Abstract: A headset for use in delivering electrical stimulation to the skin surface of the head or in sensing one or more body parameters of the head of a user.

Abstract: A garment for bringing an EM transducer to contact with a thoracic skin surface area of a wearer is disclosed. The garment comprises a thoracic garment having a EM transducer placement portion and a pressure applying element associated with the EM transducer placement portion for applying a pressure on an EM transducer secured in an associated the EM transducer placement portion when the thoracic garment is worn by a wearer so that the EM transducer applies a respective pressure on a thoracic skin surface area of the wearer.

Abstract: A headset for use in delivering electrical stimulation to the skin surface of the head or in sensing one or more parameters of the head of a user.

Abstract: A cranial orthosis configured to induce cranial remodeling to achieve symmetry of the skull, as well as adequate proportions between the head width and length, for infants suffering from positional skull deformities comprises a headpiece adapted to encircle, and to be secured to, the skull of an infant suffering from positional skull deformity in such a way that contact between a flattened occipital area of the infant and a flat surface on top of which the infant is lying is prevented so that unrestricted occipital growth (or other places) is able to be induced, wherein said headpiece is adjustable to take into account cranial growth of the infant and to redirect the head growth. The headpiece is capable of adjusting the infant's head growth, to improve symmetry and obtain adequate proportions.

Abstract: A garment for bringing an EM transducer to contact with a thoracic skin surface area of a wearer is disclosed. The garment comprises a thoracic garment having a EM transducer placement portion and a pressure applying element associated with the EM transducer placement portion for applying a pressure on an EM transducer secured in an associated the EM transducer placement portion when the thoracic garment is worn by a wearer so that the EM transducer applies a respective pressure on a thoracic skin surface area of the wearer.

Abstract: An assembly can comprise a prosthetic valve comprising a radially expandable and compressible frame and a rotatable screw connected to the frame, where the screw comprises a screw head having an annular bore with internal threads, and where the screw is rotatable to radially expand the frame. The assembly further comprises a delivery apparatus releasably coupled to the screw, the delivery apparatus comprising a first shaft and a first head disposed at an end of the first shaft, the first head comprising external threads configured to engage with the internal threads of the screw head, and a second shaft that is coaxial with and surrounds the first shaft, the second shaft comprising a second head disposed at an end of the second shaft. The second head is configured to releasably engage with an exterior surface of the screw head and rotate the screw.

Abstract: The techniques disclosed herein enable systems to train a machine learning model to classify malicious command line strings and select anomalous and uncertain samples for analysis. To train the machine learning model, a system receives a labeled data set containing command line inputs that are known to be malicious or benign. Utilizing a term embedding model, the system can generate aggregated numerical representations of the command line inputs for analysis by the machine learning model. The aggregated numerical representations can include various information such as term scores that represent a probability that an individual term of the command line string is malicious as well as numerical representations of the individual terms. The system can subsequently provide the aggregated numerical representations to the machine learning model for analysis. Based on the aggregated numerical representations, the machine learning model can learn to distinguish malicious command line inputs from benign inputs.

Abstract: Techniques are described herein that are capable of detecting a spoofed entity based on complexity of a distribution of events initiated by the spoofed entity. Frequencies with which events of event types are initiated are determined by an entity during a designated period of time. Complexity of a distribution of the events among the event types is determined based at least on the frequencies with which the events of the event types are initiated by the entity during the designated period of time. Based at least on the complexity of the distribution of the events among the event types being less than or equal to a complexity threshold, the entity is identified as a spoofed entity associated with a spoofing attack. A security action is performed with regard to the entity based at least on the entity being identified as the spoofed entity associated with the spoofing attack.

Abstract: A headset for use in delivering electrical stimulation to the skin surface of the head or in sensing one or more body parameters of the head of a user.

Abstract: A method can comprise deploying a prosthetic valve with a delivery apparatus, where the prosthetic valve comprises a frame and a rotatable screw connected to the frame, and where the screw comprises a screw head having an annular bore with internal threads. The delivery apparatus is releasably coupled to the screw and can comprise a first shaft and a first head disposed at an end of the first shaft, the first head comprising external threads releasably engaged with the internal threads of the screw head, and a second shaft that is coaxial with and surrounds the first shaft, the second shaft comprising a second head disposed at an end of the second shaft, the second head releasably engaged with an exterior surface of the screw head. Deploying the valve includes radially expanding the prosthetic valve by rotating the screw with the delivery apparatus.

