arXiv subjects

Kun Huang

Publications and source records attributed to Kun Huang.

At least 91 records · Page 5Linked to original sources

arXiv2025

A structure-preserving multiscale solver for particle-wave interaction in non-uniform magnetized plasmas

Particle-wave interaction is of fundamental interest in plasma physics, especially in the study of runaway electrons in magnetic confinement fusion. Analogous to the concept of photons and phonons, wave packets in plasma can also be treated as quasi-particles, called plasmons. To model the ``mixture" of electrons and plasmons in plasma, a set of ``collisional" kinetic equations has been derived, based on weak turbulence limit and the Wentzel-Kramers-Brillouin (WKB) approximation. There are two main challenges in solving the electron-plasmon kinetic system numerically. Firstly, non-uniform plasma density and magnetic field results in high dimensionality and the presence of multiple time scales. Secondly, a physically reliable numerical solution requires a structure-preserving scheme that enforces the conservation of mass, momentum, and energy. In this paper, we propose a struture-preserving multiscale solver for particle-wave interaction in non-uniform magnetized plasmas. The solver combines a conservative local discontinuous Galerkin (LDG) scheme for the interaction part with a trajectory averaging method for the plasmon Hamiltonian flow part. Numerical examples for a non-uniform magnetized plasma in an infinitely long symmetric cylinder are presented. It is verified that the LDG scheme rigorously preserves all the conservation laws, and the trajectory averaging method significantly reduces the computational cost.

math.NA↗

arXiv2025

A structure-preserving local discontinuous Galerkin method for the Fokker-Planck-Landau equation

In this work, we introduce a structure-preserving local discontinuous Galerkin (LDG) method \cite{cockburn1998local} for solving the non-local non-linear Fokker-Planck-Landau (FPL) equations. We rephrase the structure-preserving strategy of Shiroto and Sentoku\cite{shiroto2019structure} in the language of numerical analysis, and extend it to the LDG framework. We propose a method that is not only conservative, but also stabilized through upwind flux. The apparent contradiction between conservation laws and numerical stabilization is elegantly resolved by leveraging the properties of the jump terms inherent to the LDG framework. In the numerical experiments, our scheme is tested with benchmark examples.

math.NA↗

arXiv2025

Decentralized Min-Max Optimization with Gradient Tracking

This paper presents a novel distributed formulation of the min-max optimization problem. Such a formulation enables enhanced flexibility among agents when optimizing their maximization variables. To address the problem, we propose two distributed gradient methods over networks, termed Distributed Gradient Tracking Ascent (DGTA) and Distributed Stochastic Gradient Tracking Ascent (DSGTA). We demonstrate that DGTA achieves an iteration complexity of $\mathcal{O}(κ^2\varepsilon^{-2})$, and DSGTA attains a sample complexity of $\mathcal{O}(κ^3\varepsilon^{-4})$ for nonconvex strongly concave (NC-SC) objective functions. Both results match those of their centralized counterparts up to constant factors related to the communication network. Numerical experiments further demonstrate the superior empirical performance of the proposed algorithms compared to existing methods.

math.OC↗

arXiv2025

Study of neutron-deficient nucleus 224Np and its α decay by particle-number-conserving method in the framework of deformed shell model

The particle-number-conserving (PNC) method in the framework of the deformed shell model (DSM) is employed to study the properties of the newly discovered short-lived neutron-deficient nucleus 224Np and its α-decay. The calculated energy of α-particle lies within 300 keV of the experimental data. This is the first application of the PNC method to the region of neutron-deficient nuclei. This work provides the first attempt to combine the microscopic PNC theory with empirical formulas to study the nuclear α decay. The configurations of ground states are assigned as π5/2-[523]{\otimes}ν5/2+[633] for 224Np, π1/2-[530]{\otimes}ν3/2+[642] for 220Pa, π3/2+[651]{\otimes}ν1/2-[501] for 216Ac, and π7/2-[514]{\otimes}ν3/2-[501] for 212Fr. The absence of the Z = 92 subshell closure in 224Np is explained by analyzing the proton single-particle levels. Low-lying excited statesare predicted for nuclei along the α-decay chain by the PNC method. Based on the PNC predicted α-decay energy and the assigned configurations, the α-decay half-lives are calculated by the empirical formulas, in which the angular momentum taken away by the α particle is taken into account. The angular momentum have an important effect on the α-decay half-life. The errors of the empirical formulas calculation are in two orders of magnitude with the experimental data.

nucl-th↗

arXiv2025

Nonreciprocal and temperature-tunable light absorption in AlAs/ITO/GaAs Hybrid Metasurfaces

