arXiv subjects

Rui Guo

Publications and source records attributed to Rui Guo.

At least 19 recordsLinked to original sources

arXiv2026

LV-CARE-Diff: A Conditional Anatomy-Aware Diffusion Model for Left Ventricular Shape Reconstruction and Function Quantification from Ultra-Sparse Cine Slices

Left ventricular functional quantification is an essential examination and is routinely performed using cardiovascular magnetic resonance (CMR) cine imaging. However, conventional CMR cine protocols require the acquisition of multiple short-axis (SAX) slices to cover the entire left ventricle (LV) along with two long-axis (LAX) slices, which is time-consuming and places a considerable burden on patients who are unable to sustain repeated breath-holds, limiting its suitability for large-scale early screening. In this study, a Conditional Anatomy-Aware Diffusion Model (LV-CARE-Diff) was developed using a coarse-to-fine strategy to reconstruct the complete LV shape from ultra-sparse cine slices, namely three short-axis and two long-axis slices, with the aim of accelerating CMR cine examination. LV-CARE-Diff employs a 3DUNet to generate a coarse initial shape, which is subsequently refined through a residual diffusion model. A condition-guided input incorporating imaging plane orientation and positional metadata was constructed to enable spatial awareness, and a multi-objective training strategy jointly supervising shape, function, and anatomy was incorporated to guide high-fidelity reconstruction. LV-CARE-Diff was compared against a standalone 3DUNet, a standalone diffusion model, and a 3D UNet with diffusion-based refinement. Testing results indicated that complete LV shape could be robustly reconstructed by all deep learning models, with the highest reconstruction performance achieved by the proposed LV-CARE-Diff. Deep learning models reconstructing LV shape from sparse cine slices preserved 96% of functional quantification accuracy while reducing imaging time by 73%. The LV-CARE-Diff framework established in this study enables ultra-sparse cine acquisition to shorten CMR examination duration without sacrificing quantitative functional accuracy.

physics.med-ph↗

arXiv2026

AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks

Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative block. Under a constrained uplink, candidate messages may queue while the verifier is idle. Stop-and-wait scheduling leaves edge compute underutilized; optimistic same-request runahead can waste work when a rejection or an unexpected bonus token invalidates dependent drafts. We present AsymSpec, which addresses uplink-gated verification and invalid dependent work with two corresponding mechanisms. Its asymmetric verification protocol keeps the common-path acceptance upload compact and moves richer, rejection-only correction information to the downlink. A total-variation (TV) certificate for the residual distribution determines whether a small target top-K response suffices; if not, the protocol progressively escalates through proposal-based exact recovery before falling back to the full distribution. Its confirmed-prefix pipeline exposes only independent, valid requests to the edge scheduler and lets the cloud re-batch arrived blocks, hiding verification waits when another confirmed-prefix request is ready without using same-request runahead. Across three draft-target pairs, two workloads, and three asymmetric network profiles, our end-to-end evaluation shows that AsymSpec delivers 2.82-28.03$\times$ the output-token throughput of the strongest baseline.

cs.DC↗

arXiv2026

From Convex to Non-convex: Evolution of Rarefaction-dispersive shock interactions and the Influence of Non-convexity

In this paper, we focus on the analytical description of the interaction between a rarefaction and dispersive shock wave across both convex and non-convex cases, with particular attention to the effect of the non-convexity, within the framework of the Gardner equation. For convex structures, internal oscillations degenerate into small amplitude harmonic waves or a modulated soliton train as t tends to infinity, accompanied by either a retained rarefaction part, or a new dispersive shock wave emanating from it. Taking into account the non-convexity, when alpha>0, we find that kinks either remain non-participating in the interaction at all, or only act to switch polarities of convex structures. As for alpha<0, we solve the Gardner-Whitham equations with three varying Riemann invariants, to analyze the rarefaction-contact dispersive shock interaction where the only possible configuration is that the rarefaction wave is on the left. It is demonstrated that the rarefaction wave will be completely drawn into the interaction region, with a changed contact dispersive shock wave escaping from the left. And internal oscillations eventually degenerate into an asymptotic algebraic soliton train as t tends to infinity. In addition, we study the interaction between a rarefaction wave and composite structure consisting of the contact and classical dispersive shock parts under two distinct situations: (i) For the composite structure-rarefaction interaction, the contact part remains inactive in the interaction, and internal oscillations ultimately degenerate into a contact dispersive shock wave. (ii) For the rarefaction-composite structure case, the entire composite structure participates in the interaction, during

math-ph↗

arXiv2026

The co-directional overtaking collision problem between a dispersive shock wave and a rarefaction wave for the Hirota equation

