arXiv subjects

H. Shen

Publications and source records attributed to H. Shen.

At least 19 recordsLinked to original sources

arXiv2026

Polarization-Multiplexed Chaotic LiDAR Based on a VCSEL with Delayed Orthogonal Feedback

Light detection and ranging (LiDAR) systems are pivotal for precise distance and velocity measurement, yet widespread deployment requires solutions that balance their performance, robustness, and simplicity. Here, we propose a novel chaotic LiDAR system based on a semiconductor vertical-cavity surface-emitting laser (VCSEL) with delayed orthogonal polarization feedback. By exploiting the intrinsic competition between the transverse electric (TE) and transverse magnetic (TM) modes, the system generates a polarization-multiplexed dynamics: a chaotic TM mode serves as the reference, while a feedback-modulated TE mode probes the target. This all-in-one source eliminates the need for external optical modulators or complex coherent detection. The system's dynamics is finely tunable via a half-wave ($\lambda$/2) plate in the feedback loop and the laser injection current, enabling real-time optimization of the cross-correlation signal-to-noise ratio. Experimental results demonstrate precise linear ranging with a resolution of approximately 1.2 cm. Furthermore, the system exhibits strong inherent resistance to external optical interference, maintaining accurate ranging even in the presence of a secondary laser source. This compact, tunable, and interference-resilient platform offers a promising pathway toward low-cost, high-performance LiDAR for applications in autonomous navigation, robotics, and industrial metrology.

physics.optics↗

arXiv2026

Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network

Narrowband perfect absorbers are interesting for spectrum sensing, molecular detection, and infrared imaging. However, their design remains constrained by intuitive, iterative methods that lack flexibility, while also facing challenges in multi-objective optimization. Here, we introduce a deep learning-enabled inverse-design framework that overcomes these limitations through a conditional Wasserstein Generative Adversarial Network (WGAN). The main contribution of this work is a dual-channel image encoding scheme that jointly represents the geometry and thickness of a Si$_3$N$_4$ meta-layer, facilitating the network to learn the distribution of viable structures for a target optical response. This approach naturally solves the inherent ``one-to-many'' design issue, giving a diverse portfolio of functional candidates from a single input spectrum. The designed absorbers achieve exceptional spectral fidelity, with resonance peak errors below 5 nm, a mean squared error (MSE) on the order of $10^{-3}$, and the capacity to produce over 10 distinct, high-performance designs per target. Furthermore, we demonstrate the model's robustness under oblique illumination, showing that it can be efficiently fine-tuned to maintain spectral accuracy across incidence angles from $10^\circ$ to $40^\circ$ by transfer learning, thus extending its practical utility to non-normal operating conditions. Full-wave simulations confirm that the generated geometries support a hybrid plasmonic-dielectric resonance, leading to near-perfect absorption and strong near-field enhancement. Our study provides a robust, physics-aware design paradigm that moves beyond conventional parametric optimization. The introduced framework establishes a versatile platform for the on-demand inverse design of advanced photonic devices for sensing, spectroscopy, and optical signal processing.

physics.optics↗

arXiv2025

Wave-packet revival in a Floquet engineering quadratic potential system

We investigate the quantum dynamics of a one-dimensional tight-binding lattice driven by a spatially quadratic and time-periodic potential. Both Hermitian ($J_1 = J_2$) and non-Hermitian ($J_1 \neq J_2$) hopping regimes are analyzed. Within the framework of Floquet theory, the time-dependent Hamiltonian is mapped onto an effective static Floquet Hamiltonian, enabling a detailed study of the quasi-energy spectrum as function of the driving frequency $\omega$. By applying a gauge transformation, we find that critical frequencies $\omega_c$ emerge, at which nearly equidistant quasi-energy ladders appear, as revealed by a pronounced minimum in the normalized variance $\Delta(\omega)$ of the level spacings. This spectral regularity leads to robust periodic revivals and Bloch-like oscillations in the time evolution. Numerical simulations confirm that such coherent oscillations persist even in the non-Hermitian regime, where the periodic driving stabilizes an almost real and uniformly spaced quasi-energy ladder.

