Selected work

Representative Papers

Automated Palynological Analysis System: Integrating Deep Metric Learning and $U^{2}$-Net Detection in $H\infty$ bright field microscopy

Apr 17, 2026

Traditional pollen analysis is time-consuming (4–6 hours per sample) and highly subjective. This study proposes an automated, high-throughput microscopic analysis system that integrates brightfield imaging, $H_\infty$ robust mechanical control, and a deep learning pipeline to enable efficient, accurate counting, classification, and morphological characterization of pollen from the Bio Bío region of Chile. The approach combines U²-Net for salient object detection with a DINOv2 vision transformer classifier based on deep metric learning, augmented by a gradient-weighted attention mechanism to generate interpretable texture and diagnostic features. The system achieves a classification recall of 95.8% and processes samples six times faster than human experts.

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Partial decidability protocol for the Wang tiling problem from statistical mechanics and chaotic mapping

Jul 17, 2025

Addressing the classical undecidable problem of Wang tiling, this paper proposes a partially decidable framework integrating statistical mechanics and discrete dynamical systems theory. We construct mappings among finite tile sets and introduce effective entropy and temperature parameters to characterize the thermodynamic behavior of tile alphabets; dynamical phase transitions are identified via chaos criteria—including logistic map bifurcation analysis and Kendall Tau correlation. Results show that favorable thermodynamic behavior (low entropy, non-chaotic dynamics) strongly correlates with infinite planar tileability, whereas emergent chaotic dynamics signal the undecidable phase. The framework successfully distinguishes known tileable and non-tileable instances, establishing—for the first time—a quantitative link between thermodynamic properties and decidability. This yields a computationally tractable and interpretable paradigm for partial decidability in combinatorial undecidability problems.

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The Causal-Effect Score in Data Management

Feb 04, 2025

Existing database attribution methods—such as Shapley values—fail to accurately quantify the causal contribution strength of individual tuples to query results. Method: We propose the Causal-Effect Score (CES), the first framework to integrate structural causal models (SCMs) and counterfactual reasoning into data management, enabling unified tuple-level causal attribution for both deterministic and probabilistic databases. CES combines query semantics modeling, probabilistic inference, and efficient approximation algorithms. Contribution/Results: We provide an axiomatized definition of CES, analyze its computational complexity, and prove it satisfies key causal properties—including causal sensitivity and consistency. Experiments demonstrate that CES significantly outperforms baseline methods in attribution accuracy while maintaining strong scalability. By bridging causal inference and database systems, CES establishes a novel paradigm for interpretable, causally grounded database explanations.

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Recent publications

Latest Papers

Automated Palynological Analysis System: Integrating Deep Metric Learning and $U^{2}$-Net Detection in $H\infty$ bright field microscopy

Apr 17, 2026

Traditional pollen analysis is time-consuming (4–6 hours per sample) and highly subjective. This study proposes an automated, high-throughput microscopic analysis system that integrates brightfield imaging, $H_\infty$ robust mechanical control, and a deep learning pipeline to enable efficient, accurate counting, classification, and morphological characterization of pollen from the Bio Bío region of Chile. The approach combines U²-Net for salient object detection with a DINOv2 vision transformer classifier based on deep metric learning, augmented by a gradient-weighted attention mechanism to generate interpretable texture and diagnostic features. The system achieves a classification recall of 95.8% and processes samples six times faster than human experts.

0 citationsRead paper

Partial decidability protocol for the Wang tiling problem from statistical mechanics and chaotic mapping

Jul 17, 2025

Addressing the classical undecidable problem of Wang tiling, this paper proposes a partially decidable framework integrating statistical mechanics and discrete dynamical systems theory. We construct mappings among finite tile sets and introduce effective entropy and temperature parameters to characterize the thermodynamic behavior of tile alphabets; dynamical phase transitions are identified via chaos criteria—including logistic map bifurcation analysis and Kendall Tau correlation. Results show that favorable thermodynamic behavior (low entropy, non-chaotic dynamics) strongly correlates with infinite planar tileability, whereas emergent chaotic dynamics signal the undecidable phase. The framework successfully distinguishes known tileable and non-tileable instances, establishing—for the first time—a quantitative link between thermodynamic properties and decidability. This yields a computationally tractable and interpretable paradigm for partial decidability in combinatorial undecidability problems.

0 citationsRead paper

The Causal-Effect Score in Data Management

Feb 04, 2025

Existing database attribution methods—such as Shapley values—fail to accurately quantify the causal contribution strength of individual tuples to query results. Method: We propose the Causal-Effect Score (CES), the first framework to integrate structural causal models (SCMs) and counterfactual reasoning into data management, enabling unified tuple-level causal attribution for both deterministic and probabilistic databases. CES combines query semantics modeling, probabilistic inference, and efficient approximation algorithms. Contribution/Results: We provide an axiomatized definition of CES, analyze its computational complexity, and prove it satisfies key causal properties—including causal sensitivity and consistency. Experiments demonstrate that CES significantly outperforms baseline methods in attribution accuracy while maintaining strong scalability. By bridging causal inference and database systems, CES establishes a novel paradigm for interpretable, causally grounded database explanations.

0 citationsRead paper