103,591 research outputs found
Heat transport and spin-charge separation in the normal state of high temperature superconductors
Hill et al. have recently measured both the thermal and charge conductivities
in the normal state of a high temperature superconductor. Based on the
vanishing of the Wiedemann-Franz ratio in the extrapolated zero temperature
limit, they conclude that the charge carriers in this material are not
fermionic. Here I make a simple observation that the prefactor in the
temperature dependence of the measured thermal conductivity is unusually large,
corresponding to an extremely small energy scale K. I argue
that should be interpreted as a collective scale. Based on
model-independent considerations, I also argue that the experiment leads to two
possibilities: 1) The charge-carrying excitations are non-fermionic. And much
of the heat current is in fact carried by distinctive charge-neutral
excitations; 2) The charge-carrying excitations are fermionic, but a subtle
ordering transition occurs at .Comment: 3 pages, 1 figur
Destruction of the Kondo effect in a multi-channel Bose-Fermi Kondo model
We consider the SU(N) x SU(kappa N) generalization of the spin-isotropic
Bose-Fermi Kondo model in the limit of large N. There are three fixed points
corresponding to a multi-channel non-Fermi liquid phase, a local spin-liquid
phase, and a Kondo-destroying quantum critical point (QCP). We show that the
QCP has strong similarities with its counterpart in the single-channel model,
even though the Kondo phase is very different from the latter. We also discuss
the evolution of the dynamical scaling properties away from the QCP.Comment: 2 papes, 2 figures, submittet to SCES'0
Multi-Scale Link Prediction
The automated analysis of social networks has become an important problem due
to the proliferation of social networks, such as LiveJournal, Flickr and
Facebook. The scale of these social networks is massive and continues to grow
rapidly. An important problem in social network analysis is proximity
estimation that infers the closeness of different users. Link prediction, in
turn, is an important application of proximity estimation. However, many
methods for computing proximity measures have high computational complexity and
are thus prohibitive for large-scale link prediction problems. One way to
address this problem is to estimate proximity measures via low-rank
approximation. However, a single low-rank approximation may not be sufficient
to represent the behavior of the entire network. In this paper, we propose
Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can
handle massive networks. The basis idea of MSLP is to construct low rank
approximations of the network at multiple scales in an efficient manner. Based
on this approach, MSLP combines predictions at multiple scales to make robust
and accurate predictions. Experimental results on real-life datasets with more
than a million nodes show the superior performance and scalability of our
method.Comment: 20 pages, 10 figure
A Divide-and-Conquer Solver for Kernel Support Vector Machines
The kernel support vector machine (SVM) is one of the most widely used
classification methods; however, the amount of computation required becomes the
bottleneck when facing millions of samples. In this paper, we propose and
analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the
division step, we partition the kernel SVM problem into smaller subproblems by
clustering the data, so that each subproblem can be solved independently and
efficiently. We show theoretically that the support vectors identified by the
subproblem solution are likely to be support vectors of the entire kernel SVM
problem, provided that the problem is partitioned appropriately by kernel
clustering. In the conquer step, the local solutions from the subproblems are
used to initialize a global coordinate descent solver, which converges quickly
as suggested by our analysis. By extending this idea, we develop a multilevel
Divide-and-Conquer SVM algorithm with adaptive clustering and early prediction
strategy, which outperforms state-of-the-art methods in terms of training
speed, testing accuracy, and memory usage. As an example, on the covtype
dataset with half-a-million samples, DC-SVM is 7 times faster than LIBSVM in
obtaining the exact SVM solution (to within relative error) which
achieves 96.15% prediction accuracy. Moreover, with our proposed early
prediction strategy, DC-SVM achieves about 96% accuracy in only 12 minutes,
which is more than 100 times faster than LIBSVM
Quantum Criticality and the Kondo Lattice
Quantum phase transitions (QPTs) arise as a result of competing interactions
in a quantum many-body system. Kondo lattice models, containing a lattice of
localized magnetic moments and a band of conduction electrons, naturally
feature such competing interactions. A Ruderman-Kittel-Kasuya-Yosida (RKKY)
exchange interaction among the local moments promotes magnetic ordering.
However, a Kondo exchange interaction between the local moments and conduction
electrons favors the Kondo-screened singlet ground state. This chapter
summarizes the basic physics of QPTs in antiferromagnetic Kondo lattice
systems. Two types of quantum critical points (QCPs) are considered.
Spin-density-wave quantum criticality occurs at a conventional type of QCP,
which invokes only the fluctuations of the antiferromagnetic order parameter.
Local quantum criticality describes a new type of QCP, which goes beyond the
Landau paradigm and involves a breakdown of the Kondo effect. This critical
Kondo breakdown effect leads to non-Fermi liquid electronic excitations, which
are part of the critical excitation spectrum and are in addition to the
fluctuations of the magnetic order parameter. Across such a QCP, there is a
sudden collapse of the Fermi surface from large to small. I close with a brief
summary of relevant experiments, and outline a number of outstanding issues,
including the global phase diagram.Comment: 27 pages, 6 figures; Chapter of the book "Understanding Quantum Phase
Transitions", ed. Lincoln D. Carr (CRC Press/Taylor & Francis, Boca Raton,
2010
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