Abstract: A headset for use in delivering electrical stimulation to the skin surface of the head or in sensing one or more body parameters of the head of a user.

Abstract: Embodiments of the present disclosure provide systems, methods, and non-transitory computer storage media for detecting abnormal behavior of device in an enterprise network based on an analysis of behavioral information of the device's neighbors in network. At a high level, embodiments of the present disclosure employ a hive-mind approach to determine anomalous behavior of a device in a network based on analyzing behavior information reported by neighboring devices within the network. Embodiments identify that a device is alive and connected within the network based on multiple neighboring devices reporting behavioral information about the device; however, the device may be dysfunctional and failing to report its own information. By aggregating and analyzing behavioral information of a device based on the reporting information of its neighboring devices, embodiments of the present disclosure are able to determine whether a device is healthy even when the device is unable to report its own information.

Abstract: Embodiments of the present disclosure provide systems, methods, and non-transitory computer storage media for detecting abnormal behavior of device in an enterprise network based on an analysis of behavioral information of the device's neighbors in network. At a high level, embodiments of the present disclosure employ a hive-mind approach to determine anomalous behavior of a device in a network based on analyzing behavior information reported by neighboring devices within the network. Embodiments identify that a device is alive and connected within the network based on multiple neighboring devices reporting behavioral information about the device; however, the device may be dysfunctional and failing to report its own information. By aggregating and analyzing behavioral information of a device based on the reporting information of its neighboring devices, embodiments of the present disclosure are able to determine whether a device is healthy even when the device is unable to report its own information.

Abstract: The techniques disclosed herein enable systems to train a machine learning model to classify malicious command line strings and select anomalous and uncertain samples for analysis. To train the machine learning model, a system receives a labeled data set containing command line inputs that are known to be malicious or benign. Utilizing a term embedding model, the system can generate aggregated numerical representations of the command line inputs for analysis by the machine learning model. The aggregated numerical representations can include various information such as term scores that represent a probability that an individual term of the command line string is malicious as well as numerical representations of the individual terms. The system can subsequently provide the aggregated numerical representations to the machine learning model for analysis. Based on the aggregated numerical representations, the machine learning model can learn to distinguish malicious command line inputs from benign inputs.

Abstract: Embodiments of the present disclosure provide systems, methods, and non-transitory computer storage media for detecting abnormal behavior of device in an enterprise network based on an analysis of behavioral information of the device's neighbors in network. At a high level, embodiments of the present disclosure employ a hive-mind approach to determine anomalous behavior of a device in a network based on analyzing behavior information reported by neighboring devices within the network. Embodiments identify that a device is alive and connected within the network based on multiple neighboring devices reporting behavioral information about the device; however, the device may be dysfunctional and failing to report its own information. By aggregating and analyzing behavioral information of a device based on the reporting information of its neighboring devices, embodiments of the present disclosure are able to determine whether a device is healthy even when the device is unable to report its own information.

Abstract: An assembly can comprise a prosthetic valve comprising a radially expandable and compressible frame and a rotatable screw connected to the frame, where the screw comprises a screw head having an annular bore with internal threads, and where the screw is rotatable to radially expand the frame. The assembly further comprises a delivery apparatus releasably coupled to the screw, the delivery apparatus comprising a first shaft and a first head disposed at an end of the first shaft, the first head comprising external threads configured to engage with the internal threads of the screw head, and a second shaft that is coaxial with and surrounds the first shaft, the second shaft comprising a second head disposed at an end of the second shaft. The second head is configured to releasably engage with an exterior surface of the screw head and rotate the screw.

Abstract: An oral care implement including a handle and an oral care refill head. The handle has a gripping portion, an engagement component coupled to a distal end of the gripping portion, and a stem. The engagement component includes a plate portion and a first engagement feature. The stem extends through an opening in the engagement component and protrudes from the plate portion. The oral care refill head includes an oral care treatment portion and a sleeve portion. The sleeve portion has an inner surface that defines a sleeve cavity having a cavity axis and a second engagement feature. When the oral care refill head is detachably coupled to the handle, the first and second engagement features mate with one another to at least one of: (1) position the oral care refill head and the handle in an operational alignment; and (2) lock the oral care refill head to the handle.