arXiv subjects

Rui Guo

Publications and source records attributed to Rui Guo.

At least 37 records · Page 2Linked to original sources

arXiv2026

Feature-based Inversion of 2.5D Controlled Source Electromagnetic Data using Generative Priors

In this study, we investigate feature-based 2.5D controlled source marine electromagnetic (mCSEM) data inversion using generative priors. Two-and-half dimensional modeling using finite difference method (FDM) is adopted to compute the response of horizontal electric dipole (HED) excitation. Rather than using a neural network to approximate the entire inverse mapping in a black-box manner, we adopt a plug-andplay strategy in which a variational autoencoder (VAE) is used solely to learn prior information on conductivity distributions. During the inversion process, the conductivity model is iteratively updated using the Gauss Newton method, while the model space is constrained by projections onto the learned VAE decoder. This framework preserves explicit control over data misfit and enables flexible adaptation to different survey configurations. Numerical and field experiments demonstrate that the proposed approach effectively incorporates prior information, improves reconstruction accuracy, and exhibits good generalization performance.

physics.geo-ph↗

arXiv2025

Breather interactions and limit analysis in the second harmonic generation process via Riemann-Hilbert approach

The discovery of second harmonic generation (SHG) heralds the emergence of nonlinear optics. In this paper, we focus on the theoretical analysis of the SHG equation under phase-matching conditions. A rich family of soliton solutions are derived via the Riemann-Hilbert (RH) approach, and we characterize breather interactions corresponding to second harmonic solutions. The construction and solution of the RH problem are discussed firstly, including a detailed analysis of the discrete spectrum in the single-zero and double-zero cases. In such cases two-soliton solutions, breather solutions, two-breather solutions, and soliton-breather solutions are obtained. We numerically simulate and visually illustrate the spatiotemporal evolution of these solutions. Furthermore, through asymptotic analysis of the interaction dynamics, the exact position shift magnitudes resulting from breather-breather interaction within a nonzero background field are calculated. When the velocities are distinct, the interaction of two breathers divides the xt-plane into four asymptotic regions by the characteristic trajectories of breathers, and we show that the asymptotic behavior can be explicitly determined by the relative position between the region and the breathers.

nlin.PS↗

arXiv2025

Dynamics of the semi-discrete Gardner equation under two types of non-vanishing boundary conditions: heteropolar solitons and kinks

In this work, we will use inverse scattering transform to study the semi-discrete Gardner equation under two types of non-vanishing boundary conditions, and investigate two interesting nonlinear waves in the presence of discrete spectrum, namely heteropolar solitons and kinks. When $u_n\rightarrow -\frac{a}{2b}$ as $n\rightarrow \pm \infty$, this is a symmetric boundary condition, for which the heteropolar solitons, i.e., two kinds of single soliton solutions with different polarities will be obtained. If considering two sets of discrete eigenvalues, there will be two types of soliton collisions, head-on and overtaking collision, depending on the position of discrete spectrum. Interestingly, the energy gathered at the moment of collision with different polarities, producing the so-called rogue wave phenomenon with a large amplitude more than twice the background, and its generation mechanism is briefly analyzed. When $u_n\rightarrow \frac{c_{\pm}\sqrt{ a^2+4b }-a}{2b}$ as $n\rightarrow \pm \infty$, the kink, i.e., the undercompressive dispersive shock wave, will be obtained under the specific step-like boundary condition.

math-ph↗

arXiv2025

Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Prediction

Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems.

