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Bei Zhang

Publications and source records attributed to Bei Zhang.

17 recordsLinked to original sources
arXiv2026

The Ultimate Ishii-Pastur Theorem for Whole-Line Ergodic Block Jacobi Operators

We establish an ultimate Ishii-Pastur theorem for whole-line ergodic block Jacobi operators. We prove that, for almost every realization, the restriction of a maximal spectral measure to the region where the smallest nonnegative Lyapunov exponent is strictly positive is carried by a Borel set of zero capacity. In the scalar case, this settles the whole-line problem formulated by Damanik and Fillman \cite[Problem~4.7.18]{Damanik-Fillman-book}.

math.SP↗
arXiv2026

A 2.5D NURBS-Trace Infinite-Element Method for Moving-Load Wave Propagation and Soil--Structure Interaction in Semi-Infinite Ground

For moving-load problems whose geometry and material properties are approximately invariant along the traveling direction, 2.5D analysis retains three displacement components at lower cost than full three-dimensional discretization. We present a 2.5D Non-Uniform Rational B-spline (NURBS)-trace infinite-element method (NBIEM), formulated as a coupled finite/infinite-element scheme, for wave propagation in linear viscoelastic semi-infinite geotechnical media. The bounded near field is discretized by isogeometric analysis, while the exterior is represented by tensor products of the boundary NURBS basis and admissible outgoing or evanescent exponential radial functions. Both subdomains share the same NURBS trace space and control-point degrees of freedom, enforcing displacement continuity without projection or mortar variables. For the selected radial functions, far-field stiffness and mass contributions are evaluated through closed-form radial moments, eliminating finite radial cutoff and radial quadrature. Closed-form half-space solutions verify displacement and stress frequency-response functions in sub-Rayleigh, super-shear but sub-compressional, and super-compressional moving-load regimes. Low-frequency studies assess sensitivity to radial parameters and artificial-boundary placement. Additional tests examine complex-valued response accuracy, phase fidelity, computational cost, and the frequency-dependent working range of the default S-wave-informed exterior realization. Applications to layered media, track--subgrade systems, and buried structures demonstrate the ability to handle heterogeneous materials, multi-patch configurations, curved interfaces, and cover-depth-dependent geotechnical responses. The framework provides a geometrically consistent and computationally efficient treatment of moving-load wave propagation and soil--structure interaction in semi-infinite domains.

cs.CE↗
arXiv2026

Stabilization-free virtual element methods based on finite element interpolation

In this paper, we introduce a new framework for designing stabilization-free virtual element methods (VEMs) based on an finite element interpolation-based strategy, where we can simultaneously eliminate the stabilization terms in the discretizations of diffusion and reaction terms. The core idea is to construct a computable, polynomial-preserving, and norm-equivalent interpolation operator from the virtual element space to a (local) finite element space. Leveraging the properties of this operator, we design two types of stabilization-free schemes. The first scheme requires the interpolation to preserve the polynomial consistency related to the bilinear forms, thereby maintaining both consistency and stability as in the standard VEM. The second scheme relaxes this consistency requirement. While it may not satisfy the standard polynomial consistency, the second scheme retains optimal convergence with simpler construction, fewer degrees of freedom and, more importantly, applicable to more complex problems such as those involving nonlinearities or variable coefficients. We construct concrete interpolation operators for both conforming and nonconforming virtual elements in two and three dimensions. These operators are then employed to realize stabilization-free schemes for conforming and nonconforming VEMs. Numerical experiments confirm the optimal convergence rates of the proposed methods. The presented framework can be extended to design stabilization-free schemes for other polytopal discretization methods, such as the hybrid high-order method and the weak Galerkin method.

math.NA↗
arXiv2025

A Dual-Gate Altermagnetic Tunnel Junction Based on Bilayer Cr$_{2}$SeO

Altermagnets demonstrate significant potential in spintronics due to their unique non-relativistic spin-splitting properties, yet altermagnetic devices still face challenges in efficiently switching logic states. Here, we report electrostatically controllable spin-momentum locking in bilayer Cr$_{2}$SeO and design a dual-gate altermagnetic tunnel junction (AMTJ), which can switch between high and low resistance states without switching the N\'eel vector. First-principles calculations demonstrate that vertical electric field can induce significant spin splitting in bilayer Cr$_{2}$SeO. Reversing the electric field direction can alter the spin-momentum locking in bilayer Cr$_{2}$SeO. Leveraging this electric-field-tunable spin splitting, the dual-gate AMTJ exhibits an ultrahigh tunneling magnetoresistance (TMR) ratio of $10^{7}$. This work provides theoretical support for the design of fully electrically controlled AMTJs and demonstrates their great potential for applications in spintronic devices.

