一、往期活动回顾
I. Review of Previous Events
西北工业大学于2021年开始举办“未来AI大师”国际夏令营。至今已举办两届。活动期间邀请来自耶鲁大学、澳洲国立大学、阿德莱德大学、香港城市大学、西北工业大学等国内外高校的国际知名学者、计算机业界知名企业授课，组建了一流师资团队。来自清华大学、中国科技大学、北京理工大学、哈尔滨工业大学、天津大学、电子科技大学、西北工业大学等21所高校的百余名中外大学生营员欢聚一堂，以人工智能、国际化和领导力为主题，通过AI专业学习和项目实践，高级领导力训练和多元学习交流体验，致力于提升个人的AI知识眼界、综合素质和领导能力。
Northwestern Polytechnical University started hosting the "Future AI Master" International Summer Camp in 2021. Two editions have been held so far. During the events, internationally renowned scholars from universities such as Yale University, Australian National University, University of Adelaide, City University of Hong Kong, and Northwestern Polytechnical University, as well as prominent companies in the computer industry, were invited to give lectures, forming a top-notch faculty team. More than 100 domestic and international students from 21 universities, including Tsinghua University, University of Science and Technology of China, Beijing Institute of Technology, Harbin Institute of Technology, Tianjin University, University of Electronic Science and Technology of China, and Northwestern Polytechnical University, gathered together. The camps focused on artificial intelligence, internationalization, and leadership, aiming to enhance participants' knowledge and perspectives on AI, overall qualities, and leadership skills through AI-focused learning, project practices, advanced leadership training, and diverse learning and exchange experiences.
二、营会背景
II. Camp Background
最近，由人工智能实验室Open AI发布的对话式大型语言模型Chat GPT在全球互联网平台备受关注，它的出现代表着人类在AI领域的不断探索和进步。随着大数据、云计算、互联网、物联网等信息技术的发展，泛在感知数据和图形处理器等计算平台推动，人工智能技术飞速发展，大幅跨越了科学与应用之间的技术鸿沟，在图像分类、语音识别、知识问答、人机对弈、无人驾驶等人工智能技术实现了重大的技术突破，从现有的技术和趋势来看，未来的AI将会更加普及、更加强大、更加智能化。
Recently, Chat GPT, a conversational large-scale language model developed by the AI laboratory Open AI, has garnered global attention on internet platforms. Its emergence represents the continuous exploration and progress of humanity in the field of AI. With the development of information technologies such as big data, cloud computing, the internet, and the Internet of Things, as well as the advancements in ubiquitous sensing data and computing platforms like graphics processors, artificial intelligence technology has been rapidly advancing. It has significantly bridged the technological gap between science and application, achieving major breakthroughs in areas such as image classification, speech recognition, knowledge-based question answering, human-machine games, and autonomous driving. Based on current technologies and trends, AI in the future will become more widespread, powerful, and intelligent.
为了给学生提供共同学习探讨AI前沿技术、了解AI＋时代的行业新版图、进一步挖掘人工智能新价值的机会。培养具有“人工智能+全球胜任力”的复合型、创新型、引领型人才，西北工业大学将于2023年7月2日—7月12日举办第三届未来AI大师国际暑期学校暨首届全球IT胜任力卓越训练营（7.2-13日，根据考试结束时间会有调整）。
To provide students with opportunities for collaborative learning and exploration of cutting-edge AI technologies, understanding the new landscape of AI+ era, and further discovering the new value of artificial intelligence, Northwestern Polytechnical University will host the 3rd Future AI Masters International Summer School and the 1st Global IT Competence Excellence Training Camp from July 2nd to July 12th, 2023. （The dates may be adjusted based on the end of exams, tentatively scheduled from July 2nd to 13th）.
三、营会特色
III. Camp Features
特色一：特邀计算机领域知名企业华为、华为云、道客DaoCloud、算能、达阀等公司开设专业学习方向。学习内容包含领域内前沿技术基础课程和高水平竞赛。企业专家亲自指导实践环节。
Feature 1: Invited renowned companies in the computer field such as Huawei, Huawei Cloud, DaoCloud, SOPHGO and Dataa Robotics to offer specialized learning directions. The learning content includes cutting-edge technology foundation courses and high-level competitions. Industry experts from these companies will provide hands-on guidance during practical sessions.
特色二：根据企业特色，营会区分为不同的学习方向，可根据个人意向选择企业方向，深度领略人工智能前沿科创知识以及极富创意、想象、创新与互动的学习体验。
Feature 2: Based on the characteristics of each company, the camp is divided into different learning directions. Participants can choose their preferred company direction to deeply explore the frontiers of artificial intelligence and experience a learning environment that is highly creative, imaginative, innovative, and interactive.
