Abstract: A meaning representation parsing system 100 includes: an input unit 110 that accepts input data 210 in a text or a graph; and a serialized graph generating unit 120 that generates a token array (serialized graph 220) representing a graph structure corresponding to the input data 210. Furthermore, the token array generated by the serialized graph generating unit 120 includes at least a first token indicating a node in the graph structure corresponding to the input data 210 and a second token indicating an edge representing a relationship between the nodes.

Abstract: A text can be automatically summarized with high accuracy. A text summarization method is executed by a computer, and includes: a blocking step of receiving an input of a text and generating a blocked text in which the text is segmented into blocks in topic units; a summarizing step of summarizing content of the text for each of the blocks in the blocked text and outputting a summarized text; and a structuring step of structuring content of the summarized text and outputting the structured content.

Abstract: In some examples, a system may generate a plurality of care path patient profile models based on a plurality of care path patterns for a plurality of past patient admissions. For example, each care path patient profile model may include a trained classifier. Further, the system may receive information related to a new patient admission, and may generate features from the received information related to the new patient admission. The system may input the features generated from the received information related to the new patient admission into the plurality of care path patient profile models to obtain a respective probability of being classified in a respective care path based on an amount of similarity to the patients who have gone through each care path. In addition, the system may present, on a display, information related to at least one care path pattern in a graphical user interface.

Abstract: In some examples, a computing device may receive hierarchical data having a hierarchical structure including a plurality of levels. The computing device may determine a plurality of features based at least in part on the hierarchical data, and may select a subset of the features at a first level as candidates for consolidating to a next higher level in the hierarchical structure. The computing device may determine that a predicted loss of information from consolidating the subset of features is less than a threshold, and may revise the hierarchical structure to include a consolidated feature at the next higher level, rather than the subset of features. In some examples, a statistical model may be trained based on the revised hierarchical structure and used at least partially to make a determination, send a notification, and/or control a device.

Abstract: In some examples, a system may generate a plurality of care path patient profile models based on a plurality of care path patterns for a plurality of past patient admissions. For example, each care path patient profile model may include a trained classifier. Further, the system may receive information related to a new patient admission, and may generate features from the received information related to the new patient admission. The system may input the features generated from the received information related to the new patient admission into the plurality of care path patient profile models to obtain a respective probability of being classified in a respective care path based on an amount of similarity to the patients who have gone through each care path. In addition, the system may present, on a display, information related to at least one care path pattern in a graphical user interface.

Abstract: In some examples, a computing device may receive sensor data associated with a plurality of patient devices that are associated with a plurality of patients. The computing device may further receive caregiver records corresponding at least partially to the sensor data. At least two groups of indicators may be determined from the caregiver records, such a based on a selected subject. Further, the computing device may determine a plurality of clusters from the sensor data. Based on the plurality of clusters and the at least two groups, the computing device may determine an indication of a discrepancy in care for a patient of the plurality of patients. Based on the indication of the discrepancy, the computing device may send at least one of a notification to a caregiver computing device, a notification to a monitoring computing device, or a control signal to one of the patient devices.

Abstract: In some examples, a computing device may receive sensor data for a target and at least one of: log data for the target, or historical log data and historical sensor data for a plurality of other targets. The computing device may determine at least one event classified as a non-uniform event in at least one of the log data or the historical log data, and may determine combined features, such as a feature vector, based on the sensor data and the non-uniform event(s). The computing device may determine an analysis result from the combined features. Further, based on the analysis result, the computing device may send a control signal to a device associated with the target for controlling the device, and/or may send a communication related to the target to another computing device.

Abstract: A series data distributed processing system including a parallel processing system and a distributed series data management unit managing distributed series data, wherein: the parallel processing system includes, on each computing device, a data block, a data block processing server for processing the data block, and a block processing integration server for processing a result from the data block processing server, the data block being formed from a plurality of values each associated with one of a plurality of sequential labels in the series data; and the distributed series data management unit includes a distributed information management database for managing data blocks, which retains management data, including sequential label ranges, which refer to ranges of sequential labels in the data blocks, series IDs corresponding to value names in the data blocks, and meta-information identifying computing devices retaining the data blocks.

Abstract: In some examples, a computing device may receive sensor data for a target and at least one of: log data for the target, or historical log data and historical sensor data for a plurality of other targets. The computing device may determine at least one event classified as a non-uniform event in at least one of the log data or the historical log data, and may determine combined features, such as a feature vector, based on the sensor data and the non-uniform event(s). The computing device may determine an analysis result from the combined features. Further, based on the analysis result, the computing device may send a control signal to a device associated with the target for controlling the device, and/or may send a communication related to the target to another computing device.

Abstract: In some examples, a computing device may receive hierarchical data having a hierarchical structure including a plurality of levels. The computing device may determine a plurality of features based at least in part on the hierarchical data, and may select a subset of the features at a first level as candidates for consolidating to a next higher level in the hierarchical structure. The computing device may determine that a predicted loss of information from consolidating the subset of features is less than a threshold, and may revise the hierarchical structure to include a consolidated feature at the next higher level, rather than the subset of features. In some examples, a statistical model may be trained based on the revised hierarchical structure and used at least partially to make a determination, send a notification, and/or control a device.

Abstract: In some examples, a computing device may receive sensor data associated with a plurality of patient devices that are associated with a plurality of patients. The computing device may further receive caregiver records corresponding at least partially to the sensor data. At least two groups of indicators may be determined from the caregiver records, such a based on a selected subject. Further, the computing device may determine a plurality of clusters from the sensor data. Based on the plurality of clusters and the at least two groups, the computing device may determine an indication of a discrepancy in care for a patient of the plurality of patients. Based on the indication of the discrepancy, the computing device may send at least one of a notification to a caregiver computing device, a notification to a monitoring computing device, or a control signal to one of the patient devices.

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: 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 series data distributed processing system including a parallel processing infrastructure and a distributed series data management unit managing distributed series data, wherein: the parallel processing infrastructure includes, on each computing device, a data block, a data block processing server for processing the data block, and a block processing integration server for processing a result from the data block processing server, the data block being formed from a plurality of values each associated with one of a plurality of sequential labels in the series data; and the distributed series data management unit includes a distributed information management database for managing data blocks, which retains management data, including sequential label ranges, which refer to ranges of sequential labels in the data blocks, series IDs corresponding to value names in the data blocks, and meta-information identifying computing devices retaining the data blocks.

Abstract: A method for treating wastewater containing heavy metals comprising directing the wastewater across a reverse osmosis aromatic polyamide membrane at low pressure ranging from 40-120 psi, the membrane being capable of removing at least 90% of the target heavy metals from the wastewater.

Abstract: A method for treating wastewater containing heavy metals comprising directing the wastewater across a reverse osmosis aromatic polyamide membrane at low pressure ranging from 40-120 psi, the membrane being capable of removing at least 90% of the target heavy metals from the wastewater.