q-fin.CP↗

arXiv2025

Towards Transparent AI: A Survey on Explainable Language Models

Language Models (LMs) have significantly advanced natural language processing and enabled remarkable progress across diverse domains, yet their black-box nature raises critical concerns about the interpretability of their internal mechanisms and decision-making processes. This lack of transparency is particularly problematic for adoption in high-stakes domains, where stakeholders need to understand the rationale behind model outputs to ensure accountability. On the other hand, while explainable artificial intelligence (XAI) methods have been well studied for non-LMs, they face many limitations when applied to LMs due to their complex architectures, considerable training corpora, and broad generalization abilities. Although various surveys have examined XAI in the context of LMs, they often fail to capture the distinct challenges arising from the architectural diversity and evolving capabilities of these models. To bridge this gap, this survey presents a comprehensive review of XAI techniques with a particular emphasis on LMs, organizing them according to their underlying transformer architectures: encoder-only, decoder-only, and encoder-decoder, and analyzing how methods are adapted to each while assessing their respective strengths and limitations. Furthermore, we evaluate these techniques through the dual lenses of plausibility and faithfulness, offering a structured perspective on their effectiveness. Finally, we identify open research challenges and outline promising future directions, aiming to guide ongoing efforts toward the development of robust, transparent, and interpretable XAI methods for LMs.

cs.CL↗

arXiv2025

Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling

We propose a posterior sampling algorithm for the problem of estimating multiple independent source signals from their noisy superposition. The proposed algorithm is a combination of Gibbs sampling method and plug-and-play (PnP) diffusion priors. Unlike most existing diffusion-model-based approaches for signal separation, our method allows source priors to be learned separately and flexibly combined without retraining. Moreover, under the assumption of perfect diffusion model training, the proposed method provably produces samples from the posterior distribution. Experiments on the task of heartbeat extraction from mixtures with synthetic motion artifacts demonstrate the superior performance of our method over existing approaches.

eess.SP↗

arXiv2025

Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging

Electromagnetic (EM) imaging is an important tool for non-invasive sensing with low-cost and portable devices. One emerging application is EM stroke imaging, which enables early diagnosis and continuous monitoring of brain strokes. Quantitative imaging is achieved by solving an inverse scattering problem (ISP) that reconstructs permittivity and conductivity maps from measurements. In general, the reconstruction accuracy is limited by its inherent nonlinearity and ill-posedness. Existing methods, including learning-free and learning-based approaches, fail to either incorporate complicated prior distributions or provide theoretical guarantees, posing difficulties in balancing interpretability, distortion error, and reliability. To overcome these limitations, we propose a posterior sampling method based on latent diffusion for quantitative EM brain imaging, adapted from a generative plug-and-play (PnP) posterior sampling framework. Our approach allows to flexibly integrate prior knowledge into physics-based inversion without requiring paired measurement-label datasets. We first learn the prior distribution of targets from an unlabeled dataset, and then incorporate the learned prior into posterior sampling. In particular, we train a latent diffusion model on permittivity and conductivity maps to capture their prior distribution. Then, given measurements and the forward model describing EM wave physics, we perform posterior sampling by alternating between two samplers that respectively enforce the likelihood and prior distributions. Finally, reliable reconstruction is obtained through minimum mean squared error (MMSE) estimation based on the samples. Experimental results on brain imaging demonstrate that our approach achieves state-of-the-art performance in reconstruction accuracy and structural similarity while maintaining high measurement fidelity.

eess.SP↗

arXiv2025

Recovering the pattern speeds of edge-on barred galaxies via an orbit-superposition method

We developed an orbit-superposition method for edge-on barred galaxies and evaluated its capability to recover the bar pattern speed $\rmΩ_p$. We selected three simulated galaxies (Au-18, Au-23, and Au-28) with known pattern speeds from the Auriga simulations and created MUSE-like mock data sets with edge-on views (inclination angles $θ_{\rm T}\ge85^\circ$) and various bar azimuthal angles $φ_{\rm T}$. For mock data sets with side-on bars ($φ_{\rm T}\ge50^\circ$), the model-recovered pattern speeds $\rmΩ_p$ encompass the true pattern speeds $\rmΩ_T$ within the model uncertainties ($1σ$ confidence levels, $68\%$) for 10 of 12 cases. The average model uncertainty within the $1σ$ confidence levels is equal to $10\%$. For mock data sets with end-on bars ($φ_{\rm T}\le30^\circ$), the model uncertainties of $\rmΩ_p$ depend significantly on the bar azimuthal angles $φ_{\rm T}$, with the uncertainties of cases with $φ_{\rm T}=10^\circ$ approaching $\sim30\%$. However, by imposing a stricter constraint on the bar morphology ($p_{\rm bar}\le0.50$), the average uncertainties are reduced to $14\%$ , and $\rmΩ_p$ still encompass $\rmΩ_T$ within the model uncertainties for three of four cases. For all the models that we create in this paper, the $2σ$ ($95\%$) confidence levels of the model-recovered pattern speeds $\rmΩ_p$ always cover the true values $\rmΩ_T$.

