Institution profile

Katholieke Universiteit Leuven

Academic institutioneurope · be
Official website
Research library724linked papers
Opportunities0open roles
Selected work

Representative Papers

Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy

Feb 06, 2026

This study addresses the challenge of automatically identifying singing errors in music education by proposing the first teaching-oriented singing error detection framework. Leveraging synchronously recorded audio from both teachers and students, the authors construct a dedicated dataset with a fine-grained error annotation scheme and develop a deep learning model for error recognition. Experimental results demonstrate that the proposed method significantly outperforms traditional rule-based baselines. A systematic analysis further reveals the impact of inter-teacher instructional variability on error detection performance. This work contributes a novel benchmark dataset, an evaluation methodology, and actionable pedagogical insights for intelligent music education systems.

3 citationsRead paper

Least trimmed squares regression with missing values and cellwise outliers

Mar 04, 2026

This study addresses the limitations of traditional regression methods in simultaneously handling case-wise and cell-wise outliers as well as missing data, particularly under skewed distributions where out-of-sample prediction performance often deteriorates. To overcome these challenges, the authors propose a novel robust regression approach built upon the Least Trimmed Squares (LTS) framework. This method is the first to provide a theoretical breakdown point guarantee against cell-wise contamination and incorporates an embedded imputation mechanism tailored for asymmetric data distributions. Empirical evaluations demonstrate that the proposed technique substantially enhances both robustness and predictive accuracy in complex scenarios where outliers and missing values coexist.

1 citationsRead paper

Fixpoint Semantics for DatalogMTL with Negation

Jan 07, 2026Electronic Proceedings in Theoretical Computer Science

This work addresses the lack of a unified and rigorous semantics for DatalogMTL with negation by systematically introducing Approximation Fixpoint Theory (AFT) into the language for the first time. By integrating metric temporal logic operators with non-monotonic reasoning techniques, the paper provides concise definitions of four key semantics: stable models, well-founded models, Kripke-Kleene models, and supported models. The proposed framework establishes a formally coherent and highly expressive semantic foundation. Moreover, it demonstrates that the derived stable model semantics is equivalent to the existing definition based on here-and-there temporal logic, thereby validating the effectiveness and applicability of AFT in the context of temporal logic programming.

1 citationsRead paper

AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing

Nov 05, 2025

Manual sizing of analog/mixed-signal (AMS) circuits suffers from lengthy design cycles and error-proneness, while existing AI-driven approaches are hampered by prohibitive simulation overhead and poor interpretability. This paper introduces the first large language model (LLM)-based multi-agent collaborative framework for AMS circuit sizing, integrating a reasoning-driven workflow, adaptive simulation control, and design-history learning to enable efficient, transparent, and fully automated optimization. LLM agents collaboratively parse circuit topology and specifications, dynamically orchestrate simulation resources, substantially reduce sample complexity, and avoid common design pitfalls. Experiments across circuits of varying complexity demonstrate that our method improves sample efficiency by 2.1–3.8× over Bayesian optimization and conventional reinforcement learning, accelerates convergence by 47%–63%, and ensures full traceability and verifiability of all design decisions.

1 citationsRead paper

An open-source heuristic to reboot 2D nesting research

Sep 05, 2025arXiv.org

The two-dimensional irregular strip packing problem suffers from stagnant research progress, poor reproducibility, and underestimated optimization potential. To address these challenges, this paper introduces Sparrow, an open-source heuristic solver. Its core innovation is the “sequential feasibility decomposition” framework, which hierarchically decomposes the global optimization problem into a series of collision-free feasibility subproblems, integrating efficient collision detection, geometric processing, and heuristic search strategies. We release ten real-world industrial benchmark instances and fully open-source the implementation. Experimental results demonstrate that Sparrow significantly outperforms state-of-the-art methods across multiple benchmarks, achieving simultaneous improvements in nesting quality and computational efficiency. By establishing a transparent, reproducible, and extensible foundation, Sparrow breaks longstanding technical barriers and fosters sustainable academic advancement in irregular packing research.

1 citationsRead paper
Recent publications

Latest Papers