Core Points - The first IEEE E-CARGO and its Applications International Symposium and the third IEEE E-CARGO and its Applications International Summer School were held at Guangdong University of Technology, focusing on expanding the application of the E-CARGO model and Role-Based Collaboration (RBC) methodology [1][8] - The event attracted over 170 participants, including experts and scholars from more than 20 universities across China, Canada, the United States, Australia, and Japan [1] Group 1 - The opening ceremony featured a presentation by the Vice President of Guangdong University of Technology, highlighting the university's development history, recent high-quality growth, and future plans to become a high-level technical teacher training university rooted in the Bay Area and serving globally [3] - Experts from various institutions delivered speeches on cutting-edge topics such as artificial intelligence, low-altitude economy, robotics, smart education, smart healthcare, digital twins, autonomous intelligence, brain-like intelligence, multi-agent systems, and cyber-physical social systems [3] Group 2 - A special forum for young scientists was organized, featuring presentations from notable scholars, including recipients of the National Excellent Youth Fund and EU Marie Curie Scholars [5] - The symposium included a Best Paper Award selection process, resulting in the recognition of six outstanding papers, and discussions on the development and application of the E-CARGO model [6] Group 3 - The international summer school, held over three days, included seven tutorial sessions focused on the theme of "E-CARGO and its Applications," attracting over 40 participants from nearly 20 universities [8] - The event was organized by the IEEE Systems, Man, and Cybernetics Society and Guangdong University of Technology, with support from various international and local academic institutions [8]

Core Insights - Chen Xiang, a PhD student at Nanjing University of Science and Technology, focuses on computer vision enhancement technology in harsh environments, having published 18 papers and received over 900 citations on Google Scholar [2][4] - He has been recognized as a "Huawei Terminal Camera Academic Star," a title awarded to only 14 individuals globally, and has participated in Huawei's talent selection program [10] Group 1: Academic Achievements - Chen has consistently ranked first in his program for two consecutive years and has received multiple scholarships and awards, including the National Scholarship and the CASC Public Welfare Scholarship [2][4] - He has been invited to serve as a chair for the International Joint Conference on Neural Networks (IJCNN) and has been a reviewer for over 40 international SCI journals and conferences [4] Group 2: Research Contributions - Chen is involved in a National Natural Science Foundation project focused on image and video enhancement for ground targets in adverse conditions, addressing challenges in battlefield environments [5] - He has proposed an all-weather visual imaging enhancement technology and collaborates with military and defense organizations to innovate reconnaissance methods using AI [5] Group 3: Community Engagement - Chen actively participates in academic conferences, sharing his research with over 48,000 attendees across various platforms, and has received awards for his presentations [9] - He emphasizes the importance of balancing hard work with visionary thinking, aiming to contribute to national development through his research [9]

Core Viewpoint - The article emphasizes the significant impact of artificial intelligence (AI), particularly generative AI, on various industries and the increasing demand for AI-related content and research. Springer Nature is actively embracing these trends by publishing insightful research and developing AI tools and services to enhance academic and industrial collaboration [1]. Group 1: AI Resources and Services - Springer Nature is set to launch a new collection of AI books in 2025, covering topics from machine learning algorithms to neural network research impacting healthcare. This collection aims to provide a comprehensive resource for researchers, educators, and professionals [2]. - The AI book collection includes diverse resources such as monographs, conference proceedings, textbooks, and manuals, catering to different levels of readers and facilitating a deeper understanding of the rapidly evolving AI field [2]. Group 2: Core Research Areas - The AI book collection focuses on core research areas, including machine learning, deep learning, natural language processing, computer vision, and AI ethics, among others [3]. Group 3: AI-Driven Services - Springer Nature offers AI-driven services, including text and data mining (TDM) tools that help researchers extract valuable information from large datasets, enhancing their ability to discover patterns that traditional analysis may miss [46]. - The Methods Muse platform simplifies experimental design and optimization for life sciences research, integrating with protocols.io to streamline the research process [50]. - Nature Research Intelligence provides data-driven insights to help academic, governmental, and corporate researchers make informed decisions, track research trends, and enhance their academic impact [53][55].

Core Insights - The article emphasizes the rapid transformation of the world through artificial intelligence and programming technologies, highlighting the significance of Auckland University's computer science undergraduate program as a platform for students passionate about these fields [1]. Group 1: Program Advantages - Auckland University's computer science program boasts exceptional academic resources and a strong faculty, with the department recognized internationally for its research in artificial intelligence, data science, and cybersecurity [3]. - The faculty comprises professors from around the globe who have made significant academic contributions and maintain close collaborations with major tech companies like Google and Microsoft, integrating the latest industry trends into the curriculum [3]. - The university provides advanced learning resources, including high-performance computing clusters and virtual reality equipment, facilitating complex programming experiments and AI project development [3]. - Partnerships with numerous tech companies offer students internship and employment opportunities, allowing them to engage with real-world business projects during their studies [3]. Group 2: Application Requirements - Applicants to the computer science undergraduate program must meet specific academic and language criteria, with international students typically required to achieve an average high school score of over 80%, particularly excelling in mathematics and physics [4]. - For Chinese students, the Gaokao score is a critical reference, generally requiring a score above the provincial first-tier line; alternative qualifications like A-Level or IB scores are also accepted [4]. - Language proficiency is essential, with a minimum IELTS score of 6.5 (no individual score below 6.0) or a TOEFL score of 90 (with writing no less than 21) required for admission [4]. Group 3: Curriculum Content - The curriculum is diverse and designed to build a solid theoretical foundation and practical innovation skills, starting with introductory courses in computer science, programming basics (Python and Java), and discrete mathematics in the first year [6]. - As students progress, they encounter more specialized courses such as data structures and algorithms, computer systems principles, and database systems, deepening their understanding of computer science fundamentals [6]. - Elective courses in artificial intelligence, machine learning, computer graphics, and cybersecurity allow students to explore cutting-edge areas of interest, while project-based courses enable teamwork and problem-solving through real programming projects [6].

白交 发自 凹非寺 量子位 | 公众号 QbitAI 一个计算机领域的著名问题，在停滞50年之后终于有了进展。 MIT科学家威廉姆斯一次偶然发现：证明内存比大家认为的更强大。在所有可以想象的计算中， 少量的内存与大量的时间一样有价值 。 时间和内存（空间）是计算中最基本的两种资源 ，每个算法都需要一些时间来运行，并且在运行时需要一些空间来存储数据。迄今为止，已 知的算法里所需的空间与其运行时间基本上都成正比，研究人员认为没有更好的办法。 但现在威廉姆斯证明，存在一个数学程序，可以将任何算法转换成「占用更少空间」的形式。 由于想法过于不可思议，他当时第一想法是： 大概是自己疯了吧 。 于是开始着手证明自己错了，但想了几个小时也没找出任何瑕疵：没准万一真就自己对了呢。 经过几个月的整理和推敲，最终将成果po到网上，没想到收获大家一种好评。 一华盛顿大学科学家表示： 这是一个相当惊人的结果，也是一个巨大的进步 。 困扰计算机科学家的半世纪难题 先来看看这是一个什么问题。 如果用大白话来讲，这个问题其实源于我们一种直觉： 你可以重复使用空间，但不能重复使用时间 。 算法可以反复使用同一小块内存，而时间却不那么宽容，一 ...