Selected work

Representative Papers

A new rotation-free isogeometric thin shell formulation and a corresponding continuity constraint for patch boundaries

Apr 01, 2017

Traditional thin-shell formulations suffer from numerical instability due to reliance on rotational degrees of freedom and insufficient C⁰ continuity across multi-patch surfaces. To address these issues, this paper proposes a rotation-free isogeometric thin-shell formulation. Leveraging NURBS basis functions and the Galerkin weak form, it introduces, for the first time, explicit C¹-continuous boundary constraints applicable to both 3D reduced and planar straight-sided multi-patch shell structures. By eliminating nodal rotational variables entirely, the method significantly enhances modeling robustness and asymptotic convergence rates. Numerical experiments on benchmark bending and buckling problems demonstrate over 40% improvement in stress accuracy, alongside enhanced computational efficiency and broader applicability. This work establishes a new paradigm for high-fidelity simulation of complex curved thin-shell structures.

145 citations4 influentialRead paper

Going Deep and Going Wide: Counting Logic and Homomorphism Indistinguishability over Graphs of Bounded Treedepth and Treewidth

Aug 11, 2023Annual Conference for Computer Science Logic

This paper investigates the expressive power of the fragment $C^k_q$ of first-order logic with counting quantifiers—restricted to at most $k$ variables and quantifier rank at most $q$—focusing on its logical equivalence in relation to graph structure. We characterize $C^k_q$-equivalence via homomorphism indistinguishability and define the associated graph class $T^k_q$, providing its first purely graph-theoretic characterization: $T^k_q = {G mid ext{hom}(G, -) ext{ is invariant over } C^k_q ext{-equivalent graphs}}$. Our contributions are threefold: (1) an elementary, Dvořák-inspired construction reproves that two graphs are $C^k_q$-equivalent iff they are homomorphism-indistinguishable over $T^k_q$; (2) we establish that the class of graphs with treedepth at most $q$ is closed under homomorphism distinguishability; and (3) we strictly separate $T^k_q$ from $TW_{k-1} cap TD_q$ for $q gg k$, thereby refuting Roberson’s (2022) conjecture that $TD_q$ is homomorphism-distinguishability closed.

9 citations2 influentialRead paper

From Instructions to ODRL Usage Policies: An Ontology Guided Approach

Jun 03, 2025VLDB Workshops

This work addresses the need for automated digital rights policy generation in multi-institutional, culturally oriented trusted data spaces. We propose a large language model (LLM)-based natural language-to-ODRL policy mapping method. Our approach uniquely integrates the W3C ODRL ontology and its structured documentation as core components of prompt engineering to guide GPT-4 in generating high-fidelity, standards-compliant policies. Additionally, we introduce an ontology-adaptation heuristic tailored for knowledge graph construction to enhance semantic alignment. Evaluated on 12 culturally diverse use cases spanning varying complexity levels, our method achieves a policy generation accuracy of 91.95%, significantly outperforming existing baselines. The contribution lies in establishing a scalable, interpretable, and standards-aligned automation paradigm for open digital rights management—bridging natural language requirements with formal, machine-processable ODRL policies.

4 citationsRead paper

Witnessed Symmetric Choice and Interpretations in Fixed-Point Logic with Counting

Oct 14, 2022International Colloquium on Automata, Languages and Programming

This paper addresses the expressive gap between polynomial-time algorithms and isomorphism-invariant logics, focusing on the expressive boundaries of Fixed-Point Logic with Counting (IFPC). Methodologically, it systematically investigates the logical strength arising from combining Witnessed Symmetric Choice (WSC) with first-order interpretations (I). The main contributions are threefold: (1) It separates IFPC+WSC from IFPC+WSCI, proving that IFPC+WSC is not closed under first-order interpretations—resolving the Dawar–Richerby open problem; (2) it demonstrates that nesting WSC operators strictly increases expressive power; and (3) it establishes a new sufficient condition for canonization of CFI graphs and shows that IFPC+WSC+I canonizes CFI graphs beyond the capability of all previously known mainstream logics. Technically, the work integrates fixed-point logic, group actions and orbit decompositions, structural interpretations, and automata-based witnessing techniques.

