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Università della Svizzera italiana

Academic institutioneurope · ch
Official website
Research library235linked papers
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Selected work

Representative Papers

SAT-MapIt: A SAT-based Modulo Scheduling Mapper for Coarse Grain Reconfigurable Architectures

Apr 01, 2023Design, Automation and Test in Europe

This paper addresses the efficient mapping of compute-intensive loops onto coarse-grained reconfigurable arrays (CGRAs), targeting minimization of the initiation interval (II). We propose a SAT-based modulo scheduling approach, whose core innovation is Kernel Movement Scheduling (KMS)—a novel scheduling representation that uniformly encodes mapping constraints as Boolean logic formulas, thereby overcoming the search limitations inherent in conventional graph-based algorithms. Integrating modulo scheduling theory, dataflow graph analysis, and iterative feasibility verification, our method systematically generates and validates legal mappings for a given II. Experimental evaluation demonstrates that our approach outperforms state-of-the-art techniques on 47.72% of benchmarks, achieving lower IIs and uncovering several previously unrecognized valid mappings.

13 citationsRead paper

PIF: Anomaly detection via preference embedding

Jan 10, 2021International Conference on Pattern Recognition

This paper addresses anomaly detection in structured data by proposing Preference-based Isolation Forest (PIF), a novel method that maps raw data into a preference-driven high-dimensional embedding space and constructs a PI-Forest tree structure for efficient anomaly scoring. Its core contribution lies in the first integration of adaptive isolation mechanisms with learnable preference embeddings: this enables flexible anomaly modeling under arbitrary distance metrics while enhancing both separability and robustness of anomalies in a semantically coherent preference space. Extensive experiments on multiple synthetic and real-world datasets demonstrate that PIF significantly outperforms state-of-the-art methods, validating its dual advantages in precise distance-aware modeling and effective anomaly isolation.

4 citationsRead paper

Beyond the noise: intrinsic dimension estimation with optimal neighbourhood identification

May 24, 2024arXiv.org

Estimating intrinsic dimensionality (ID) from real-world data is highly sensitive to neighborhood scale: small scales overestimate ID due to noise, while large scales introduce bias from manifold curvature and topology. This work proposes a self-consistent scale selection protocol that identifies the optimal “sweet spot” for ID estimation by enforcing local density constancy. Our key contribution is the first formal coupling of ID estimation and scale selection, resolved via iterative optimization that yields a theoretically guaranteed robust decoupling—effectively suppressing both noise and curvature effects. The method integrates local neighborhood graph construction, asymptotic statistical analysis, and rigorous error-bound derivation. Evaluated on diverse synthetic and real-world datasets, it reduces ID estimation error by over 30% compared to state-of-the-art methods, while significantly improving stability and noise robustness.

3 citationsRead paper

Hashing for Structure-Based Anomaly Detection

May 16, 2025International Conference on Image Analysis and Processing

This work addresses anomaly detection on low-dimensional manifold-structured data. We propose an efficient isolation-based method that embeds data into a high-dimensional semantic-enhanced preference space and employs Locality-Sensitive Hashing (LSH) to accelerate sparse neighborhood estimation, thereby identifying the most isolated samples as anomalies. To our knowledge, this is the first approach to integrate LSH into a preference-space isolation framework, achieving both theoretical soundness and computational efficiency. Extensive experiments on multiple benchmark datasets demonstrate state-of-the-art detection performance, with inference speed improved by 3–5× over existing methods, alongside substantial reductions in time and memory overhead. The source code is publicly available.

2 citationsRead paper

Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement

Jan 27, 2026

This work addresses the challenges of multiword expression (MWE) identification, particularly the issues of class imbalance and the difficulty in accurately detecting discontinuous and noun-type MWEs. The authors propose a lightweight yet effective framework that reformulates the task as a token-level binary classification problem with START/END/INSIDE labels, integrating noun phrase chunking and dependency syntactic features while employing oversampling to mitigate data imbalance. Evaluated on the CoAM dataset, their DeBERTa-v3-large–based model achieves an F1 score of 69.8%, surpassing the previous state-of-the-art by 12 percentage points while using only 1/165th the number of parameters of comparable large models. Further evaluation on STREUSLE yields an F1 of 78.9%, demonstrating the method’s strong generalization and effectiveness.

1 citationsRead paper
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