Abstract: Anchor tree cross-merge within a distributed storage system. A computer system identifies a data structure that includes (i) a root anchor tree and (ii) an ordered set of anchor trees that are ordered based on their creation, such that a last anchor tree of the ordered set of anchor trees is most-recently created. The data structure stores index data for a set of objects stored in a non-volatile storage. The computer system creates a new anchor tree in the ordered set of anchor trees. The computer system identifies, from a set of delta tables, index data representing one or more objects that are stored on the non-volatile storage. The computer system merges the index data representing the one or more objects into the new anchor tree.

Abstract: Anchor tree cross-merge within a distributed storage system. A computer system identifies a data structure that includes (i) a root anchor tree and (ii) an ordered set of anchor trees that are ordered based on their creation, such that a last anchor tree of the ordered set of anchor trees is most-recently created. The data structure stores index data for a set of objects stored in a non-volatile storage. The computer system creates a new anchor tree in the ordered set of anchor trees. The computer system identifies, from a set of delta tables, index data representing one or more objects that are stored on the non-volatile storage. The computer system merges the index data representing the one or more objects into the new anchor tree.

Abstract: A distributed storage system includes non-volatile storage storing portions of a first object. The first object encompasses data having a first range of addresses and each portion includes data for a respective range of addresses that is a proper subset of the first range. A first data structure stores, for each portion, data indicating the respective range of addresses and a pointer to where the portion is stored. The first data structure includes a root tree and a set of trees ordered by creation data such that a last tree is most-recently created. The non-volatile storage stores received write data and a write buffer stores index data pointing to storage locations of the received write data. An index management system stores the index data from the write buffer into the last tree and, if the ordered set is empty, creates a tree in the ordered set before the storing.

Abstract: A text preparation apparatus is configured to in the decoding processing: perform first-layer recurrent neural network processing for phrase types to be used in the text and second-layer recurrent neural network processing for words appropriate for each of the phrase types; determine a phrase appropriate for each of the phrase types based on outputs of the second-layer recurrent neural network processing; generate a first vector set from a state vector of a previous step in the first-layer recurrent neural network processing and the feature vector sets, each vector of the first vector set being generated based on similarity degrees between individual vectors in one of the feature vector sets and the state vector; generate a second vector based on similarity degrees between individual vectors in the first vector set and the state vector; and input the second vector to a given step in the first-layer recurrent neural network processing.

Abstract: A distributed storage system includes non-volatile storage storing portions of a first object. The first object encompasses data having a first range of addresses and each portion includes data for a respective range of addresses that is a proper subset of the first range. A first data structure stores, for each portion, data indicating the respective range of addresses and a pointer to where the portion is stored. The first data structure includes a root tree and a set of trees ordered by creation data such that a last tree is most-recently created. The non-volatile storage stores received write data and a write buffer stores index data pointing to storage locations of the received write data. An index management system stores the index data from the write buffer into the last tree and, if the ordered set is empty, creates a tree in the ordered set before the storing.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label?unlabeled?anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: A text preparation apparatus is configured to in the decoding processing: perform first-layer recurrent neural network processing for phrase types to be used in the text and second-layer recurrent neural network processing for words appropriate for each of the phrase types; determine a phrase appropriate for each of the phrase types based on outputs of the second-layer recurrent neural network processing; generate a first vector set from a state vector of a previous step in the first-layer recurrent neural network processing and the feature vector sets, each vector of the first vector set being generated based on similarity degrees between individual vectors in one of the feature vector sets and the state vector; generate a second vector based on similarity degrees between individual vectors in the first vector set and the state vector; and input the second vector to a given step in the first-layer recurrent neural network processing.

Abstract: The problem solved by this invention is to convert text information in a geology report to numerical values which reflects geological characteristics of a well's subsurface. Prior art referred above cannot be applicable to this problem. Since text information in the geology report is in the natural language form. This information is not widely used in this industry, due to the fact that the text information can be hardly extracted and summarized into numerical values and integrated into current physical geology models or statistical models. This invention makes the text information in geology report, which is often in a natural language form, easier to be integrated into current geology physical models or statistical models. Also, the numerical values extracted from the geology report can be integrated with other kinds of data, such as seismic data and well-logging data, to obtain more accurate and comprehensive analysis results.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: The problem solved by this invention is to convert text information in a geology report to numerical values which reflects geological characteristics of a well's subsurface. Prior art referred above cannot be applicable to this problem. Since text information in the geology report is in the natural language form. This information is not widely used in this industry, due to the fact that the text information can be hardly extracted and summarized into numerical values and integrated into current physical geology models or statistical models. This invention makes the text information in geology report, which is often in a natural language form, easier to be integrated into current geology physical models or statistical models. Also, the numerical values extracted from the geology report can be integrated with other kinds of data, such as seismic data and well-logging data, to obtain more accurate and comprehensive analysis results.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label unlabeled anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label?unlabeled?anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label unlabeled anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.