读研想少走弯路、快速出成果？靠自己瞎摸索费时间费精力还没结果，找个厉害的榜样"抄作业"，才是最 直接的办法。 导师介绍 毕业于知名计算机名校。曾在多家公司担任算法研究员，并进行计算机视觉，高效模型压缩算法，多模态 大语言模型的研究，包括模型量化，剪枝，蒸馏，编译以及高效稀疏化训练与推理。 博士期间研究方向聚焦为计算机视觉，高效的深度学习训练和推理方法，大语言模型轻量化与高效微调技 术。 这套路看着"功利"，但真能让你在科研路上跑快点，别人还在绕小道，你已经上了高速。 厉害的榜样通常 来说，就是那些论文专利一大堆的导师学长学姐，但苦于和这些榜样搭不上话， 现在如何让入场甩开同 行，别人摸路你超车？ 自动驾驶之心联合业内知名LLM/MLLM方向学者推出了1v6指导小班课。从模型理论到代码实践， 业内大 牛手把手带走科研全流程，帮助大家形成自己的知识体系， 掌握LLM/MLLM论文的算法设计及创新思路。 扫码免费咨询 【科研成果】 在国际顶级会议CVPR,ICCV, EMNLP等发表十余篇论文， 并担任CVPR,ICCV,ECCV,ICML,ICLR, NeurIPS 等重要会议和期刊的审稿人。多项发明专利，已经指 ...

Core Viewpoint - The article discusses the illegal ticket scalping activities surrounding a popular museum in Shanghai, highlighting how a group exploited system vulnerabilities to purchase tickets in bulk and resell them at inflated prices, significantly impacting regular visitors' ability to buy tickets at official prices [2][10][37]. Group 1: Ticket Scalping Operations - A ticket scalping gang utilized technical means to infiltrate a museum's ticketing system, leading to tickets being sold out within minutes of release [2][10]. - The gang's operations involved using a ticket scalping software that allowed them to bypass normal purchasing restrictions, enabling them to secure tickets before the general public [16][22]. - The gang's activities resulted in the sale of over 9,000 tickets, with significant profits made through partnerships with travel agencies that sold tickets at 7 to 10 times the original price [24][35]. Group 2: Financial Implications - The original ticket prices were 30 yuan for adults, while the scalped tickets were sold for 200 to 300 yuan, leading to a substantial markup [6][35]. - The gang sold tickets to travel agencies at prices between 50 to 70 yuan, earning a profit of 20 to 30 yuan per ticket, while the agencies resold them at inflated prices [31][35]. - The total revenue from these operations reached over 440,000 yuan, with profits exceeding 200,000 yuan after deducting costs [33]. Group 3: Legal Consequences - The actions of the scalping gang were classified as illegal acquisition of computer information system data, leading to criminal charges against the main perpetrators [37][38]. - The case has prompted discussions on improving ticketing systems and regulations to ensure fair access for all visitors [40][42]. - Legal measures are being considered to deter such scalping activities, emphasizing the potential for significant prison sentences for offenders [45].

中国科学技术大学常务副校长潘建伟表示，未来20年，人工智能与量子计算的融合将成为重塑人类文明 的关键方向。在量子计算领域，目前超导量子计算相对更具优势，但未来可能是光和超导结合的路径。 未来科学大奖十周年庆典7月8日在上海举行。多位科学家围绕"未来20年最具颠覆性的科学变革""未来 50年最具突破潜力的方向"等前沿领域，共话科学未来发展趋势。 上海交通大学李政道研究所所长张杰指出，2022年12月5日，美国实现了净能量增益的惯性约束核聚变 反应，标志着人类首次掌握了可控核聚变能技术，对人类社会向非碳基终极能源的变革具有极其深远的 影响。张杰预计20年内聚变能将走进千家万户，为人类生活带来巨大变革。 上海交通大学李政道研究所副所长丁洪指出，从未来时间维度看，20年内最具颠覆性的当属通用量子计 算机，未来50年则要聚焦AI for Science。 在香港科技大学校董会主席沈向洋看来，大模型是一个涵盖技术、商业、治理等多要素的概念，将赋能 千行百业，而多模态是大模型发展中的重要里程碑，涉及算力、算法、数据等多方面因素。未来，增强 模型的理解和推理能力是融合多模态数据过程中的关键技术难点。同时，如何发展以人为本的机 ...

