Selected work

Representative Papers

Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification

Jul 22, 2026

Standard data augmentation techniques exhibit limited efficacy in laser speckle-based material classification because they overlook the structural statistical properties inherent to speckle patterns, which arise from coherent interference. This work proposes a parameterized augmentation framework to systematically evaluate the impact of various perturbations—including rotation, Gaussian blur, independent noise, spatially correlated speckle-aware noise, intensity jitter, and spatial masking—on classification performance. Leveraging ResNet18 and EfficientNet-B0 models alongside ordinary least squares analysis, the study demonstrates that augmentation effectiveness hinges on preserving the spatial and frequency-domain structure of speckle rather than the magnitude of perturbation. Structure-preserving augmentations, such as spatially correlated noise, substantially enhance robustness, whereas Gaussian blur and independent noise degrade performance. The proposed framework accounts for up to 87.9% of performance variance, establishing a design principle centered on physically informed, structure-preserving augmentation.

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LM2Alloy: Investigating LLM-Generated Formal Specifications for Automated Test Derivation in Production Software

Jul 20, 2026

Traditional testing approaches struggle to uncover deep logical flaws that violate explicit or implicit constraints, particularly when discrepancies exist between requirements and implementation. This work proposes a novel method that leverages large language models to automatically generate Alloy formal specifications from both requirement documents and source code, using these specifications as an intermediate representation to derive executable test cases. By integrating large language models, formal methods, static analysis, and automated testing, the approach effectively exposes constraint-level defects missed by conventional test generation techniques. Empirical evaluation reveals a real-world vulnerability in the Flipper library and uncovers undocumented implicit abstractions in Cerberus. Moreover, tests derived from code-generated specifications demonstrate superior stability compared to those based solely on requirements.

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The Quantum Learning Pyramid (QLP): A Novel, Holistic, Industry-Ready Curriculum and Pedagogical Methodology for Quantum Computing Education

Jul 12, 2026

This work addresses the systemic gap in quantum computing education driven by industrial demands and national strategic priorities by proposing a four-tiered Quantum Learning Pyramid (QLP) framework. Integrating phenomenological understanding, computational thinking, hardware awareness, and societal context, the QLP employs a spiraling curriculum, competency-oriented pathways, and authentic assessments grounded in active and project-based learning. The framework innovatively unifies theory, practice, hardware engagement, and societal implications within an interdisciplinary curriculum, incorporating cloud-accessible quantum processors, simulation platforms, and hybrid experimental environments. It spans core modules from foundational principles to quantum algorithms, error correction, and cryptography, thereby establishing a scalable roadmap for talent development that effectively bridges academic training with industry needs, cultivating both “quantum-ready” practitioners with hands-on capabilities and scientifically literate citizens.

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Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure

Jun 03, 2026

Existing crack segmentation methods often suffer from fragmented predictions and missed fine branches under domain shift, while also lacking reliable uncertainty estimates. To address these limitations, this work proposes CrackGeoFM, a multi-task foundational model for crack analysis that leverages a frozen vision foundation backbone augmented with three key components: a Frequency-guided Crack Enhancement Module (FCEM), a Crack-domain Feature Adaptation Module (CFAM), and a Structure-aware Multi-task Decoder (SMTD). This unified framework jointly performs pixel-level segmentation, skeleton reconstruction, and uncertainty calibration. By integrating frequency enhancement, domain adaptation, and structure-aware multi-task learning into a foundational model architecture—novel in the crack analysis domain—the method achieves state-of-the-art performance across 20 datasets, significantly improving topological completeness and uncertainty reliability, and enabling effective few-shot transfer with as few as five annotated images.

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Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition

May 06, 2026

Accurate traffic congestion classification requires models that jointly capture roadway scene context and non-stationary traffic motion, yet most prior work treats these requirements in isolation. Vision-based methods often depend on appearance cues with standard temporal pooling, which can bias predictions toward static infrastructure, whereas signal-based approaches characterize temporal dynamics but lack the spatial context needed for scene-level localization. These complementary limitations motivate a unified framework that links motion evidence to spatial feature selection while preserving data-adaptive temporal characterization. This study therefore proposes FLO-EMD, a hybrid approach that couples motion-guided attention with empirical, data-driven temporal decomposition. Dense optical flow guides channel and spatial attention so that RGB features are refined toward motion-relevant regions. In parallel, aggregated flow statistics form compact motion traces that are decomposed using Empirical Mode Decomposition (EMD) to extract intrinsic temporal components. The resulting EMD embedding is fused with learned spatiotemporal representations to classify light, medium, and heavy congestion. Experiments on 1,050 five-second clips from four surveillance networks show that FLO-EMD achieves 97.5% overall test accuracy (weighted F1 = 0.9742), outperforming established baselines and remaining robust across diverse environmental conditions; ablation and sensitivity analyses further quantify the contributions of EMD, the number of intrinsic mode functions, and the selected motion descriptors.

