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 $T_0 \approx 0.15$ K. I argue that $T_0$ 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 $T_0$.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 $10^{-6}$ 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

Food poisoning due to yam flour consumption in Kano (Northwest) Nigeria

Food poisoning is known to occur sporadically from time to time due to poor hygienic preparations. Its occurrence rarely assumes epidemic proportion. The objective was to report the occurrence of food poisoning due to yam flour among three families which occurred almost in quick succession between March and July 2007 among three families in Kano. They presented with diarrhea, vomiting, abdominal pain, convulsion and loss of consciousness. They all recovered within 48hours of admission. Investigations indicated that the use of certain lethal preservatives for the processing of the yam flour might be responsible. Poisoning from consumption of yam flour should be a differential diagnosis of acute seizure disorders or occurrence of vomiting, diarrhea and abdominal pain in the tropics. It is recommended that education on proper processing of all food products in view of the public health implicatio

High-speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201

Spin Dynamics in the Normal State of High T_c Superconductors

We summarize our recent theoretical studies on the spin dynamics in the normal state of the metallic cuprates. The contrasting wave vector dependence of the dynamical spin structure factor $S({\bf q}, \omega )$ in LaSrCuO and YBaCuO systems are attributed to the differences in the fermiology, in conjunction with strong Coulomb correlations. These effects are found to account also for the anomalous temperature and frequency dependence of $S({\bf q}, \omega )$. We conclude that the low energy spin dynamics of the metallic cuprates are described in terms of correlated quasiparticles with a Luttinger Fermi surface and a non-zero antiferromagnetic exchange interaction.Comment: 30 pages, REVTE

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