来源：科技日报 IBM量子计划副总裁杰·甘贝塔评价道，这种混合模式虽暂未超越超级计算机的独立性能，但已展现出 与传统方法分庭抗礼的实力。计算化学领域权威、美国克利夫兰诊所肯尼思·莫瑞兹教授也表示，其团 队开发的空间量子动力学算法改良版已能模拟溶液环境中的分子，这使化学实验建模更贴近现实。他预 言，算法优化后，量子—经典计算机组合有望在一年内展现显著优势。 与此同时，科技巨头英伟达也已开发出支持混合计算的软件平台。微软公司强调，量子计算、超级计算 与人工智能的"三重奏"，将重塑化学和材料科学。 不过，苏黎世联邦理工学院马库斯·雷埃教授持审慎态度。他表示，实验的结果令人鼓舞，但这种方法 能否成为进行量子化学计算的首选，仍需观察其在大分子模拟中的表现。 目前，日本理化学研究所实验室已升级搭载错误率更低的IBM新型量子处理器。研究团队正双管齐下： 既优化空间量子动力学算法，又提升"鹭"与"富岳"的协同效率。 科技日报北京7月7日电 （记者刘霞）美国IBM公司与日本理化学研究所科学家携手，让量子计算机与 超级计算机联合，成功模拟了多种分子的量子行为。这项发表于《科学进展》和《物理化学B》杂志的 研究成果，为化学及药物研 ...

如果您有任何科研辅导需求，欢迎联系我们！ 自驾方向： 大模型、VLA、端到端自动驾驶、3DGS、BEV感知、目标跟踪、毫米波雷达视觉融合、激光视觉融合、 多传感器标定、多传感器融合、车道线检测、在线地图、轨迹预测、世界模型、3D目标检测、Occupancy、高性能计 算、NeRF、语义分割、决策规划等。 具身方向： VLA、视觉语言导航、端到端、强化学习、Diffusion Policy、sim2real、具身交互、抓取点预测与位姿估 计、机器人决策规划、运动规划、3DGS、SLAM、触觉感知、双足/四足机器人、遥控操作、零样本学习等； 3D视觉 相关： 点云处理、3DGS、SLAM等； 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 今年的CVPR和ICCV工作陆续放出，从今年的2大顶会来看热点研究方向，主要集中在四个部分：通用cv、自动驾驶 相关、具身相关、3D视觉相关。下面是4个领域中细分的子方向。 计算机视觉与图像：diffusion、图像质量评估、半监督学习、零样本、开放世界检测等； 自动驾驶相关，集中在端到端、闭环仿真3DGS、多模态大模型、扩 ...

文 | 王启隆 出品 | CSDN（ID：CSDNnews） 投稿或寻求报道 | zhanghy@csdn.net 在计算机技术七十余年的演进史上，一个幽灵始终在行业上空徘徊——"程序员即将被机 器取代"。 然而，这并非 ChatGPT 爆火后才出现的新近焦虑，而是贯穿整个信息时代、最具韧性的预言之一。 每当一项旨在简化软件开发、降低技术门槛的重大技术跃迁出现时，"程序员末日论"便会应声而起， 以不同的技术名义，在每一个时代向同一个职业宣判死刑。 从 1950 年代编译器的诞生，到今天大 语言模型的崛起，历史已经上演了整整八轮几乎一模一样的"替代"故事。 所以，让我们一起追溯历史上这八次主要的"程序员替代论"浪潮，看看程序员如何"死而复生"，探究 程序员们"不死的秘密"。 自动化的黎明（1950 年代） 没有显示器，没有键盘，更没有我们熟悉的 IDE。 这个年代所谓的"程序员"，是一小群数学家和逻辑学家。他们的工作，是在一张张表格上手动填写一 长串令人费解的八进制码，每一个数字都对应着机器的一条指令、一个内存地址。然后，这些编码被 送去穿孔，变成一叠厚厚的卡片。 约翰·巴克斯 ，后来 FORTRAN 语言的发 ...

