Selected work

Representative Papers

Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

Sep 05, 2026International Conference on Pattern Recognition

This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.

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Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes

Jul 06, 2026

This work addresses the challenge of non-convergence in quantum convolutional neural networks (QCNNs) on current noisy quantum devices due to the absence of efficient error correction. For the first time, bivariate bicycle (BB) codes—characterized by high thresholds, constant encoding rates, and linear code distance—are integrated into QCNNs, with a distance-4 BB code employed to achieve low-overhead quantum error correction. Simulation results under realistic noisy hardware models demonstrate that this approach substantially reduces resource overhead and effectively resolves the convergence failure observed in unprotected four-qubit QCNNs, thereby significantly enhancing their learning performance and practical viability.

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General Equilibrium Effects of Carbon Offsets

Jun 24, 2026

This study addresses the uncertainty surrounding the real-world impacts of carbon offset policies on aggregate emissions and welfare, which stems in part from conventional carbon accounting metrics’ inability to capture general equilibrium spillovers. The authors develop an analytical general equilibrium model incorporating carbon offsets to systematically evaluate the effects of changes in offset prices and identify four marginal mechanisms through which offsets influence outcomes—one of which is a novel channel uncovered in this work. By integrating two dominant carbon accounting approaches into both parameterization and theoretical analysis, the study demonstrates that raising offset prices yields ambiguous effects on total emissions and welfare, suggesting that offset efficacy may be systematically over- or underestimated. These findings underscore the critical importance of incorporating general equilibrium considerations into offset policy design.

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Approaching human parity in the quality of automated organoid image segmentation

May 04, 2026

Existing methods struggle to achieve high-accuracy and consistent automatic segmentation of organoid images across varying experimental conditions. This work proposes a hybrid approach that integrates the general-purpose vision foundation model Segment Anything Model (SAM) with domain-specific segmentation tools, marking the first application of such a combined framework for automated measurement of size and morphology in pluripotent stem cell–derived spheroids. The method delivers stable and accurate segmentation across the majority of tested images, achieving performance on par with or approaching inter-human annotator agreement. This advancement significantly enhances the automation and reliability of organoid image analysis, offering a robust solution for quantitative phenotypic assessment in organoid-based research.

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Minimax estimation for Varying Coefficient Model via Laguerre Series

Mar 09, 2026

This study addresses the estimation and inference of functional coefficients in varying-coefficient models under Laguerre–Sobolev spaces. By approximating the functional coefficients via truncated Laguerre series and estimating the empirical coefficients through least squares, the proposed method achieves, for the first time in this function space, the minimax optimal rate of convergence. Furthermore, the asymptotic normality of the estimator is established, providing a theoretical foundation for constructing pointwise confidence intervals and conducting hypothesis tests. Numerical simulations demonstrate strong finite-sample performance, and empirical analysis shows that the method outperforms existing approaches, offering both theoretical optimality and practical effectiveness.

0 citationsRead paper

Recent publications

Latest Papers

Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

Sep 05, 2026International Conference on Pattern Recognition

This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.

0 citationsRead paper

Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes

Jul 06, 2026

This work addresses the challenge of non-convergence in quantum convolutional neural networks (QCNNs) on current noisy quantum devices due to the absence of efficient error correction. For the first time, bivariate bicycle (BB) codes—characterized by high thresholds, constant encoding rates, and linear code distance—are integrated into QCNNs, with a distance-4 BB code employed to achieve low-overhead quantum error correction. Simulation results under realistic noisy hardware models demonstrate that this approach substantially reduces resource overhead and effectively resolves the convergence failure observed in unprotected four-qubit QCNNs, thereby significantly enhancing their learning performance and practical viability.

0 citationsRead paper

General Equilibrium Effects of Carbon Offsets

Jun 24, 2026

This study addresses the uncertainty surrounding the real-world impacts of carbon offset policies on aggregate emissions and welfare, which stems in part from conventional carbon accounting metrics’ inability to capture general equilibrium spillovers. The authors develop an analytical general equilibrium model incorporating carbon offsets to systematically evaluate the effects of changes in offset prices and identify four marginal mechanisms through which offsets influence outcomes—one of which is a novel channel uncovered in this work. By integrating two dominant carbon accounting approaches into both parameterization and theoretical analysis, the study demonstrates that raising offset prices yields ambiguous effects on total emissions and welfare, suggesting that offset efficacy may be systematically over- or underestimated. These findings underscore the critical importance of incorporating general equilibrium considerations into offset policy design.

0 citationsRead paper

Approaching human parity in the quality of automated organoid image segmentation

May 04, 2026

Existing methods struggle to achieve high-accuracy and consistent automatic segmentation of organoid images across varying experimental conditions. This work proposes a hybrid approach that integrates the general-purpose vision foundation model Segment Anything Model (SAM) with domain-specific segmentation tools, marking the first application of such a combined framework for automated measurement of size and morphology in pluripotent stem cell–derived spheroids. The method delivers stable and accurate segmentation across the majority of tested images, achieving performance on par with or approaching inter-human annotator agreement. This advancement significantly enhances the automation and reliability of organoid image analysis, offering a robust solution for quantitative phenotypic assessment in organoid-based research.

0 citationsRead paper

Minimax estimation for Varying Coefficient Model via Laguerre Series

Mar 09, 2026

This study addresses the estimation and inference of functional coefficients in varying-coefficient models under Laguerre–Sobolev spaces. By approximating the functional coefficients via truncated Laguerre series and estimating the empirical coefficients through least squares, the proposed method achieves, for the first time in this function space, the minimax optimal rate of convergence. Furthermore, the asymptotic normality of the estimator is established, providing a theoretical foundation for constructing pointwise confidence intervals and conducting hypothesis tests. Numerical simulations demonstrate strong finite-sample performance, and empirical analysis shows that the method outperforms existing approaches, offering both theoretical optimality and practical effectiveness.

0 citationsRead paper