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Event bubbling is a type of DOM event propagation [1] where the event first triggers on the innermost target element, and then successively triggers on the ancestors (parents) of the target element in the same nesting hierarchy till it reaches the outermost DOM element or document object [2] (Provided the handler is initialized). It is one way ...
event.stopPropagation(): the event is stopped after all event listeners attached to the current event target in the current event phase are finished; event.stopImmediatePropagation(): the event is stopped immediately and no further event listeners are executed; When an event is stopped it will no longer travel along the event path.
Gaussian belief propagation is a variant of the belief propagation algorithm when the underlying distributions are Gaussian. The first work analyzing this special model was the seminal work of Weiss and Freeman. [15] The GaBP algorithm solves the following marginalization problem:
LDPC codes functionally are defined by a sparse parity-check matrix. This sparse matrix is often randomly generated, subject to the sparsity constraints—LDPC code construction is discussed later. These codes were first designed by Robert Gallager in 1960. [6] Below is a graph fragment of an example LDPC code using Forney's factor graph notation.
GPOPS-II (pronounced "GPOPS 2") is a general-purpose MATLAB software for solving continuous optimal control problems using hp-adaptive Gaussian quadrature collocation and sparse nonlinear programming.
Event propagation models, such as bubbling, capturing, and pub/sub, define how events are distributed and handled within a system. Other key aspects include event loops, event queueing and prioritization, event sourcing, and complex event processing patterns. These mechanisms contribute to the flexibility and scalability of event-driven systems.
In computing, reactive programming is a declarative programming paradigm concerned with data streams and the propagation of change. With this paradigm, it is possible to express static (e.g., arrays) or dynamic (e.g., event emitters) data streams with ease, and also communicate that an inferred dependency within the associated execution model exists, which facilitates the automatic propagation ...
The basic backtracking algorithm runs by choosing a literal, assigning a truth value to it, simplifying the formula and then recursively checking if the simplified formula is satisfiable; if this is the case, the original formula is satisfiable; otherwise, the same recursive check is done assuming the opposite truth value.