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Recent publications

Latest Papers

Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification

Jul 22, 2026

Standard data augmentation techniques exhibit limited efficacy in laser speckle-based material classification because they overlook the structural statistical properties inherent to speckle patterns, which arise from coherent interference. This work proposes a parameterized augmentation framework to systematically evaluate the impact of various perturbations—including rotation, Gaussian blur, independent noise, spatially correlated speckle-aware noise, intensity jitter, and spatial masking—on classification performance. Leveraging ResNet18 and EfficientNet-B0 models alongside ordinary least squares analysis, the study demonstrates that augmentation effectiveness hinges on preserving the spatial and frequency-domain structure of speckle rather than the magnitude of perturbation. Structure-preserving augmentations, such as spatially correlated noise, substantially enhance robustness, whereas Gaussian blur and independent noise degrade performance. The proposed framework accounts for up to 87.9% of performance variance, establishing a design principle centered on physically informed, structure-preserving augmentation.

0 citationsRead paper

LM2Alloy: Investigating LLM-Generated Formal Specifications for Automated Test Derivation in Production Software

Jul 20, 2026

Traditional testing approaches struggle to uncover deep logical flaws that violate explicit or implicit constraints, particularly when discrepancies exist between requirements and implementation. This work proposes a novel method that leverages large language models to automatically generate Alloy formal specifications from both requirement documents and source code, using these specifications as an intermediate representation to derive executable test cases. By integrating large language models, formal methods, static analysis, and automated testing, the approach effectively exposes constraint-level defects missed by conventional test generation techniques. Empirical evaluation reveals a real-world vulnerability in the Flipper library and uncovers undocumented implicit abstractions in Cerberus. Moreover, tests derived from code-generated specifications demonstrate superior stability compared to those based solely on requirements.

0 citationsRead paper

The Quantum Learning Pyramid (QLP): A Novel, Holistic, Industry-Ready Curriculum and Pedagogical Methodology for Quantum Computing Education

Jul 12, 2026

This work addresses the systemic gap in quantum computing education driven by industrial demands and national strategic priorities by proposing a four-tiered Quantum Learning Pyramid (QLP) framework. Integrating phenomenological understanding, computational thinking, hardware awareness, and societal context, the QLP employs a spiraling curriculum, competency-oriented pathways, and authentic assessments grounded in active and project-based learning. The framework innovatively unifies theory, practice, hardware engagement, and societal implications within an interdisciplinary curriculum, incorporating cloud-accessible quantum processors, simulation platforms, and hybrid experimental environments. It spans core modules from foundational principles to quantum algorithms, error correction, and cryptography, thereby establishing a scalable roadmap for talent development that effectively bridges academic training with industry needs, cultivating both “quantum-ready” practitioners with hands-on capabilities and scientifically literate citizens.

0 citationsRead paper

Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure

Jun 03, 2026

Existing crack segmentation methods often suffer from fragmented predictions and missed fine branches under domain shift, while also lacking reliable uncertainty estimates. To address these limitations, this work proposes CrackGeoFM, a multi-task foundational model for crack analysis that leverages a frozen vision foundation backbone augmented with three key components: a Frequency-guided Crack Enhancement Module (FCEM), a Crack-domain Feature Adaptation Module (CFAM), and a Structure-aware Multi-task Decoder (SMTD). This unified framework jointly performs pixel-level segmentation, skeleton reconstruction, and uncertainty calibration. By integrating frequency enhancement, domain adaptation, and structure-aware multi-task learning into a foundational model architecture—novel in the crack analysis domain—the method achieves state-of-the-art performance across 20 datasets, significantly improving topological completeness and uncertainty reliability, and enabling effective few-shot transfer with as few as five annotated images.

0 citationsRead paper

Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition

May 06, 2026

Accurate traffic congestion classification requires models that jointly capture roadway scene context and non-stationary traffic motion, yet most prior work treats these requirements in isolation. Vision-based methods often depend on appearance cues with standard temporal pooling, which can bias predictions toward static infrastructure, whereas signal-based approaches characterize temporal dynamics but lack the spatial context needed for scene-level localization. These complementary limitations motivate a unified framework that links motion evidence to spatial feature selection while preserving data-adaptive temporal characterization. This study therefore proposes FLO-EMD, a hybrid approach that couples motion-guided attention with empirical, data-driven temporal decomposition. Dense optical flow guides channel and spatial attention so that RGB features are refined toward motion-relevant regions. In parallel, aggregated flow statistics form compact motion traces that are decomposed using Empirical Mode Decomposition (EMD) to extract intrinsic temporal components. The resulting EMD embedding is fused with learned spatiotemporal representations to classify light, medium, and heavy congestion. Experiments on 1,050 five-second clips from four surveillance networks show that FLO-EMD achieves 97.5% overall test accuracy (weighted F1 = 0.9742), outperforming established baselines and remaining robust across diverse environmental conditions; ablation and sensitivity analyses further quantify the contributions of EMD, the number of intrinsic mode functions, and the selected motion descriptors.

0 citationsRead paper