Combining classical electrodynamics and density functional theory (DFT) calculations, we develop a general and rigorous theoretical framework that describes the energetics of metal surfaces under high electric fields....
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We extend Ravenel-Wilson Hopf ring techniques to C2-equivariant homotopy theory. Our main application and motivation is a computation of the RO(C2)-graded homology of C2-equivariant Eilenberg-MacLane spaces. The resul...
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The set of unrestricted homotopy classes [M, Sn] where M is a closed and connected spin (n+1)manifold is called the n-th cohomotopy group πn(M) of M. Moreover it is known that πn(M) = Hn(M;Z) Z2 by methods from homo...
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Behaviors of the synthetic characters in current military simulations are limited since they are generally generated by rule-based and reactive computational models with minimal intelligence. Such computational models...
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Behaviors of the synthetic characters in current military simulations are limited since they are generally generated by rule-based and reactive computational models with minimal intelligence. Such computational models cannot adapt to reflect the experience of the characters, resulting in brittle intelligence for even the most effective behavior models devised via costly and labor-intensive processes. Observation-based behavior model adaptation that leverages machine learning and the experience of synthetic entities in combination with appropriate prior knowledge can address the issues in the existing computational behavior models to create a better training experience in military training simulations. In this paper, we introduce a framework that aims to create autonomous synthetic characters that can perform coherent sequences of believable behavior while being aware of human trainees and their needs within a training simulation. This framework brings together three mutually complementary components. The first component is a Unity-based simulation environment - Rapid Integration and Development Environment (RIDE) - supporting One World Terrain (OWT) models and capable of running and supporting machine learning experiments. The second is Shiva, a novel multi-agent reinforcement and imitation learning framework that can interface with a variety of simulation environments, and that can additionally utilize a variety of learning algorithms. The final component is the Sigma Cognitive Architecture that will augment the behavior models with symbolic and probabilistic reasoning capabilities. We have successfully created proof-of-concept behavior models leveraging this framework on realistic terrain as an essential step towards bringing machine learning into military simulations: (1) in order to improve the quality and complexity of non-player characters in training simulations;(2) in order to create more realistic and challenging training experiences while reducing the cost
A recent development in the theory of fractional differential equations with variable coefficients has been a method for obtaining an exact solution in the form of an infinite series involving nested fractional integr...
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In the first part of this article, we consider a Groebner basis of the differential ideal [x21] with respect to "the" weighted lexicographical monomial order and show that its computation is related with an ...
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Let G be a finite Chevalley group. We are concerned with computing the values of the unipotent characters of G by making use of Lusztig’s theory of character sheaves. In this framework, one has to find the transforma...
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Using a simple Gaussian-like Ansatz for the phase distribution of a theory with a complex action, we show how the thimble integration for the average phase factor can be plagued by a strong residual sign problem when ...
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An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human coll...
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In the article, we outline the set of Matlab functions that enable the computation of elliptic Integrals and Jacobian elliptic functions for real arguments. Correctness, robustness, efficiency and accuracy of the func...
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