arXiv subjects

Hong Liang

Publications and source records attributed to Hong Liang.

At least 19 recordsLinked to original sources

arXiv2026

Sub-Terahertz Channel Performance under Snowfall

The terahertz (THz) band promises terabit-per-second links but is highly sensitive to snowfall. Natural snowflakes are non-spherical. Yet existing THz studies treat them as spheres under Mie theory, and no ITU-R model covers THz snow attenuation. This work combines line-of-sight measurements at 120, 140, and 160 GHz with physics-based scattering modeling. The measured loss is compared against the ITU-R P.1817-1 optical model, Mie models, and a discrete dipole approximation (DDA) for randomly oriented hexagonal-plate ice crystals, each with the Scott and Gunn-Marshall size distributions. Over the measured band, ITU-R P.1817-1 overestimates and the Mie models underestimate the loss. The shape-aware DDA-Scott model agrees best, with the lowest RMSE at every frequency. From DDA-Scott, we derive a compact modified ITU-R expression in carrier frequency and liquid-water-equivalent (LWE) rate. It reproduces the reference to within 2.5 dB/km over 100-500 GHz and 0-3 mm/h. A Rician K-factor analysis shows the channel stays LoS-dominated, so snowfall degrades the link mainly through attenuation, not multipath fading. A QPSK/16-QAM link-budget analysis then quantifies the cost of the spherical assumption. Mie-based margins overestimate the tolerable snowfall rate by 3.4 across 120-160 GHz, rising toward 5.8 in the upper transparency windows by model extrapolation. The model is further mapped into snow-limited range and adaptive-modulation switching boundaries. These results support future ITU-R recommendations for THz channels under snowfall.

physics.app-ph↗

arXiv2026

Opportunistic Lower-Terahertz Rainfall Estimation with DSD-Constrained Channel Characterization

Rain-induced attenuation and scattering become significant at terahertz (THz) frequencies, and exploiting this rain sensitivity is necessary both to safeguard link reliability and to enable opportunistic environmental sensing without dedicated instrumentation, a capability that remains largely unvalidated on real outdoor channels above 100 GHz. This article investigates opportunistic rainfall estimation using measured lower-terahertz (THz) channels at 140 and 229 GHz. Outdoor measurements over a 41.5-m rain-exposed path are used to characterize rain-induced attenuation and the rainfall dependence of an effective Rician K-factor. Because the path-representative drop-size distribution (DSD) is unavailable, several propagation-model scenarios based on ITU-R P.838-3 and Mie theory with canonical DSDs are employed to quantify model-form sensitivity. These channel characteristics are then used to generate physics-constrained synthetic received-power sequences for training RainFormer, a compact attention-convolution regression network that combines temporal features with explicit attenuation and fluctuation statistics. Under matched synthetic conditions, RainFormer achieves RMSEs of 0.1782 and 0.2925 mm/h at 140 and 229 GHz, respectively, and outperforms the investigated convolutional and Transformer baselines in most metric-frequency combinations. Direct application to the independent measured dataset produces physically consistent rainfall estimates at 140 GHz and demonstrates that received-power fluctuations provide useful information beyond mean attenuation. The results establish a measurement-informed framework for evaluating lower-THz links as opportunistic rainfall sensors while explicitly accounting for propagation-model uncertainty.

physics.app-ph↗

arXiv2026

Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale

Data-parallel (DP) load balancing has emerged as a first-order bottleneck in large-scale LLM serving. When a model is sharded across devices via tensor parallelism (TP) or expert parallelism (EP) and replicated across many DP workers, every decode step ends in a synchronization barrier whose latency is set by the most heavily loaded worker; even modest persistent imbalance across DP workers compounds, step after step, into a substantial fraction of wasted compute. The problem is hard for reasons specific to LLM decoding: assignments are sticky (migrating KV caches has a high cost), per-request loads grow over time, arrivals are non-stationary, and the router must decide within a sub-100\,ms decode budget over hundreds of waiting requests and tens of workers. We present \textbf{BalanceRoute}, a family of practical online routing algorithms that target this bottleneck. The first, \textbf{BR-0}, requires no prediction infrastructure and uses a piecewise-linear F-score that captures the sharp asymmetry between admissions that fill safe margin and those that overflow into the envelope; a two-stage decomposition keeps per-step cost compatible with millisecond-scale scheduling. The second, \textbf{BR-H}, generalizes BR-0 with a short, constant lookahead $H$ and a lightweight termination-classifier interface, extending the F-score to a horizon-discounted form. We deploy BalanceRoute on a 144-NPU cluster and evaluate against vLLM baselines on both a proprietary production trace and the public Azure-2024 trace. Across both workloads, BalanceRoute substantially reduces average DP imbalance and improves end-to-end serving throughput.