quant-ph↗

arXiv2025

Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning

Precise control of plasma shape and position is essential for stable tokamak operation and achieving commercial fusion energy. Traditional control methods rely on equilibrium reconstruction and linearized models, limiting adaptability and real-time performance. Here,the first application of deep reinforcement learning (RL) for magnetic plasma control on the mid-size DIII-D tokamak is presented, demonstrating a nonlinear approach that improves robustness and flexibility across plasma scenarios. Using the Soft Actor-Critic algorithm, this method eliminates the need for equilibrium reconstruction, enabling high-speed control execution and scalability on larger fusion devices. NSFsim, a 2D Grad-Shafranov equilibration solver with a circuit equation and a 1D transport solver, is used to train the agent. Its capability of reproducing the kinetic parameter evolution alongside magnetic equilibria evolution appears to be an essential factor significantly affecting control quality. RL-based controllers demonstrated robust magnetic control in experimental application at DIII-D, preserving control performance in transient events during plasma discharges, and reaching target parameters from the first discharge without additional tuning or modifications. The approach itself has significant generalization potential across devices and targets. This work represents a step toward AI-driven, real-time plasma control, advancing the feasibility of next-generation fusion reactors.

physics.plasm-ph↗

arXiv2024

The convolutional neural networks for analysing the micro-cavity array multi-mode quantum frequency comb spectrum features

The research on sensing the sensitivity of the light field in the whispering gallery mode (WGM) to the micro-cavity environment has already appeared, which uses the frequency shift of the light field in the WGM or the sensitivity of the resonance peak frequency shift. Multi-mode comb teeth of optical frequency comb(OFC) generated by nonlinear micro-cavity have excellent sensitivity to micro-cavity environment, and they have more sensitivity degrees of freedom compared with WGM light field (the strength of each comb tooth can be influenced by micro-cavity environment). The influence of different substances on the environmental parameters of micro-cavity is complex and nonlinear, so we use machine learning method to automatically extract the spectrum characteristics, the average accuracy of single-parameter identification attains to 99.5%, and the average accuracy of double parameter identification attains to 97.0%. Based on the integration of micro-cavity OFC and wave-guide coupling structure, we propose an set of fluid characteristics detection integrated device in theoretically.

physics.optics↗

arXiv2021

Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook

In recent years, the unprecedented growth in digital payments fueled consequential changes in fraud and financial crimes. In this new landscape, traditional fraud detection approaches such as rule-based engines have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest. Graph neural networks and emerging adaptive solutions provide compelling opportunities for the future of fraud and financial crime detection. However, implementing the graph-based solutions in financial transaction processing systems has brought numerous obstacles and application considerations to light. In this paper, we overview the latest trends in the financial crimes landscape and discuss the implementation difficulties current and emerging graph solutions face. We argue that the application demands and implementation challenges provide key insights in developing effective solutions.

cs.LG↗

arXiv2021

Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

cs.CR↗

arXiv2021

Optimization of the high-frequency magnetoimpedance response in melt-extracted Co-rich microwires through novel multiple-step Joule heating

The optimization of high frequency giant magnetoimpedance (GMI) effect and its magnetic field sensitivity in melt-extracted Co69.25Fe4.25Si13B12.5Nb1 amorphous microwires, through a multi-step Joule annealing (MSA) technique, was systematically studied. The surface morphology, microstructure, surface magnetic property, and high frequency GMI response of the Co-rich microwires were explored using scanning electron microscopy (SEM), magneto-optical Kerr effect (MOKE) magnetometry, transmission electron microscopy (TEM), and impedance analyzer, respectively. An initial dc current (idc) of 20 mA, which was then increased by 20 mA at every time-step (10 min) up to 300 mA, was applied to the microwires. The MSA of 20 mA to 100 mA remarkably improved the GMI ratio and its field sensitivity up to 760% (1.75 time of that of the as-prepared), and 925%/Oe (more than 17.92 times of that of the as-prepared) at an operating frequency of 20 MHz, respectively. Our study indicates that the MSA technique can enhance the microstructures and the surface magnetic domain structures of the Co-rich magnetic microwires, giving rise to the GMI enhancement. This technique is suitable for improving the GMI sensitivity at small magnetic fields, which is highly promising for biomedical sensing and healthcare monitoring.

