arXiv subjects

Hao Lyu

Publications and source records attributed to Hao Lyu.

At least 19 recordsLinked to original sources

arXiv2026

Dual migration modes of unfaulted disconnections on curved twin boundaries

Grain boundary migration governs microstructural evolution in crystalline materials, directly influencing mechanical properties such as strength and thermal stability. Disconnections, which are line defects formed at grain boundaries in response to local curvature, have been identified as critical carriers of boundary migration. Here, we investigate the glide of unfaulted disconnections (UFDs) on a coherent twin boundary in aluminum at elevated temperatures using molecular dynamics simulations combined with the Nudged Elastic Band (NEB) method. Our results reveal a striking bifurcation in migration behavior depending on the disconnection core structure. UFDs with a pure edge Burgers vector migrate via a thermally activated double-kink mechanism, exhibiting a migration velocity that increases monotonically with temperature. In contrast, UFDs containing a screw dipole component possess an energy barrier approximately eight times lower, and their core structure undergoes a continuous transformation during glide, giving rise to stochastic, bidirectional motion with no systematic temperature dependence. These findings demonstrate that the disconnection core structure fundamentally dictates the migration mode and kinetics of twin boundaries, offering new mechanistic insights into disconnection-mediated grain boundary migration.

cond-mat.mtrl-sci↗

arXiv2026

SimFuzz: Similarity-guided Block-level Mutation for RISC-V Processor Fuzzing

The Instruction Set Architecture (ISA) defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to processor design and encourages widespread adoption, but also exposes processors to security risks such as functional bugs. Processor fuzzing is a powerful technique for automatically detecting these bugs. However, existing fuzzing methods suffer from two main limitations. First, their emphasis on redundant test case generation causes them to overlook cross-processor corner cases. Second, they rely too heavily on coverage guidance. Current coverage metrics are biased and inefficient, and become ineffective once coverage growth plateaus. To overcome these limitations, we propose SimFuzz, a fuzzing framework that constructs a high-quality seed corpus from historical bug-triggering inputs and employs similarity-guided, block-level mutation to efficiently explore the processor input space. By introducing instruction similarity, SimFuzz expands the input space around seeds while preserving control-flow structure, enabling deeper exploration without relying on coverage feedback. We evaluate SimFuzz on three widely used open-source RISC-V processors: Rocket, BOOM, and XiangShan, and discover 17 bugs in total, including 14 previously unknown issues, 7 of which have been assigned CVE identifiers. These bugs affect the decode and memory units, cause instruction and data errors, and can lead to kernel instability or system crashes. Experimental results show that SimFuzz achieves up to 73.22% multiplexer coverage on the high-quality seed corpus. Our findings highlight critical security bugs in mainstream RISC-V processors and offer actionable insights for improving functional verification.

cs.CR↗

arXiv2026

A Large Scale Empirical Analysis on the Adherence Gap between Standards and Tools in SBOM

A Software Bill of Materials (SBOM) is a machine-readable artifact that systematically organizes software information, enhancing supply chain transparency and security. To facilitate the exchange and utilization of SBOMs, organizations such as the Linux Foundation and OWASP have proposed SBOM standards. Following standards, organizations have developed tools for generating and utilizing SBOMs. However, limited research has examined the adherence of these SBOM tools to standard specifications, a gap that could lead to compliance failures and disruptions in SBOM utilization. This paper presents the first large-scale, two-stage empirical analysis of the adherence gap, using our automated evaluation framework, SAP. The evaluation, comprising a baseline evaluation and a one-year longitudinal follow-up, covers 55,444 SBOMs generated by six SBOM tools from 3,287 real-world repositories. Our analysis reveals persistent, fundamental limitations in current SBOM tools: (1) inadequate compliance support with policy requirements; (2) poor tool consistencies, including inter-tool consistency rates as low as 7.84% to 12.77% for package detection across languages, and significant longitudinal inconsistency, where tools show low consistency with their own prior versions; and (3) mediocre to poor accuracy for detailed software information, e.g., accuracy of package licenses below 20%. We analyze the root causes of these gaps and provide practical solutions. All the code, replication docker image, evaluation results are open sourced at [GitHub](https://github.com/dw763j/SAP) and [Zenodo](https://doi.org/10.5281/zenodo.14998624) for further researches.

