arXiv subjects

Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 19 recordsLinked to original sources

arXiv2026

Inflationary Magnetogenesis with $f(R,\phi)$ Coupling

Inflationary magnetogenesis provides a promising mechanism for generating primordial large-scale magnetic fields, but faces challenges such as the strong coupling problem and backreaction issues. In this paper, we extend the Ratra model by introducing a coupling between the electromagnetic field and the background geometry, parameterized as $K(R)I^2(\phi)$. Starting from a general action with $f^2(R,\phi)F_{\mu\nu}F^{\mu\nu}$, we adopt $f^2(R,\phi)=K(R)I^2(\phi)$ as a concrete realization. Rather than focusing on the slow-roll inflationary stage (which reduces to the standard Ratra scenario), we concentrate on the post-inflationary reheating epoch, where the broken-power-law evolution of the scale factor and coupling function across the inflation-to-reheating transition allows us to derive analytic expressions for the magnetic and electric energy density spectra. Three key theoretical constraints are imposed on the model parameter space: the strong coupling condition, the backreaction constraint, and the CMB isotropy requirement. We obtain predictions for the present-day magnetic field strength $B_0$ and coherence length $L_{c0}$ for various combinations of the inflationary energy scale $H_f$ and the reheating temperature $T_r$. By comparing with observational constraints from radio observations and Fermi-LAT gamma-ray data, we demonstrate that the inflationary energy scale $H_f$, the reheating temperature $T_r$, the parameter $\beta$, and the e-folding numbers $N_f$, $N_r$ must satisfy stringent joint constraints. This work provides a viable theoretical framework for inflationary magnetogenesis that simultaneously satisfies theoretical consistency conditions and current observational bounds, with the reheating-stage nonlinear MHD evolution serving as a crucial ingredient for producing observationally compatible magnetic fields.

astro-ph.CO↗

arXiv2026

DBRepro: Automated Database Synthesis via a Hybrid Constraint-Solving Approach for Reproducing Slow Queries

Slow queries frequently cause severe performance bottlenecks in database management systems. Diagnosing their root causes online risks exacerbating resource contention, while data privacy regulations often prohibit copying production data to test environments. Synthesizing a proxy database from non-intrusive metadata that induces the query optimizer to generate the same physical execution plans is therefore critical for offline diagnosis. High-fidelity reproduction requires preserving global statistical distributions while enforcing exact local cardinalities. Existing data-driven and workload-aware approaches cannot satisfy both requirements simultaneously. We present DBRepro, an automated end-to-end framework that formulates database generation as a constrained distribution synthesis problem. DBRepro initializes a global distribution from lightweight column statistics, extracts execution constraints from target queries, and progressively adjusts the distribution to satisfy these constraints while preserving the global distribution. Experiments on TPC-H and SSB show that DBRepro reduces cardinality error by up to 20.3% over a data-driven baseline while maintaining identical plan consistency. Compared with a workload-aware baseline, it reproduces 15% more consistent execution plans and reduces latency proportion error by 21.5%. We further validate DBRepro on a nearly 1 TB real-world dataset managed by KingbaseES, where it reproduces the execution performance of complex slow queries with high fidelity.

cs.DB↗

arXiv2026

DBcover: A White-box SQL Test Generation Framework for Coverage Improvement

Relational Database Management Systems (RDBMSs) are the backbone of modern data-intensive applications, making reliability and robustness critical. However, achieving high coverage in RDBMS testing remains challenging because of large codebases and complex execution logic. Traditional fuzzing relies on random SQL generation and cannot capture the correspondence between SQL inputs and internal execution paths, while symbolic execution suffers from prohibitive cost and scalability limitations. We propose DBcover, an LLM-driven white-box SQL test generation framework based on contextual reasoning. DBcover uses lightweight dynamic analysis to extract SQL-to-path correspondence and call graphs as global context, and collects source-level information around target functions as local context. These contexts are organized in a unified knowledge graph for efficient retrieval and reuse. DBcover then performs two-phase test generation: it first selects a semantically relevant seed whose execution path is close to the uncovered target, and then guides the LLM with global and local context to generate SQL test cases that trigger previously uncovered code regions. Experiments show that DBcover achieves 80.1% and 82.3% coverage on PostgreSQL and MySQL, and is also effective on the enterprise RDBMS KingbaseES, demonstrating its practical applicability to closed-source systems.