physics.ins-det↗

arXiv2021

Impact of strong magnetic fields on the inner crust of neutron stars

We study the impact of strong magnetic fields on the pasta phases that are expected to exist in the inner crust of neutron stars. We employ the relativistic mean field model to describe the nucleon interaction and use the self-consistent Thomas-Fermi approximation to calculate the nonuniform matter in neutron star crust. The properties of pasta phases and crust-core transition are examined. It is found that as the magnetic field strength $B$ is less than $10^{17}$ G, the effects of magnetic field are not evident comparing with the results without magnetic field. As $B$ is stronger than $10^{18}$ G, the onset densities of pasta phases and crust-core transition density decrease significantly, and the density distributions of nucleons and electrons are also changed obviously.

nucl-th↗

arXiv2019

Nuclear symmetry energy and hadron-quark mixed phase in neutron stars

We study the hadron-quark mixed phase, which may occur in the interior of neutron stars. The relativistic mean-field model is employed to describe the hadronic phase, while the Nambu--Jona-Lasinio model is used for the quark phase. We examine the effects of nuclear symmetry energy in the hadronic phase and repulsive vector interaction in the quark phase. For the treatment of hadron-quark mixed phase, we describe and compare four methods: (1) energy minimization method; (2) coexisting phases method; (3) Gibbs construction; and (4) Maxwell construction. The finite-size effects like surface and Coulomb energies are taken into account in the energy minimization and coexisting phases methods, which play a key role in determining the pasta configuration during the hadron-quark phase transition. It is found that massive neutron stars may contain hadron-quark pasta phases, but pure quark matter is unlikely to occur in the interior of neutron stars.

nucl-th↗

arXiv2016

Direct observation of dynamical quantum phase transitions in an interacting many-body system

Dynamical quantum phase transitions (DQPTs) extend the concept of phase transitions and thus universality to the non-equilibrium regime. In this letter, we investigate DQPTs in a string of ions simulating interacting transverse-field Ising models. We observe non-equilibrium dynamics induced by a quantum quench and show for strings of up to 10 ions the direct detection of DQPTs by measuring a quantity that becomes non-analytic in time in the thermodynamic limit. Moreover, we provide a link between DQPTs and the dynamics of other relevant quantities such as the magnetization, and we establish a connection between DQPTs and entanglement production.

quant-ph↗

arXiv2016

Channeling and Radiation of 855 MeV Electrons and Positrons in Straight and Bent Tungsten (110) Crystals

Planar channeling of 855 MeV electrons and positrons in straight and bent tungsten (110) crystal is simulated by means of the \MBNExplorer software package. The results of simulations for a broad range of bending radii are analyzed in terms of the channel acceptance, dechanneling length, and spectral distribution of the emitted radiation. Comparison of the results with predictions of other theories as well as with the data for (110) oriented diamond, silicon and germanium crystals is carried out.

physics.acc-ph↗

arXiv2016

Scalable photonic network architecture based on motional averaging in room temperature gas

Quantum interfaces between photons and ensembles of atoms have emerged as powerful tools for quantum technologies. A major objective for such interfaces is high fidelity storage and retrieval of a photon in a collective quantum state of many atoms. This requires long-lived collective superposition states, which is typically achieved with immobilized atoms. Thermal atomic vapors, which present a simple and scalable resource, have, so far, only been used for continuous variable processing or for discrete variable processing on short time scales where atomic motion is negligible. We develop a theory based on the concept of motional averaging to enable room temperature discrete variable quantum memories and coherent single photon sources. We show that by choosing the interaction time so that atoms kept under spin protecting conditions can cross the light beam several times during the interaction combined with suitable spectral filtering, we erase the "which atom" information and obtain an efficient and homogenous coupling between all atoms and the light. Heralded single excitations can thus be created and stored as collective spinwaves, which can later be read out to produce coherent single photons in a scalable fashion. We demonstrate the feasibility of this approach to scalable quantum memories with a proof-of-principle experiment with room temperature atoms contained in microcells with spin protecting coating, placed inside an optical cavity. The experiment is performed at conditions corresponding to a few photons per pulse and clearly demonstrates a long coherence time of the forward scattered photons, which is the essential feature of the motional averaging.

