Multiple Fermi pockets revealed by Shubnikov-de Haas oscillations in WTe2

We use magneto-transport measurements to investigate the electronic structure of WTe2 single crystals. A non-saturating and parabolic magnetoresistance is observed in the temperature range between 2.5 to 200 K and magnetic fields up to 8 T. Shubnikov - de Haas oscillations with beating patterns are observed. The fast Fourier transform of the SdH oscillations reveals three oscillation frequencies, corresponding to three pairs of Fermi pockets with comparable effective masses , m* ~ 0.31 me. By fitting the Hall resistivity, we infer the presence of one pair of electron pockets and two pairs of hole pockets, together with nearly perfect compensation of the electron-hole carrier concentration. These magnetotransport measurements reveal the complex electronic structure in WTe2, explaining the nonsaturating magnetoresistance.Comment: Submitted to journal on 1 April, 2015, 4 Figure

Observation of topological transition of Fermi surface from a spindle-torus to a torus in large bulk Rashba spin-split BiTeCl

The recently observed large Rashba-type spin splitting in the BiTeX (X = I, Br, Cl) bulk states due to the absence of inversion asymmetry and large charge polarity enables observation of the transition in Fermi surface topology from spindle-torus to torus with varying the carrier density. These BiTeX systems with high spin-orbit energy scales offer an ideal platform for achieving practical spintronic applications and realizing non-trivial phenomena such as topological superconductivity and Majorana fermions. Here we use Shubnikov-de Haas oscillations to investigate the electronic structure of the bulk conduction band of BiTeCl single crystals with different carrier densities. We observe the topological transition of the Fermi surface (FS) from a spindle-torus to a torus. The Landau level fan diagram reveals the expected non-trivial {\pi} Berry phase for both the inner and outer FSs. Angle-dependent oscillation measurements reveal three-dimensional FS topology when the Fermi level lies in the vicinity of the Dirac point. All the observations are consistent with large Rashba spin-orbit splitting in the bulk conduction band.Comment: 28 pages, supplementary informatio

A train dispatching model based on fuzzy passenger demand forecasting during holidays

Purpose: The train dispatching is a crucial issue in the train operation adjustment when passenger flow outbursts. During holidays, the train dispatching is to meet passenger demand to the greatest extent, and ensure safety, speediness and punctuality of the train operation. In this paper, a fuzzy passenger demand forecasting model is put up, then a train dispatching optimization model is established based on passenger demand so as to evacuate stranded passengers effectively during holidays. Design/methodology/approach: First, the complex features and regularity of passenger flow during holidays are analyzed, and then a fuzzy passenger demand forecasting model is put forward based on the fuzzy set theory and time series theory. Next, the bi-objective of the train dispatching optimization model is to minimize the total operation cost of the train dispatching and unserved passenger volume during holidays. Finally, the validity of this model is illustrated with a case concerned with the Beijing-Shanghai high-speed railway in China. Findings: The case study shows that the fuzzy passenger demand forecasting model can predict outcomes more precisely than ARIMA model. Thus train dispatching optimization plan proves that a small number of trains are able to serve unserved passengers reasonably and effectively. Originality/value: On the basis of the passenger demand predictive values, the train dispatching optimization model is established, which enables train dispatching to meet passenger demand in condition that passenger flow outbursts, so as to maximize passenger demand by offering the optimal operation plan.Peer Reviewe

Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM

This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.Comment: 28 pages, 7 figures, 3 tables, 1 algorithm. Proof details are provided in the main body of the pape