The single-band high-efficiency light absorption of nanostructures finds extensive applications in var ious fields such as photothermal conversion, optical sensing, and biomedicine. In this paper, a vertically stacked nanohybrid structure is designed with aluminum arsenide (AlAs), indium tin ox ide (ITO) and gallium arsenide (GaAs) stacked, and the photon absorption characteristics of this structure under near-infrared light at a single wavelength of 1240 nm are exploredbased on the finite difference time domain (FDTD) method. When AlAs, ITO, and GaAs are stacked and incident light enters from the GaAs side, a local light enhancement phenomenon occurs. The absorption rate can reach 91.67%, and the temperature change rate reaches 55. 53%, allowing for a wide-range regulation the absorption rate by temperature. In addition, the AlAs/ITO/GaAs sandwich-type hybrid structure also exhibits obvious nonreciprocity. With the change in temperature, the absorption rate of different structural sizes varies differently. The structure can be optimized and designed according to the requirements, providing new ideas for the design of multifunctional optoelectronic devices.

physics.optics↗

arXiv2025

Double-optical phase-transition in a three level Rydberg state in thermal Rubidium vapor

We report on the observation of electromagnetically induced transparency (EIT) with intrinsic phase transitions in a three-level ladder system within rubidium atomic vapor. The observed abrupt transitions between low and high Rydberg occupancy states manifest in the probe beam transmission, depending on the principal quantum number, the Rabi frequency of the coupling field, atomic density, and probe beam detuning. Our study elucidates the underlying interaction mechanisms governing the EIT phase transition and enriches the existing experiments of multi-parameter regulation phase transitions. These findings establish a robust platform for investigating nonequilibrium phase transitions in atomic ensembles, bridging the gap between classical mean-field theories and microscopic quantum dynamics.

physics.optics↗

arXiv2025

An Accelerated Distributed Stochastic Gradient Method with Momentum

In this paper, we introduce an accelerated distributed stochastic gradient method with momentum for solving the distributed optimization problem, where a group of $n$ agents collaboratively minimize the average of the local objective functions over a connected network. The method, termed ``Distributed Stochastic Momentum Tracking (DSMT)'', is a single-loop algorithm that utilizes the momentum tracking technique as well as the Loopless Chebyshev Acceleration (LCA) method. We show that DSMT can asymptotically achieve comparable convergence rates as centralized stochastic gradient descent (SGD) method under a general variance condition regarding the stochastic gradients. Moreover, the number of iterations (transient times) required for DSMT to achieve such rates behaves as $\mathcal{O}(n^{5/3}/(1-λ))$ for minimizing general smooth objective functions, and $\mathcal{O}(\sqrt{n/(1-λ)})$ under the Polyak-Łojasiewicz (PL) condition. Here, the term $1-λ$ denotes the spectral gap of the mixing matrix related to the underlying network topology. Notably, the obtained results do not rely on multiple inter-node communications or stochastic gradient accumulation per iteration, and the transient times are the shortest under the setting to the best of our knowledge.

math.OC↗

arXiv2025

Distributed Random Reshuffling Methods with Improved Convergence

This paper proposes two distributed random reshuffling methods, namely Gradient Tracking with Random Reshuffling (GT-RR) and Exact Diffusion with Random Reshuffling (ED-RR), to solve the distributed optimization problem over a connected network, where a set of agents aim to minimize the average of their local cost functions. Both algorithms invoke random reshuffling (RR) update for each agent, inherit favorable characteristics of RR for minimizing smooth nonconvex objective functions, and improve the performance of previous distributed random reshuffling methods both theoretically and empirically. Specifically, both GT-RR and ED-RR achieve the convergence rate of $O(1/[(1-λ)^{1/3}m^{1/3}T^{2/3}])$ in driving the (minimum) expected squared norm of the gradient to zero, where $T$ denotes the number of epochs, $m$ is the sample size for each agent, and $1-λ$ represents the spectral gap of the mixing matrix. When the objective functions further satisfy the Polyak-Łojasiewicz (PL) condition, we show GT-RR and ED-RR both achieve $O(1/[(1-λ)mT^2])$ convergence rate in terms of the averaged expected differences between the agents' function values and the global minimum value. Notably, both results are comparable to the convergence rates of centralized RR methods (up to constant factors depending on the network topology) and outperform those of previous distributed random reshuffling algorithms.

