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Found 10 papers, 1 papers with code
Explaining mountain pine beetle dynamics: From life history traits to large, episodic outbreaks
However, two life history traits -- MPB's size-dependent fecundity and preference for large trees -- are responsible for 25% of the peak number of beetles.
From policy to prediction: Forecasting COVID-19 dynamics under imperfect vaccination
Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model [WWR+].
A hypothesis-free bridging of disease dynamics and non-pharmaceutical policies
Typically, a hypothesis is made on the form of the transmission rate with respect to time.
Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions
Quadratic Unconstrained Binary Optimization (QUBO) is recognized as a unifying framework for modeling a wide range of problems.
QUBO transformation using Eigenvalue Decomposition
Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose modeling framework for combinatorial optimization problems and is a requirement for quantum annealers.
Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems
The broad applicability of Quadratic Unconstrained Binary Optimization (QUBO) constitutes a general-purpose modeling framework for combinatorial optimization problems and are a required format for gate array and quantum annealing computers.
Goal Seeking Quadratic Unconstrained Binary Optimization
The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers.
Robust Optimization of Unconstrained Binary Quadratic Problems
In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers.
Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis
The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems.
Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems
The quadratic unconstrained binary optimization (QUBO) problem arises in diverse optimization applications ranging from Ising spin problems to classical problems in graph theory and binary discrete optimization.