cs.DB↗

arXiv2026

Bright dual-pulse betatron X-ray generation from a laser wakefield accelerator

Pump-probe experiments using dual ultrashort X-ray pulses provide unique opportunities for resolving non-equilibrium dynamics initiated by intense X-ray excitation. Betatron radiation from laser wakefield accelerators offers femtosecond duration, micrometer-scale source size, and intrinsic synchronization with the driving laser, making it a promising candidate for compact ultrafast X-ray sources. Here, we experimentally demonstrate a high-flux, dual-pulse betatron X-ray source based on a density-tailored gas-mixture target. Two electron bunches are generated within a single plasma wakefield through ionization-induced and shock-front-triggered injection, subsequently producing twin X-ray pulses. The measured electron spectra and dual-component X-ray angular profiles, together with particle-in-cell simulations, identify the contributions of the two electron populations to the radiation. The total X-ray photon yield reaches the level of 10^{10} photons per shot with a 40-TW laser system. These results establish a compact, single-stage route toward high-flux dual-pulse betatron sources for laboratory-scale ultrafast X-ray spectroscopy.

physics.plasm-ph↗

arXiv2026

Predictive Relative-Velocity Steering for Safe Robotic Manipulator Teleoperation in Dynamic Environments

Recent advances in teleoperation have enabled robotic manipulators to perform dexterous, human-arm-like motions. However, human operators may fail to avoid suddenly appearing obstacles promptly and effectively, particularly under network latency or limited attention, thereby creating safety risks. To address this issue, we propose a lightweight and modular framework for proactive collision avoidance, operating directly at the end-effector velocity-command level. After preprocessing the point cloud, the framework first predicts potential collisions based on time-to-collision (TTC) with integrated overshoot protection, and subsequently rotates the relative-velocity vector using Rodrigues' rotation formula. The deflection changes only the direction of the relative velocity while preserving its magnitude, thereby mitigating the deadlock problem commonly encountered by conventional artificial potential field (APF) methods. The prediction module compensates for point-cloud processing latency introduced by complex teleoperation pipelines, while the lightweight design enables the high-frequency control required for teleoperation. Simulations across diverse scenarios show that the proposed method achieves a higher end-effector collision avoidance rate than the baseline methods. Experiments on a physical robotic system further validate its collision-avoidance effectiveness.

cs.RO↗

arXiv2026

Absolute charge calibration of DRZ phosphor screens for relativistic electron bunches

Laser-plasma accelerators have been the subject of extensive research in recent years. The electron beams they generate exhibit a broad energy spread. To conveniently characterize beams from laser wakefield acceleration (LWFA), electron spectrometers employing scintillating screens coupled with CCD cameras are typically used. In this work, we calibrate a series of DRZ phosphor screens and measure the spectra of the light they emit. The calibration was performed using the radio-frequency linear electron accelerator at Tsinghua University, which provided monoenergetic electron beams with peak energy of approximately 30 MeV.

physics.acc-ph↗

arXiv2026

The Ollivier Ricci flow with prescribed curvature on infinite graphs

In this paper, we consider the Ricci flow with prescribed curvature on infinite graphs, which reads as \begin{equation*}\label{flow-equation3} \frac{d}{dt}\omega(t)=-(\kappa(t)-\kappa^*)\omega(t),~~ t>0, \end{equation*} where $\omega$ is the edge weight, $\kappa$ and $\kappa^*$ are Lin-Lu-Yau Ricci curvature and the prescribed curvature on the set of edges, respectively. First, we establish the existence and uniqueness of the solution to the Ricci flow. Furthermore, we prove the convergence of the Ricci flow for graphs with girth at least 6 under two different conditions. Our convergence result aligns with the conclusion of Rodin and Sullivan (J Differ Geom, 26(2) 1987) that a circle packing in the plane with the hexagonal pattern is the regular hexagonal packing.

math.DG↗

arXiv2026

HARVE: Hacking-Aware Reward-Head Vector Editing for Robust Reward Models

Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we introduce RewardHackBench containing 13 reward-hacking patterns covering real life high-stakes domains and general settings, and we find severe failures on specific subcategories across eight reward models. To mitigate these failures, we propose HARVE, a training-free reward-head editing method for scalar reward models. Instead of fine-tuning the reward model, HARVE identifies a multi-directional hacking subspace from residual stream directions associated with selected hacking subcategories, and removes the component of the reward-head vector aligned with that subspace. This directly reduces the reward head's sensitivity to hacking-related features using only a small set of contrastive gold-hacked examples, without gradient updates or fine-tuning. Comprehensive experiments across eight reward models indicates that \model improves hacking robustness, outperforms fine-tuning baselines, and preserves reward-models' general capability. Further analyses suggest that reward hacking is better captured as a multidimensional residual-space structure than by isolated surface cues.

cs.LG↗

arXiv2026

A second-order product-type implicit-explicit Runge-Kutta method preserving unit length and energy dissipation structures for gradient flows of vector fields

Gradient flows of unit vector fields arise in a wide range of physical models such as harmonic map heat flows, nematic liquid crystals, and magnetization dynamics. Designing numerical schemes that simultaneously preserve the unit length constraint and dissipate energy is essential for reliable simulations of such systems. Although projection methods can effectively enforce the unit length constraint, ensuring energy dissipation under projection, especially in high-order schemes, remains challenging. Unlike traditional implicit-explicit Runge-Kutta (IMEX-RK) methods, in this work we propose a general methodology for constructing product-type IMEX-RK schemes that offers greater adaptability to various models with the goal of designing structure-preserving numerical schemes. For gradient flows of unit vector fields with Dirichlet energy, we design a linear and second-order numerical scheme that simultaneously preserves energy dissipation and the unit length constraint by using product-type IMEX-RK methods and projection techniques. Numerical experiments verify the accuracy, stability, and structure-preserving properties of the scheme. According to our best knowledge, this is the first second-order linear scheme that can preserve both the unit length and the original Dirichlet energy for harmonic map heat flows.

math.NA↗

arXiv2026

Remote sensing data imputation using deep learning for multispectral imagery

Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method (i.e., linear interpolation) with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures (i.e., CNN, Inception Resnet, and Autoencoder) and CNN-LSTM-based architectures (i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM). Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices (i.e., Green/Red and NDCI) derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.

cs.CV↗

arXiv2026

Staged Laser Wakefield Acceleration for Saturated Lasing of Bandwidth-Tunable Free-Electron Lasers from EUV to X-ray

Free-electron lasers (FELs) provide a revolutionary tool for capturing the structure and dynamics of matter in real time at the atomic scale. The size and cost of FELs can be substantially reduced by using laser wakefield acceleration (LWFA), which offers acceleration gradients orders of magnitude beyond radiofrequency technology, producing multi-GeV electron beams within tens of centimeters. This compactness opens the possibility of integrating multiple operating modes - from the EUV to X-rays including broadband operation - into one facility. Realizing this vision, however, faces key challenges: current LWFA bunches are too short to sustain sufficient radiation slippage, limiting FEL pulse energy at EUV wavelengths, while the large energy spread and emittance make X-ray lasing even more demanding. Here we present a LWFA-driven FEL scheme that addresses these challenges, enabling multi-mode operation spanning different wavelengths and bandwidths within a single facility. The scheme employs staged acceleration to reach multi-GeV energies while preserving beam quality, combined with a dual-chicane beamline that stretches the bunch to mitigate the radiation slippage for EUV FEL and tailors the energy chirp for diverse FEL bandwidth modes. Simulations demonstrate that the scheme can generate high-quality electron beams with energies up to 7 GeV and tunable energy chirp, enabling both FEL saturation from the EUV to X-ray wavelengths and large bandwidth operation with a bandwidth of up to 11%. This work provides a roadmap for compact, multi-mode FELs based on plasma acceleration, and the high-energy, high-quality beams achieved also point toward compact injectors for next-generation storage-ring light sources.

physics.acc-ph↗

arXiv2026

Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents

Modern database management systems (DBMSs) expose hundreds of configuration knobs that critically influence performance. Existing automated tuning methods either adopt a data-driven paradigm, which incurs substantial overhead, or rely on manual-driven heuristics extracted from database documentation, which are often limited and overly generic. Motivated by the fact that the control logic of configuration knobs is inherently encoded in the DBMS source code, we argue that promising tuning strategies can be mined directly from the code, uncovering fine-grained insights grounded in system internals. To this end, we propose SysInsight, a code-driven database tuning system that automatically extracts fine-grained tuning knowledge from DBMS source code to accelerate and stabilize the tuning process. SysInsight combines static code analysis with LLM-based reasoning to identify knob-controlled execution paths and extract semantic tuning insights. These insights are then transformed into quantitative and verifiable tuning rules via association rule mining grounded in tuning observations. During online tuning, system diagnosis is applied to identify critical knobs, which are adjusted under the rule guidance. Evaluations demonstrate that compared to the SOTA baseline, SysInsight converges to the best configuration on average 7.11X faster while achieving a 19.9% performance improvement.

cs.DB↗

arXiv2026

Quantitative Dynamic Phase Mapping via Single-Arm Field-Correlation Ghost Imaging

We demonstrate a single-arm optical platform for phase-retrieval-free, quantitative dynamic phase mapping of continuous transparent media via field-correlation ghost imaging. By modeling the medium as a dynamic pure-phase object, we spatially encode and compress its two-dimensional (2D) complex transmittance into a single bucket detector. Balanced heterodyne detection downconverts the optical frequencies for direct digitization. Crucially, by mapping spatial information into the temporal domain, this single-pixel architecture exploits high-speed digitization to continuously resolve 2D phase dynamics, effectively bypassing the frame-rate bottlenecks of traditional array sensors. Coupled with intermediate-frequency spectral analysis, this establishes a direct linear mapping from the recorded signal to the physical phase. The complex amplitude is thus deterministically extracted via field-correlation, enabling the spatial reconstruction of 2D acoustic pressure distributions using a pseudo-inverse algorithm. Experimental validations in an acoustic levitator confirm that the optically extracted acoustic wavelengths strictly match theoretical dispersion models, exhibiting a robust linear correlation between the retrieved phase shift and local sound pressure levels. This deterministic methodology provides a real-time-capable metrological tool for characterizing rapidly evolving phenomena, including transient aeroacoustic flows, shockwaves, and microfluidic biological dynamics.

physics.optics↗

arXiv2026

Broad-band Mid-infrared Laser Generation via Cascading Deceleration in Plasma Channels

Plasma-based mid-infrared (MIR) laser generation has garnered significant interest owing to its advantage of high output power, continuous wavelength tunability, and ultrashort pulse durations. However, existing methodologies predominantly depend on high-intensity inputs at the hertz frequency level, with spectral energy concentrated near the central frequency, rendering them unsuitable for spectroscopic applications. This paper proposes and demonstrates a cascaded deceleration scheme that enables the generation of broadband MIR lasers with low energy inputs compatible with high-repetition-rate laser systems. By confining the input laser within a plasma channel, this approach preserves the laser intensity, which not only sustains the decelerating field strength but also enables the cumulative effect of deceleration across multiple distinct bubbles. Numerical simulations demonstrate that more than 30% of the 23 mJ input energy is converted into a broadband MIR output spanning wavelength from 0.58 to 6.86 {\mu}m, achieving peak powers on the order of gigawatts. The output exhibits unique time-frequency characteristics, defined by spectral sub-bands organized in a temporal sequence, wherein each sub-band comprises few-cycle pulses. Parametric analyses reveal that the spectral bandwidth broadens with increasing laser intensity, provided that the plasma density being adequate to ensure a sufficiently short deceleration length. This approach provides a practical, efficient route to broadband ultra-intense mid-infrared sources, promising for applications in Fourier transform spectroscopy and laser-induced electron diffraction.

physics.optics↗

arXiv2026

The Ricci flow with prescribed curvature on graphs

In this paper, we consider the Ricci flow with prescribed curvature on the finite graph $G=(V,E)$. For any $e$ in $E$, $$\frac{d\omega(t,e)}{dt} = -(\kappa(t,e)-\kappa^*(e))\omega(t,e), t > 0,$$ where $\omega$ is the weight function, $\kappa$ is Lin-Lu-Yau Ricci curvature, and $\kappa^*$ is the prescribed curvature. By imposing invariance of the graph distance with respect to time $t$, the Ricci flow introduced above characterizes the weight evolution governed by the Lin-Lu-Yau curvature. We first establish the existence and uniqueness of the solution to this equation on general graphs. Furthermore, for graphs with girth of at least 6, we prove that the Ricci flow converges exponentially to weights of $\kappa^*$ if and only if $\kappa^*$ is attainable (namely, there exist weights realizing $\kappa^*$). In particular, we prove that the weights for constant curvature exist if and only if $$\max_{\emptyset \neq \Omega \subsetneq V} \frac{|E(\Omega)|}{|\Omega|} < \frac{|E|}{|V|},$$ where $E(\Omega)$ denotes the set of edges within the induced subgraph of $\Omega$, and $|A|$ is the cardinality of the set $A$. Viewing edge weights as metrics on surface tilings with girth of at least 5 or the duals of triangulations with vertex degrees exceeding 5, we demonstrate that our constant Lin-Lu-Yau curvature flow serves as an analog to the 2D combinatorial Ricci flow for piecewise constant curvature metrics, thereby providing an affirmative answer to Question 2 posed by Chow and Luo (J Differ Geom, 63(1) 2002).