math.OC↗

arXiv2025

Tunable Absorption in Near Infrared Based on Indium Tin Oxide

We propose a serrated ultra-broadband infrared absorber based on the multi-layer repetitive stacking of indium tin oxide (ITO) and silicon materials. This absorber achieves continuously tunable absorption within a wide infrared light range. By changing the size and materials of the absorber, its average absorption rate can reach a relatively high level. Simulation results reveal wavelength-dependent electric field localization: shorter wavelengths concentrate in narrower upper regions, while longer wavelengths localize in wider lower areas. This design offers potential applications in near-infrared detectors, optical communication, and smart windows.

physics.optics↗

arXiv2025

OpenGV 2.0: Motion prior-assisted calibration and SLAM with vehicle-mounted surround-view systems

The present paper proposes optimization-based solutions to visual SLAM with a vehicle-mounted surround-view camera system. Owing to their original use-case, such systems often only contain a single camera facing into either direction and very limited overlap between fields of view. Our novelty consist of three optimization modules targeting at practical online calibration of exterior orientations from simple two-view geometry, reliable front-end initialization of relative displacements, and accurate back-end optimization using a continuous-time trajectory model. The commonality between the proposed modules is given by the fact that all three of them exploit motion priors that are related to the inherent non-holonomic characteristics of passenger vehicle motion. In contrast to prior related art, the proposed modules furthermore excel in terms of bypassing partial unobservabilities in the transformation variables that commonly occur for Ackermann-motion. As a further contribution, the modules are built into a novel surround-view camera SLAM system that specifically targets deployment on Ackermann vehicles operating in urban environments. All modules are studied in the context of in-depth ablation studies, and the practical validity of the entire framework is supported by a successful application to challenging, large-scale publicly available online datasets. Note that upon acceptance, the entire framework is scheduled for open-source release as part of an extension of the OpenGV library.

cs.RO↗

arXiv2025

Modulation of supernarrow EIT pair via atomic coherence

We report the phenomena of electromagnetically induced transparency (EIT) and electromagnetically induced absorption (EIA) using two identical beams in rubidium atomic vapor. The Λ-type EIT configuration is employed to examine the EIT spectrum for the D1 line in 87Rb F=2 characteristics16 by varying parameters such as frequency detuning, Iprobe/Ipump, the total power of probe and pump beam. Notably, the pump beam is also investigated in this process, which has not been previously studied. We study the effect of of the phase between the two applied fields and find that EIA and EIT can transform into each other by adjusting the relative phase. These finding may have applications in light drag or storage, optical switching, and sensing.

physics.atom-ph↗

arXiv2025

Inhomogeneous graph trend filtering via a l2,0 cardinality penalty

We study estimation of piecewise smooth signals over a graph. We propose a $\ell_{2,0}$-norm penalized Graph Trend Filtering (GTF) model to estimate piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness across the nodes. We prove that the proposed GTF model is simultaneously a k-means clustering on the signal over the nodes and a minimum graph cut on the edges of the graph, where the clustering and the cut share the same assignment matrix. We propose two methods to solve the proposed GTF model: a spectral decomposition method and a method based on simulated annealing. In the experiment on synthetic and real-world datasets, we show that the proposed GTF model has a better performances compared with existing approaches on the tasks of denoising, support recovery and semi-supervised classification. We also show that the proposed GTF model can be solved more efficiently than existing models for the dataset with a large edge set.

cs.LG↗

arXiv2024

Weak solutions for weak turbulence models in electrostatic plasmas

The weak turbulence model, also known as the quasilinear theory in plasma physics, has been a cornerstone in modeling resonant particle-wave interactions in plasmas. This reduced model stems from the Vlasov-Poisson/Maxwell system under the weak turbulence assumption, incorporating the random phase approximation and ergodicity. The interaction between particles and waves (plasmons) can be treated as a stochastic process, whose transition probability bridges the momentum space and the spectral space. Therefore, the operators on the right hand side resemble collision forms, such as those in Boltzmann and Landau interacting models. For them, there have been results on well-posedness and regularity of solutions. However, as far as we know, there is no such preceding work for the quasilinear theory addressed in this manuscript. In this paper, we establish the existence of global weak solutions for the system modeling electrostatic plasmas in one dimension. Our key contribution consists of associating the original integral-differential system to a degenerate inhomogeneous porous medium equation(PME) with nonlinear source terms, and leveraging advanced techniques from the PME literature. This approach opens a novel pathway for analyzing weak turbulence models in plasma physics. Moreover, our work offers new tools for tackling related problems in the broader context of nonlinear nonlocal PDEs.

