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2023 Future Science Prize
2023未来科学大奖
The Future Science Prize announced eight laureates for 2023 on Wednesday in Beijing.The Future Science Prize has announced 35 winners since it was founded in 2016.
2023未来科学大奖8月16日揭晓，共8位科学家获奖。2016年创设至今,未来科学大奖共评选出35位获奖者。
At present, a new round of sci-tech revolution and industrial transformation is advancing by leaps and bounds, interdisciplinary integration is growing, the paradigm of scientific research is undergoing profound changes, the integration of science and technology with economic and social development is accelerating, the application cycle of basic research is significantly shortened, and international competition in basic research is moving to the forefront.To cope with international sci-tech competition, achieve greater self-reliance, foster a new development pattern, and promote high-quality development, there is an urgent need to strengthen basic research and address key technologies from the source and from the bottom.
当前，新一轮科技革命和产业变革突飞猛进，学科交叉融合不断发展，科学研究范式发生深刻变革，科学技术和经济社会发展加速渗透融合，基础研究转化周期明显缩短，国际科技竞争向基础前沿前移。应对国际科技竞争、实现高水平自立自强，推动构建新发展格局、实现高质量发展，迫切需要我们加强基础研究，从源头和底层解决关键技术问题。
We will deepen structural scientific and technological reform and reform of the system for appraisal of scientific and technological advances. We will increase investment in science and technology through diverse channels and strengthen legal protection of intellectual property rights, in order to establish a foundational system for all-around innovation. We will nurture a culture of innovation, encourage dedication to science, cultivate fine academic conduct, and foster an enabling environment for innovation. We will expand science and technology exchanges and cooperation with other countries, cultivate an internationalized environment for research, and create an open and globally-competitive innovation ecosystem.
深化科技体制改革，深化科技评价改革，加大多元化科技投入，加强知识产权法治保障，形成支持全面创新的基础制度。培育创新文化，弘扬科学家精神，涵养优良学风，营造创新氛围。扩大国际科技交流合作，加强国际化科研环境建设，形成具有全球竞争力的开放创新生态。
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相关词汇:
high-temperature superconducting materials
高温超导材料
deep residual learning
深度残差学习
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