arXiv subjects

Bing Luo

Publications and source records attributed to Bing Luo.

42 records · Page 3Linked to original sources

arXiv2022

Electrochemically-driven insulator-metal transition in ionic-liquid-gated antiferromagnetic Mott-insulating NiS$_2$ single crystals

Motivated by the existence of superconductivity in pyrite-structure CuS$_2$, we explore the possibility of ionic-liquid-gating-induced superconductivity in the proximal antiferromagnetic Mott insulator NiS$_2$. A clear gating-induced transition from a two-dimensional insulating state to a three-dimensional metallic state is observed at positive gate bias on single crystal surfaces. No evidence for superconductivity is observed down to the lowest measured temperature of 0.45 K, however. Based on transport, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, atomic force microscopy, and other techniques, we deduce an electrochemical gating mechanism involving a substantial decrease in the S:Ni ratio (over hundreds of nm), which is both non-volatile and irreversible. This is in striking contrast to the reversible, volatile, surface-limited, electrostatic gate effect in pyrite FeS$_2$. We attribute this stark difference in electrochemical vs. electrostatic gating response in NiS$_2$ and FeS$_2$ to the much larger S diffusion coefficient in NiS$_2$, analogous to the different behaviors observed among electrolyte-gated oxides with differing O-vacancy diffusivities. The gating irreversibility, on the other hand, is associated with the lack of atmospheric S; this is in contrast to the better understood oxide case, where electrolysis of atmospheric H$_2$O provides an O reservoir. This study of NiS$_2$ thus provides new insight into electrolyte gating mechanisms in functional materials, in a previously unexplored limit.

cond-mat.mtrl-sci↗

arXiv2021

Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probabilities. Based on the bound, we analytically establish the relationship between the total learning time and sampling probabilities, which results in a non-convex optimization problem for training time minimization. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes. Notably, our scheme in hardware prototype spends 73% less time than the uniform sampling baseline for reaching the same target loss.

cs.LG↗

arXiv2021

Cost-Effective Federated Learning in Mobile Edge Networks

Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.

cs.LG↗

arXiv2020

Cost-Effective Federated Learning Design

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.

cs.LG↗

arXiv2017

Resistive MHD Modelling of Quasi-Single Helicity State in the KTX Regimes

The potential formation of the quasi-single-helicity (QSH) state in the Keda Torus eXperiment (KTX) is investigated in resistive MHD simulations using the NIMROD code. We focus on the effects of finite resistivity on the mode structure and characteristics of the dominant linear and nonlinear resistive tearing-mode in a finite $β$, cylindrical configuration of reversed field pinch model for KTX. In the typical resistive regimes of KTX where Lundquist number $S=5 \times 10^4$, the plasma transitions to a steady QSH state after evolving through an initial transient phase with multiple helicities. The dominant mode of the QSH state develops from the dominant linear tearing mode instability. In lower $β$ regime, the QSH state are intermittent and short in duration; in higher $β$ regime, the QSH state persists for a longer time and should be more observable in experiment.

physics.plasm-ph↗

arXiv2007

X-Ray Properties of the Point Source Population in the Spiral Galaxy NGC 5055 (M63) with Chandra

By analyzing the Chandra ACIS S3 data we studied the X-ray properties of the low-mass and high-mass X-ray binary populations in the nearby spiral galaxy NGC 5055. A total of 43 X-ray point sources were detected within the 2 effective radii, with 31 sources located on the disk and the rest 12 sources in the bulge. The resolved point sources dominate the total X-ray emission of the galaxy by accounting for about 80% of the total counts in 0.3--10 keV. By carrying out the spectral fittings we calculated the 0.3--10.0 keV luminosities of all the detected X-ray point sources and found that they span a wide range from a few 10^{37} erg s^{-1} to over 10^{39} erg s^{-1}. After compensating for the incompleteness at the low luminosity end, we find that the corrected XLF of the bulge population is well fitted with a broken power-law model with a break at 1.57^{+0.21}_{-0.20}\times 10^{38} erg s^{-1}, while the profile of the disk population's XLF agrees with a single power-law distribution with a slope of 0.93^{+0.07}_{-0.06}. The disk population is significantly richer at {}^{>}_{\sim}2\times10^{38} erg s^{-1} than the bulge population, inferring that the disk may have undergone relatively recent, strong starbursts that significantly increased the HMXB population, although ongoing starbursts are also observed in the nuclear region. Similar XLF profiles of the bulge and disk populations were found in M81. However, in most other spiral galaxies different patterns of the spatial variation of the XLF profiles from the bulge to the disk have been observed, indicating that the star formation and evolution history may be more complex than we have expected.

astro-ph↗