cond-mat.mtrl-sci↗
arXiv2025

Plasmon-driven Ultrafast and Highly Efficient Saturable Absorption for Ultrashort Pulse Generation Based on 2D V2C

Plasmon-driven ultrafast nonlinearities hold promise for advanced photonics but remain challenging to harness in two-dimensional materials at telecommunication wavelengths. Here, we demonstrate few-layer V2C MXene as a high-performance saturable absorber by leveraging its tailored surface plasmon resonance. Combining transient absorption spectroscopy and first-principles calculations, we unveil a plasmon-driven relaxation mechanism dominated by interfacial high-energy hot electron generation (~100 fs), enabling giant ultrafast nonlinearities. Crucially, at the communication band (1550 nm), V2C exhibits a high saturable absorption coefficient of -1.35 cm/GW. Integrating this into an erbium-doped fiber laser, we generate mode-locked pulses with a duration of 486 fs at 1569 nm, a 39.51 MHz repetition rate, and exceptional stability (92 dB SNR). This work establishes plasmonic MXenes as a paradigm for tailored ultrafast photonic devices.

physics.optics↗
arXiv2025

Characterization of atomization and delivery efficiency of exogenous surfactant in preterm infant lungs using an ex vivo respiratory model

Administration of pulmonary surfactant is crucial for the treatment of respiratory distress syndrome (RDS) in preterm infants. The aim of this study is to evaluate the potential of Curosurf atomization via the Endosurf device, a recently developed spray technology, as a promising approach for surfactant delivery in infants with RDS. A comprehensive analysis was performed to evaluate the physicochemical properties of atomized Curosurf, including its surface tension and rheology. The size distribution of Curosurf vesicles was also investigated. An ex vivo respiratory model based on rabbit lungs breathing through an instrumented hypobaric chamber representing the thorax of a preterm infant was developed to provide proof of concept for regional aerosol deposition of atomized Curosurf. The atomization of Curosurf with the innovative Endosurf device did not significantly alter surface tension, but reduced vesicle size and promoted homogeneous distribution of Curosurf in the lungs. Rheological measurements showed the viscoelastic complexity of atomized Curosurf. This preliminary study confirmed the promising potential of Curosurf atomization via the Endosurf device for the distribution of surfactant in the lungs of infants with RDS. These advances could help to improve the treatment of RDS in preterm infants and offer new perspectives for healthcare professionals and affected families.

physics.med-ph↗
arXiv2025

The spectrum of the multi-frequency quasi-periodic CMV matrices contains intervals

We investigate the spectral structure of multi-frequency quasi-periodic CMV matrices with Verblunsky coefficients defined by shifts on the $d$-dimensional torus. Under the positive Lyapunov exponent regime and standard Diophantine frequency conditions, we establish that the spectrum of these operators contains intervals on the unit circle.

math.SP↗
arXiv2025

Anderson localization for the multi-frequency quasi-periodic CMV matrices and quantum walks

In this paper we prove Anderson localization for multi-frequency quasi-periodic extended CMV matrices with analytic Verblunsky coefficients in the regime of positive Lyapunov exponents. By constructing a suitable semialgebraic set and combining the Avalanche Principle with a Large Deviation Theorem, we overcome the key obstruction of eliminating double resonances along the orbit, where multi-frequency potentials introduce significant challenges compared to the single-frequency case. As a direct application, we establish Anderson localization for corresponding analytic multi-frequency quasi-periodic quantum walks via unitary equivalence.

math.SP↗
arXiv2024

Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels

Inspired by the concept of the male gaze (Mulvey, 1975) in literature and media studies, this paper proposes a framework for analyzing gender bias in terms of female objectification: the extent to which a text portrays female individuals as objects of visual pleasure. Our framework measures female objectification along two axes. First, we compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities. Next, by analyzing the word embedding space induced by a text (Caliskan et al., 2017), we compute an appearance bias score that indicates whether female entities are more closely associated with appearance-related words than male entities. Applying our framework to 19th and 20th century novels reveals evidence of female objectification in literature: we find that novels written from a male perspective systematically objectify female characters, while novels written from a female perspective do not exhibit statistically significant objectification of any gender.

cs.CL↗
arXiv2022

A hybrid volume-surface integral equation method for rapid electromagnetic simulations in MRI

Objective: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. Methods: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid- VSIE with other VSIE methods for realistic MRI simulation setups. Results: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. Conclusion: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. Significance: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups.

