14,264 research outputs found
Large-N reduction of SU(N) Yang-Mills theory with massive adjoint overlap fermions
We study four dimensional large-N SU(N) Yang-Mills theory coupled to adjoint
overlap fermions on a single site lattice. Lattice simulations along with
perturbation theory show that the bare quark mass has to be taken to zero as
one takes the continuum limit in order to be in the physically relevant
center-symmetric phase. But, it seems that it is possible to take the continuum
limit with any renormalized quark mass and still be in the center-symmetric
physics. We have also conducted a study of the correlations between Polyakov
loop operators in different directions and obtained the range for the Wilson
mass parameter that enters the overlap Dirac operator.Comment: 8 pages, 5 figure
Lacunary Fourier series and a qualitative uncertainty principle for compact Lie groups
We define lacunary Fourier series on a compact connected semisimple Lie group
. If has lacunary Fourier series, and vanishes on a non empty
open set, then we prove that vanishes identically. This may be viewed as a
qualitative uncertainty principle
Truncated Overlap Fermions
In this talk I propose a new computational scheme with overlap fermions and a
fast algorithm to invert the corresponding Dirac operator.Comment: LATTICE99(algorithms
Two dimensional fermions in three dimensional YM
Dirac fermions in the fundamental representation of SU(N) live on the surface
of a cylinder embedded in and interact with a three dimensional SU(N)
Yang Mills vector potential preserving a global chiral symmetry at finite .
As the circumference of the cylinder is varied from small to large, the chiral
symmetry gets spontaneously broken in the infinite limit at a typical bulk
scale. Replacing three dimensional YM by four dimensional YM introduces
non-trivial renormalization effects.Comment: 21 pages, 7 figures, 5 table
An Automated Social Graph De-anonymization Technique
We present a generic and automated approach to re-identifying nodes in
anonymized social networks which enables novel anonymization techniques to be
quickly evaluated. It uses machine learning (decision forests) to matching
pairs of nodes in disparate anonymized sub-graphs. The technique uncovers
artefacts and invariants of any black-box anonymization scheme from a small set
of examples. Despite a high degree of automation, classification succeeds with
significant true positive rates even when small false positive rates are
sought. Our evaluation uses publicly available real world datasets to study the
performance of our approach against real-world anonymization strategies, namely
the schemes used to protect datasets of The Data for Development (D4D)
Challenge. We show that the technique is effective even when only small numbers
of samples are used for training. Further, since it detects weaknesses in the
black-box anonymization scheme it can re-identify nodes in one social network
when trained on another.Comment: 12 page
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