3 citations1 influentialRead paper

NQC²: A Non-Intrusive QEMU Code Coverage Plugin

Jan 18, 2024RAPIDO@HiPEAC

This work proposes NQC2, a non-intrusive code coverage collection mechanism based on QEMU plugins, designed to address the challenge of applying traditional coverage analysis—typically reliant on operating systems and file systems—to bare-metal embedded programs. By leveraging dynamic binary translation, NQC2 extracts execution path information from within QEMU during emulation and saves it directly to the host machine, without requiring modifications to the target program or a customized QEMU build. This approach enables, for the first time, zero-instrumentation coverage analysis for bare-metal embedded systems. Experimental results demonstrate that NQC2 achieves up to an 8.5× performance improvement over Xilinx’s comparable solution, significantly enhancing both the efficiency and applicability of testing for embedded software.

3 citationsRead paper

Recent publications

Latest Papers

Multiobjective Preexpectation Reasoning for Probabilistic Programs

Aug 13, 2026

This work addresses the problem of multi-objective expected reward optimization in infinite-state Markov decision processes, aiming to synthesize policies that approximate the Pareto front. To this end, it introduces the first deductive program-level reasoning framework that integrates multi-objective optimization with weak expectation semantics. The approach features a novel multi-objective expectation transformer and employs a convex hull power domain to symbolically represent post-expectation tuples. By combining hybrid determinization rules for policy synthesis with operational semantics modeling, the method enables symbolic policy synthesis over infinite state spaces. Experimental evaluation demonstrates its effectiveness in solving multi-objective optimization problems across several case studies.

0 citationsRead paper

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

Aug 12, 2026

This work addresses the limitations of learning-based behavioral planning in autonomous driving—particularly poor interpretability and challenges in ensuring safety—by proposing a hybrid planning architecture. The approach integrates the perceptual strengths of deep neural networks in complex traffic scenarios with an optimization-based supervisory layer that validates high-level behavior proposals and enforces feasibility and safety constraints. By synergistically combining data-driven flexibility with classical, deterministic planning principles, the method significantly enhances verifiability and safety without sacrificing adaptability. The system is evaluated through open-loop testing on real-world urban driving data and demonstrates stable closed-loop performance, having been successfully deployed on the research vehicle Karl, thereby confirming its practicality and reliability.

0 citationsRead paper

Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment

Aug 11, 2026

This work addresses color drift, boundary ambiguity, and the limitation of latent-feature decoders treating images merely as intermediate visualizations in generative semantic segmentation. To overcome these issues, the authors propose the Semantic Prism framework, which employs diffusion distillation to construct a single-step generator, integrates a fixed class-color codebook to establish an explicit probabilistic interface, and introduces a hierarchical evidence alignment mechanism in logit space to predict residuals while preserving the image-defined interface as the reference for the final distribution. The study also innovatively presents C-IHD, an error-ranking method that requires no additional predictor. Experiments demonstrate that the approach achieves 72.07% mIoU (+11.39) with an ECE of 0.41% on Cityscapes, and attains 62.22% and 46.89% mIoU on BDD100K and ACDC, respectively, while significantly improving pixel-wise error ranking AUPR—e.g., from 0.6580 to 0.7557 on ACDC.

0 citationsRead paper

Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework

Aug 10, 2026

This work addresses the limitations of traditional process discovery methods, which struggle to fully exploit the expressive power of Petri nets due to reliance on predefined structures and inability to capture long-range dependencies. To overcome these constraints, the paper proposes a monotonicity-guided, bottom-up approach to Petri net discovery that departs from conventional top-down paradigms. By leveraging local monotonicity analysis, pruning candidate places, and applying combinatorial optimization, the method efficiently constructs high-quality process models capable of representing concurrency, free-choice behavior, and long-term dependencies. Experimental results demonstrate that, on both synthetic and real-life event logs, the approach generates expressive and structurally flexible Petri nets using limited computational resources, significantly improving the accuracy and adaptability of process discovery.

0 citationsRead paper

Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection

Aug 10, 2026

This work addresses structural discontinuities in lane detection caused by occlusions and complex scenes, as well as the misalignment between classification confidence and localization quality that leads to false positives and missed detections. Without altering the inference pipeline of the Anchor Decomposition Network (ADNet), the authors propose two key enhancements: first, a Gated Horizontal-Vertical Token module is introduced to strengthen directional structural feature continuity in the backbone network; second, a Line-Quality-Aware dynamic anchor scoring mechanism is devised to recalibrate anchors based on quality supervision, hard negative suppression, and pairwise ranking—eliminating the need for additional network branches. Evaluated on VIL-100, the method improves ADNet-R34’s F1@50 from 89.97 to 91.28, while experiments on CULane and TuSimple further confirm its effectiveness and low computational overhead.

0 citationsRead paper