16,996 research outputs found
Computing the Unique Information
Given a pair of predictor variables and a response variable, how much
information do the predictors have about the response, and how is this
information distributed between unique, redundant, and synergistic components?
Recent work has proposed to quantify the unique component of the decomposition
as the minimum value of the conditional mutual information over a constrained
set of information channels. We present an efficient iterative divergence
minimization algorithm to solve this optimization problem with convergence
guarantees and evaluate its performance against other techniques.Comment: To appear in 2018 IEEE International Symposium on Information Theory
(ISIT); 18 pages; 4 figures, 1 Table; Github link to source code:
https://github.com/infodeco/computeU
Abstraction in directed model checking
Abstraction is one of the most important issues to cope with large and infinite state spaces in model checking and to reduce the verification efforts. The abstract system is smaller than the original one and if the abstract system satisfies a correctness specification, so does the concrete one. However, abstractions may introduce a behavior violating the specification that is not present in the original system.
This paper bypasses this problem by proposing the combination of abstraction with heuristic search to improve error detection. The abstract system is explored in order to create a database that stores the exact distances from abstract states to the set of abstract error states. To check, whether or not the abstract behavior is present in the original system, effcient exploration algorithms exploit the database as a guidance
Nonlinear Basis Pursuit
In compressive sensing, the basis pursuit algorithm aims to find the sparsest
solution to an underdetermined linear equation system. In this paper, we
generalize basis pursuit to finding the sparsest solution to higher order
nonlinear systems of equations, called nonlinear basis pursuit. In contrast to
the existing nonlinear compressive sensing methods, the new algorithm that
solves the nonlinear basis pursuit problem is convex and not greedy. The novel
algorithm enables the compressive sensing approach to be used for a broader
range of applications where there are nonlinear relationships between the
measurements and the unknowns
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