math.DG↗

arXiv2026

BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off

Virtual try-off (VTOFF) aims to recover canonical flat-garment representations from images of dressed persons for standardized display and downstream virtual try-on. Prior methods often treat VTOFF as direct image translation driven by local masks or text-only prompts, overlooking the gap between on-body appearances and flat layouts. This gap frequently leads to inconsistent completion in unobserved regions and unstable garment structure. We propose BridgeDiff, a diffusion-based framework that explicitly bridges human-centric observations and flat-garment synthesis through two complementary components. First, the Garment Condition Bridge Module (GCBM) builds a garment-cue representation that captures global appearance and semantic identity, enabling robust inference of continuous details under partial visibility. Second, the Flat Structure Constraint Module (FSCM) injects explicit flat-garment structural priors via Flat-Constraint Attention (FC-Attention) at selected denoising stages, improving structural stability beyond text-only conditioning. Extensive experiments on standard VTOFF benchmarks show that BridgeDiff achieves state-of-the-art performance, producing higher-quality flat-garment reconstructions while preserving fine-grained appearance and structural integrity.

cs.CV↗

arXiv2026

LISTA-Transformer Model Based on Sparse Coding and Attention Mechanism and Its Application in Fault Diagnosis

Driven by the continuous development of models such as Multi-Layer Perceptron, Convolutional Neural Network (CNN), and Transformer, deep learning has made breakthrough progress in fields such as computer vision and natural language processing, and has been successfully applied in practical scenarios such as image classification and industrial fault diagnosis. However, existing models still have certain limitations in local feature modeling and global dependency capture. Specifically, CNN is limited by local receptive fields, while Transformer has shortcomings in effectively modeling local structures, and both face challenges of high model complexity and insufficient interpretability. In response to the above issues, we proposes the following innovative work: A sparse Transformer based on Learnable Iterative Shrinkage Threshold Algorithm (LISTA-Transformer) was designed, which deeply integrates LISTA sparse encoding with visual Transformer to construct a model architecture with adaptive local and global feature collaboration mechanism. This method utilizes continuous wavelet transform to convert vibration signals into time-frequency maps and inputs them into LISTA-Transformer for more effective feature extraction. On the CWRU dataset, the fault recognition rate of our method reached 98.5%, which is 3.3% higher than traditional methods and exhibits certain superiority over existing Transformer-based approaches.

cs.CV↗

arXiv2026

Decoupling Spatio-Temporal Dynamics: Microvibration Imaging Using Coherent Detection Ghost Imaging Lidar

Imaging the full-field microvibration of extended targets remains a formidable challenge for conventional remote sensing. Traditional array-based sensors are often severely constrained by data throughput and sensitivity limits when scaling to high spatial resolutions, while point-scanning interferometric systems lack the instantaneous full-field capability required to capture transient, spatially coupled vibration modes. To overcome these limitations, we propose a Coherent Detection-Ghost Imaging (CD-GI) framework that synergizes the spatial multiplexing capability of single-pixel imaging with the high-dimensional sensitivity of coherent detection. We establish a comprehensive mathematical model that describes the coupling mechanism of the target's spatial distribution and temporal micro-dynamics within a 1D bucket detector signal. To resolve the resulting inverse problem, we develop a frequency-channel self-calibration scheme. This approach effectively decouples the micro-Doppler signatures from spatial speckle patterns without requiring prior knowledge of the vibration frequency. Experimental results demonstrate that our system successfully reconstructs the spatially resolved microvibration patterns of adjacent targets with a frequency difference as small as 1 Hz, achieving sub-wavelength vibration sensitivity. This work bridges the gap between computational imaging and coherent metrology, offering a robust solution for non-invasive, high-precision structural health monitoring.

physics.optics↗