Abstract: A processor for a cryptosystem. The processor comprises a hybrid processor architecture including a hardware processor, a software processor and an interconnection interface arranged to exchange data between the hardware processor and the software processor; wherein the hardware processor comprises a plurality of hardware accelerator modules arranged to perform computational tasks including at least one of number theoretic transforms (NTT) computation, arithmetic operations which are more time-consuming when being performed instead by the software-processor.

Abstract: A computing system that is configured for a federated wallet with cryptographically secure signature delegation. The system may be configured to receive a session public key corresponding to a decentralized application and a user. The system may be further configured to receive an unsigned transaction of a blockchain, the unsigned transaction corresponding to the user. The system may be further configured to provide a symmetric encryption key to the user's device for encrypting the user's private signing key. The system may be further configured to determine, using the session public key, that the unsigned transaction is valid. Based on the validity of the unsigned transaction, the system may send the unsigned transaction to the user's device. The system may send the symmetric encryption key to the user's device to decrypt the private signing key. The system may be further configured to receive a signed transaction for submission to the blockchain.

Abstract: A computing system that is configured for a federated wallet with cryptographically secure signature delegation. The system may be configured to receive a session public key corresponding to a decentralized application and a user. The system may be further configured to receive an unsigned transaction of a blockchain, the unsigned transaction corresponding to the user. The system may be further configured to provide a symmetric encryption key to the user's device for encrypting the user's private signing key. The system may be further configured to determine, using the session public key, that the unsigned transaction is valid. Based on the validity of the unsigned transaction, the system may send the unsigned transaction to the user's device. The system may send the symmetric encryption key to the user's device to decrypt the private signing key. The system may be further configured to receive a signed transaction for submission to the blockchain.

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: 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: A first system receives an encrypted data vector representing a text search query from a second system and second encrypted data from a third system that may include a first vector and a second vector representing text of an electronic document. The first system may multiply the vectors by a random vector. The first system may determine a first difference between the encrypted data vector and the first vector, and a second difference between the encrypted data vector and the second vector. The first system may determine a product of the first and second difference. The first system may send the product to the third system and then receive a value representing the decrypted difference. The first system may determine if the value satisfies a condition and send the result of the determination to the second system.

Abstract: The present invention provides a system for implementing a logistic regression classification mechanism to measure and assess a depth of anesthesia of an animal subject based on electroencephalography (EEG), which includes a signal pre-processor, an epoch generator, a feature extractor, a classifier, and a predictor. Related method of how to pre-process the raw data of EEG signal, epoch generation thereof, feature extraction from each epoch, classification based on extracted features, and prediction of different states of the animal subject based on a prediction decision mechanism is also provided. Classification accuracy of the present invention for 1-second and 10% overlapping epochs is about 94% with an average total system delay of about 12 ?s and low on-chip power consumption. The present system is entirely optimized, which leads to a 100% accurate channel prediction after a 7-second run-time on average.

Abstract: The present invention provides a hardware-friendly framework for implementing a point-of-care diagnosis hardware tool for practical end-user convenience, power saving and resource utilization. The hardware tool is non-invasive and comfortable for the patient, as a primary means of differential diagnosis between two neuromuscular diseases such as neuropathy and myopathy. The provided hard-ware tool comprises a feature extractor configured to receive electrodiagnostic signals (preferably EMG signals) of a patient and extract one or more features from the collected signals; and a classifier configured to receive the extracted features and classify a neuromuscular disease for the patient based on the extracted features. The classifier is a single layer machine-learning perceptron trained with datasets consisted of electrodiagnostic signals of patients to perform a linearly separable binary classification.

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: 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: A first system receives an encrypted data vector representing a text search query from a second system and second encrypted data from a third system that may include a first vector and a second vector representing text of an electronic document. The first system may multiply the vectors by a random vector. The first system may determine a first difference between the encrypted data vector and the first vector, and a second difference between the encrypted data vector and the second vector. The first system may determine a product of the first and second difference. The first system may send the product to the third system and then receive a value representing the decrypted difference. The first system may determine if the value satisfies a condition and send the result of the determination to the second 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: 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: 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 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 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.