115,587 research outputs found
Effects of Ru Substitution on Dimensionality and Electron Correlations in Ba(Fe_{1-x}Ru_x)_2As_2
We report a systematic angle-resolved photoemission spectroscopy study on
Ba(FeRu)As for a wide range of Ru concentrations (0.15
\emph{x} 0.74). We observed a crossover from two-dimension to
three-dimension for some of the hole-like Fermi surfaces with Ru substitution
and a large reduction in the mass renormalization close to optimal doping.
These results suggest that isovalent Ru substitution has remarkable effects on
the low-energy electron excitations, which are important for the evolution of
superconductivity and antiferromagnetism in this system.Comment: 4 pages, 4 figure
Ground states of one and two fractional vortices in long Josephson 0-kappa-junctions
Half integer Josephson vortices in 0--junctions, discussed theoretically
and observed experimentally, spontaneously appear at the point where the
Josephson phase is -discontinuous. The creation of \emph{arbitrary}
discontinuities of the Josephson phase has been demonstrated recently. Here we
study fractional vortices formed at an arbitrary -discontinuity,
discuss their stability and possible ground states. The two stable states are
not mirror symmetric. Furthermore, the possible ground states formed at two
-discontinuities separated by a distance are investigated, and the
energy and the regions of stability of each ground state are calculated. We
also show that the ground states may strongly depend on the distance
between the discontinuities. There is a crossover distance such that for
the ground states may be qualitatively different.Comment: 7 figures, submitted to PRB In v.2 one figure is added, and refs are
updated In v.3 major revision, many issues fixe
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Machine-learning techniques are widely used in security-related applications,
like spam and malware detection. However, in such settings, they have been
shown to be vulnerable to adversarial attacks, including the deliberate
manipulation of data at test time to evade detection. In this work, we focus on
the vulnerability of linear classifiers to evasion attacks. This can be
considered a relevant problem, as linear classifiers have been increasingly
used in embedded systems and mobile devices for their low processing time and
memory requirements. We exploit recent findings in robust optimization to
investigate the link between regularization and security of linear classifiers,
depending on the type of attack. We also analyze the relationship between the
sparsity of feature weights, which is desirable for reducing processing cost,
and the security of linear classifiers. We further propose a novel octagonal
regularizer that allows us to achieve a proper trade-off between them. Finally,
we empirically show how this regularizer can improve classifier security and
sparsity in real-world application examples including spam and malware
detection
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