553 research outputs found
Homo Sapiens Sapiens Meets Homo Strategicus at the Laboratory
Homo Strategicus populates the vast plains of Game Theory. He knows all logical implications of his knowledge (logical omniscience) and chooses optimal strategies given his knowledge and beliefs (rationality). This paper investigates the extent to which the logical capabilities of Homo Sapiens Sapiens resemble those possessed by Homo Strategicus. Controlling for other-regarding preferences and beliefs about the rationality of others, we show, in the laboratory, that the ability of Homo Sapiens Sapiens to perform complex chains of iterative reasoning is much better than previously thought. Subjects were able to perform about two to three iterations of reasoning on average.iterative reasoning; depth of reasoning; logical omniscience; rationality; experiments; other-regarding preferences
Transonic Flutter Suppression Control Law Design, Analysis and Wind Tunnel Results
The benchmark active controls technology and wind tunnel test program at NASA Langley Research Center was started with the objective to investigate the nonlinear, unsteady aerodynamics and active flutter suppression of wings in transonic flow. The paper will present the flutter suppression control law design process, numerical nonlinear simulation and wind tunnel test results for the NACA 0012 benchmark active control wing model. The flutter suppression control law design processes using (1) classical, (2) linear quadratic Gaussian (LQG), and (3) minimax techniques are described. A unified general formulation and solution for the LQG and minimax approaches, based on the steady state differential game theory is presented. Design considerations for improving the control law robustness and digital implementation are outlined. It was shown that simple control laws when properly designed based on physical principles, can suppress flutter with limited control power even in the presence of transonic shocks and flow separation. In wind tunnel tests in air and heavy gas medium, the closed-loop flutter dynamic pressure was increased to the tunnel upper limit of 200 psf The control law robustness and performance predictions were verified in highly nonlinear flow conditions, gain and phase perturbations, and spoiler deployment. A non-design plunge instability condition was also successfully suppressed
JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition
This paper proposes a novel algorithm to reassemble an arbitrarily shredded
image to its original status. Existing reassembly pipelines commonly consist of
a local matching stage and a global compositions stage. In the local stage, a
key challenge in fragment reassembly is to reliably compute and identify
correct pairwise matching, for which most existing algorithms use handcrafted
features, and hence, cannot reliably handle complicated puzzles. We build a
deep convolutional neural network to detect the compatibility of a pairwise
stitching, and use it to prune computed pairwise matches. To improve the
network efficiency and accuracy, we transfer the calculation of CNN to the
stitching region and apply a boost training strategy. In the global composition
stage, we modify the commonly adopted greedy edge selection strategies to two
new loop closure based searching algorithms. Extensive experiments show that
our algorithm significantly outperforms existing methods on solving various
puzzles, especially those challenging ones with many fragment pieces
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
Towards the Gravity Dual of Quarkonium in the Strongly Coupled QCD Plasma
We build a "bottom-up" holographic model of charmonium by matching the
essential spectral data. We argue that this data must include not only the
masses but also the decay constants of the J/psi and psi' mesons. Relative to
the "soft-wall" models for light mesons, such a matching requires two new
features in the holographic potential: an overall upward shift as well as a
narrow "dip" near the holographic boundary. We calculate the spectral function
as well as the position of the complex singularities (quasinormal frequencies)
of the retarded correlator of the charm current at finite temperatures. We
further extend this analysis by showing that the residues associated with these
singularities are given by the boundary derivative of the appropriately
normalized quasinormal mode. We find that the "melting" of the J/psi spectral
peak occurs at a temperature of about 540 MeV, or 2.8 T_c, in good agreement
with lattice results.Comment: 13 pages, 9 figure
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