特色三：企业为营员提供算力支持，保证营员拥有实战操作环境和真实的AI体验。
Feature 3: Companies provide computing power support to ensure that participants have practical operating environments and real AI experiences.
Feature 4: The camp provides participants with opportunities to observe and experience AI technology companies on-site.
特色五：部分留学生可申请生产实习认定。
Feature 5: Some international students can apply for internship recognition.
特色六：营会活动充满趣味，涉及讲座、授课、团队游戏和IT名企参观、大唐文化之旅等多种形式的活动。
Feature 6: The camp activities are fun-filled and include lectures, classes, team games, visits to IT industry leaders, and cultural trips to the Tang Dynasty, among other forms of activities.
特色七：方向二中优秀作品可直接参与昇腾AI创新大赛2023陕西区域比赛。
Feature 7: Outstanding works from Direction 2 can directly participate in the 2023 Shaanxi Regional Competition of the Ascend AI Innovation Contest.
四、学习实践项目方向
IV: Learning and Practical Project Directions
方向一：大国重器与计算报国
Direction 1: National Key Projects and Computing for the Nation
·该方向具体安排将在QQ群内通知
· Specific arrangements for this direction will be notified within the QQ group.
部分培训方向简介:
Introduction to Some Training Direction:
达闼是智能机器人领域的独角兽头部企业，全球领先的云端机器人创造者、制造商和运营商。达闼具有行业领先的云端机器人全栈技术解决方案，创新性地提出“云网边端”（“云脑+安全专网+边缘部署＋端侧机器人”）架构并成功实现云端机器人的商业化。2022年科技部批复由达闼建设[云端机器人国家新一代人工智能开放创新平台]。HarixRDK作为达闼的机器人能力开发套件，可以实现机器人在智能语音、行为控制、动作编辑、移动导航等方面的能力开发，并完成数字孪生环境和真实机器人的虚实同步验证。
Dataa Robotics is a unicorn leader in the field of intelligent robotics, a global pioneer, manufacturer, and operator of cloud-based robots. Dataa Robotics has industry-leading full-stack technology solutions for cloud-based robots and has innovatively proposed the "Cloud-Edge-End" ("Cloud AI Brain + Secure Private Network + Edge Deployment + End-side Robot") architecture, successfully commercializing cloud-based robots. In 2022, the Ministry of Science and Technology approved Dataa Robotics to establish the "Cloud-Based Robot National New Generation Artificial Intelligence Open Innovation Platform." HarixRDK, as Dataa Robotics's robot capability development kit, enables the development of robot capabilities in intelligent voice, behavior control, motion editing, and mobile navigation, as well as the synchronization verification of digital twin environments and real robots.
方向二：基于华为昇腾Atlas200DK A2的深度学习
Direction 2: Deep Learning Based on Huawei Ascend Atlas200DK A2
1.模块一：人工智能基础概论知识
Module 1: Introduction to Artificial Intelligence Fundamentals
课程内容：ChatGPT\文心一言\天工\星火、stable diffusion\dalle2\mid journey等热点应用技术分析、人工智能的定义概念、人工智能的发展历程、人工智能的技术应用分类，人工智能在图像、语言、文本、自动化方向的应用场景和案例，以及人工智能项目的开发流程，包括数据采集标注处理、模型设计训练优化、模型量化剪枝部署等。
Course Content: Analysis of hot application technologies such as ChatGPT, Wenxin Yiyuan, Tiangong, Xinghuo, Stable Diffusion, DALLE2, Mid Journey, etc. Definition and concept of artificial intelligence, the development history of artificial intelligence, classification of artificial intelligence technology applications, application scenarios and cases of artificial intelligence in image, language, text, and automation domains, as well as the development process of AI projects, including data collection, annotation, and processing, model design, training optimization, model quantization, pruning, and deployment.
学习目标：通过实际的热点应用了解最新的技术发展方向，为未来的学习奠定基础，激发学习兴趣，掌握基本的人工智能常识，明白当前人工智能所处的发展阶段，对常见的人工智能应用能够分辨其技术类别；掌握人工智能在不同应用方向下的价值，对各应用方向的实际案例能够做出简要技术分析
Learning Objectives: Gain an understanding of the latest technological development trends through practical hot applications, lay a foundation for future learning, stimulate interest in learning, grasp basic knowledge of artificial intelligence, understand the development stage of current artificial intelligence, identify the technical categories of common artificial intelligence applications, recognize the value of artificial intelligence in different application domains, and conduct brief technical analysis of practical cases in various application domains.
