unit which determines, based on at least the calculation result, whether or not the target text entails the hypothesis text.

Entailment evaluation device, entailment evaluation method, and recording medium

A synonym relation determination device comprises: a synonym expression candidate storage unit which associates and stores a synonym candidate (EW) with the synonym source (OW); a text gathering unit which associates and gathers text with an issuing time; a synonym candidate search unit which calculates from the issuing time of the text a time interval (PD) in which the synonym candidate is searched in a text set (TX); a synonym source search unit which searches for a synonym source from the text set of a period which overlaps with the time interval in which the synonym candidate is searched for and calculates an occurrence of the synonym source; and synonym relation extraction unit which, when the occurrence of the synonym source is present in the time interval in which the synonym candidate is searched for, extracts a synonym relation between the synonym candidate and the synonym source.

Synonym relation determination device, synonym relation determination method, and program thereof

The disclosed apparatus uses a training data generation apparatus, which generates training data used for creating characteristic expression extraction rules. The training data generation apparatusincludes: a training data candidate clustering unit, which clusters a plurality of training data candidates assigned labels indicating annotation classes based on feature values containing respective context information, and a training data generation unitwhich, by referring to each cluster obtained using the clustering results, obtains the distribution of the labels of the training data candidates within the cluster, identifies training data candidates that meet a preset condition based on the obtained distribution, and generates training data using the identified training data candidates.

Training data generation apparatus, characteristic expression extraction system, training data generation method, and computer-readable storage medium

An information classification device () is provided with an union of sets determination unit () which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit () which applies a plurality of individual determination rules to the content to be classified which has been determined as correct, determines whether the content matches the condition, and performs correct/incorrect determination again regarding the content to be classified which has been determined as correct on the basis of the determination result of each individual determination rule. The union of sets determination rule is created using a result of correct/incorrect determination previously performed by two or more people with respect to a plurality of contents which are different from the contents to be classified, and also using feature amounts of respective different contents. Each individual determination rule is created for respective person using the aforementioned result of correct/incorrect determination and the feature amount.

Growing community of inventors

Tokyo, Japan

Masaaki Tsuchida