Phase Transition in Binary Compressed Sensing via Annealing with Adaptive Regularization
研究通过结合系统参数搜索与随机森林回归的方法，解决了二进制压缩感知中正则化选择问题，优化了模拟退火的恢复性能。
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Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents
本文探讨了通过整合大型语言模型、知识库和推理能力来构建下一代AI代理，以实现通用具身智能，并分析了面临的五大挑战。
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Semi-Automatic Flute Robot and Its Acoustic Sensing
This work proposes a semi-autonomous flute-playing robot that addresses the challenge of simultaneously managing complex fingering and the embouchure adjustments required for low-register airflow redirection—tasks that existing systems struggle to synchronize. The system automates full-range fingering across C4–C7 via 14 servos driven by MIDI input and mechanically rotates the headjoint by 22 degrees in the low register to assist airflow angulation, enabling human performers to supply only a steady airstream. Leveraging a wire-driven transmission, rack-and-pinion servo mechanisms, a programmable MIDI response module, and an acoustic analysis unit, the design achieves, for the first time, fully automated fingering and low-register airflow assistance without modifying the instrument or relying on manual embouchure control. Experimental results show fingering response times ≤77.50 ms, headjoint rotation within ≤40.00 ms, and a significant improvement in second-harmonic intensity difference (ΔSPL), confirming the efficacy of the airflow redirection mechanism.
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A Formal Descriptive Language for Learning Dynamics: A Five-Layer Structural Coordinate System
Existing learning modeling approaches fragment critical constructs—such as cognitive load, comprehension evolution, and subjective evaluation—lacking a unified, scalable formal framework. Method: This paper introduces a five-layer formal description language for learning dynamics, grounded in state variables, hierarchical mappings, and separation of concerns. It implements multi-faceted co-characterization through explicit structural mechanisms. Contribution/Results: We propose the novel “hierarchical responsibility separation” architecture, explicitly decoupling load generation, comprehension transformation, observation, and evaluation. Cognitive load is redefined as an interactional quantity between internal and external factors; subjective evaluation is abstracted as a minimal regulatory interface. The framework imposes no prior assumptions on functional forms or optimization objectives. Leveraging formal syntax, structured coordinates, and multi-level modeling, it provides a theoretically rigorous yet empirically compatible foundational description layer for human learning analysis and AI-driven adaptive educational systems.
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Motional representation; the ability to predict odor characters using molecular vibrations
Predicting odor perception from molecular structure remains a fundamental challenge. This work introduces CNN_vib, a novel convolutional neural network regression model that systematically investigates molecular vibrational spectra—as opposed to conventional static structural representations—as a paradigm for odor prediction. We construct a parameterized vibrational spectral representation and benchmark it against molecular fingerprints and logistic regression across multiple odor descriptors (e.g., “sweet,” “pungent,” “woody”). Results demonstrate that vibrational spectra achieve predictive performance comparable to or exceeding that of molecular fingerprints. Crucially, we show that molecular dynamic features—particularly low-frequency vibrational modes—encode essential olfactory information sufficient for odor prediction in isolation. CNN_vib significantly enhances the modeling capacity for vibrational spectral data. This study breaks the long-standing reliance on static molecular structures in computational olfaction, establishing vibrational spectroscopy as a theoretically grounded and technically viable foundation for odor prediction.
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Phase Transition in Binary Compressed Sensing via Annealing with Adaptive Regularization
研究通过结合系统参数搜索与随机森林回归的方法，解决了二进制压缩感知中正则化选择问题，优化了模拟退火的恢复性能。
0 citationsRead paper
Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents
本文探讨了通过整合大型语言模型、知识库和推理能力来构建下一代AI代理，以实现通用具身智能，并分析了面临的五大挑战。
0 citationsRead paper
Semi-Automatic Flute Robot and Its Acoustic Sensing
This work proposes a semi-autonomous flute-playing robot that addresses the challenge of simultaneously managing complex fingering and the embouchure adjustments required for low-register airflow redirection—tasks that existing systems struggle to synchronize. The system automates full-range fingering across C4–C7 via 14 servos driven by MIDI input and mechanically rotates the headjoint by 22 degrees in the low register to assist airflow angulation, enabling human performers to supply only a steady airstream. Leveraging a wire-driven transmission, rack-and-pinion servo mechanisms, a programmable MIDI response module, and an acoustic analysis unit, the design achieves, for the first time, fully automated fingering and low-register airflow assistance without modifying the instrument or relying on manual embouchure control. Experimental results show fingering response times ≤77.50 ms, headjoint rotation within ≤40.00 ms, and a significant improvement in second-harmonic intensity difference (ΔSPL), confirming the efficacy of the airflow redirection mechanism.
0 citationsRead paper
A Formal Descriptive Language for Learning Dynamics: A Five-Layer Structural Coordinate System
Existing learning modeling approaches fragment critical constructs—such as cognitive load, comprehension evolution, and subjective evaluation—lacking a unified, scalable formal framework. Method: This paper introduces a five-layer formal description language for learning dynamics, grounded in state variables, hierarchical mappings, and separation of concerns. It implements multi-faceted co-characterization through explicit structural mechanisms. Contribution/Results: We propose the novel “hierarchical responsibility separation” architecture, explicitly decoupling load generation, comprehension transformation, observation, and evaluation. Cognitive load is redefined as an interactional quantity between internal and external factors; subjective evaluation is abstracted as a minimal regulatory interface. The framework imposes no prior assumptions on functional forms or optimization objectives. Leveraging formal syntax, structured coordinates, and multi-level modeling, it provides a theoretically rigorous yet empirically compatible foundational description layer for human learning analysis and AI-driven adaptive educational systems.
0 citationsRead paper
Motional representation; the ability to predict odor characters using molecular vibrations
Predicting odor perception from molecular structure remains a fundamental challenge. This work introduces CNN_vib, a novel convolutional neural network regression model that systematically investigates molecular vibrational spectra—as opposed to conventional static structural representations—as a paradigm for odor prediction. We construct a parameterized vibrational spectral representation and benchmark it against molecular fingerprints and logistic regression across multiple odor descriptors (e.g., “sweet,” “pungent,” “woody”). Results demonstrate that vibrational spectra achieve predictive performance comparable to or exceeding that of molecular fingerprints. Crucially, we show that molecular dynamic features—particularly low-frequency vibrational modes—encode essential olfactory information sufficient for odor prediction in isolation. CNN_vib significantly enhances the modeling capacity for vibrational spectral data. This study breaks the long-standing reliance on static molecular structures in computational olfaction, establishing vibrational spectroscopy as a theoretically grounded and technically viable foundation for odor prediction.
0 citationsRead paper