金元证券近日发布计算机行业月报：本月计算机行业正向催化事件包括英伟达重返全球 市值第一和华为云盘古大模型5.5的重磅发布，另外，本月苹果WWDC2025似乎未达市场预 期。计算机行业指数在本周的强势表现或将归结为特殊的时间节点，从历史上看计算机行业 在7月难有表现，目前判定计算机行业中期趋势性行情的条件尚不充分。 本月计算机行业正向催化事件包括英伟达重返全球市值第一和华为云盘古大模型5.5的 重磅发布，另外，本月苹果WWDC2025似乎未达市场预期。计算机行业指数在本周的强势 表现或将归结为特殊的时间节点，从历史上看计算机行业在7月难有表现，目前判定计算机 行业中期趋势性行情的条件尚不充分。 维持计算机行业增持的投资评级，目前市场多个行业对于资金的争夺并未终结，建议继 续观察行业在7月初的市场表现以确定月末行情性质。 风险因素分析：宏观刺激政策不及预期风险、境外对中出台针对性措施风险、中美关税 战进一步升级等。（金元证券 周强） 【责任编辑：杨梓安 】 以下为研究报告摘要： 我们5月28日报告预测由于计算机行业的投资热度逐步下降，市场热点正向一些传统行 业转移，计算机行业难于在6月走出独立行情。 A股6月内大 ...

Core Insights - The report titled "Global AI Research Landscape Report (2015-2024)" analyzes the evolution of AI research over the past decade, highlighting the competitive landscape between China and the United States in AI talent and publication output [2][7]. Group 1: AI Research Trends - The report identifies four distinct phases in AI research: initial phase (2015-2016), rapid development phase (2017-2019), maturity peak phase (2020-2023), and adjustment phase (2024) [4][5]. - The number of AI papers published globally increased significantly, with a peak of 17,074 papers in 2023, representing nearly a fourfold increase from 2015 [5][6]. - The year 2024 is expected to see a decline in publication volume to 14,786 papers, indicating a shift towards more specialized and application-oriented research [6]. Group 2: Talent Distribution - China has emerged as the second-largest hub for AI talent, with a total of 52,000 researchers by 2024, growing at a compound annual growth rate of 28.7% since 2015 [8]. - The United States leads with over 63,000 AI researchers, with significant contributions from institutions like Stanford and MIT, as well as tech giants like Google and Microsoft [8][9]. - Chinese institutions such as the Chinese Academy of Sciences, Tsinghua University, and Peking University are leading in terms of publication output and talent concentration [7][9]. Group 3: Institutional and Corporate Performance - The Chinese Academy of Sciences published 4,639 top-tier papers, while Tsinghua University and Peking University followed closely, showcasing China's institutional strength in AI research [7][9]. - In contrast, U.S. companies like Google, Microsoft, and Meta have a significantly higher average publication output compared to their Chinese counterparts, reflecting a disparity in research investment and output capabilities [9][10]. - The top three U.S. companies published 5,896 papers, which is 1.8 times the output of the top three Chinese companies [9][10]. Group 4: Gender Disparity in AI Talent - The report highlights a significant gender imbalance in AI research, with women making up only 9.3% of AI talent in China compared to 20.1% in the U.S. [12][13]. - Chinese institutions like Tsinghua University and Peking University have low female representation in AI, at 7.88% and 9.18% respectively, compared to 25%-30% in top U.S. institutions [12][13]. Group 5: Future Trends in AI Research - The report indicates that "deep learning" has been the dominant focus in AI research over the past decade, but its growth rate is expected to slow down, suggesting a need for new approaches [14][15]. - Emerging technologies such as "Transformers" are gaining traction, particularly in natural language processing and multimodal AI, indicating a shift in research focus [15]. - The integration of traditional AI fields with deep learning techniques is becoming more prevalent, reflecting a trend towards collaborative and interdisciplinary research [15].

Group 1 - The event aims to promote academic exchange and innovation collaboration among global youth AI researchers, responding to the theme of the World Artificial Intelligence Conference [3] - The initiative seeks to provide a high-standard academic exchange platform for young scholars and technology developers, facilitating the collision of academic ideas and the transformation and dissemination of research results [3] Group 2 - The call for submissions focuses on cutting-edge explorations in the field of artificial intelligence, covering areas such as large models, generative AI, computer vision, reinforcement learning, AI ethics, and interdisciplinary applications [4][5][6][7][8] - Submissions can take the form of academic posters or preprint papers, with specific guidelines for each format, including visual presentation requirements and structural expectations for research papers [8][9] Group 3 - Selected works will be showcased at the World Artificial Intelligence Conference, with opportunities for authors to present their findings and engage with industry leaders [10] - Authors may apply for direct recommendations to top international journals, including Nature Machine Intelligence, with expedited review processes for recommended papers [10][14] Group 4 - Submission materials must include a title, author information, and either a poster design file or a full preprint paper, along with a brief research highlight summary [11] - The submission deadline is July 10, 2025, with notifications of review results by July 15, 2025, and the conference scheduled for July 27, 2025, in Shanghai [12]

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].