TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
本文提出TetrisCNN，一种具有不同形状滤波器的卷积架构，用于从实验量子模拟器数据中检测物质相，并通过可解释的潜在表示揭示其属性。
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Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework
本文通过深度学习YOLOv11框架解决双星系核与前景恒星等偶然叠加难以区分的问题，提高了双星系核候选体识别的准确性和数量。
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The ethics of artificial intelligence in the life sciences: Universality, cultural diversity and an architecture of care
This work addresses the unique ethical challenges posed by the application of artificial intelligence in the life sciences, arguing for a governance framework that moves beyond traditional “constraint”-based paradigms. Integrating insights from neuroscience, cognitive psychology, and AI ethics, the study proposes a novel “nurturing”-oriented governance model grounded in the Global Neuronal Workspace theory and a non-maximizing affective reward mechanism. This approach emulates the human cognitive-affective cycle of “desire–affection–satisfaction,” balancing ethical universality with moral diversity. The framework introduces a low-computational-cost architecture alongside a care-centered institutional design, offering both a theoretical foundation and a set of critical open questions to guide the ethical development and societal integration of AI systems in the life sciences.
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TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
本文提出TetrisCNN，一种具有不同形状滤波器的卷积架构，用于从实验量子模拟器数据中检测物质相，并通过可解释的潜在表示揭示其属性。
0 citationsRead paper
Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework
本文通过深度学习YOLOv11框架解决双星系核与前景恒星等偶然叠加难以区分的问题，提高了双星系核候选体识别的准确性和数量。
0 citationsRead paper
The ethics of artificial intelligence in the life sciences: Universality, cultural diversity and an architecture of care
This work addresses the unique ethical challenges posed by the application of artificial intelligence in the life sciences, arguing for a governance framework that moves beyond traditional “constraint”-based paradigms. Integrating insights from neuroscience, cognitive psychology, and AI ethics, the study proposes a novel “nurturing”-oriented governance model grounded in the Global Neuronal Workspace theory and a non-maximizing affective reward mechanism. This approach emulates the human cognitive-affective cycle of “desire–affection–satisfaction,” balancing ethical universality with moral diversity. The framework introduces a low-computational-cost architecture alongside a care-centered institutional design, offering both a theoretical foundation and a set of critical open questions to guide the ethical development and societal integration of AI systems in the life sciences.
0 citationsRead paper