Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: Multiple data sources encrypt data using encryption key data received from a first system; a second system does not have access to the encryption key data. The second system receives the encrypted data from the multiple data sources. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The second system may send the encrypted summation data to the first system, which may then decrypt the encrypted summation data to create unencrypted summation data.

Abstract: A first component determines encrypted data representing an event and encrypted threshold data corresponding to an outlier of the event. The first system may process the data using, for example, one or more composite integers, and may send the result to a second system. This second system may subtract the data to determine of the encrypted data is greater than, less than, or equal to the encrypted threshold. If so, the second system may determine that the encrypted data corresponds to an outlier of the data. The second system may send an indication of this determination to a third system.

Abstract: Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A secure processing component may process data using a transformation layer and may send and receive data to and from a first system. Multiple data-provider systems may send vertically partitioned data to the secure-processing component, which may determine output data corresponding to the multiple data-provider systems.

Abstract: A first component determines encrypted data representing an event and encrypted threshold data corresponding to an outlier of the event. The first system may process the data using, for example, one or more composite integers, and may send the result to a second system. This second system may subtract the data to determine of the encrypted data is greater than, less than, or equal to the encrypted threshold. If so, the second system may determine that the encrypted data corresponds to an outlier of the data. The second system may send an indication of this determination to a third system.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A secure processing component may process data using a transformation layer and may send and receive data to and from a first system. Multiple data-provider systems may send vertically partitioned data to the secure-processing component, which may determine output data corresponding to the multiple data-provider systems.

Abstract: Multiple data sources encrypt data using encryption key data received from a first system; a second system does not have access to the encryption key data. The second system receives the encrypted data from the multiple data sources. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The second system may send the encrypted summation data to the first system, which may then decrypt the encrypted summation data to create unencrypted summation data.

Abstract: Multiple data sources encrypt data using encryption key data received from a first system; a second system does not have access to the encryption key data. The second system receives the encrypted data from the multiple data sources. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The second system may send the encrypted summation data to the first system, which may then decrypt the encrypted summation data to create unencrypted summation data.

Abstract: A prosthetic valve may be used to repair a diseased or otherwise damaged heart valve such as the mitral valve. The prosthetic valve may include atraumatic anchor tabs which may be radiopaque or echogenic. A cinching mechanism may also be coupled to the prosthetic valve to control radial expansion of the prosthetic valve. The cinching mechanism may include a wire lasso or a belt. The prosthetic valve may include an expandable frame which has a reduced number of strut connection nodes to allow a lower profile collapsed configuration and tighter crimping. The commissure posts of the prosthetic valve may extend beyond an edge of the ventricular skirt portion of the device.

Abstract: Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

Abstract: A first system creates and sends encryption key data to multiple data sources. A second system receives data encrypted using the encryption key data from the multiple data sources; the data may include noise data such that, even if decrypted, the original data cannot be discovered. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The first system separately receives the noise data encrypted using the same technique as the encrypted data. The second system may send the encrypted summation data to the first system, which may then remove the noise data from the encrypted summation data to create unencrypted summation data.

Abstract: Multiple data sources encrypt data using encryption key data received from a first system; a second system does not have access to the encryption key data. The second system receives the encrypted data from the multiple data sources. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The second system may send the encrypted summation data to the first system, which may then decrypt the encrypted summation data to create unencrypted summation data.

Abstract: The present disclosure provides systems and methods for dynamically signaling encoder capabilities of vocoders of corresponding communication nodes. In one embodiment, during a call between a first communication node and a second communication node, a control node (e.g., base station controller or mobile switching center) for the first communication node sends capability information for a voice encoder of a vocoder of the first communication node to a control node for the second communication node. As a result, the second communication node is enabled to select and request a preferred encoder mode for the voice encoder of the vocoder of the first communication node based on the capabilities of the voice encoder of the vocoder of the first communication node.

Abstract: The present disclosure provides systems and methods for dynamically signaling encoder capabilities of vocoders of corresponding communication nodes. In one embodiment, during a call between a first communication node and a second communication node, a control node (e.g., base station controller or mobile switching center) for the first communication node sends capability information for a voice encoder of a vocoder of the first communication node to a control node for the second communication node. As a result, the second communication node is enabled to select and request a preferred encoder mode for the voice encoder of the vocoder of the first communication node based on the capabilities of the voice encoder of the vocoder of the first communication node.

Abstract: The present disclosure provides systems and methods for dynamically signaling encoder capabilities of vocoders of corresponding communication nodes. In one embodiment, during a call between a first communication node and a second communication node, a control node (e.g., base station controller or mobile switching center) for the first communication node sends capability information for a voice encoder of a vocoder of the first communication node to a control node for the second communication node. As a result, the second communication node is enabled to select and request a preferred encoder mode for the voice encoder of the vocoder of the first communication node based on the capabilities of the voice encoder of the vocoder of the first communication node.

Abstract: A fishing reel employs a driving mechanism having a cooperative pinion gear and a face gear. The face gear has a tooth form with a cross-section wherein the cross-section of the inner pitch portion is less than the cross-section of an outer pitch portion. The pinion gear effectively engages the inner pitch portion less than an outer pitch portion to provide a more uniform distribution of torque and to improve the wearing rate of the gear mechanism.

Abstract: A system and method for user authentication utilizing PKI credentials and user credentials on an electronic device comprising a mobile communication device, smart phone, a computer or other computing device.