In this paper, we mainly investigate the overtaking collision problem between a dispersive shock wave (DSW) and a rarefaction wave (RW) propagating in the same direction in the defocusing Hirota equation framework. Based on the Whitham theory, the zero-phase and single-phase modulation systems corresponding to the defocusing Hirota equation are given, along with explicit expressions of Riemann invariants. For step-type initial conditions with left-side DSW and right-side RW propagating identically, the global initial configuration is constructed.~The modulation solution within the nonlinear collision domain is obtained via the generalized hodograph transformation and solutions of the Euler-Poisson-Darboux equation. Thus, by matching the Riemann invariants at the boundaries of DSW and RW, we can provide a detailed analytical description for the dynamical behaviors of the collision across all evolutionary stages. The accuracy of theoretical predictions is verified by comparison with direct numerical simulations.

math-ph↗

arXiv2026

X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving

Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive perception-action mapping. While integrating Predictive World Models (PWMs) addresses this gap, existing approaches either incur prohibitive cascaded latency or act as shallow terminal tasks that fail to deeply embed forward-looking reasoning. To endow VLA models with this reasoning capability, we propose X-Mind. Rather than treating PWMs as an external auxiliary module, this framework internalizes them as the Visual Chain-of-Thought (Visual CoT). By enforcing a world rollout prior to action, the model is constrained to imagine future evolution first, yielding a driving policy that is robustly grounded in environmental dynamics and aware of the future consequences its actions will unfold. The challenge here is efficiency, and we tackle it on two fronts. First, we introduce a compact representation of visual thinking: an abstract sketch that fuses a Bird's-Eye-View (BEV) layout with abstract driving priors (e.g., navigation intents and traffic rules). Rather than rolling out dense future frames, the model reasons over this sketch as a mental canvas; aided by a Deep Compression Autoencoder (DC-AE), a 12-frame future rollout is reduced to merely 96 tokens, alleviating the long-context computational bottleneck. Second, to accelerate generation further, we propose a recurrent block diffusion scheme that unrolls the denoising steps across the layers of the large drive model, folding iterative refinement into the backbone's one forward pass. Trained and validated on large-scale real-world data, X-Mind achieves competitive end-to-end driving performance, which makes it a highly practical, low-latency solution that successfully deploys large-scale cognitive reasoning directly onto resource-constrained vehicle platforms.

cs.CV↗

arXiv2026

X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling

Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive world modeling enables VLA to internalize physical dynamics and long-term causality by predicting future video from past observations. However, naive next-frame prediction faces two challenges: 1) unlike semantically distinct text tokens, video tokens are low-entropy and redundant, causing prediction to degenerate into trivial extrapolation. 2) world modeling poses a temporal dilemma: dense prediction captures instantaneous dynamics, but cannot efficiently model long-horizon causality. To learn world knowledge effectively, we introduce X-Foresight, a predictive world model integrated directly into the VLA architecture to jointly learn world modeling and real-time action control. At its core lies a long-horizon chunk-wise auto-regressive strategy that addresses both challenges: by predicting semantically distant chunks rather than adjacent frames, it escapes trivial extrapolation, while preserving dense intra-chunk frames for instantaneous dynamics and sparse inter-chunk transitions for long-term causality. A curriculum learning schedule progressively extends prediction horizons and stabilizes long-horizon training. To capture long-term causality effectively, we present temporal importance sampling, which concentrates supervision on safety-critical chunks identified by ego-motion and behavioral signals. We further delegate photorealistic synthesis to a diffusion-based multi-view renderer, improving photorealistic appearance. Comprehensive experiments demonstrate that X-Foresight significantly outperforms VLA baselines in planning performance while maintaining strong generative fidelity, establishing a robust paradigm for world-knowledge-driven autonomous systems.

