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第四届中国研究生金融科技创新大赛在南京收官
Xin Lang Cai Jing· 2025-12-31 06:33
中新网北京12月31日电 (高晓烜)第四届中国研究生金融科技创新大赛近日在南京落幕。经过层层选拔, 112支晋级全国总决赛的队伍展开激烈角逐,经专家评审,最终评选出冠亚季军及其他奖项。大赛各主 办方、承办方和支持单位有关领导、业界专家、企业代表、参赛师生、行业伙伴和新闻媒体代表600余 人现场参会。 据悉,本届大赛由教育部学位管理与研究生教育司、中国人民银行科技司、全国金融专业学位研究生教 育指导委员会共同指导,中国学位与研究生教育学会和中国科协青少年科技中心联合主办,南京大学与 南京建邺区人民政府共同承办,江苏省金融学会特别支持,江苏省数字金融重点实验室、江苏省金科数 字与科技金融研究院共同协办。 赛事自7月10日启动以来,共吸引了全国226所高校和科研院所的1377支队伍、8089名师生报名参赛,彰 显了赛事品牌的强劲影响力与社会认可度。 经过四年的发展,大赛已成为中国研究生创新实践大赛中特色鲜明的品牌活动,更是全国高校金融科技 领域极具标杆性的专业赛事,为培养既懂金融理论又掌握前沿技术的复合型拔尖创新人才提供了重要平 12月28日,第四届中国研究生金融科技创新大赛在南京落幕。(主办方供图) 台。 作为金 ...
300551前实控人,操纵市场,有期徒刑六年
Shang Hai Zheng Quan Bao· 2025-12-18 13:08
12月18日晚,古鳌科技(300551)发布公告,公司近日收到公司前控股股东、实际控制人陈崇军家属送 达的山东省青岛市中级人民法院《刑事判决书》。陈崇军犯操纵证券市场罪,判处有期徒刑六年,并处 罚金人民币四百万元。 公司分别于2024年4月25日、2024年5月28日披露了《关于公司实际控制人被刑事拘留的公告》《关于公 司实际控制人被批准逮捕的公告》,陈崇军因涉嫌操纵证券市场罪于2024年4月18日被刑事拘留,同年5 月24日被逮捕。 对于公司的影响,古鳌科技表示,陈崇军为公司前任控股股东、前任实际控制人,上述判决为股东个人 行为,不会对公司生产经营产生重大不利影响。目前,公司经营情况正常。 公开履历显示,陈崇军生于1968年,为古鳌科技创始人,此前曾在相当长的一段时间内身兼公司董事 长、总经理两大要职。2021年10月,陈崇军因个人原因辞去公司总经理职务;2023年5月,古鳌科技董 事会换届,陈崇军不再担任董事,亦不再任董事长职务。此后未在公司担任具体职务。记者注意到,目 前在古鳌科技官网还能看到陈崇军本人的照片。 就在几天前,陈崇军刚刚让出古鳌科技实控人的位置。公告显示,12月12日,陈崇军与徐迎辉签订《 ...
南农大梨新品种家族集体“出道”
Ke Ji Ri Bao· 2025-07-08 02:07
Core Viewpoint - The introduction of new pear varieties, particularly "Ningli Early Dew," showcases advancements in breeding techniques aimed at enhancing taste, appearance, and cultivation efficiency in the pear industry [1][2]. Group 1: New Pear Varieties - "Ningli Early Dew" is a new pear variety that matures in late June, which is half a month earlier than traditional early-ripening pears, with a growth period of approximately 90 days from flowering to maturity [1][2]. - The variety has a fruit weight of 280-320 grams and features a small core, providing a sweet and juicy taste experience [1]. - Other new varieties presented include "Ning Early Gold," "Ning Late Green," and a red-skinned pear series, all developed by the Nanjing Agricultural University pear innovation team [1][2]. Group 2: Breeding Techniques - The breeding process for new pear varieties traditionally takes 12 to 15 years; however, the research team has implemented image recognition and machine learning technologies to accelerate this process [2]. - The development of the "Cloud Shang Hou Ji" breeding information platform has standardized data collection and improved the efficiency of new pear variety creation [2]. - The combination of hybrid breeding, bud mutation, and molecular marker selection has significantly enhanced the speed and effectiveness of new variety development [2]. Group 3: Market Impact - The newly introduced varieties cover a range of maturity periods from extremely early to mid-late, ensuring a continuous supply of fresh pears in Jiangsu from late June to early September [4].
玻色子采样用于量子AI图像识别 为现实应用打开新窗口
Ke Ji Ri Bao· 2025-06-29 23:22
Group 1 - The core viewpoint of the articles highlights the significant advancement in quantum artificial intelligence (AI) through the application of boson sampling for image recognition, marking a crucial step towards practical quantum AI applications in real-world scenarios [1][2]. - The research team from Okinawa Institute of Science and Technology successfully utilized a quantum AI system for image classification using only three photons and a linear optical network, demonstrating the potential for low-energy, hybrid quantum methods [1][2]. - The developed quantum AI architecture compresses grayscale image data into single-photon quantum states, which are then processed through a complex optical network to create high-dimensional patterns, achieving superior accuracy compared to traditional machine learning methods [2]. Group 2 - The principle of boson sampling is explained through an analogy of a "marble board" game, illustrating how photons, unlike marbles, exhibit wave-like behavior that leads to complex interference patterns that are difficult to predict even for supercomputers [2]. - The experimental results indicate that the quantum AI system outperformed traditional machine learning approaches across all tested image datasets, showcasing its effectiveness in image recognition tasks [2].