astro-ph.GA↗

arXiv2025

SVAgent: AI Agent for Hardware Security Verification Assertion

Verification using SystemVerilog assertions (SVA) is one of the most popular methods for detecting circuit design vulnerabilities. However, with the globalization of integrated circuit design and the continuous upgrading of security requirements, the SVA development model has exposed major limitations. It is not only inefficient in development, but also unable to effectively deal with the increasing number of security vulnerabilities in modern complex integrated circuits. In response to these challenges, this paper proposes an innovative SVA automatic generation framework SVAgent. SVAgent introduces a requirement decomposition mechanism to transform the original complex requirements into a structured, gradually solvable fine-grained problem-solving chain. Experiments have shown that SVAgent can effectively suppress the influence of hallucinations and random answers, and the key evaluation indicators such as the accuracy and consistency of the SVA are significantly better than existing frameworks. More importantly, we successfully integrated SVAgent into the most mainstream integrated circuit vulnerability assessment framework and verified its practicality and reliability in a real engineering design environment.

cs.CR↗

arXiv2025

RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation

Dense depth estimation using millimeter-wave radar typically requires dense LiDAR supervision, generated via multi-frame projection and interpolation, for guiding the learning of accurate depth from sparse radar measurements and RGB images. However, this paradigm is both costly and data-intensive. To address this, we propose RaCalNet, a novel framework that eliminates the need for dense supervision by using sparse LiDAR to supervise the learning of refined radar measurements, resulting in a supervision density of merely around 1\% compared to dense-supervised methods. RaCalNet is composed of two key modules. The Radar Recalibration module performs radar point screening and pixel-wise displacement refinement, producing accurate and reliable depth priors from sparse radar inputs. These priors are then used by the Metric Depth Optimization module, which learns to infer scene-level scale priors and fuses them with monocular depth predictions to achieve metrically accurate outputs. This modular design enhances structural consistency and preserves fine-grained geometric details. Despite relying solely on sparse supervision, RaCalNet produces depth maps with clear object contours and fine-grained textures, demonstrating superior visual quality compared to state-of-the-art dense-supervised methods. Quantitatively, it achieves performance comparable to existing methods on the ZJU-4DRadarCam dataset and yields a 34.89\% RMSE reduction in real-world deployment scenarios. We plan to gradually release the code and models in the future at https://github.com/818slam/RaCalNet.git.

cs.CV↗

arXiv2025

Formation of the Two-Armed Phase Spiral from Multiple External Perturbations

Recent studies using the Gaia DR3 data have revealed a two-armed phase spiral in the $Z-V_Z$ phase space in the inner disk. In this study, we present new features of the two-armed phase spiral revealed by the Gaia Data and a new mechanism to explain such features with multiple external perturbations. By segmenting the Gaia DR3 RVS catalog based on $J_ϕ$ (or $R_{g}$) and $θ_ϕ$, we confirm the existence of the clear two-armed phase spiral in the inner disk. Moreover, we identify a different two-armed phase spiral pattern at slightly larger radii, resembling a weak secondary branch along with the prominent major branch. At a given radius, with the azimuthal angle increasing, we observe a systematic transition of the two-armed phase spiral, with the significance of one branch weakened and another branch enhanced. This two-armed phase spiral may be due to the overlapping of distinct one-armed phase spirals. At different radii, the perturbation times estimated from each branch of the two-armed phase spiral are $\sim 320$ Myr and $\sim 500$ Myr, respectively, suggesting that the Galactic disk could be impacted by double external perturbers separated by $\sim 180$ Myr. We also performed test particle simulations of the disk perturbed by two satellite galaxies, which successfully generated a two-armed phase spiral similar to the observation. Both the observation and simulation results suggest that the signature in the $Z-V_Z$ phase space of earlier perturbations may not be completely erased by the more recent one.

