作者 | 刘古月课题组 在大规模智能计算和未来网络快速演进的背景下，业界迫切需要更高带宽、更低成本、更智能化和更安全的网络基础设施，以支撑大语言模型训练、生 产网络运维与数据中心安全等多样化场景。 围绕这些需求，北京大学计算机学院网络与高能效计算研究所刘古月助理教授课题组长期聚焦于智能计算时代的网络体系结构、运维智能化和安全防护 研究，并从不同层面呼应行业痛点，形成互补合力，来推动新一代智能网络系统发展。 在今年的 ACM SIGCOMM 2025 上，该课题组共有 5 篇高水平论文（4 篇长文 +1 篇短文）入选，成为本年度 SIGCOMM 全球发文数量最多的高校课 题组 。据悉，SIGCOMM 作为计算机网络系统领域历史最悠久、最具权威性的学术会议，以严苛的录用标准著称。今年共有 461 篇投稿，录用仅 74 篇，录取率仅为 16.1%。这五篇论文的研究内容大概如下： 基于光交换收发器的大语言模型数据中心规模高带宽域架构 大语言模型（LLM）的训练依赖多维并行，其中高带宽域（High-Bandwidth Domain，HBD）是支撑张量并行等通信密集型并行方式的关键。 然而，现有 HBD 架构在可扩展性、 ...

I might have some overlap with focus. So yeah, but I'm going to give my insights as a computer guy. So a few months ago, exactly six months, I joined a research project which like mo most work today required some help from AI.However, the hope that I got from AI in this project isn't a typical writing of code that does exact sense. It reshaped how I see AI not just as a tool but as a collaborator. It challenged my perception of the role of AI in academia and even in society and has got me thinking ever sinc ...

曾几何时计算机科学还是人人追捧的"金饭碗"，高薪就业代名词，而哲学则被嘲讽为"毕业即失业"的典 型代表。然而时代的风向说变就变，最新就业市场数据显示计算机科学毕业生失业率飙升至百分之六点 一，几乎是哲学专业三点二失业率的两倍。那些曾经嘲笑哲学找不到工作的人突然发现，这个世界已经 开始奖励深度思考者，而曾经稳坐神坛的码农们正在经历行业寒冬的残酷洗礼。这场就业市场的惊天逆 转背后，究竟是短期波动还是长期趋势？哪些力量在悄悄重塑我们的职业价值观？ 计算机行业的黄金时代似乎正遭遇前所未有的挑战。过去十多年里计算机科学被奉为"最稳的选择"，高 薪有前景容易就业成为无数学生和家长的坚定信念。然而就在最近这段时间，这个神话突然破碎。曾经 引以为傲的失业率数据已经飙到令人担忧的水平，所谓的"铁饭碗"正在投资者眼前裂开。更让人意想不 到的是，这种就业困境并非个别现象，而是整个专业的系统性调整，从麻省理工学院斯坦福到卡内基梅 隆伯克利等精英院校的毕业生，进入科技大厂的比例从百分之二十五骤降至百分之十一到十二，整体就 业率也从八成跌到七成，在短短两年时间内实现直线腰斩。 那些曾经自信满满的计算机毕业生突然发现，自己不仅要与同龄人竞 ...

允中 发自 凹非寺 量子位 | 公众号 QbitAI 2025中国计算机大会（CNCC2025）定于10月22日至25日在黑龙江省哈尔滨市举办，大会主题为"数智赋能，无限可能"。 9月12日，CNCC2025新闻发布会在北京成功举行。中国青年报、中国科学报、人民网、南方都市报、央广网、财经、搜狐科技、网易科技、 智东西等媒体及机构出席发布会。 （CNCC2025指导委员会主席、CCF理事长，中国工程院院士，中国科学院计算所研究员孙凝晖） 大会副主席、程序委员会主席 於志文 教授就大会报告及论坛组织情况作了说明。本届大会紧扣"数智赋能，无限可能"主题，结合地域特色， 共设置19场特邀报告、3场大会论坛及154场专题论坛。已邀请的特邀报告嘉宾包括欧洲科学院院士，意大利博洛尼亚大学教授 Sumi Helal ；香港浸会大学教授，美国国家工程院院士 C. Mohan ；CCF荣誉会员、东京大学教授 喜连川优 ；CCF会士、副理事长，中国科学院院 士，清华大学教授 胡事民 ；中国科学院院士，西北工业大学教授 黄维 ；CCF会士，中国科学院院士，哈尔滨工业大学教授 李惠 ；CCF会 士，中国工程院院士，北京航空航天大学教 ...

