Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.

There is provided a system and method for generating predictions. The predictions are generated using a model configured to associate text with at least one action associated with at least one of a plurality of applications.

Systems and methods for iris authentication are disclosed. In one aspect, a deep neural network (DNN) with a triplet network architecture can be trained to learn an embedding (e.g., another DNN) that maps from the higher dimensional eye image space to a lower dimensional embedding space. The DNN can be trained with segmented iris images or images of the periocular region of the eye (including the eye and portions around the eye such as eyelids, eyebrows, eyelashes, and skin surrounding the eye). With the triplet network architecture, an embedding space representation (ESR) of a person's eye image can be closer to the ESRs of the person's other eye images than it is to the ESR of another person's eye image. In another aspect, to authenticate a user as an authorized user, an ESR of the user's eye image can be sufficiently close to an ESR of the authorized user's eye image.

Systems and methods for monitoring food intake include an air pressure sensor for detecting ear canal deformation, according to some implementations. For example, the air pressure sensor detects a change in air pressure in the ear canal resulting from mandible movement. Other implementations include systems and methods for monitoring food intake that include a temporalis muscle activity sensor for detecting temporalis muscle activity, wherein at least a portion of the temporalis muscle activity sensor is coupled adjacent a temple portion of eyeglasses and disposed between the temple tip and the frame end piece. The temporalis muscle activity sensor may include an accelerometer, for example, for detecting movement of the temple portion due to mandibular movement from chewing.

A method, a device, and a non-transitory storage medium to receive from customer devices, sensor messages indicating a power state of on or off, a location, and a timestamp; select an element of a utility system based on the sensor messages; determine a power state of on or off for the element based on the sensor messages and a location and time pertaining to the element; store a temporal and spatial model that includes an outage event; receive weather data pertaining to the element; generate an outage model based on the temporal and spatial model and the weather data; receive forecasted weather data; calculate a predicted outage pertaining to one or more elements of the utility system based on the outage model and the forecasted weather data; and transmit a message that includes the predicted outage.

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.

Implementations of the present application disclose a method for paging, an access network device and a terminal device. The method includes: an access network device receives movement information of a terminal device sent by the terminal device; the access network device determines, according to the movement information, a target paging area where the access network device initiates paging to the terminal device; and the access network device sends the target paging area to the terminal device. The method, the access network device and the terminal device according to the implementations of the present application are beneficial for the access network device to configure a reasonable paging area, thereby reducing signaling overhead.

Methods, systems, and apparatus for solving computational tasks using quantum computing resources. In one aspect a method includes receiving, at a quantum formulation solver, data representing a computational task to be performed; deriving, by the quantum formulation solver, a formulation of the data representing the computational task that is formulated for a selected type of quantum computing resource; routing, by the quantum formulation solver, the formulation of the data representing the computational task to a quantum computing resource of the selected type to obtain data representing a solution to the computational task; generating, at the quantum formulation solver, output data including data representing a solution to the computational task; and receiving, at a broker, the output data and generating one or more actions to be taken based on the output data.

The present technology addresses the problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, an exploration strategy comprising diverse exploration (DE) is employed, which learns and deploys a diverse set of safe policies to explore the environment. DE theory explains why diversity in behavior policies enables effective exploration without sacrificing exploitation. An empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.