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Yokohama National University

Academic institutionasia · jp
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Research library55linked papers
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Selected work

Representative Papers

Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems

Jul 12, 2023Annual Conference on Genetic and Evolutionary Computation

Traditional Michigan-style Learning Fuzzy Classifier Systems (LFCS) suffer from limited generalization in continuous domains due to fixed, pre-specified rule representations that cannot adapt to unknown data characteristics. To address this, we propose an adaptive rule representation mechanism featuring evolvable “fuzzy indicators”—parameters that dynamically select between crisp (hyper-rectangular) and fuzzy (triangular) membership functions, enabling online, context-aware rule-shape adaptation. This approach transcends rigid structural assumptions by unifying fuzzy logic, genetic evolution, and supervised learning within a single cohesive framework. Empirical evaluation across multiple continuous-domain benchmark tasks demonstrates that our method achieves significantly higher classification accuracy than the conventional UCS, while exhibiting superior robustness and stability under uncertainty—including noise corruption and missing values.

4 citationsRead paper

LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Jan 15, 2026

Existing test-time alignment methods either rely on trajectory-level signals or suffer from low sampling efficiency, making it challenging to balance performance and generation diversity. This work proposes LLMdoctor, a novel framework that introduces token-level reward acquisition and Trajectory-Flow Preference Optimization (TFPO). Leveraging a patient-doctor architecture, LLMdoctor employs fine-grained token-level preference signals at test time to guide a small "doctor" model in efficiently aligning a frozen large language model. By enforcing flow consistency across sub-trajectories, the method achieves precise token-by-token alignment while preserving output diversity. Experiments demonstrate that LLMdoctor significantly outperforms current test-time alignment approaches across multiple benchmarks and even surpasses full fine-tuning methods such as DPO in terms of performance.

2 citationsRead paper

Real-Time Video Prediction With Fast Video Interpolation Model and Prediction Training

Oct 27, 2024International Conference on Information Photonics

To address perceptual latency degradation in real-time video transmission, which impairs interactive user experience, this paper proposes IFRVP—a zero-latency video prediction framework. Methodologically, we design IFRNet, a lightweight convolutional architecture incorporating ELAN-based residual modules to balance accuracy and efficiency, and introduce three novel frame interpolation training paradigms specifically tailored for predictive tasks. Furthermore, we propose a mid-level feature refinement mechanism to enable end-to-end inter-frame interpolation modeling. Experimental results demonstrate that IFRVP achieves a state-of-the-art trade-off between prediction accuracy and inference speed, enabling real-time prediction at over 30 FPS and significantly reducing end-to-end perceptual latency. The source code and demonstration videos are publicly available.

1 citationsRead paper
Recent publications

Latest Papers

Quantifying Different Gains from Trade in Quality

Aug 04, 2026

This study investigates how cross-country differences in quality preferences and quality-enhancing technologies generate heterogeneous gains from trade. Embedding an endogenous quality choice mechanism into a multi-country, multi-sector general equilibrium model—extending the ANTONIADES framework—the paper combines structural estimation with counterfactual simulations to quantify the role of the quality channel in shaping the distribution of trade gains. The analysis reveals that high-income countries exhibit systematically stronger quality preferences, that omitting the quality channel substantially understates their welfare losses from trade liberalization, and that the benefits of global quality upgrading accrue disproportionately to larger economies. This work provides the first systematic identification of quality spillovers in a multi-country, multi-sector setting, offering a novel perspective for accurately evaluating the effects of trade policy.

0 citationsRead paper