cs.SE↗

arXiv2025

Quantized Thouless Pumping of Dark Solitons

Nonlinearity enables the emergence of localized waves such as solitons that maintain their shapes during propagation. Solitons are broadly classified into bright and dark solitons. While a bright soliton exhibits a density peak, a dark soliton presents as a defect on a continuous wave background. A distinctive feature of dark solitons is the abrupt phase change in their wave function, which can host Majorana zero modes in topological fermionic superfluids. Recent studies have shown that bright solitons can undergo quantized transport through Thouless pumping, where the bright soliton functions as a Wannier function. However, it remains unclear whether Thouless pumping can also occur for dark solitons, which fundamentally differ from bright solitons. Here, we theoretically demonstrate the occurrence of both integer and fractional Thouless pumping for dark solitons within both a continuous model under optical lattices and a tight-binding model. Specifically, we find that a dark soliton is transported by one or half a unit cell, following the center-of-mass position of a Wannier function, as a system parameter is slowly varied over one cycle. Our work opens new avenues for exploring Thouless pumping for defects with phase changes, such as dark solitons, vortex solitons, ring dark solitons, and vortices.

nlin.PS↗

arXiv2025

Bayesian hierarchical non-stationary hybrid modeling for threshold estimation in peak over threshold approach

Extreme value theory (EVT) has been utilized to estimate crash risk from traffic conflicts with the peak over threshold approach. However, it's challenging to determine a suitable threshold to distinguish extreme conflicts in an objective way. The subjective and arbitrary selection of the threshold in the peak over threshold approach can result in biased estimation outcomes. This study proposes a Bayesian hierarchical hybrid modeling (BHHM) framework for the threshold estimation in the peak over threshold approach. Specifically, BHHM is based on a piecewise function to model the general conflicts with specific distribution while model the extreme conflicts with generalized Pareto distribution (GPD). The Bayesian hierarchical structure is used to combine traffic conflicts from different sites, incorporating covariates and site-specific unobserved heterogeneity. Five non-stationary BHHM models, including Normal-GPD, Cauchy-GPD, Logistic-GPD, Gamma-GPD, and Lognormal-GPD models, were developed and compared. Traditional graphical diagnostic and quantile regression approaches were also used for comparison. Traffic conflicts collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The results show that the proposed BHHM approach could estimate the threshold parameter objectively. The Lognormal-GPD model is superior to the other four BHHM models in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the BHHM outperform those estimated based on the graphical diagnostic and quantile regression approaches, indicating the superiority of the proposed threshold determination approach. The findings of this study contribute to enhancing the existing EVT methods for providing a threshold determination approach as well as producing reliable crash estimations.

stat.OT↗

arXiv2025

Band-edge superfluid of Bose-Einstein condensates in the spin-orbit-coupled Zeeman lattice

Since the first experimental realization of Bose-Einstein condensates in a spin-orbit-coupled Zeeman lattice, a wide range of applications have been found in these systems. Here, we systematically study the ground-state phase diagram of the systems. We address that the band-edge phase in the ground-state phase diagram is exotic and exists in a very broad parameter regime. The superfluidity of the band-edge states is identified by elementary excitations and superfluid fraction.

cond-mat.quant-gas↗

arXiv2025

Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach

Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Existing efforts lack the extraction and capture of vehicle behavior interaction features at the platoon scale. More importantly, maintaining high modeling accuracy without losing physical analyzability remains to be solved. To this end, this paper proposes a novel physics-encoded deep learning network, named PeMTFLN, to model the nonlinear vehicle platoon dynamics. Specifically, an analyzable parameters encoded computational graph (APeCG) is designed to guide the platoon to respond to the driving behavior of the lead vehicle while ensuring local stability. Besides, a multi-scale trajectory feature learning network (MTFLN) is constructed to capture platoon following patterns and infer the physical parameters required for APeCG from trajectory data. The human-driven vehicle trajectory datasets (HIGHSIM) were used to train the proposed PeMTFLN. The trajectories prediction experiments show that PeMTFLN exhibits superior compared to the baseline models in terms of predictive accuracy in speed and gap. The stability analysis result shows that the physical parameters in APeCG is able to reproduce the platoon stability in real-world condition. In simulation experiments, PeMTFLN performs low inference error in platoon trajectories generation. Moreover, PeMTFLN also accurately reproduces ground-truth safety statistics. The code of proposed PeMTFLN is open source.

