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

Optimistic versus Pessimistic--Optimal Judgemental Bias with Reference Point

This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over-pessimism depends on the real chance of success and optimistic decision makers prefer receiving early information. In the portfolio choice problem, pessimistic investors tend to trade conservatively, however, they might trade aggressively if they are sophisticated enough to recognise the biases since low expectation can reduce their fear of loss

Commissioning and Operation of the New CMS Phase-1 Pixel Detector

The Phase-1 upgrade of the CMS pixel detector is built out of four barrel layers (BPix) and three forward disks in each endcap (FPix). It comprises a total of 124M pixel channels in 1,856 modules, and it is designed to withstand instantaneous luminosities of up to $2 \times 10^{34}\,$cm$^{-2}$s$^{-1}$. Different parts of the detector were assembled over the last year and later brought to CERN for installation inside the CMS tracker. At various stages during the assembly tests have been performed to ensure that the readout and power electronics and the cooling system meet the design specifications. After tests of the individual components, system tests were performed before the installation inside CMS. In addition to reviewing these tests, we also present results from the final commissioning of the detector in-situ using the central CMS DAQ system. Finally we review results from the initial operation of the detector first with cosmic rays and then with pp collisions.Comment: Talk presented at the APS Division of Particles and Fields Meeting (DPF 2017), July 31-August 4, 2017, Fermilab. C17073

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