Large-N scaling behavior of the quantum fisher information in the Dicke model

Quantum Fisher information (QFI) of the reduced two-atom state is employed to capture the quantum criticality of the superradiant phase transition in the Dicke model in the infinite size and finite-$N$ systems respectively. The analytical expression of the QFI of its ground state is evaluated explicitly. And finite-size scaling analysis is performed with the large accessible system size due to the effective bosonic coherent-state technique. We also investigate the large-size scaling behavior of the scaled QFI of the reduced $N$-atom state and show the accurate exponent.Comment: 6pages,2figure

Generalized rotating-wave approximation to biased qubit-oscillator systems

The generalized rotating-wave approximation with counter-rotating interactions has been applied to a biased qubit-oscillator system. Analytical expressions are explicitly given for all eigenvalues and eigenstates. For a flux qubit coupled to superconducting oscillators, spectra calculated by our approach are in excellent agreement with experiment. Calculated energy levels for a variety of biases also agree well with those obtained via exact diagonalization for a wide range of coupling strengths. Dynamics of the qubit has also been examined, and results lend further support to the validity of the analytical approximation employed here. Our approach can be readily implemented and applied to superconducting qubit-oscillator experiments conducted currently and in the near future with a biased qubit and for all accessible coupling strengths

Amphiphilic blockers punch through a mutant CLC-0 pore.

Intracellularly applied amphiphilic molecules, such as p-chlorophenoxy acetate (CPA) and octanoate, block various pore-open mutants of CLC-0. The voltage-dependent block of a particular pore-open mutant, E166G, was found to be multiphasic. In symmetrical 140 mM Cl(-), the apparent affinity of the blocker in this mutant increased with a negative membrane potential but, paradoxically, decreased when the negative membrane potential was greater than -80 mV, a phenomenon similar to the blocker "punch-through" shown in many blocker studies of cation channels. To provide further evidence of the punch-through of CPA and octanoate, we studied the dissociation rate of the blocker from the pore by measuring the time constant of relief from the block under various voltage and ionic conditions. Consistent with the voltage dependence of the effect on the steady-state current, the rate of CPA dissociation from the E166G pore reached a minimum at -80 mV in symmetrical 140 mM Cl(-), and the direction of current recovery suggested that the bound CPA in the pore can dissociate into both intracellular and extracellular solutions. Moreover, the CPA dissociation depends upon the Cl(-) reversal potential with a minimal dissociation rate at a voltage 80 mV more negative than the Cl(-) reversal potential. That the shift of the CPA-dissociation rate follows the Cl(-) gradient across the membrane argues that these blockers can indeed punch through the channel pore. Furthermore, a minimal CPA-dissociation rate at a voltage 80 mV more negative than the Cl(-) reversal potential suggests that the outward blocker movement through the CLC-0 pore is more difficult than the inward movement

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio