Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: Method and system for remote medical information exchanging are disclosed. The system for remote medical information exchanging comprises a computer application program and a data storage server. The computer application program is electrically coupled with a data storage server via communication network, comprising an end user end, configured to capture video stream of face of the end user; and a doctor end, configured to show the video stream of the face of end user and one or more physiological inference in accordance with the video stream. The data storage server, configured to store a scalable video server module, one or more artificial intelligence modules, a poly-omics uni-matrix pipeline, and a combined inference module.

Abstract: The technology disclosed relates to a system and method of drug adherence. The system includes an optical character recognition engine configured to process at least one image that depicts data characterizing medication-under-analysis and generate text identifying at least a name of the medication-under-analysis. The system comprises a name entity recognition engine to attribute the name of the medication-under-analysis to at least one family of medication. The system comprises a data augmenter engine configured to supplement the attributed medication name with a plurality of multiomics channels and generate an augmented set of channels. The system includes runtime logic to select a drug-specific adverse event mapper from a plurality of drug-specific adverse event mappers.

Abstract: The technology disclosed relates to a system and method of conducting virtual clinical trials. The system comprises a sponsor server configured to specify a target mapping of a clinical trial objective mapper. The target mapping maps participant-specific clinical data to an objective of a virtual clinical trial. The system comprises a plurality of edge devices accessible by respective participants in a plurality of participants. The system comprises a clinical trial conductor server configured to distribute coefficients of the clinical trial objective mapper to respective edge devices to implement distributed training of the clinical trial objective mapper. The clinical trial conductor server is configured to receive participant-specific gradients generated during the distributed training in response to processing participant-specific clinical data.

Abstract: The technology disclosed relates to a system and method for training processing engines. A processing engine can have at least a first processing module and a second processing module. The first processing module in each processing engine is different from a corresponding first processing module in every other processing engine. The second processing module in each processing engine is same as a corresponding second processing module in every other processing engine. The system can include a deployer that deploys each processing engine to a respective hardware module for training. The system can comprise a forward propagator which during forward pass stage can process inputs through first processing modules and produce an intermediate output for each first processing module. The system can comprise a backward propagator which during backward pass stage can determine gradients for each second processing module on corresponding final outputs and ground truths.

Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.

Abstract: A federated training system comprises a plurality of models, a plurality of training datasets, and a runtime intermediary. Models in the plurality of models have model coefficients responsive to training. Training datasets in the plurality of training datasets are annotated with ground truth labels to train the models. The training datasets are accompanied with training provisioning parameters and privacy parameters. The runtime intermediary is interposed between the models and the training datasets, and configured to receive requests for training the models on the training datasets, the requests accompanied with training acquisition parameters, to respond to the requests by matching the models with the training datasets based on evaluating the training acquisition parameters against the training provisioning parameters, to train the models on the matched training datasets in accordance with the privacy parameters to generate gradients with respect to the model coefficients.

Abstract: Method and system for remote medical information exchange are disclosed. The system for information exchange comprises a mirror configured to capture the facial information of a user and display inference information about the user. The system can comprise an image capture module configured to capture the facial information of the user while the user is looking into the mirror. The system can include a displaying module, coupled to a display integrated into the mirror and configured to cause display of inference information about the user. An on-mirror computation device, coupled in communication with the image capture module and the displaying module, is configured to receive and process facial information of the user and produce inference information about the user. The on-mirror computation device can comprise one or more artificial intelligence modules.

Abstract: Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.

Abstract: Method and system for remote medical information exchanging are disclosed. The system for remote medical information exchanging comprises a computer application program and a data storage server. The computer application program is electrically coupled with a data storage server via communication network, comprising an end user end, configured to capture video stream of face of the end user; and a doctor end, configured to show the video stream of the face of end user and one or more physiological inference in accordance with the video stream. The data storage server, configured to store a scalable video server module, one or more artificial intelligence modules, a poly-omics uni-matrix pipeline, and a combined inference module.