A system for machine learning architecture for time series data prediction. The system may be configured to: maintain a data set representing a neural network having a plurality of weights; obtain time series data associated with a data query; generate, using the neural network and based on the time series data, a predicted value based on a sampled realization of the time series data and a normalizing flow model, the normalizing flow model based on a latent continuous-time stochastic process having a stationary marginal distribution and bounded variance; and generate a signal providing an indication of the predicted value associated with the data query.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

A controller for controlling a system is provided. The controller performs measuring variables via an interface to generate a vector of variables, providing a cost function, with respect to the system, based on the vector variables using weighting factors, wherein the vector variables are represented by a time-step, computing first-derivative of the cost function at an initial time-step, obtaining a convergence time from the first-derivative of the cost function, computing second derivative of the cost function and generating an optimization differential equation based on the first and second derivatives of the cost function, proceeding, starting with the initial time-step, to obtain a value of the optimization differential equation by solving the optimization differential equation, in an iteration manner, with a predetermined time step being multiplied with the value of the solved differential equation to obtain next vector variables corresponding to a next iteration time-step, until the time-step reaches the convergence time, and outputting optimal values of the vector of variables and the cost function.

A controller for controlling a system is provided. The controller performs measuring variables via an interface to generate a vector of variables, providing a cost function, with respect to the system, based on the vector variables using weighting factors, wherein the vector variables are represented by a time-step, computing first-derivative of the cost function at an initial time-step, obtaining a convergence time from the first-derivative of the cost function, computing second derivative of the cost function and generating an optimization differential equation based on the first and second derivatives of the cost function, proceeding, starting with the initial time-step, to obtain a value of the optimization differential equation by solving the optimization differential equation, in an iteration manner, with a predetermined time step being multiplied with the value of the solved differential equation to obtain next vector variables corresponding to a next iteration time-step, until the time-step reaches the convergence time, and outputting optimal values of the vector of variables and the cost function.

The present invention discloses a method for expressing traffic flow characteristics based on a quantum harmonic oscillator model, including: (1) constructing an energy eigenequation of a quantum harmonic oscillator (QHO) for vehicle movement, and converting the energy eigenequation to an Hermite polynomial; (2) solving traffic flow characteristic parameters using K-order Hermite polynomial approximation; and (3) expressing the traffic flow characteristic parameters on a sphere. On the premise of the autonomous decision of a driving strategy by a driver and centering on the objective limitation that the individual accurate state information in the long-distance expressway traffic flow is not observable, the dynamic evolution of the speed and the state of the vehicle is described using a quantum state, the driving state of the vehicle is expressed as the superposition state of three driving states, and the probability of the three states is represented using QHO model parameters.

The present invention discloses a method for expressing traffic flow characteristics based on a quantum harmonic oscillator model, including: (1) constructing an energy eigenequation of a quantum harmonic oscillator (QHO) for vehicle movement, and converting the energy eigenequation to an Hermite polynomial; (2) solving traffic flow characteristic parameters using K-order Hermite polynomial approximation; and (3) expressing the traffic flow characteristic parameters on a sphere. On the premise of the autonomous decision of a driving strategy by a driver and centering on the objective limitation that the individual accurate state information in the long-distance expressway traffic flow is not observable, the dynamic evolution of the speed and the state of the vehicle is described using a quantum state, the driving state of the vehicle is expressed as the superposition state of three driving states, and the probability of the three states is represented using QHO model parameters.

A method for incremental update of materialized views and a system for answering queries against relational databases, or object-oriented databases, or graph databases, are provided. The system comprises a Storage Engine subsystem, configured to store original data as well as materialized views and subviews in a dedicated subsystem, and a Diff Engine subsystem configured to translate Natural Algebra representations of a Natural Algebra view definition into derived Natural Algebra expressions. The system further comprises an Optimizer configured to translate derived Natural Algebra expressions into Incremental View Maintenance plans, and a Delta Extractor subsystem configured to extract any transactional changes to the original data or batches of the said changes in a form that can be passed as input to the Incremental View Maintenance plans in order to compute the changes to the materialized views.

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

According to one embodiment, a calculating device includes a processor repeating a processing procedure. The processing procedure includes first, second, and third variable updates. The first variable update includes updating an ith entry of a first variable x.sub.i by adding an ith entry of a first function to the first variable x.sub.i. The second variable update includes updating the second variable y.sub.i by adding, to the second variable y.sub.i, an arithmetic result of an ith entry of a second function, an ith entry of a third function, and an ith entry of a first element function. The third variable update includes updating the third variable z by adding an ith entry of a second element function to the third variable z. The processor performs at least an output of at least one of the first variable x.sub.i or a function of the first variable x.sub.i.