2.模块二：人工智能神经网络模型知识
Module 2: Artificial Intelligence Neural Network Models
课程内容：
Course Content:
人工神经网络的发展、全连接神经网络(MLP)、卷积神经网络(CNN)、循环神经网络(RNN)、生成式对抗神经网络(GAN)、深度强化学习网络（DRL）
Development of artificial neural networks, full connect neural network (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), deep reinforcement learning networks (DRL).
学习目标：掌握人工神经网络的作用和诞生及演变过程，理解人工神经网络的结构组成和运行过程，理解重要数学函数的作用，熟悉常见的神经网络模型，理解不同类型的神经网络模型的优势和应用方向，能够对实际问题提出使用何种网络模型来解决。
Learning Objectives: Master the role, birth, and evolution of artificial neural networks, understand the structure and operation of artificial neural networks, comprehend the functions of important mathematical functions, be familiar with common neural network models, understand the advantages and application directions of different types of neural network models, and be able to propose suitable network models for solving practical problems.
3.模块三：人工智能项目实战
Module 3: Artificial Intelligence Project Practical Application
课程内容：
Course Content:
（1）人工智能开发环境搭建：Anaconda、PyTorch、PyCharm、CUDA、CUDNN
(1) Artificial intelligence development environment setup: Anaconda, PyTorch, PyCharm, CUDA, CUDNN.
（2）无人机检测项目实战：数据的采集和处理、模型的搭建、模型训练和评估、结果测试、部署华为atlas200DK A2
(2) Drone detection project practical application: Data collection and processing, model construction, model training and evaluation, result testing, deployment on Huawei Atlas200DK A2.
（3）华为云开发者认证考试：考取华为颁发的开发者证书
(3) Huawei Cloud developer certification exam: Obtain developer certification awarded by Huawei.
学习目标：通过目标检测模型，学习人工智能的实际应用项目，掌握人工智能项目开发的重点，并能将模型部署到华为终端设备atlas200DK，同时考取华为颁发的认证证书。
Learning Objectives: Through the target detection model, learn practical application projects of artificial intelligence, master the key points of AI project development, be able to deploy models to Huawei terminal device Atlas200DK, and obtain certification from Huawei.
方向三：基于MLIR的AI编译器介绍及应用
Direction 3: Introduction and Application of AI Compiler based on MLIR
1.在人工智能领域，为了处理大规模的数据和复的计算任务，硬件加速器成为了不可或缺的工具。Tensor Processing Unit(TPU)作为一种专门针对机器学习任务优化的硬件加速器，具有高性能和能耗效率的优势。为了充分发挥TPU的性能，就需要开发针对TPU优化的编译器。
1. In the field of artificial intelligence, hardware accelerators have become indispensable tools for handling large-scale data and complex computing tasks. Tensor Processing Unit (TPU) is a hardware accelerator specifically optimized for machine learning tasks, offering high performance and energy efficiency advantages. To fully leverage the performance of TPUs, it is necessary to develop compilers optimized for TPUs.
2.TPU-MLIR作为一个开源A编译器工程，专注于针对TPU的编译器优化和工具开发，它使用MLIR作为中间表示语言，提供了一种模块化的方式来定义和组合编译器优化和转换。通过使用TPU-MLIR，提供了一套完整的工具链，其可以将不同框架下的神经网络，转化为可以在算能TPU上高效运算的二进制文件。
2. TPU-MLIR, as an open-source compiler project, focuses on compiler optimization and tool development for TPUs. It uses MLIR as an intermediate representation language, providing a modular way to define and combine compiler optimizations and transformations. By using TPU-MLIR, a complete toolchain is provided that can convert neural networks from different frameworks into binary files that can be efficiently executed on TPU.
3为了满足不断增长的对A编译器开发和优化的需求，本课程旨在向学生介绍A编译器的基本原理和技术，并结合TPU-MLIR项目进行实际应用。通过该课程，学生将学习到如何设计、开发和优化A编译器，以实现对TPU的最佳利用。这将为学生提供深入理解A编译器的能力，并为他们在人工智能领域的职业发展奠定坚实的基础。
3. To meet the growing demand for compiler development and optimization in the AI field, this course aims to introduce students to the basic principles and techniques of compiler development and apply them in the context of the TPU-MLIR project. Through this course, students will learn how to design, develop, and optimize compilers for achieving the best utilization of TPUs. This will provide students with a deep understanding of compilers and lay a solid foundation for their professional development in the field of artificial intelligence.
