Abstract: A method for classifying types of heartbeats includes obtaining a dataset including multiple heartbeat waveform data, generating, from the dataset, input data regarding a heartbeat waveform for training and generating a learning model to which the generated input data is input and from which a heartbeat type of the heartbeat waveform for training is output, training the learning model by determining a loss weight of each batch sampled from the dataset and determining a loss function based on the loss weight of each batch, and inputting a heartbeat waveform for test to the learning model and classifying a heartbeat type of the heartbeat waveform for test.

Abstract: A method for classifying types of heartbeats includes obtaining a dataset including multiple heartbeat waveform data, generating, from the dataset, input data regarding a heartbeat waveform for training and generating a learning model to which the generated input data is input and from which a heartbeat type of the heartbeat waveform for training is output, training the learning model by determining a loss weight of each batch sampled from the dataset and determining a loss function based on the loss weight of each batch, and inputting a heartbeat waveform for test to the learning model and classifying a heartbeat type of the heartbeat waveform for test.

Abstract: A method that includes generating, in a query pre-processor, a set of pre-computed materialized sub-graphs by executing a pre-processing dynamic random-walk based search for a bin of terms. The method also includes receiving, in a query processor, a search query having at least one search query term. In response to receiving the search query, the method includes accessing the set of pre-computed materialized sub-graphs. The accessing includes accessing a text index based on the search query term to retrieve a corresponding term group identifier and accessing the corresponding pre-computed materialized sub-graph based on the term group identifier. The method also includes executing a dynamic random-walk based search on only the corresponding pre-computed materialized sub-graph and based on the executing, retrieving nodes in the dataset and transmitting the nodes as results of the query.

Abstract: A method that includes generating, in a query pre-processor, a set of pre-computed materialized sub-graphs by executing a pre-processing dynamic random-walk based search for a bin of terms. The method also includes receiving, in a query processor, a search query having at least one search query term. In response to receiving the search query, the method includes accessing the set of pre-computed materialized sub-graphs. The accessing includes accessing a text index based on the search query term to retrieve a corresponding term group identifier and accessing the corresponding pre-computed materialized sub-graph based on the term group identifier. The method also includes executing a dynamic random-walk based search on only the corresponding pre-computed materialized sub-graph and based on the executing, retrieving nodes in the dataset and transmitting the nodes as results of the query.

Abstract: According to one embodiment of the present invention, a method for processing a query is provided. The method includes generating a set of pre-computed materialized sub-graphs from a dataset and receiving a search query having one or more search query terms. A particular one of the pre-computed materialized sub-graphs is accessed and a dynamic authority-based keyword search is executed on the particular one of the pre-computed materialized sub-graphs. Nodes in the dataset are then retrieved based on the executing, and a response to the search query is provided which includes the retrieved nodes.

Abstract: An apparatus and a method to generate an atrial fibrillation prediction model, and an apparatus and a method to predict atrial fibrillation are provided. An atrial fibrillation (AF) prediction model generating apparatus includes a feature extractor configured to extract T-wave features in a predetermined time period from electrocardiogram data and generate a T-wave feature profile based on the extracted features, and a prediction model generator configured to classify the generated T-wave feature profile and generate an AF prediction model using the classified feature profile.

Abstract: An apparatus and a method to generate an atrial fibrillation prediction model, and an apparatus and a method to predict atrial fibrillation are provided. An atrial fibrillation (AF) prediction model generating apparatus includes a feature extractor configured to extract T-wave features in a predetermined time period from electrocardiogram data and generate a T-wave feature profile based on the extracted features, and a prediction model generator configured to classify the generated T-wave feature profile and generate an AF prediction model using the classified feature profile.

Abstract: A system, method and computer program product for executing a query on linked data sources. Embodiments of the invention generate an instance graph expressing relationships between objects in the linked data sources and receive a query including at least first and second search terms. The first search term is then executed on the instance graph and a summary graph is generated using the results of the executing step. A second search term is then executed on the summary graph.

Abstract: Described is a search-related technology in which context information regarding a user's prior search actions is used in making query recommendations for a current user action, such as a query or click. To determine whether each set or subset of context information is relevant to the user action, data obtained from a query log is evaluated. More particularly, a query transition (query-query) graph and a query click (query-URL) graph are extracted from the query log; vectors are computed for the current action and each context/sub-context and evaluated against vectors in the graphs to determine current action-to-context similarity. Also described is using similar context to provide the query recommendations, using parameters to control the similarity strictness, and/or whether more recent context information is more relevant than less recent context information, and using context information to distinguish between user sessions.

Abstract: According to one embodiment of the present invention, a method for processing a query is provided. The method includes generating a set of pre-computed materialized sub-graphs from a dataset and receiving a search query having one or more search query terms. A particular one of the pre-computed materialized sub-graphs is accessed and a dynamic authority-based keyword search is executed on the particular one of the pre-computed materialized sub-graphs. Nodes in the dataset are then retrieved based on the executing, and a response to the search query is provided which includes the retrieved nodes.

Abstract: A system, method and computer program product for executing a query on linked data sources. Embodiments of the invention generate an instance graph expressing relationships between objects in the linked data sources and receive a query including at least first and second search terms. The first search term is then executed on the instance graph and a summary graph is generated using the results of the executing step. A second search term is then executed on the summary graph.