Selected work

Representative Papers

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

Jun 09, 2026

This study addresses the challenge of inconsistent time-frequency representation dimensions in lung sound signals caused by variable respiratory cycle lengths. To resolve this, the authors propose an adaptive-length windowing strategy that standardizes the spatiotemporal dimensions of both MFCCs and log-Mel spectrograms. Building upon this unified representation, they employ CNNs to extract sub-phase features and systematically evaluate fusion approaches, including direct concatenation, GRU, and GRU with attention mechanisms. Experimental results demonstrate that the MFCC-based model achieves the best F1 scores of 0.877 and 0.855 at the respiratory-cycle and subject levels, respectively, significantly outperforming models based on log-Mel spectrograms and VAR. The findings also underscore the critical importance of real-world data, while revealing that more complex fusion strategies and data augmentation techniques such as mixup do not yield further performance gains.

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QEC and EAQEC Codes from Hermitian Sums and Hulls of Cyclic Codes over $\mathbb{F}_2 \times (\mathbb{F}_2+v\mathbb{F}_2)$

Jun 01, 2026

This study investigates the construction of high-performance quantum error-correcting (QEC) codes and entanglement-assisted quantum error-correcting (EAQEC) codes from cyclic codes over the composite ring $\mathbb{F}_2 \times (\mathbb{F}_2 + v\mathbb{F}_2)$. By analyzing for the first time the Hermitian hull and Hermitian sum structures of cyclic codes over this ring, the work integrates quantum Construction X, matrix-product codes, and linear complementary dual (LCD) code techniques to propose novel constructions of QEC and EAQEC codes. This approach not only extends the design framework for EAQEC codes but also yields several new families of quantum codes, thereby enriching the theoretical foundation of quantum error correction based on non-traditional algebraic structures.

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Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation

May 28, 2026

This study addresses the limitations of existing Turkish sentence embedding models—namely, restricted contextual capacity, suboptimal performance, and high training costs—by proposing an efficient three-stage adaptation framework. The approach begins with optimizing a multilingual tokenizer through cross-lingual vocabulary pruning and frequency-based analysis to reduce vocabulary size. Next, a teacher model is cloned and its embedding layer is reinitialized for compatibility. Finally, offline distillation is performed using precomputed target vectors. This method reduces model parameters by 33% and achieves full training in under four hours on a single GPU at a cost of merely \$5–20. On the STSb-TR benchmark, it attains a Pearson correlation of 77.55%, and scores 63.9% on average across the TR-MTEB suite, outperforming even larger teacher models.

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A Reconfigurable Pneumatic Joint Enabling Localized Selective Stiffening and Shape Locking in Vine-Inspired Robots

Apr 17, 2026

This work addresses the challenges of deploying vine-like robots in free space, which are hindered by low axial stiffness, weak load-bearing capacity, and an inability to maintain curved configurations. To overcome these limitations, the authors propose a reconfigurable pneumatic joint (RPJ) architecture featuring symmetrically arranged pneumatic chamber modules distributed along the robot’s body. This design enables localized, pressure-tunable stiffness and shape locking without impeding continuous growth, while integrating tendon-driven steering and a compact aerial inversion base. Notably, the approach decouples global compliance from local rigidity for the first time in a continuously growing soft robot. Experimental results demonstrate that the system can execute inversions under low pressure, significantly suppress gravity-induced deflection, support cascaded retraction, and reliably transport payloads up to 200 grams.

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From Labels to Facets: Building a Taxonomically Enriched Turkish Learner Corpus

Jan 30, 2026

This study addresses the limitations of existing learner corpora, which typically employ flat annotation schemes that hinder the disentanglement of multiple linguistic dimensions and impede fine-grained analysis of error origins. To overcome this, the authors propose a linguistically grounded multidimensional taxonomy and develop a semi-automatic annotation expansion framework that transforms flat labels into rich, multifaceted structures integrating linguistic features and metadata. As a first application of this approach, they construct a Turkish learner corpus conforming to the proposed taxonomy, accompanied by detailed annotation guidelines and supporting tools to enable cross-dimensional error pattern mining. Experimental results demonstrate that the method achieves 95.86% accuracy at the facet level, substantially enhancing both query capabilities and analytical depth of the corpus.