cs.CE↗
arXiv2020

On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control

Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-policy reinforcement learning (RL) algorithm, soft actor-critic (SAC) is adopted to train AI agents with multi-thread offline training and periodic online training for regulating voltages and transmission losses without violating thermal constraints of lines. A software prototype was developed and deployed in the control center of SGCC Jiangsu Electric Power Company that interacts with their Energy Management System (EMS) every 5 minutes. Massive numerical studies using actual power grid snapshots in the real-time environment verify the effectiveness of the proposed approach. Well-trained SAC agents can learn to provide effective and subsecond control actions in regulating voltage profiles and reducing transmission losses.

math.OC↗
arXiv2020

Deriving AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation

Optimal power flow (OPF) is a very fundamental but vital optimization problem in the power system, which aims at solving a specific objective function (ex.: generator costs) while maintaining the system in the stable and safe operations. In this paper, we adopted the start-of-the-art artificial intelligence (AI) techniques to train an agent aiming at solving the AC OPF problem, where the nonlinear power balance equations are considered. The modified IEEE-14 bus system were utilized to validate the proposed approach. The testing results showed a great potential of adopting AI techniques in the power system operations.

eess.SY↗
arXiv2019

AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs

This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constraints and uncertainties. Several AI techniques including supervised learning and deep reinforcement learning (DRL) are adopted and improved to train effective AI agents for achieving the desired performance. First, imitation learning (IL) is used to provide a good initial policy for the AI agent. Then, the agent is trained by DRL algorithms with a novel guided exploration technique, which significantly improves the training efficiency. Finally, an Early Warning (EW) mechanism is designed to help the agent find good topology control strategies for long testing periods, which helps the agent to determine action timing using power system domain knowledge; thus, effectively increases the system error-tolerance and robustness. Effectiveness of the proposed approach is demonstrated in the "2019 Learn to Run a Power Network (L2RPN)" global competition, where the developed AI agents can continuously and safely control a power grid to maximize ATCs without operator's intervention for up to 1-month's operation data and eventually won the first place in both development and final phases of the competition. The winning agent has been open-sourced on GitHub.

eess.SP↗
arXiv2019

A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatch

The stochastic and dynamic nature of renewable energy sources and power electronic devices are creating unique challenges for modern power systems. One such challenge is that the conventional mathematical systems models-based optimal active power dispatch (OAPD) method is limited in its ability to handle uncertainties caused by renewables and other system contingencies. In this paper, a deep reinforcement learning-based (DRL) method is presented to provide a near-optimal solution to the OAPD problem without system modeling. The DRL agent undergoes offline training, based on which, it is able to obtain the OAPD points under unseen scenarios, e.g., different load patterns. The DRL-based OAPD method is tested on the IEEE 14-bus system, thereby validating its feasibility to solve the OAPD problem. Its utility is further confirmed in that it can be leveraged as a key component for solving future model-free AC-OPF problems.

math.OC↗
arXiv2017

High Impedance Detector Arrays for Magnetic Resonance

Resonant inductive coupling is commonly seen as an undesired fundamental phenomenon emergent in densely packed resonant structures, such as nuclear magnetic resonance phased array detectors. The need to mitigate coupling imposes rigid constraints on the detector design, impeding performance and limiting the scope of magnetic resonance experiments. Here we introduce a high impedance detector design, which can cloak itself from electrodynamic interactions with neighboring elements. We verify experimentally that the high impedance detectors do not suffer from signal-to-noise degradation mechanisms observed with traditional low impedance elements. Using this new-found robustness, we demonstrate an adaptive wearable detector array for magnetic resonance imaging of the hand. The unique properties of the detector glove reveal new pathways to study the biomechanics of soft tissues, and exemplify the enabling potential of high-impedance detectors for a wide range of demanding applications that are not well suited to traditional coil designs.

physics.ins-det↗
arXiv2015

Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC

A natural method for approximating out-of-sample predictive evaluation is leave-one-out cross-validation (LOOCV) --- we alternately hold out each case from a full data set and then train a Bayesian model using Markov chain Monte Carlo (MCMC) without the held-out; at last we evaluate the posterior predictive distribution of all cases with their actual observations. However, actual LOOCV is time-consuming. This paper introduces two methods, namely iIS and iWAIC, for approximating LOOCV with only Markov chain samples simulated from a posterior based on a \textit{full} data set. iIS and iWAIC aim at improving the approximations given by importance sampling (IS) and WAIC in Bayesian models with possibly correlated latent variables. In iIS and iWAIC, we first integrate the predictive density over the distribution of the latent variables associated with the held-out without reference to its observation, then apply IS and WAIC approximations to the integrated predictive density. We compare iIS and iWAIC with other approximation methods in three real data examples that respectively use mixture models, models with correlated spatial effects, and a random effect logistic model. Our empirical results show that iIS and iWAIC give substantially better approximates than non-integrated IS and WAIC and other methods.

stat.ME↗