astro-ph.GA↗

arXiv2025

UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment

This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices. To this end, we introduce a Mixture-of-Experts strategy for local state estimation, with several specialized decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-Softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder, and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pre-trained, scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones), and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance.

cs.CV↗

arXiv2025

BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems

Serving systems for Large Language Models (LLMs) are often optimized to improve quality of service (QoS) and throughput. However, due to the lack of open-source LLM serving workloads, these systems are frequently evaluated under unrealistic workload assumptions. Consequently, performance may degrade when systems are deployed in real-world scenarios. This work presents BurstGPT, an LLM serving workload with 10.31 million traces from regional Azure OpenAI GPT services over 213 days. BurstGPT captures LLM serving characteristics from user, model and system perspectives: (1) User request concurrency: burstiness variations of requests in Azure OpenAI GPT services, revealing diversified concurrency patterns in different services and model types. (2) User conversation patterns: counts and intervals within conversations for service optimizations. (3) Model response lengths: auto-regressive serving processes of GPT models, showing statistical relations between requests and their responses. (4) System response failures: failures of conversation and API services, showing intensive resource needs and limited availability of LLM services in Azure. The details of the characteristics can serve multiple purposes in LLM serving optimizations, such as system evaluation and trace provisioning. In our demo evaluation with BurstGPT, frequent variations in BurstGPT reveal declines in efficiency, stability, or reliability in realistic LLM serving. We identify that the generalization of KV cache management, scheduling and disaggregation optimizations can be improved under realistic workload evaluations. BurstGPT is publicly available now at https://github.com/HPMLL/BurstGPT and is widely used to develop prototypes of LLM serving frameworks in the industry.

cs.DC↗

arXiv2025

The coexistence of possible magnetic and chiral rotation in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$: a microscopic investigation

A microscopic investigation of the rotational properties in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$ was carried out using the three-dimensional tilted axis cranking covariant density functional theory (3DTAC-CDFT). The calculations reveal the coexistence of magnetic and chiral rotation built on identical qusiparticle configurations $π{h}_{11/2}^{1}\otimes ν{h}_{11/2}^{-2}$ in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$, establishing a new type of shape coexistence. The calculations predict the deformation parameters for magnetic rotation in $^{129}\mathrm{Cs}$ ($β\approx0.23$, $γ\approx41^\circ$) and $^{131}\mathrm{La}$ ($β\approx0.25,$ $γ\approx42^\circ$), along with those for possible chiral rotation in $^{129}\mathrm{Cs}$ ($β\approx0.20,$ $γ\approx29^\circ$) and in $^{131}\mathrm{La}$ ($β\approx0.20,$ $γ\approx27^\circ$). The energy spectra, the relation between the spin and the rotational frequency, and the reduced $M1$ and $E2$ transition probabilities are obtained with the various configurations. The experimental characteristics of band B8 in $^{129}\mathrm{Cs}$ and band 13 in $^{131}\mathrm{La}$ are well reproduced. Moreover, a distinctive rotational mode transition is uncovered in this work, progressing from the principal-axis rotation to the planar rotation and finally to the chiral rotation, as the rotational frequency evolves through different regimes.

nucl-th↗

arXiv2025

A Tale of Two Sides of Wafer: Physical Implementation and Block-Level PPA on Flip FET with Dual-sided Signals

As the conventional scaling of logic devices comes to an end, functional wafer backside and 3D transistor stacking are consensus for next-generation logic technology, offering considerable design space extension for powers, signals or even devices on the wafer backside. The Flip FET (FFET), a novel transistor architecture combining 3D transistor stacking and fully functional wafer backside, was recently proposed. With symmetric dual-sided standard cell design, the FFET can deliver around 12.5% cell area scaling and faster but more energy-efficient libraries beyond other stacked transistor technologies such as CFET. Besides, thanks to the novel cell design with dual-sided pins, the FFET supports dual-sided signal routing, delivering better routability and larger backside design space. In this work, we demonstrated a comprehensive FFET evaluation framework considering physical implementation and block-level power-performance-area (PPA) assessment for the first time, in which key functions are dual-sided routing and dual-sided RC extraction. A 32-bit RISC-V core was used for the evaluation here. Compared to the CFET with single-sided signals, the FFET with single-sided signals achieved 23.3% post-P&R core area reduction, 25.0% higher frequency and 11.9% lower power at the same utilization, and 16.0 % higher frequency at the same core area. Meanwhile, the FFET supports dual-sided signals, which can further benefit more from flexible allocation of cell input pins on both sides. By optimizing the input pin density and BEOL routing layer number on each side, 10.6% frequency gain was realized without power degradation compared to the one with single-sided signal routing. Moreover, the routability and power efficiency of FFET barely degrades even with the routing layer number reduced from 12 to 5 on each side, validating the great space for cost-friendly design enabled by FFET.

