20,164 research outputs found
Truth tables to binary decision diagrams
Model transformation tools have reached a considerable level of maturity in the core features, and are currently developing in many directions. Some tools are focusing on providing higher performance for large models or complex transformations. Others focus on bidirectionality, visualisation, traceability, or verifiability, among other research directions. Whereas past cases in TTC have focused on specific research directions, this case study presents a well-known simple transformation and welcomes researchers to apply their research to it. The aim of this case is to serve as a showcase of the various directions that model transformation research is going towards at the moment
DDMF: An Efficient Decision Diagram Structure for Design Verification of Quantum Circuits under a Practical Restriction
Recently much attention has been paid to quantum circuit design to prepare
for the future "quantum computation era." Like the conventional logic
synthesis, it should be important to verify and analyze the functionalities of
generated quantum circuits. For that purpose, we propose an efficient
verification method for quantum circuits under a practical restriction. Thanks
to the restriction, we can introduce an efficient verification scheme based on
decision diagrams called
Decision Diagrams for Matrix Functions (DDMFs). Then, we show analytically
the advantages of our approach based on DDMFs over the previous verification
techniques. In order to introduce DDMFs, we also introduce new concepts,
quantum functions and matrix functions, which may also be interesting and
useful on their own for designing quantum circuits.Comment: 15 pages, 14 figures, to appear IEICE Trans. Fundamentals, Vol.
E91-A, No.1
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Background: Many biological systems are modeled qualitatively with discrete
models, such as probabilistic Boolean networks, logical models, Petri nets, and
agent-based models, with the goal to gain a better understanding of the system.
The computational complexity to analyze the complete dynamics of these models
grows exponentially in the number of variables, which impedes working with
complex models. Although there exist sophisticated algorithms to determine the
dynamics of discrete models, their implementations usually require
labor-intensive formatting of the model formulation, and they are oftentimes
not accessible to users without programming skills. Efficient analysis methods
are needed that are accessible to modelers and easy to use. Method: By
converting discrete models into algebraic models, tools from computational
algebra can be used to analyze their dynamics. Specifically, we propose a
method to identify attractors of a discrete model that is equivalent to solving
a system of polynomial equations, a long-studied problem in computer algebra.
Results: A method for efficiently identifying attractors, and the web-based
tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other
analysis methods for discrete models. ADAM converts several discrete model
types automatically into polynomial dynamical systems and analyzes their
dynamics using tools from computer algebra. Based on extensive experimentation
with both discrete models arising in systems biology and randomly generated
networks, we found that the algebraic algorithms presented in this manuscript
are fast for systems with the structure maintained by most biological systems,
namely sparseness, i.e., while the number of nodes in a biological network may
be quite large, each node is affected only by a small number of other nodes,
and robustness, i.e., small number of attractors
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