cs.RO↗

arXiv2025

Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control

Mitigating traffic oscillations in mixed flows of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is critical for enhancing traffic stability. A key challenge lies in modeling the nonlinear, heterogeneous behaviors of HDVs within computationally tractable predictive control frameworks. This study proposes an adaptive deep Koopman predictive control framework (AdapKoopPC) to address this issue. The framework features a novel deep Koopman network, AdapKoopnet, which represents complex HDV car-following dynamics as a linear system in a high-dimensional space by adaptively learning from naturalistic data. This learned linear representation is then embedded into a Model Predictive Control (MPC) scheme, enabling real-time, scalable, and optimal control of CAVs. We validate our framework using the HighD dataset and extensive numerical simulations. Results demonstrate that AdapKoopnet achieves superior trajectory prediction accuracy over baseline models. Furthermore, the complete AdapKoopPC controller significantly dampens traffic oscillations with lower computational cost, exhibiting strong performance even at low CAV penetration rates. The proposed framework offers a scalable and data-driven solution for enhancing stability in realistic mixed traffic environments. The code is made publicly available.

eess.SY↗

arXiv2024

Variational quantum compiling for three-qubit gates design in quantum dots

Semiconductor quantum dots offer a promising platform for controlling spin qubits and realizing quantum logic gates, essential for scalable quantum computing. In this work, we utilize a variational quantum compiling algorithm to design efficient three-qubit gates using a time-independent Hamiltonian composed of only physical interaction terms. The resulting gates, including the Toffoli and Fredkin gates, demonstrate high fidelity and robustness against both coherent and incoherent noise sources, including charge and nuclear spin noise. This method is applicable to a wide range of physical systems, such as superconducting qubits and trapped ions, paving the way for more resilient and universal quantum computing architectures.

quant-ph↗

arXiv2024

Tunable nonlinear Landau-Zener tunnelings in a spin-orbit-coupled spinor Bose-Einstein condensate

Nonlinear Landau-Zener tunneling is an important nonlinear phenomenon. We propose to stimulate the nonlinear tunneling in a spin-orbit-coupled spinor Bose-Einstein condensate. The system provides an experimentally tunable nonlinearity as well as multiple avoided crossings with tunable gap size in nonlinear dispersion relations. The nonlinearity generates tilted cusp and loop structures around the avoided crossings, and the physical consequence of these nonlinear structures is the nonlinear Landau-Zener tunneling. The spin-momentum locking induced by the spin-orbit coupling leads to a time-resolved observation of the nonlinear tunneling by measuring atom populations.

cond-mat.quant-gas↗

arXiv2024

Thouless pumping and trapping of two-component gap solitons

We study Thouless pumping and arresting of gap solitons in a two-component Bose gas loaded into an optical superlattice. We show that, depending on the atomic interactions and chemical potentials, the two solitons can be simultaneously pumped or trapped, but we also identify regimes where one soliton is pumped and the other arrested. These behaviors can be understood by considering an effective model of the system based on a variational approach, which reveals that the soliton width is a suitable qualifier for the observed behaviours: solitons with a larger width get transported, while solitons with a smaller width get trapped. Since these width can be controlled by tuning interactions, it should be possible to observe all behaviours experimentally.

cond-mat.quant-gas↗

arXiv2024

Supercurrent-carrying supersolid in spin-orbit-coupled Bose-Einstein condensates

One of brilliant achievements in spin-orbit-coupled Bose-Einstein condensates is the discovery and observation of the supersolid stripe states. So far, all studied supersolid stripe states do not carry supercurrent. In this work, we reveal the existence of supercurrent-carrying supersolids in spin-orbit-coupled Bose-Einstein condensates. The supersolid family has a parabolic-like dispersion relation and carries supercurrent which is proportional to the quasimomentum. Energetic and dynamical instabilities can break supercurrent-carrying ability of this supersolid family. An insightful interpretation of the dynamical instability of supercurrent-carrying supersolids from the pure plane-wave phase is provided.