cs.CV↗

arXiv2026

TV-Regularized Frequency-Domain Full-Waveform Inversion for Single-Sided Linear Ultrasound Array Data

Quantitative speed-of-sound (SoS) and attenuation of tissues are closely related to pathology; however, conventional B-mode images are limited to qualitative visualization. Existing ultrasound full-waveform inversion (FWI) methods for quantitative SoS reconstruction are primarily developed under double-sided or ring-shaped arrays, which limits their applicability to widely adopted routine clinical acquisitions. In this work, we develop a frequency-domain, total variation (TV)-regularized FWI framework tailored for single-sided linear ultrasound arrays, which enables quantitative reconstruction of SoS maps using standard clinical probes. To address the severe ill-posedness and computational challenges in this setup, efficient forward modeling, fast gradient evaluation, ADMM-based optimization, and multi-GPU parallelization are integrated into the inversion framework. Numerical experiments in a thyroid cyst imaging scenario demonstrate that the proposed method reconstructs the SoS of both simple (fluid-filled) and solid cysts with improved visual and quantitative performance compared to conventional FWI. Additional 2D and 3D simulations across different target and array apertures further elucidate the capabilities and limitations of single-sided ultrasound FWI.

eess.SP↗

arXiv2026

IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment

Prompt-conditioned foundation segmenters have emerged as a dominant paradigm for image segmentation, where explicit spatial prompts(e.g., points, boxes, masks) guide mask decoding. However, many real-world deployments require fully automatic segmentation, creating a structural mismatch: the decoder expects prompts that are unavailable at inference. Existing adaptations typically modify intermediate features, inadvertently bypassing the model's native prompt interface and weakening prompt-conditioned decoding. We propose IP-SAM, which revisits adaptation from a prompt-space perspective through prompt-space conditioning. Specifically, a Self-Prompt Generator (SPG) distills image context into complementary intrinsic prompts that serve as coarse regional anchors. These cues are projected through SAM2's frozen prompt encoder, restoring prompt-guided decoding without external intervention. To suppress background-induced false positives, Prompt-Space Gating (PSG) leverages the intrinsic background prompt as an asymmetric suppressive constraint prior to decoding. Under a deterministic no-external-prompt protocol, IP-SAM achieves state-of-the-art performance across four camouflaged object detection benchmarks with only 21.26M trainable parameters. Furthermore, the proposed conditioning strategy generalizes beyond COD to medical polyp segmentation.

cs.CV↗

arXiv2026

Why Are Some Optically Red Spirals NUV-r Blue?

To understand the complicated formation processes of disk galaxies, we carry out a comparative study for NUV-r blue and red spiral galaxies drawn from a parent sample of u-r red spirals with $M_{*} > 10^{10.5} M_{\odot}$ at 0.02 < z < 0.07, based on the optical data from the Sloan Digital Sky Survey (SDSS) and the ultraviolet (UV) data from the Galaxy Evolution Explorer (GALEX). The analyses of the images and surface brightness profiles in the NUV and optical bands show that the differences between NUV-r blue and red spirals mainly occur in the outer disks (1-3 $R_{\rm e}$), and the contrast in NUV band is much larger than that in the optical bands. Both the positions on the star formation main sequence diagram and the NUV-r color profiles suggest that NUV-r red spirals have been fully quenched, whereas NUV-r blue spirals host quenched bulges and inner disks, as well as star-forming outer disks. Particularly, the disk mass-size relations indicate that, at a given disk mass, NUV-r blue spirals possess larger optical disks than NUV-r red spirals, by a factor of $\sim 1.20$. The environments and optical morphologies are consistent with the scenario that NUV-r blue spirals obtained fresh fuel for star formation either by interacting or merging with gas-rich galaxies or through accreting surrounding HI gas.

astro-ph.GA↗

arXiv2026

The Local Tremaine-Weinberg Method for Galactic Pattern Speed: Theory and its Application to IllustrisTNG

