Abstract: In an embodiment, operations include receiving a graph representative of a domain. The operations further include extracting first sub-graphs from the graph and reducing each first sub-graph to obtain a set of reduced sub-graphs. The operations further include executing a set of operations comprising: determining a closest reduced sub-graph, from the set of reduced sub-graphs, corresponding to each first sub-graph; determining coverage metrics based on the extracted first sub-graphs and the closest reduced sub-graph corresponding to each first sub-graph; determining whether the coverage metrics satisfy coverage conditions; and re-iterating reduction of the extracted first sub-graphs if the coverage metrics do not satisfy the coverage conditions.

Abstract: In an embodiment, each of a set of subgraphs associating an entity from an entity graph with an item is extracted from a graph database. A label score, which is an importance of an item to a respective entity is computed for each subgraph. A training dataset including the set of subgraphs and the label score for each subgraph is generated. A set of ML regression models is trained on respective entity-specific subsets of the training dataset. An ML regression model associated with a second entity generates a prediction score for an unseen graph. From the set of subgraphs, one or more subgraphs associated with the second entity are determined based on the prediction score. A recommendation for one or more items is determined, based on the one or more subgraphs. The recommendation is displayed on a user device of the first entity.

Abstract: A system for assessing a spinal disorder includes a wearable electronic device having one or more sensors and an assessment system. The wearable electronic device is configured to be positioned on a portion of a lower back of a wearer, and the one or more sensors are configured to obtain patient data associated with the wearer. The system receives patient data from the one or more sensors, where the patient data includes movement data associated with movement of the lower back of the wearer, classifies the movement data into an initial grouping, further classifies the at least a portion of the movement data into one of the plurality of activity categories, generate a score corresponding to the at least a portion of the movement data based on the activity category to which the movement data is classified, and cause the score to be displayed via a client electronic device.

Abstract: A nanopore cell may include a well having a working electrode at a bottom of the well. The well may be formed within a dielectric layer, where the sidewalls of the well may be formed of the dielectric or other materials coating the walls of the dielectric layer. Various materials having different hydrophobicity and hydrophilicity can be used to provide desired properties of the cell. The nanopore in the nanopore cell can be inserted in a membrane formed over the well. Various techniques can be used for providing a desired shape and other properties e.g., of materials and processes for forming the well.

Abstract: A nanopore cell may include a well having a working electrode at a bottom of the well. The well may be formed within a dielectric layer, where the sidewalls of the well may be formed of the dielectric or other materials coating the walls of the dielectric layer. Various materials having different hydrophobicity and hydrophilicity can be used to provide desired properties of the cell. The nanopore in the nanopore cell can be inserted in a membrane formed over the well. Various techniques can be used for providing a desired shape and other properties e.g., of materials and processes for forming the well.

Abstract: A system for assessing a spinal disorder includes a wearable electronic device having one or more sensors and an assessment system. The wearable electronic device is configured to be positioned on a portion of a lower back of a wearer, and the one or more sensors are configured to obtain patient data associated with the wearer. The system receives patient data from the one or more sensors, where the patient data includes movement data associated with movement of the lower back of the wearer, classifies the movement data into an initial grouping, further classifies the at least a portion of the movement data into one of the plurality of activity categories, generate a score corresponding to the at least a portion of the movement data based on the activity category to which the movement data is classified, and cause the score to be displayed via a client electronic device.

Abstract: A nanopore cell may include a well having a working electrode at a bottom of the well. The well may be formed within a dielectric layer, where the sidewalls of the well may be formed of the dielectric or other materials coating the walls of the dielectric layer. Various materials having different hydrophobicity and hydrophilicity can be used to provide desired properties of the cell. The nanopore in the nanopore cell can be inserted in a membrane formed over the well. Various techniques can be used for providing a desired shape and other properties e.g., of materials and processes for forming the well.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: The present invention pertains to video playback systems, devices and methods for searching events contained within a video image sequence. A video playback system in accordance with an illustrative embodiment of the present invention includes a video playback device adapted to run a sequential searching algorithm for sequentially presenting video images within an image sequence, and a user interface that can be used by an operator to detect the occurrence of an event contained within the image sequence. Methods of searching for an event of interest contained within an image sequence are also disclosed herein.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.