课程目标:
Course Objectives:
1理解MLIR和AI编译器的基本原理和关键技术
1. Understand the basic principles and key technologies of MLIR and AI compilers.
2.学习TPU-MLIR项目的整体架构和功能设计
2. Learn about the overall architecture and functionality design of the TPU-MLIR project.
3使用TPU-MLIR进行Resnet图像分类算法迁移
3. Use TPU-MLIR for migrating the ResNet image classification algorithm.
4. 动手设计一个简单的MLIR编译器
4. Hands-on experience in designing a simple MLIR compiler.
算能公司介绍
About SOPHGO:
算能致力于成为全球领先的通用算力提供商。算能专注于AI、RISC-V CPU等算力产品的研发和推广应用，以自研产品为核心打造了覆盖“云、边、端”的全场景应用矩阵为城市大脑、智算中心、智慧安防、智慧交通、安全生产、工业质检、智能终端等应用提供算力产品及整体解决方案 。公司在北京、上海、深圳、青岛、厦门等国内 10 多个城市及美国、新加坡等国家设有研发中心。
SOPHGO is committed to becoming the world's leading general computing power provider. SOPHGO focuses on the development and promotion of AI, RISC-V CPU and other computing products. With the self-developed chips as the core, SOPHGO has created a matrix of computing power products, which covers the whole scene of "cloud, edge and terminal" and provides computing power products and overall solutions for urban brains, intelligent computing centers, intelligent security, intelligent transportation, safety production, industrial quality inspection, intelligent terminals and others. SOPHGO has set up R&D centers in more than 10 cities and countries, including Beijing, Shanghai, Shenzhen, Qingdao, Xiamen, the United States and Singapore.
方向四：基于KubeEdge的云原生边缘计算平台实践
Direction 4: Cloud-Native Edge Computing Platform Practice based on KubeEdge
1. 随着5G和物联网的快速发展，越来越多的应用和数据在网络边缘部署和产生，这使得边缘计算的重要性日益凸显。Kubernetes作为云原生容器编排领域的事实标准，在边缘计算中发挥着至关重要的作用，它能够管理和调度边缘设备上运行的容器应用，引领我们进入云原生边缘计算的新时代。
1. With the rapid development of 5G and the Internet of Things (IoT), more and more applications and data are being deployed and generated at the network edge, highlighting the increasing importance of edge computing. Kubernetes, as the factual standard in the cloud-native container orchestration field, plays a crucial role in edge computing. It can manage and schedule container applications running on edge devices, leading us into a new era of cloud-native edge computing.
2. KubeEdge是专为边缘计算场景设计，基于Kubernetes的云原生边缘计算框架，它聚焦于提供一致的云边资源协同、数据协同、智能协同和应用协同体验。作为业界首个云原生边缘计算框架和CNCF唯一的孵化级边缘计算项目，KubeEdge采用了开放的社区治理模式，连接云原生和边缘计算生态，为用户提供云边端一体化解决方案。
2. KubeEdge is a cloud-native edge computing framework designed specifically for edge computing scenarios. It is built on Kubernetes and focuses on providing a consistent experience for cloud-edge resource collaboration, data collaboration, intelligent collaboration, and application collaboration. As the industry's first cloud-native edge computing framework and the only incubating-level edge computing project under CNCF, KubeEdge adopts an open community governance model, connecting the cloud-native and edge computing ecosystems to provide users with integrated cloud-edge-end solutions.
3. 本课程旨在向学生介绍业界首个云原生边缘计算平台KubeEdge的基本原理和架构，并基于KubeEdqe进行实用案例开发。通过该课程，学生将学习到云原生边缘计算基本原理、关键技术以及在产业中的应用案例。这将为学生深入理解边缘计算的能力，并为他们在云原生边缘计算领域的职业发展奠定坚实的基础。
This course aims to introduce students to the basic principles and architecture of KubeEdge, the industry's first cloud-native edge computing platform, and practical case development based on KubeEdge. Through this course, students will learn about the basic principles of cloud-native edge computing, key technologies, and application cases in the industry. This will provide students with the ability to deeply understand edge computing and lay a solid foundation for their career development in the field of cloud-native edge computing.
课程目标:
Course Objectives:
1. 了解云原生边缘计算行业发展现状及前景
1. Understand the current status and prospects of the cloud-native edge computing industry.
2.学习KubeEdge平台的整体架构和特性设计
2. