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: 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: 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 a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Abstract: The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions.

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic biometric identifiers. The method includes generating a scannable code upon receiving a success nonce from a registration server. The registration server can access a user identifier and a hash of at least a signature using the success nonce. The signature can be generated based at least in part upon a biometric identifier of a user. The method includes recreating the hash of the signature stored by the registration server. The method includes generating the scannable code by encrypting the success nonce and the recreated hash. The biometric identifier of the user is generated by feeding a plurality of non-deterministic biometric inputs to a trained machine learning model producing a plurality of feature vectors. The method includes projecting the plurality of feature vectors onto a surface of a unit hyper-sphere and computing a characteristic identity vector representing the user.

Abstract: The technology disclosed relates to systems and methods for deploying cross-platform applications to tiny ML hardware. The system provides tools to maintain definitions of a first, a second and a third declarative instruction. The system provides tools to maintain compiled bytecode for a procedure block and a tiny ML model that runs on the tiny ML hardware. The compiled bytecode further includes a manifest of one or more capabilities, one or more procedure blocks and at least one output supported by the tiny ML model. The system provides a loader that connects to an instance of the tiny ML hardware. The loader includes logic to verify that the instance of the ML hardware supports the one or more capabilities, the one or more procedure blocks and at least one output specified in the manifest. The loader loads the bytecode and verifies integrity of the load.

Abstract: The technology disclosed relates to a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

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 systems and methods for deploying cross-platform applications to tiny ML hardware. The system provides tools to maintain definitions of a first, a second and a third declarative instruction. The system provides tools to maintain compiled bytecode for a procedure block and a tiny ML model that runs on the tiny ML hardware. The compiled bytecode further includes a manifest of one or more capabilities, one or more procedure blocks and at least one output supported by the tiny ML model. The system provides a loader that connects to an instance of the tiny ML hardware. The loader includes logic to verify that the instance of the ML hardware supports the one or more capabilities, the one or more procedure blocks and at least one output specified in the manifest. The loader loads the bytecode and verifies integrity of the load.

Abstract: The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions.

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 a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint.

Abstract: The technology disclosed relates to a system and method for detecting when to diverge to a tangential conversation from a linearly structured steps of a cognitive behavioral therapy (CBT) session in a series of sessions for a mental health disorder. The system includes logic to determine when to merge the tangential conversation back to flow of linearly structured steps in the therapy session. The system can bookmark the last completed step in the flow of linearly structured steps of the CBT therapy session before starting the tangential conversation. The system can calculate a return to CBT session score after each succeeding utterance from the patient in the tangential conversation. The system resumes flow of the linearly structured steps of the CBT session upon detecting that the return to CBT session score has exceeded a threshold.

Abstract: The technology disclosed relates to a system and method for remembering content received from a patient for future use and understood by computerized natural language processing during a chatbot therapy session. The system includes a graph of entities and relationships in an artificial memory graph data structure. The entity nodes in the graph represent entities that are related to a patient root node representing the patient. The entity nodes are connected to patient root node by one or more relationship edges that have named roles. The entity nodes and/or relationship edges can be slotted for at least a distinguishing name, a patient sentiment towards the entity and an entity state.

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 a system and method for assigning participants to groups in a clinical trial. The system includes a federated server configured with group assignability data specifying a plurality of groups assignable to participants in a clinical trial and group distribution data specifying distribution of the participants into groups. The groups include at least one placebo group and one or more treatment groups. The system includes an intervention server configured to generate group encryption keys for encrypting the group assignability data. The system includes edge devices of each of the participants. The edge devices are in communication with the federated server.

Abstract: System and method for remote medical information exchange are disclosed. The system comprises a patient-end digital device configured to capture video stream of face of the patient. The system comprises a doctor-end digital device configured to show the video stream of the face of the patient and one or more physiological inference in accordance with the video stream. The system comprises a processor configured to receive a facial image from the captured video stream. The processor can provide the facial image to a trained neural network configured to predict a sentiment of the patient based on features in the facial image. The processor is configured to receive the sentiment of the patient from the trained neural network indicating how the patient is feeling. The processor is further configured to provide the predicted sentiment to the doctor-end digital device to aid in diagnosis of the patient.

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.