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Recent publications

Latest Papers

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

Jun 09, 2026

This study addresses the challenge of inconsistent time-frequency representation dimensions in lung sound signals caused by variable respiratory cycle lengths. To resolve this, the authors propose an adaptive-length windowing strategy that standardizes the spatiotemporal dimensions of both MFCCs and log-Mel spectrograms. Building upon this unified representation, they employ CNNs to extract sub-phase features and systematically evaluate fusion approaches, including direct concatenation, GRU, and GRU with attention mechanisms. Experimental results demonstrate that the MFCC-based model achieves the best F1 scores of 0.877 and 0.855 at the respiratory-cycle and subject levels, respectively, significantly outperforming models based on log-Mel spectrograms and VAR. The findings also underscore the critical importance of real-world data, while revealing that more complex fusion strategies and data augmentation techniques such as mixup do not yield further performance gains.

0 citationsRead paper

QEC and EAQEC Codes from Hermitian Sums and Hulls of Cyclic Codes over $\mathbb{F}_2 \times (\mathbb{F}_2+v\mathbb{F}_2)$

Jun 01, 2026

This study investigates the construction of high-performance quantum error-correcting (QEC) codes and entanglement-assisted quantum error-correcting (EAQEC) codes from cyclic codes over the composite ring $\mathbb{F}_2 \times (\mathbb{F}_2 + v\mathbb{F}_2)$. By analyzing for the first time the Hermitian hull and Hermitian sum structures of cyclic codes over this ring, the work integrates quantum Construction X, matrix-product codes, and linear complementary dual (LCD) code techniques to propose novel constructions of QEC and EAQEC codes. This approach not only extends the design framework for EAQEC codes but also yields several new families of quantum codes, thereby enriching the theoretical foundation of quantum error correction based on non-traditional algebraic structures.

0 citationsRead paper

Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation

May 28, 2026

This study addresses the limitations of existing Turkish sentence embedding models—namely, restricted contextual capacity, suboptimal performance, and high training costs—by proposing an efficient three-stage adaptation framework. The approach begins with optimizing a multilingual tokenizer through cross-lingual vocabulary pruning and frequency-based analysis to reduce vocabulary size. Next, a teacher model is cloned and its embedding layer is reinitialized for compatibility. Finally, offline distillation is performed using precomputed target vectors. This method reduces model parameters by 33% and achieves full training in under four hours on a single GPU at a cost of merely \$5–20. On the STSb-TR benchmark, it attains a Pearson correlation of 77.55%, and scores 63.9% on average across the TR-MTEB suite, outperforming even larger teacher models.

0 citationsRead paper

A Reconfigurable Pneumatic Joint Enabling Localized Selective Stiffening and Shape Locking in Vine-Inspired Robots

Apr 17, 2026

This work addresses the challenges of deploying vine-like robots in free space, which are hindered by low axial stiffness, weak load-bearing capacity, and an inability to maintain curved configurations. To overcome these limitations, the authors propose a reconfigurable pneumatic joint (RPJ) architecture featuring symmetrically arranged pneumatic chamber modules distributed along the robot’s body. This design enables localized, pressure-tunable stiffness and shape locking without impeding continuous growth, while integrating tendon-driven steering and a compact aerial inversion base. Notably, the approach decouples global compliance from local rigidity for the first time in a continuously growing soft robot. Experimental results demonstrate that the system can execute inversions under low pressure, significantly suppress gravity-induced deflection, support cascaded retraction, and reliably transport payloads up to 200 grams.

0 citationsRead paper

From Labels to Facets: Building a Taxonomically Enriched Turkish Learner Corpus

Jan 30, 2026

This study addresses the limitations of existing learner corpora, which typically employ flat annotation schemes that hinder the disentanglement of multiple linguistic dimensions and impede fine-grained analysis of error origins. To overcome this, the authors propose a linguistically grounded multidimensional taxonomy and develop a semi-automatic annotation expansion framework that transforms flat labels into rich, multifaceted structures integrating linguistic features and metadata. As a first application of this approach, they construct a Turkish learner corpus conforming to the proposed taxonomy, accompanied by detailed annotation guidelines and supporting tools to enable cross-dimensional error pattern mining. Experimental results demonstrate that the method achieves 95.86% accuracy at the facet level, substantially enhancing both query capabilities and analytical depth of the corpus.

0 citationsRead paper