quant-ph↗

arXiv2016

Effects of finite size and symmetry energy on the phase transition of stellar matter at subnuclear densities

We study the liquid-gas phase transition of stellar matter with the inclusion of the finite-size effect from surface and Coulomb energies. The equilibrium conditions for two coexisting phases are determined by minimizing the total free energy including the surface and Coulomb contributions, which are different from the Gibbs conditions used in the bulk calculations. The finite-size effect can significantly reduce the region of the liquid-gas mixed phase. The influence of the symmetry energy on the liquid-gas phase transition is investigated with the inclusion of finite-size effects. It is found that the slope of the symmetry energy plays an important role in determining the boundary and properties of the mixed phase.

nucl-th↗

arXiv2015

Impact of the symmetry energy on nuclear pasta phases and crust-core transition in neutron stars

We study the impact of the symmetry energy on properties of nuclear pasta phases and crust-core transition in neutron stars. We perform a self-consistent Thomas--Fermi calculation employing the relativistic mean-field model. The properties of pasta phases presented in the inner crust of neutron stars are investigated and the crust-core transition is examined. It is found that the slope of the symmetry energy plays an important role in determining the pasta phase structure and the crust-core transition. The correlation between the symmetry energy slope and the crust-core transition density obtained in the Thomas--Fermi approximation is consistent with that predicted by the liquid-drop model.

nucl-th↗

arXiv2014

Symmetry energy of hot nuclei in the relativistic Thomas-Fermi approximation

We develop a self-consistent description of hot nuclei within the relativistic Thomas--Fermi approximation using the relativistic mean-field model for nuclear interactions. The temperature dependence of the symmetry energy and other physical quantities of a nucleus are calculated by employing the subtraction procedure in order to isolate the nucleus from the surrounding nucleon gas. It is found that the symmetry energy coefficient of finite nuclei is significantly affected by the Coulomb polarization effect. We also examine the dependence of the results on nuclear interactions and make a comparison between the results obtained from relativistic and nonrelativistic Thomas-Fermi calculations.

nucl-th↗

arXiv2014

Effects of the symmetry energy on properties of neutron star crusts near the neutron drip density

We study the effects of the symmetry energy on the neutron drip density and properties of nuclei in neutron star crusts. The nonuniform matter around the neutron drip point is calculated by using the Thomas--Fermi approximation with the relativistic mean-field model. The neutron drip density and the composition of the crust are found to be correlated with the symmetry energy and its slope. We compare the self-consistent Thomas--Fermi approximation with other treatments of surface and Coulomb energies and find that these finite-size effects play an essential role in determining the equilibrium state at low density.

nucl-th↗

arXiv2014

Relativistic equation of state at subnuclear densities in the Thomas-Fermi approximation

We study the non-uniform nuclear matter using the self-consistent Thomas--Fermi approximation with a relativistic mean-field model. The non-uniform matter is assumed to be composed of a lattice of heavy nuclei surrounded by dripped nucleons. At each temperature $T$, proton fraction $Y_p$, and baryon mass density $ρ_B$, we determine the thermodynamically favored state by minimizing the free energy with respect to the radius of the Wigner--Seitz cell, while the nucleon distribution in the cell can be determined self-consistently in the Thomas--Fermi approximation. A detailed comparison is made between the present results and previous calculations in the Thomas--Fermi approximation with a parameterized nucleon distribution that has been adopted in the widely used Shen EOS.

nucl-th↗