arXiv subjects

Guoping Zhang

Publications and source records attributed to Guoping Zhang.

6 recordsLinked to original sources

arXiv2026

Theory of Andreev reflection spectroscopy with anisotropic spin-dependent scattering

Spintronic technologies require efficient generation and control of spin-polarized currents. Conventional ferromagnet-based methods suffer from sensitivity to external magnetic fields. Andreev reflection spectroscopy is vital for measuring spin polarization and superconducting gaps, yet prevailing theories assume isotropic interface scattering. This neglects ubiquitous anisotropy in heterostructures, causing misinterpretation of material properties. To resolve this, we develop a generalized model incorporating spin-dependent anisotropic scattering. Introducing distinct interface barriers for spin-up and spin-down electrons extends both the Blonder-Tinkham-Klapwijk formalism and the Chen-Tesanovic-Chien extension. This unified framework describes transport from normal metals to half-metals. Solving the Bogoliubov-de Gennes equations with modified boundary conditions yields current formulae with a transmission probability judgment function identifying dominant spin channels. In non-magnetic metals, interfacial anisotropy generates sizable spin-polarized currents via transmission spin filtering, suppressing Andreev reflection and reducing sub-gap conductance. For positively polarized ferromagnets, anisotropy nonlinearly modulates polarization, enhancing Andreev reflection to a threshold before suppression. Negatively polarized materials exhibit inverse spectra, enabling unambiguous polarization sign determination via conductance comparisons. Epitaxial Co film measurements validate the model, resolving subtle anisotropies. This refines Andreev spectrum interpretation and supports interference-resistant spin sources using non-magnetic platforms, benefiting magnetoresistive devices and superconducting quantum technologies.

cond-mat.mes-hall↗

arXiv2026

The Completeness of Eigenstates in Quantum Mechanics

We delineate the scope of research on the completeness of eigenstates in quantum mechanics. Based on the limit of the potential function at infinity, the proof of completeness is divided into eight cases, and theoretical proofs or numerical simulations are provided for each case. We present the definition of orthonormalization for general free states and the solution to the normalization coefficients, as well as a general set of initial states, which simplifies and concretizes the proof of completeness. Additionally, we define the spectral function for continuous energy eigenvalues. By taking the spectral function as the original integral variable of the expansion function, the relationship between the measured probability amplitude and the expansion function is endowed with the physical meaning of coordinate-momentum transformation.

quant-ph↗

arXiv2025

Joint Transmit and Pinching Beamforming Optimization in Pinching Antenna-Assisted Symbiotic Radio Systems

This paper investigates a novel downlink symbiotic radio framework enabled by the pinching antenna system (PASS), designed to enhance both primary and secondary transmissions through reconfigurable antenna positioning. This reconfigurability introduces additional degrees of freedom for adaptive pinching beamforming, thereby enabling constructive signal enhancement and interference suppression tailored to the locations of the backscatter device, the Internet of Things (IoT) receiver, and the primary receivers. To fully exploit these benefits, we formulate a joint transmit and pinching beamforming optimization problem that maximizes the achievable sum rate while satisfying the IoT receiver's detection error probability constraint and feasible deployment constraints for the pinching antennas. The resulting problem is inherently nonconvex and highly coupled. To address this challenge, we develop two complementary solution approaches. The first is a learning-aided gradient descent method, where the constrained optimization is reformulated into a differentiable form and solved through end-to-end learning. In this approach, the pinching antenna position matrix is reparameterized to automatically satisfy minimum spacing constraints, while transmit power and waveguide length limits are enforced via projection and normalization. The second approach is an optimization-based successive convex approximation-particle swarm optimization method, which first determines the transmit beamforming solution using successive convex approximation and subsequently optimizes pinching beamforming via a particle swarm optimization search over candidate pinching antenna placements.

eess.SP↗

arXiv2025

Widespread remote introgression in the grass genomes

Genetic transfers are pervasive across both prokaryotes and eukaryotes, encompassing canonical genomic introgression between species or genera and horizontal gene transfer (HGT) across kingdoms. However, DNA transfer between phylogenetically distant species, here defined as remote introgression (RI), has remained poorly explored in evolutionary genomics. In this study, we present RIFinder, a novel phylogeny-based method for RI event detection, and apply it to a comprehensive dataset of 122 grass genomes. Our analysis identifies 622 RI events originating from 543 distinct homologous genes, revealing distinct characteristics among grass subfamilies. Specifically, the subfamily Pooideae exhibits the highest number of introgressed genes while Bambusoideae contains the lowest. Comparisons among accepted genes, their donor copies and native homologs demonstrate that introgressed genes undergo post-transfer localized adaptation, with significant functional enrichment in stress-response pathways. Notably, we identify a large Triticeae-derived segment in a Chloridoideae species Cleistogenes songorica, which is potentially associated with its exceptional drought tolerance. Furthermore, we provide compelling evidence that RI has contributed to the origin and diversification of biosynthetic gene clusters of gramine, a defensive alkaloid chemical, across grass species. Collectively, our study establishes a robust method for RI detection and highlights its critical role in adaptive evolution.

q-bio.PE↗

arXiv2021

A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

Particle image velocimetry (PIV) is essential in experimental fluid dynamics. In the current work, we propose a new velocity field estimation paradigm, which achieves a synergetic combination of the deep learning method and the traditional cross-correlation method. Specifically, the deep learning method is used to optimize and correct a coarse velocity guess to achieve a super-resolution calculation. And the cross-correlation method provides the initial velocity field based on a coarse correlation with a large interrogation window. As a reference, the coarse velocity guess helps with improving the robustness of the proposed algorithm. This fully convolutional network with embedded cross-correlation is named as CC-FCN. CC-FCN has two types of input layers, one is for the particle images, and the other is for the initial velocity field calculated using cross-correlation with a coarse resolution. Firstly, two pyramidal modules extract features of particle images and initial velocity field respectively. Then the fusion module appropriately fuses these features. Finally, CC-FCN achieves the super-resolution calculation through a series of deconvolution layers to obtain the single-pixel velocity field. As the supervised learning strategy is considered, synthetic data sets including ground-truth fluid motions are generated to train the network parameters. Synthetic and real experimental PIV data sets are used to test the trained neural network in terms of accuracy, precision, spatial resolution and robustness. The test results show that these attributes of CC-FCN are further improved compared with those of other tested PIV algorithms. The proposed model could therefore provide competitive and robust estimations for PIV experiments.

physics.flu-dyn↗

arXiv2017

Comparisons of Some Iterative Algorithms for Biot Equations

In this paper, we aim at solving the Biot model under stabilized finite element discretizations. To solve the resulting generalized saddle point linear systems, some iterative methods are proposed and compared. In the first method, we apply the GMRES algorithm as the outer iteration. In the second method, the Uzawa method with variable relaxation parameters is employed as the outer iteration method. In the third approach, Uzawa method is treated as a fixed-point iteration, the outer solver is the so-called Anderson acceleration. In all these methods, the inner solvers are preconditioners for the generalized saddle point problem. In the preconditioners, the Schur complement approximation is derived by using Fourier analysis approach. These preconditioners are implemented exactly or inexactly. Extensive experiments are given to justify the performance of the proposed preconditioners and to compare all the algorithms.

math.NA↗