physics.app-ph↗

arXiv2024

Construction and analysis of multi-lump solutions of dispersive long wave equations via integer partitions

In this paper, the relation between the integer partition theory and a kind of rational solution of the dispersion long wave equations is studied. For the integer partition λ= (λ1,λ2,... ,λn) of positive integer N, with the degree vector m = (m1,m2,... ,mn), the corresponding M lump solution can be obtained where M = N + n mn. Combined with the generalized Schur polynomial and heat polynomial, the asymptotic positions of peaks are studied, and the arrangement of multi-peak groups in multi-lump solutions are obtained, as well as the relationship between the patterns formed by single-peak groups and the corresponding integer partition.

math-ph↗

arXiv2024

Deciphering the Kinematic Substructure of Local Dark Matter with LAMOST K Giants

Numerical simulations indicate that correlations exist between the velocity distributions of stars and dark matter (DM). We study the local DM velocity distribution based on these correlations. We select K giants from LAMOST DR8 cross-matched with {\gaia} DR3, which have robust measurements of velocity and metallicity, and separate them into the disk, halo substructure and isotropic halo components in the chemodynamical space utilizing the Gaussian Mixture Model. The substructure component is highly radially anisotropic, and possibly related to the \gaia-Enceladus-Sausage (GES) merger event, while the isotropic halo component is accreted from the earliest mergers following the Maxwell-Boltzmann distribution (Standard Halo Model, SHM). We find that the GES-like substructure contributes $\sim85\%$ of the local nondisk stars in the Solar neighbourhood, which is nearly invariant when applying different volume cuts or additional angular momentum constraints. Utilizing the metallicity-stellar mass relation and the stellar mass-halo mass relation, we find that $\sim25_{-15}^{+24}\%$ of local DM is in the kinematic substructure. Combined with the stellar distributions of nondisk components, we modify the heliocentric velocity distribution of local DM. It shifts to a lower speed with a sharper peak compared to the SHM, and updates the detection limits of DM direct detection experiments. We discuss extensively the degeneracies present in the GMM fitting and propose that more kinematic and chemical information such as $α$ abundance could help to break the degeneracy in the future. Our work confirms that the local DM velocity distribution deviates significantly from the SHM, and needs to be properly accounted for in the DM detection experiments.

astro-ph.GA↗

arXiv2024

Mixed single, double, and triple poles solutions for the space-time shifted nonlocal DNLS equation with nonzero boundary conditions via Riemann--Hilbert approach

In this paper, we investigate the space-time shifted nonlocal derivative nonlinear Schrödinger (DNLS) equation under nonzero boundary conditions using the Riemann--Hilbert (RH) approach for the first time. To begin with, in the direct scattering problem, we analyze the analyticity, symmetries, and asymptotic behaviors of the Jost eigenfunctions and scattering matrix functions. Subsequently, we examine the coexistence of $N$-single, $N$-double, and $N$-triple poles in the inverse scattering problem. The corresponding residue conditions, trace formulae, $θ$ condition, and symmetry relations of the norming constants are obtained. Moreover, we derive the exact expression for the mixed single, double, and triple poles solutions with the reflectionless potentials by solving the relevant RH problem associated with the space-time shifted nonlocal DNLS equation. Furthermore, to further explore the remarkable characteristics of soliton solutions, we graphically illustrate the dynamic behaviors of several representative solutions, such as three-soliton, two-breather, and soliton-breather solutions. Finally, we analyze the effects of shift parameters through graphical simulations.

math-ph↗