cond-mat.quant-gas↗

arXiv2024

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning

Physics-informed neural network (PINN) is a data-driven solver for partial and ordinary differential equations(ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems. Through two case studies, we discovered that transfer learning can effectively train PINN to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We elaborately described our training strategy, including optimizer selection, and suggested guidelines for using transfer learning to train neural networks for solving more complex problems.

cs.LG↗

arXiv2023

Spin-orbit-coupling-induced phase separation in trapped Bose gases

In a trapped spin-1/2 Bose-Einstein condensate with miscible interactions, a two-dimensional spin-orbit coupling can introduce an unconventional spatial separation between the two components. We reveal the physical mechanism of such a spin-orbit-coupling-induced phase separation. Detailed features of the phase separation are identified in a trapped Bose-Einstein condensate. We further analyze differences of phase separation in Rashba and anisotropic spin-orbit-coupled Bose gases. An adiabatic splitting dynamics is proposed as an application of the phase separation.

cond-mat.quant-gas↗

arXiv2023

Quantum phases in spin-orbit-coupled Floquet spinor Bose gases

We propose a spin-orbit-coupled Floquet spinor Bose-Einstein condensate (BEC) which can be implemented by Floquet engineering of a quadratic Zeeman field. The Floquet spinor BEC has a Bessel-function-modulated Rabi frequency and a Floquet-induced spin-exchange interaction. The quantum phase diagram of the spin-orbit-coupled Floquet spinor BEC is investigated by considering antiferromagnetic or ferromagnetic spin-spin interactions. In comparison with the usual spin-orbit-coupled spin-1 BEC, we find that a stripe phase for antiferromagnetic interactions can exist in a large quadratic Zeeman field regime, and a different stripe phase with an experimentally favorable contrast for ferromagnetic interactions is uncovered.

cond-mat.quant-gas↗

arXiv2022

Detection of roton and phonon excitations in a spin-orbit coupled Bose-Einstein condensate with a moving barrier

We propose to detect phonon and roton excitations in a two-dimensional Bose-Einstein condensate with Raman-induced spin-orbit coupling by perturbing the atomic cloud with a weak barrier. The two excitation modes can be observed by moving the barrier along different directions in appropriate parameter regimes. Phonon excitations are identified by the appearance of solitary waves, while roton excitations lead to distinctive spatial density modulations. We show that this method can also be used to determine the anisotropic critical velocities of superfluid.

cond-mat.quant-gas↗

arXiv2022

Elementary excitations in a spin-orbit-coupled spin-1 Bose-Einstein condensate

While a spin-orbit-coupled spin-1 Bose-Einstein condensate has been experimentally observed, its elementary excitations remain unclear in the stripe phase. Here, we systematically study the elementary excitations in three distinct phases of a spin-orbit-coupled spin-1 Bose-Einstein condensate. We find that the excitation spectrum as well as the corresponding static response function and structure factor depend strongly on spin-orbit coupling parameters such as the quadratic Zeeman field and the Rabi frequency. In the stripe phase, besides two gapless Goldstone modes, we show the existence of roton excitations. Finally, we demonstrate that quantum phase transitions between these different phases including the zero-momentum, plane wave and stripe phases are characterized by the sound velocities and the quantum depletion.

cond-mat.quant-gas↗

arXiv2022

Self-interfering dynamics in Bose-Einstein condensates with engineered dispersions

Optical lattice and spin-orbit coupling are typical experimental approaches to engineer dispersion. We reveal a self-interfering dynamics in a noninteracting Bose-Einstein condensate with the engineered dispersion by optical lattice or spin-orbit coupling. The self-interference results from the co-occupation of positive and negative effect mass regimes in the engineered dispersion. The physical origination of the self-interference is explained by the Wigner distribution function of the self-interfering wave-packet. We characterize detail features of the self-interference pattern.

cond-mat.quant-gas↗