cs.DC↗

arXiv2026

Kinetic simulation of magnetic-field-tuned hydrodynamic electron transport in graphene corbino disk

Hydrodynamic electron transport, in which electrical transport in solids resembles fluid hydrodynamics when momentum-conserving electron-electron scattering dominates, has attracted much attention over the past decade. However, its thermal aspects have received considerably less attention. In this paper, electron transport in a graphene Corbino disk is systematically simulated by solving the stationary Boltzmann transport equation with a dual-relaxation-time Callaway model, where momentum-conserving and momentum-relaxing scatterings are explicitly distinguished. By varying the magnetic field intensity and the scattering rates, the electric charge and heat flux responses are compared across the diffusive-to-hydrodynamic crossover under electric-field or temperature-gradient drives. It is shown that magnetic-field-induced deflection of both fluxes is strongly enhanced in the hydrodynamic regime but nearly suppressed in the diffusive regime. Under electric-field driving, a pronounced temperature rise is observed in the hydrodynamic regime due to reduced dissipation, while the diffusive regime remains nearly isothermal. Under temperature-gradient driving, the deflection is reversed relative to the electric-field case. These findings establish that thermal behaviors could provide a sensitive and independent diagnostic of electron hydrodynamics, with the magnetic field being identified as an effective discriminator between collective and dissipative conduction.

cond-mat.mes-hall↗

arXiv2026

Semi-implicit Lax-Wendroff kinetic scheme for electron-phonon coupling

A semi-implicit Lax-Wendroff scheme is developed for electron-phonon coupling process in metals based on the two-temperature kinetic equations. The core of this method is to integrate the evolution information of physical equations into the numerical modeling process, which leads to that the time step or cell size is not limited by the relaxation time and mean free path. Specifically, the finite difference method is used to solve the kinetic model again when reconstructing the interfacial distribution function, through which the particle migration, scattering and electron-phonon coupling processes are coupled together within a single time step. Numerical tests demonstrate that this method could efficiently capture electron-phonon coupling or heat conduction processes from the ballistic to diffusive regimes. It provides a new tool for describing electron-phonon coupling or thermal management in microelectronic devices.

physics.comp-ph↗

arXiv2026

Semi-implicit Lax-Wendroff kinetic scheme for hydrodynamic phonon transport

A semi-implicit Lax-Wendroff kinetic scheme is developed for hydrodynamic phonon transport in solid materials based on the Boltzmann transport equation under the double relaxation time approximation, in which both the normal and resistive scattering processes are accounted. The trapezoidal and midpoint rules are adopted for the temporal integration of the scattering and migration terms under the framework of finite volume method, respectively. Instead of direct numerical interpolation, the kinetic equation is solved again when reconstructing the interfacial flux, in order to realize the coupling of phonon migration and scattering within a numerical time step. Specifically, the finite difference scheme is introduced and the second-order upwind or central schemes are used for the reconstruction of the interfacial distribution function and its spatial gradient. Consequently, the cell size and time step of the present method could be larger than the phonon mean free path and relaxation time in the limit of small Knudsen numbers. Numerical tests demonstrate that the present method can accurately capture multi-scale thermal conduction phenomena within different normal or resistive scattering rates.

physics.comp-ph↗

arXiv2026

Analytical Provisioning for Attention-FFN Disaggregated LLM Serving under Stochastic Workloads

Attentio-FFN disaggregation (AFD) is an emerging architecture for LLM decoding that separates state-heavy, KV-cache-dominated Attention computation from stateless, compute-intensive FFN computation, connected by per-step communication. While AFD enables independent scaling of memory and compute resources, its performance is highly sensitive to the Attention/FFN provisioning ratio: mis-sizing induces step-level blocking and costly device idle time. We develop an analytical provisioning framework for AFD bundles in an $r$A--$1$F topology under stochastic workloads. Two sources of randomness shape the problem: per-slot Attention workload evolves as KV caches grow and completed requests are replenished with random prompt and decode lengths, and synchronized execution across Attention workers introduces a barrier governed by the slowest worker. We address both via a renewal-reward characterization of the per-slot stationary token load, identifying a single workload statistic $\theta$ that governs provisioning under arbitrary prefill-decode distributions and admits a nonparametric estimator from request traces. The analysis yields a closed-form mean-field rule for the optimal A/F ratio decomposing into Attention-, communication-, and FFN-bottleneck regimes, together with a Gaussian barrier-aware refinement that quantifies cross-worker synchronization overhead. A trace-calibrated AFD simulator supports the framework across workloads: the predicted optimal ratio matches the simulation-optimal within 10%. Together, these results provide a compact, calibratable account of how stochastic workload structure determines provisioning in disaggregated LLM serving.