Core Insights - Yoshua Bengio has been recognized as the world's most cited scientist across all fields, achieving a total citation count of over 973,655, with 698,008 citations in the last five years [4][5][6] - The top 10 list of highly cited scientists includes prominent figures in computer science, with four of them being key contributors to the field of artificial intelligence [7][8] Group 1: Yoshua Bengio - Yoshua Bengio is a Turing Award winner and a leading figure in deep learning, holding the top position in citation metrics globally [2][4] - His significant contributions include foundational work in machine learning and artificial intelligence, with a remarkable citation record that reflects his influence in the field [5][6] Group 2: Other Top Cited Scientists - Geoffrey Hinton ranks second globally, with a total citation count of 952,643 and over 577,970 citations in the last five years, recognized for his pivotal role in deep neural networks [8][9][10] - Kaiming He, known for developing deep residual networks (ResNets), ranks fifth with a total citation count of 733,529, and 617,328 citations in the last five years [13][14][15] - Ilya Sutskever, co-founder of OpenAI, has a total citation count of 670,000, with 500,000 citations in the last five years, contributing significantly to advancements in AI [16][18] Group 3: Citation Ranking Methodology - The AD Scientific Index ranks scientists based on total citation counts and citations over the last five years, evaluating their academic performance and impact [26][29] - The ranking system incorporates various metrics, including H-index and i10-index, to provide a comprehensive assessment of researchers' contributions [31][32]

Core Viewpoint - The article discusses the introduction of GUAVA, a novel framework developed by researchers from Tsinghua University and IDEA, which enables the creation of upper-body 3D avatars from a single image in just 0.1 seconds, without the need for multi-view videos or individual training [1][5][36]. Summary by Sections Introduction - GUAVA is recognized for its ability to create realistic and expressive upper-body avatars, which is valuable in fields such as film, gaming, and virtual meetings [4]. Challenges and Innovations - Creating avatars from a single image has been a significant challenge, particularly in achieving real-time rendering and ease of creation. GUAVA addresses these challenges by allowing inference reconstruction in seconds and supporting real-time animation [5][6]. Methodology - GUAVA introduces the Expressive Human Model (EHM) to enhance facial expression capture, overcoming limitations of existing models [12][36]. - The framework employs a two-branch model for avatar reconstruction, combining a "template Gaussian" and a "UV Gaussian" to maintain geometric structure while capturing detailed textures [14][15]. - Real-time animation is achieved by deforming the Ubody Gaussian based on new pose parameters, followed by optimization through a neural refiner to enhance rendering quality [16][17]. Experimental Results - The dataset for experiments included over 620,000 frames, focusing on upper-body videos, with evaluations based on ID consistency, efficiency, and viewpoint control [18][20]. - GUAVA outperformed existing 2D and 3D methods in rendering quality and efficiency, achieving approximately 50 FPS and a reconstruction time of around 0.1 seconds [22][23]. - In self-reenactment scenarios, GUAVA showed superior performance across all metrics compared to 2D methods, while also maintaining ID consistency in cross-reenactment scenarios [22][25]. Conclusion - GUAVA represents a significant advancement in the field of 3D avatar reconstruction, demonstrating improved rendering quality and efficiency over existing methods, with a reconstruction time of approximately 0.1 seconds and support for real-time animation [36][37].

Child Development & Education - Early childhood experiences significantly impact future potential [4] - Opportunities in arts, science, math, and writing can spark passion and future careers [5] - Encouraging curiosity and exploration in children is crucial for development [3][11] Creativity & Innovation - Creativity evolves throughout life, adapting to different stages and challenges [6][7] - Engaging in creative activities is linked to higher life satisfaction [7] - Even seemingly uncreative jobs require unique forms of creativity [10] Job Market & Career Trends - Computer science fields are projected to grow by 26% from 2023 to 2033 [9] - Creative thinking is increasingly valued in various professions [8][9] - Diverse career paths benefit from creative problem-solving skills [10]

