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Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 37 records · Page 2Linked to original sources
arXiv2026

Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios

With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.

cs.RO↗
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

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}ω(t)=-(κ(t)-κ^*)ω(t),~~ t>0, \end{equation*} where $ω$ is the edge weight, $κ$ and $κ^*$ 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

Realization of Precise Perforating Using Dynamic Threshold and Physical Plausibility Algorithm for Self-Locating Perforating in Oil and Gas Wells

Accurate depth measurement is critical for targeting designated perforation intervals to maximize hydrocarbon recovery. While next-generation automated wireless perforating techniques reduce reliance on costly surface infrastructure and personnel, they lack the continuous depth correlation provided by conventional wireline cables. Consequently, correlating real-time casing collar locator (CCL) signals with a pre-recorded casing tally is essential for automatic depth determination. However, implementing this measurement remains challenging: downhole instruments must process CCL signals in real-time to identify collar signatures from complex interference, a task severely restricted by the limited computational resources and power budget of high-temperature downhole electronics. To address these constraints, this work proposes the Dynamic Threshold and Physical Plausibility Depth Measurement and Perforation Control (DTPPMP) system. This integrated solution enables in situ depth calibration by correlating CCL signals with the casing tally using lightweight algorithms for dynamic-threshold-based collar recognition and physical plausibility verification. Field tests demonstrate a collar recognition F1-score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 μs per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms.

eess.SY↗
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

ac strain based thermodynamic criterion for vortex lattice in type-II superconductors

In type-I superconductors, zero electrical resistivity and perfect diamagnetism define two fundamental criteria for superconducting behavior. In contrast, type-II superconductors exhibit more complex mixed state physics, where magnetic flux penetrates the material above the lower critical field Hc1 in the form of quantized vortices, each carrying a single flux quantum. These vortices form a two dimensional lattice which persists up to another irreversible field (Hirr) and then melts into a dissipative liquid phase. The vortex lattice is fundamental to the magnetic and electrical properties of type II superconductors, ac strain susceptibility-a thermodynamic criterion-for identifying this phase has remained elusive. Here, we report the discovery of a dynamic magnetostrictive effect, wherein the geometry of the superconductor oscillates only under an applied alternating magnetic field due to the disturbance of the vortex lattice. This effect is detected by a thin piezoelectric transducer, which converts the excited geometric deformation into an in-phase ac voltage. Notably, we find a direct and nearly linear relationship between the signal amplitude and the vortex density in lattice across several representative type-II superconductors. In the vortex liquid phase above Hirr, the signal amplitude rapidly decays to zero near the upper critical field (Hc2), accompanied by a pronounced out-of-phase component due to enhanced dissipation. This dynamic magnetostrictive effect not only reveals an unexplored magnetoelastic property of the vortex lattice but also establishes a fundamental criterion for identifying the type-II superconductors.

cond-mat.supr-con↗
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 $E$, $$\frac{dω(t,e)}{dt} = -(κ(t,e)-κ^*(e))ω(t,e), t > 0,$$ where $ω$ is the weight function, $κ$ is Lin-Lu-Yau Ricci curvature, and $κ^*$ 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 $κ^*$ if and only if $κ^*$ is attainable (namely, there exist weights realizing $κ^*$). In particular, we prove that the weights for constant curvature exist if and only if $$\max_{\emptyset \neq Ω\subsetneq V} \frac{|E(Ω)|}{|Ω|} < \frac{|E|}{|V|},$$ where $E(Ω)$ denotes the set of edges within the induced subgraph of $Ω$, 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

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

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 μm, 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

Light Propagation through Space-Time Non-Markovian Random Media

Here, we introduce a stochastic partial differential equation (SPDE) formulation driven by temporally correlated noise to describe light propagation beyond the standard Markov approximation. By representing the squared refractive index fluctuations as a random field with explicit long-range temporal correlations, we demonstrate that the propagation dynamics map exactly onto the hyperbolic Anderson model. This rigorous mapping enables the derivation of new quantitative scaling relations that connect the environment's non-Markovian memory effects to the statistical properties of the emergent light field. We experimentally validate these analytical predictions in an outdoor atmospheric environment, confirming the memory-dependent statistical signatures of the propagated light. Our results establish a precise physical foundation for understanding memory-driven wave phenomena, providing crucial insights for free-space optical communication, remote sensing, and coherent imaging.

physics.optics↗
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

A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments

Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced magnetic interference, while stringent size and power budgets limit the use of computationally intensive algorithms and specific operations require real-time, in-situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1,985~parameters and 8,208~MACs, and deployed on an ARM Cortex-M7 based embedded system using TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1,000 inference per second and 343.2 μs latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints.

eess.SY↗
arXiv2026

Data-Augmented Deep Learning for Downhole Depth Sensing and Validation

Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing, and random cropping are fundamental prerequisites for model training, while label smoothing regularization, time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations.

cs.LG↗
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↗
arXiv2026

BCH Coding Assisted Imaging

In modern correlation imaging systems, also known as ghost imaging (GI), particularly under low-light or noisy conditions, preserving high image fidelity presents a significant challenge. This paper introduces an innovative approach by integrating Bose-Chaudhuri-Hocquenghem (BCH) error control coding (ECC) into CGI systems to assist imaging. By encoding target image with BCH codes and using order-statistic decoding (OSD) for error correction during reconstruction, this approach significantly improves image quality across various signal-to-noise ratio (SNR) conditions. Simulation and experiment results validate that BCH coding assisted imaging achieves significantly enhanced robustness against additive white Gaussian noise (AWGN) and improved image reconstruction quality. In addition, the imaging performance of different BCH codes varies, with each code exhibiting distinct advantages based on factors such as code length and coding efficiency.

physics.optics↗