cs.LG↗

arXiv2025

Generalized Gauss-Jacobi rules for discrete velocity method in Multiscale Flow Simulations

The discrete velocity method (DVM) is a powerful framework for simulating gas flows across continuum to rarefied regimes, yet its efficiency remains limited by existing quadrature rules. Conventional infinite-domain quadratures, such as Gauss-Hermite, distribute velocity nodes globally and perform well near equilibrium but fail under strong nonequilibrium conditions. In contrast, finite-interval quadratures, such as Newton-Cotes, enable local refinement but lose efficiency near equilibrium. To overcome these limitations, we propose a generalized Gauss-Jacobi quadrature (GGJQ) for DVM, built upon a new class of adjustable weight functions. This framework systematically constructs one- to three-dimensional quadratures and maps the velocity space into polar or spherical coordinates, enabling flexible and adaptive discretization. The GGJQ accurately captures both near-equilibrium and highly rarefied regimes, as well as low- and high-Mach flows, achieving superior computational efficiency without compromising accuracy. Numerical experiments over a broad range of Knudsen numbers confirm that GGJQ consistently outperforms traditional Newton-Cotes and Gauss-Hermite schemes, offering a robust and efficient quadrature strategy for multiscale kinetic simulations.

physics.flu-dyn↗

arXiv2025

Bell-State Quantum Holography with Metasurfaces

Metasurfaces composed of subwavelength nanostructures enable simultaneous control of polarization and wavefront, greatly enhancing holographic information capacity. Building on this capability, we extend holography into the quantum domain by experimentally realizing Bell-state holograms-distinct holographic images encoded in polarization-entangled Bell states of photon pairs. A polarization-multiplexed dielectric metasurface generates spatial modes conditioned on both input and output polarizations, entangling the holographic pattern with the two-photon state. To characterize these quantum holograms, we further develop quantum hologram tomography, reconstructing the full density matrix of the holographic state pixel by pixel. The reconstructed density-matrix hologram reveals tailor-made holographic symbols attached to individual Bell states through the metasurface, with contrast built up among the different Bell components as theory shows. This framework unifies metasurface photonics with quantum-state reconstruction and provides a scalable route toward high-dimensional quantum communication, encryption and information processing based on holographically encoded quantum light.

physics.optics↗

arXiv2025

Communications to Circulations: Real-Time 3D Wind Field Prediction Using 5G GNSS Signals and Deep Learning

Accurate atmospheric wind field information is crucial for various applications, including weather forecasting, aviation safety, and disaster risk reduction. However, obtaining high spatiotemporal resolution wind data remains challenging due to limitations in traditional in-situ observations and remote sensing techniques, as well as the computational expense and biases of numerical weather prediction (NWP) models. This paper introduces G-WindCast, a novel deep learning framework that leverages signal strength variations from 5G Global Navigation Satellite System (GNSS) signals to forecast three-dimensional (3D) atmospheric wind fields. The framework utilizes Forward Neural Networks (FNN) and Transformer networks to capture complex, nonlinear, and spatiotemporal relationships between GNSS-derived features and wind dynamics. Our preliminary results demonstrate promising accuracy in real-time wind forecasts (up to 30 minutes lead time). The model exhibits robustness across forecast horizons and different pressure levels, and its predictions for wind fields show superior agreement with ground-based radar wind profiler compared to concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Furthermore, we show that the system can maintain excellent performance for localized forecasting even with a significantly reduced number of GNSS stations (e.g., around 100), highlighting its cost-effectiveness and scalability. This interdisciplinary approach underscores the transformative potential of exploiting non-traditional data sources and deep learning for advanced environmental monitoring and real-time atmospheric applications.