Core Insights - The article discusses the acceptance of 10 papers from a laboratory at the 20th ICCV International Conference on Computer Vision, highlighting advancements in 3D vision and related technologies [25]. Paper Summaries Paper 1: Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds - This paper addresses domain generalization in 3D scene segmentation, proposing a framework that couples geometric embedding with semantic learning to enhance model generalization [1]. Paper 2: Hierarchical Variational Test-Time Prompt Generation for Zero-Shot Generalization - The authors introduce a hierarchical variational method for dynamic prompt generation during inference, significantly improving the zero-shot generalization capabilities of visual language models [3]. Paper 3: Knowledge-Guided Part Segmentation - A new framework is proposed that utilizes structural knowledge to enhance the segmentation of fine-grained object parts, improving understanding of complex structures [5][6]. Paper 4: TopicGeo: An Efficient Unified Framework for Geolocation - TopicGeo presents a unified framework for geolocation that improves computational efficiency and accuracy by directly matching query images with reference images [9]. Paper 5: Vision-Language Interactive Relation Mining for Open-Vocabulary Scene Graph Generation - This paper explores a model that enhances the understanding of relationships in open-vocabulary scene graph generation through multimodal interaction learning [11]. Paper 6: VGMamba: Attribute-to-Location Clue Reasoning for Quantity-Agnostic 3D Visual Grounding - The authors propose a mechanism that combines attribute and spatial information to improve the accuracy of 3D visual grounding tasks [13]. Paper 7: Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels - A new metric called Dynamic Center Distance is introduced to enhance the learning process in the presence of noisy labels by focusing on hard samples [15]. Paper 8: Learning Separable Fine-Grained Representation via Dendrogram Construction from Coarse Labels for Fine-grained Visual Recognition - The paper presents a method for learning fine-grained representations from coarse labels without predefined category numbers, enhancing adaptability to dynamic semantic structures [17]. Paper 9: Category-Specific Selective Feature Enhancement for Long-Tailed Multi-Label Image Classification - This research addresses the issue of label imbalance in multi-label image classification by enhancing feature sensitivity for underrepresented categories [19]. Paper 10: Partially Matching Submap Helps: Uncertainty Modeling and Propagation for Text to Point Cloud Localization - The authors redefine the task of text to point cloud localization by allowing partial spatial matches, improving the model's ability to handle real-world ambiguities [21].

Talent Acquisition & Retention - Recommends offering significant tax benefits, such as five years tax-free or no taxation on stock options, to attract computer science graduates and tech professionals to relocate to Europe [1] - Suggests leveraging Europe's attractiveness as a desirable place to live to attract talent to London, Berlin, Munich, and other cities [2] Competitive Landscape - Notes that Europe faces challenges in competing with less capital, a more scattered landscape, more bureaucracy, and less talent [1] - Argues that solving the talent shortage is crucial for Europe's success [2]

Core Viewpoint - The article discusses a groundbreaking research by Ryan Williams that challenges the long-held belief in computer science regarding the relationship between time and space in algorithm execution, suggesting that a small amount of computational memory is theoretically more valuable than a large amount of computational time [1][3]. Group 1: Historical Context - In 1965, Juris Hartmanis and Richard Stearns established rigorous mathematical definitions for "time" and "space," providing a common language for researchers to categorize problems into complexity classes [5][6]. - The complexity class P includes problems solvable in reasonable time, while PSPACE includes problems solvable with a reasonable amount of space, with researchers believing PSPACE is significantly larger than P [7][8]. Group 2: Breakthrough in Complexity Theory - For 50 years, researchers struggled to prove that PSPACE is strictly larger than P, facing a fundamental barrier due to the limitations of previous simulation methods [8][9]. - In 2023, James Cook and Ian Mertz overturned a long-standing assumption about memory usage in algorithms, leading to a new algorithm that could solve the tree evaluation problem with significantly less space than previously thought [10][12]. Group 3: Williams' Revolutionary Approach - Ryan Williams recognized that the new algorithm by Cook and Mertz could serve as a universal space compression tool, allowing for the design of a new simulation mechanism that links time and space complexity more effectively [14][15]. - Williams' method involves breaking down the computation process into blocks and transforming it into a tree evaluation problem, optimizing the space complexity to O(√t log t), where t is the total computation time [16].