The Tremaine-Weinberg (TW) method and its variations provide the most direct means to measure the pattern speeds of galactic bars. We establish a unifying framework by deriving an integral form of the continuity equation over an arbitrary closed loop. This naturally defines a local pattern speed for any chosen region in a galactic disk (including bars and spirals). We demonstrate that this intuitive formalism recovers all standard variants of the TW method as special cases corresponding to specific choices of the integration loop. In this paper, we validate this framework and demonstrate its diagnostic power. By applying it to a diverse set of test cases from the TNG50 simulation, including face-on prototype barred galaxies and highly constrained Mock Milky Way standard configurations, we show that this formalism accurately recovers both constant global pattern speeds and radially varying profiles. Rather than relying on rigid geometric approximations, our method naturally differentiates coherent solid-body rotators (bars) from spirals. Our results validate that this unified integral framework provides a robust, geometrically flexible, and practically extensible tool for decoding complex dynamics of galactic structures.

astro-ph.GA↗

arXiv2026

Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection

Edema is a potential indicator of underlying pathological changes. However, its low-contrast signature is often masked in conventional B-mode imaging by strong scatterers, making reliable detection challenging. Ultrasound (US) provides a non-invasive, non-ionizing, and cost-efficient imaging option that is widely used. Conventional techniques, which rely on beamforming, often lack sufficient physical interpretability. Quantitative US (QUS) can estimate physical properties such as the speed of sound (SoS) and density by solving a physics-based inverse problem directly on the measured US wavefields, i.e., the raw per-element channel data (CD), to recover their spatial distribution. However, state-of-the-art physics-based inversion methods, including full waveform inversion (FWI) and model-based quantitative radar and US (MB-QRUS), are computationally intensive and susceptible to local minima, which constrains their clinical utility. We introduce deep unfolded FWI (DUFWI), a physics-faithful unfolded iterative inversion method that exhibits FWI-like refinement behavior while learning the update rule from data, requiring only a small number of iterations for real-time SoS reconstruction. Across both simulated datasets and hardware measurements acquired with a Verasonics US system, the DUFWI significantly outperforms classical FWI and MB-QRUS in reconstruction quality while maintaining high computational efficiency. These results demonstrate real-time edema diagnosis in both simulation and hardware experiments, with phantom-based validation using cylindrical rods, supporting practical deployment under typical US imaging setting.

eess.SP↗

arXiv2026

Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling

In signal processing, the data collected from sensing devices is often a noisy linear superposition of multiple components, and the estimation of components of interest constitutes a crucial pre-processing step. In this work, we develop a Bayesian framework for signal component decomposition, which combines Gibbs sampling with plug-and-play (PnP) diffusion priors to draw component samples from the posterior distribution. Unlike many existing methods, our framework supports incorporating component-wise model-driven and data-driven priors into diffusion models in a unified manner. Moreover, the proposed posterior sampler allows component priors to be learned separately and flexibly combined for different decomposition tasks at inference time. Under suitable assumptions, the proposed Diffusion-within-Gibbs (DiG) sampler provably produces samples from the posterior distribution. We also show that DiG can be interpreted as an extension of a class of recently proposed diffusion-based samplers, and that, for suitable classes of sensing operators, DiG better exploits the structure of the measurement model. Numerical experiments demonstrate the superior performance of our method over existing approaches.

eess.SP↗

arXiv2026

Optical dispersive shock waves of initial pulses in optical fibers with high-order dispersion and quintic nonlinearity effects

This paper probes the dispersive shock waves (DSWs) theory in nonlinear optical systems through Whitham modulation theory for the high-order Chen-Lee-Liu (HOCLL) equation. We systematically derived the one-phase periodic solutions and the corresponding Whitham equations. For all feasible initial discontinuous conditions, we delineate a classification of wave structures during the evolutionary process by virtue of the initial distribution of Riemann invariants, including both convex and non-convex cases. In this classification, we find that the evolved wave structures in non-convex cases incorporate an additional and more complex region-contact DSW region, compared with those in convex cases. So we analyze the propagation behavior of this region. Additionally, we also take into account the problem that the initial wave at the instant of wave breaking approximated by the cubic root function, and elaborately analyze the propagation of photons in optical fibers under the effects of high-order dispersion and quintic nonlinearity.

math-ph↗

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↗