cs.LG↗

arXiv2025

Terahertz Channel performance in ULEO Satellite-to-Ground Communications

The exponential growth in satellite data traffic demands communication systems exceeding current microwave capacity limitations, while the terahertz (THz) frequency band (0.1-10 THz) offers unprecedented bandwidth potential with superior weather resilience compared to optical systems, particularly when combined with ultra low Earth orbit (ULEO) satellite deployments below 300 km altitude. This article presents a comprehensive performance evaluation for ULEO-THz satellite to ground communications, analyzing three distinct transmission architectures direct satellite to ground (S2G), satellite relay ground (SRG) forwarding, and satellite-to-high altitude base station (S2H) with fiber backhaul. Our analysis leverages altitude-resolved atmospheric propagation models validated using year long meteorological data from four high-altitude stations in Tibet and Qinghai, China. It incorporates frequency-dependent atmospheric absorption using ITU-R standards, free space path loss with curved atmospheric modeling, and regional atmospheric variations to derive total channel path loss, available bandwidth capacity, and bit error rate (BER) performance under both AWGN and Weibull fading conditions across multiple THz frequencies. Results demonstrate that direct S2G transmission at lower THz frequencies achieves optimal practical performance with maximum available bandwidth under QPSK modulation, while SRG suffers prohibitive cumulative losses from multiple hops, and S2H is rendered impractical for long-haul links by substantial electro-optical conversion and fiber transmission losses.

physics.app-ph↗

arXiv2025

Coevolution of Actions and Opinions in Networks of Coordinating and Anti-Coordinating Agents

In this paper, we investigate the dynamics of coordinating and anti-coordinating agents in a coevolutionary model for actions and opinions. In the model, the individuals of a population interact on a two-layer network, sharing their opinions and observing others' action, while revising their own opinions and actions according to a game-theoretic mechanism, grounded in the social psychology literature. First, we consider the scenario of coordinating agents, where convergence to a Nash equilibrium (NE) is guaranteed. We identify conditions for reaching consensus configurations and establish regions of attraction for these equilibria. Second, we study networks of anti-coordinating agents. In this second scenario, we prove that all trajectories converge to a NE by leveraging potential game theory. Then, we establish analytical conditions on the network structure and model parameters to guarantee the existence of consensus and polarized equilibria, characterizing their regions of attraction.

cs.GT↗

arXiv2025

Non-equilibrium transport and phonon branch-resolved size effects based on a multi-temperature kinetic model

Non-equilibrium transport and phonon branch-resolved size effects in single-layer graphene materials are studied under a multi-temperature kinetic model, which is developed for capturing the branch-dependent electron-phonon coupling. Compared with typical macroscopic multi-temperature models, the assumption of diffusive phonon transport is abandoned in this model and replaced by the free migration and scattering of particles. The phonon branch- and size-dependent effective thermal conductivity is predicted in nanosized graphene as well as the temperature slips near the boundaries. Compared with other phonon branches, the ZA branch contributes the most to thermal conduction regardless of system sizes. Furthermore, in nanosized homogeneous graphene with a hotspot at the center, the branch-dependent thermal conductivity increases from the inside to the outside even if the system size is fixed. The thermal conductivity of ZA branch is even higher than the lattice thermal conductivity when the system size is hundreds of nanometers.

cond-mat.mes-hall↗

arXiv2024

Terahertz channel power and BER performance in rain

Terahertz (THz) communications have emerged as a promising technology for 6G networks due to their potential for achieving terabit-per-second data rates. However, the impact of rainfall on THz channel characteristics remains incompletely understood, particularly regarding power attenuation mechanisms and bit error rate (BER) performance. This article presents a systematic measurement-based and theoretical investigation of line-of-sight (LoS) THz channel behavior under rainfall conditions, methodically examining both power attenuation mechanisms and bit error rate (BER) performance. Our experimental campaign, conducted at frequencies of 220-230 GHz over a 54-meter outdoor channel, is complemented by analytical frameworks incorporating ITU-R and Mie scattering models. The study reveals that while rain induces significant power attenuation, multipath scattering effects remain minimal, with Rician K-factors maintaining high values. Notably, we observe substantial variations in power loss under constant rain rates, attributed to dynamic changes in raindrop size distribution. Comparative analysis demonstrates superior BER performance of Quadrature Amplitude Modulation (QAM) in rainfall conditions, while revealing increased environmental sensitivity at higher frequencies. These findings underscore the necessity for adaptive modulation schemes and strategic frequency planning in future THz communication systems.