math.AP↗

arXiv2024

Multifunctional metalens for trapping and characterizing single atoms

Precise control and manipulation of neutral atoms are essential for quantum technologies but largely dependent on conventional bulky optical setups. Here, we demonstrate a multifunctional metalens that integrates an achromatic lens with large numerical aperture, a quarter-wave plate, and a polarizer for trapping and characterizing single Rubidium atoms. The metalens simultaneously focuses a trapping beam at 852\,nm and collects single-photon fluorescence at 780\,nm. We observe a strong dependence of the trapping lifetime on an external bias magnetic field, suggests a complex interplay between the circularly polarized trapping light and the atom's internal states. Our work showcases the potential of metasurfaces in realizing compact and integrated quantum systems based on cold atoms, opening up new possibilities for studying quantum control and manipulation at the nanoscale.

quant-ph↗

arXiv2024

Multi-task Geometric Estimation of Depth and Surface Normal from Monocular 360° Images

Geometric estimation is required for scene understanding and analysis in panoramic 360° images. Current methods usually predict a single feature, such as depth or surface normal. These methods can lack robustness, especially when dealing with intricate textures or complex object surfaces. We introduce a novel multi-task learning (MTL) network that simultaneously estimates depth and surface normals from 360° images. Our first innovation is our MTL architecture, which enhances predictions for both tasks by integrating geometric information from depth and surface normal estimation, enabling a deeper understanding of 3D scene structure. Another innovation is our fusion module, which bridges the two tasks, allowing the network to learn shared representations that improve accuracy and robustness. Experimental results demonstrate that our MTL architecture significantly outperforms state-of-the-art methods in both depth and surface normal estimation, showing superior performance in complex and diverse scenes. Our model's effectiveness and generalizability, particularly in handling intricate surface textures, establish it as a new benchmark in 360° image geometric estimation. The code and model are available at \url{https://github.com/huangkun101230/360MTLGeometricEstimation}.

cs.CV↗

arXiv2024

360° Stereo Image Composition with Depth Adaption

360° images and videos have become an economic and popular way to provide VR experiences using real-world content. However, the manipulation of the stereo panoramic content remains less explored. In this paper, we focus on the 360° image composition problem, and develop a solution that can take an object from a stereo image pair and insert it at a given 3D position in a target stereo panorama, with well-preserved geometry information. Our method uses recovered 3D point clouds to guide the composited image generation. More specifically, we observe that using only a one-off operation to insert objects into equirectangular images will never produce satisfactory depth perception and generate ghost artifacts when users are watching the result from different view directions. Therefore, we propose a novel per-view projection method that segments the object in 3D spherical space with the stereo camera pair facing in that direction. A deep depth densification network is proposed to generate depth guidance for the stereo image generation of each view segment according to the desired position and pose of the inserted object. We finally combine the synthesized view segments and blend the objects into the target stereo 360° scene. A user study demonstrates that our method can provide good depth perception and removes ghost artifacts. The per-view solution is a potential paradigm for other content manipulation methods for 360° images and videos.

cs.GR↗

arXiv2024

Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

Towards human-like dialogue systems, current emotional dialogue approaches jointly model emotion and semantics with a unified neural network. This strategy tends to generate safe responses due to the mutual restriction between emotion and semantics, and requires rare emotion-annotated large-scale dialogue corpus. Inspired by the "think twice" behavior in human dialogue, we propose a two-stage conversational agent for the generation of emotional dialogue. Firstly, a dialogue model trained without the emotion-annotated dialogue corpus generates a prototype response that meets the contextual semantics. Secondly, the first-stage prototype is modified by a controllable emotion refiner with the empathy hypothesis. Experimental results on the DailyDialog and EmpatheticDialogues datasets demonstrate that the proposed conversational outperforms the comparison models in emotion generation and maintains the semantic performance in automatic and human evaluations.

cs.CL↗

arXiv2024

CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence

In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting. We study a compressed decentralized stochastic gradient method, termed ``compressed exact diffusion with adaptive stepsizes (CEDAS)", and show the method asymptotically achieves comparable convergence rate as centralized { stochastic gradient descent (SGD)} for both smooth strongly convex objective functions and smooth nonconvex objective functions under unbiased compression operators. In particular, to our knowledge, CEDAS enjoys so far the shortest transient time (with respect to the graph specifics) for achieving the convergence rate of centralized SGD, which behaves as $\mathcal{O}(n{C^3}/(1-λ_2)^{2})$ under smooth strongly convex objective functions, and $\mathcal{O}(n^3{C^6}/(1-λ_2)^4)$ under smooth nonconvex objective functions, where $(1-λ_2)$ denotes the spectral gap of the mixing matrix, and $C>0$ is the compression-related parameter. In particular, CEDAS exhibits the shortest transient times when $C < \mathcal{O}(1/(1 - λ_2)^2)$, which is common in practice. Numerical experiments further demonstrate the effectiveness of the proposed algorithm.

math.OC↗