arXiv subjects

Li-Ming Liu

Publications and source records attributed to Li-Ming Liu.

5 recordsLinked to original sources

arXiv2026

Topological phase rectification via Aharonov-Bohm interference in a Majorana--quantum-dot interferometer

We propose and theoretically investigate a topological superconducting rectifier based on a quantum-dot--Majorana interferometer. The Aharonov-Bohm phase, controlled by a magnetic flux threading the interferometer loop, tunes the quantum interference between a trivial $2\pi$-periodic quantum-dot channel and a topological $4\pi$-periodic Majorana channel. At non-integer flux, this interference generates a persistent current background $I_{\rm off}$ that shifts the current-phase relation into a unipolar regime, in which the supercurrent flows strictly in one direction. We introduce a signed unipolarity factor $\eta_u$, with $|\eta_u|>0.5$ defining the unipolar regime, and establish its quantitative relationship to the conventional diode efficiency $\eta$. The unipolarity proves robust against variations of the quantum-dot level, spin polarization, and Majorana hybridization, is enhanced by stronger Majorana coupling and Rashba spin-orbit interaction, and persists at realistic temperatures and under quasiparticle poisoning. We further propose a topological diode figure of merit $\mathcal{Z}_{\rm TD}$, defined from the Fourier spectrum of $\eta_u$, whose nonzero value provides a model-independent signature of the $4\pi$-periodic Majorana channel and distinguishes topological from trivial rectification mechanisms. Our findings establish the quantum-dot--Majorana interferometer as a promising route toward high-performance topological superconducting diodes with clear experimental signatures accessible via standard dc transport measurements.

cond-mat.mes-hall↗

arXiv2020

High-sensitivity millimeter imaging of molecular outflows in nine nearby high-mass star-forming regions

We present a study of molecular outflows using six molecular lines (including 12CO/13CO/C18O/HCO+(J = 1-0) and SiO/CS(J = 2-1)) toward nine nearby high-mass star-forming regions with accurate known distances. This work is based on the high-sensitivity observations obtained with the 14-m millimeter telescope of Purple Mountain Observatory Delingha (PMODLH) observatory. The detection rate of outflows (including 12CO, 13CO, HCO+, and CS) is 100\%. However, the emission of SiO was not detected for all sources. The full line widths ($\Delta V$) at 3$\sigma$ above the baseline of these molecular lines have the relationship $\Delta V_{\rm ^{12}CO} > \Delta V_{\rm HCO^{+}} > \Delta V_{\rm CS} \approx \Delta V_{\rm ^{13}CO} > \Delta V_{\rm ^{18}CO}$. 12CO and HCO+ can be used to trace relatively high-velocity outflows, while 13CO and CS can be employed to trace relatively low-velocity outflows. The dynamical timescales of the 13CO and CS outflows are longer than those of the 12CO and HCO+ outflows. The mechanical luminosities, masses, mass-loss rates and forces of all outflows (including 12CO, 13CO, HCO+, and CS) are correlated with the bolometric luminosities of their central IRAS sources.

astro-ph.GA↗

arXiv2020

Principal Component Analysis Based on T$\ell_1$-norm Maximization

Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise. Therefore PCA based on $\ell_1$-norm and $\ell_p$-norm ($0 < p < 1$) have been studied. Among them, the ones based on $\ell_p$-norm seem to be most interesting from the robustness point of view. However, their numerical performance is not satisfactory. Note that, although T$\ell_1$-norm is similar to $\ell_p$-norm ($0 < p < 1$) in some sense, it has the stronger suppression effect to outliers and better continuity. So PCA based on T$\ell_1$-norm is proposed in this paper. Our numerical experiments have shown that its performance is superior than PCA-$\ell_p$ and $\ell_p$SPCA as well as PCA, PCA-$\ell_1$ obviously.

cs.LG↗

arXiv2019

A general model for plane-based clustering with loss function

In this paper, we propose a general model for plane-based clustering. The general model contains many existing plane-based clustering methods, e.g., k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector clustering (TWSVC) and its extensions. Under this general model, one may obtain an appropriate clustering method for specific purpose. The general model is a procedure corresponding to an optimization problem, where the optimization problem minimizes the total loss of the samples. Thereinto, the loss of a sample derives from both within-cluster and between-cluster. In theory, the termination conditions are discussed, and we prove that the general model terminates in a finite number of steps at a local or weak local optimal point. Furthermore, based on this general model, we propose a plane-based clustering method by introducing a new loss function to capture the data distribution precisely. Experimental results on artificial and public available datasets verify the effectiveness of the proposed method.

cs.LG↗

arXiv2017

Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems

Stochastic gradient descent algorithm has been successfully applied on support vector machines (called PEGASOS) for many classification problems. In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for classification. Compared with PEGASOS, the proposed stochastic gradient twin support vector machines (SGTSVM) is insensitive on stochastic sampling for stochastic gradient descent algorithm. In theory, we prove the convergence of SGTSVM instead of almost sure convergence of PEGASOS. For uniformly sampling, the approximation between SGTSVM and twin support vector machines is also given, while PEGASOS only has an opportunity to obtain an approximation of support vector machines. In addition, the nonlinear SGTSVM is derived directly from its linear case. Experimental results on both artificial datasets and large scale problems show the stable performance of SGTSVM with a fast learning speed.

cs.LG↗