physics.app-ph↗

arXiv2024

Parametric Gaussian quadratures for Discrete Unified Gas Kinetic Scheme

The discrete unified gas kinetic scheme (DUGKS) has emerged as a promising Boltzmann solver capable of effectively capturing flow physics across all Knudsen numbers. However, simulating rarefied flows at high Knudsen numbers remains computationally demanding. This paper introduces a parametric Gaussian quadrature (PGQ) rule designed to improve the computational efficiency of DUGKS. The PGQ rule employs Gaussian functions for weighting and introduces several novel forms of higher-dimensional Gauss-Hermite quadrature. Initially, the velocity space is mapped to polar or spherical coordinates using a parameterized integral transformation method, which converts multiple integrals into repeated parametric integrals. Subsequently, Gaussian points and weight coefficients are computed based on the newly defined parametric weight functions. The parameters in PGQ allow the distribution of Gaussian points to be adjusted according to computational requirements, addressing the limitations of traditional Gaussian quadratures where Gaussian points are difficult to match the distribution of real particles in rarefied flows. To validate the proposed approach, numerical examples across various Knudsen numbers are provided. The simulation results demonstrate that PGQ offers superior computational efficiency and flexibility compared to the traditional Newton-Cotes rule and the half-range Gaussian Hermite rule, achieving computational efficiency that is tens of times higher than that of the Newton-Cotes method. This significantly enhances the computational efficiency of DUGKS and augments its ability to accurately simulate rarefied flow dynamics.

physics.flu-dyn↗

arXiv2024

An unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks

Mesh generation is essential for accurate and efficient computational fluid dynamics simulations. To resolve critical features in the flow, adaptive mesh refinement (AMR) is routinely employed in certain regions of the computational domain, where gradients or error estimates of the solution are often considered as the refining criteria. In many scenarios, however, these indicators can lead to unnecessary refinement over a large region, making the process a matter of trial and error and resulting in slow convergence of the computation. To this end, we propose a heuristic strategy that employs the residuals of the governing partial differential equations (PDEs) as a novel criterion to adaptively guide the mesh refining process. In particular, we leverage on the physics-informed neural networks (PINNs) to integrate imprecise data obtained on a coarse mesh and the governing PDEs. Once trained, PINNs are capable of identifying regions of highest residuals of the Navier-Stokes/Euler equations and suggesting new potential vertices for the coarse mesh cells. Moreover, we put forth two schemes to maintain the quality of the refined mesh through the strategic insertion of vertices and the implementation of Delaunay triangulation. By applying the residuals-guided AMR to address a multitude of typical incompressible/compressible flow problems and comparing the outcomes with those of gradient-based methods, we illustrate that the former effectively attains a favorable balance between the computational accuracy and cost.

physics.flu-dyn↗

arXiv2024

MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification

The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and clinical symptoms), drive us to develop novel designs to address unique challenges in multiomics prediction. In this paper, we propose the multi-view knowledge transfer learning (MVKTrans) framework, which transfers intra- and inter-omics knowledge in an adaptive manner by reviewing data heterogeneity and suppressing bias transfer, thereby enhancing classification performance. Specifically, we design a graph contrastive module that is trained on unlabeled data to effectively learn and transfer the underlying intra-omics patterns to the supervised task. This unsupervised pretraining promotes learning general and unbiased representations for each modality, regardless of the downstream tasks. In light of the varying discriminative capacities of modalities across different diseases and/or samples, we introduce an adaptive and bi-directional cross-omics distillation module. This module automatically identifies richer modalities and facilitates dynamic knowledge transfer from more informative to less informative omics, thereby enabling a more robust and generalized integration. Extensive experiments on four real biomedical datasets demonstrate the superior performance and robustness of MVKTrans compared to the state-of-the-art. Code and data are available at https://github.com/Yaolab-fantastic/MVKTrans.

cs.LG↗

arXiv2024

Semi-implicit Lax-Wendroff kinetic scheme for multi-scale phonon transport

Fast and accurate predictions of the spatiotemporal distributions of temperature are crucial to the multi-scale thermal management and safe operation of microelectronic devices. To realize it, an efficient semi-implicit Lax-Wendroff kinetic scheme is developed for numerically solving the transient phonon Boltzmann transport equation (BTE) from the ballistic to diffusive regime. The phonon BTE at the cell center is discretized under the framework of finite volume method, where the trapezoidal and midpoint rules are used to deal with the temporal integration of phonon scattering and convection terms, respectively. For the reconstruction of the interfacial distribution function, the phonon BTE at the cell interface is discretized in the form of finite difference method and solved numerically, where second-order upwind and central scheme are used to deal with the spatial interpolation and gradient of interfacial distribution function, respectively. The macroscopic governing equations are invoked for the evolution of macroscopic fields at both the cell center and interface, where the macroscopic flux is obtained by taking the moment of the interfacial distribution function. Numerical results show that the present scheme could accurately predict the steady/unsteady heat conduction in solid materials from the ballistic to diffusive regime, and its time and cell size are not limited by the relaxation time and phonon mean free path. The present work could provide a useful tool for the efficient predictions of the macroscopic spatiotemporal distributions in the multi-scale thermal engineering.

physics.comp-ph↗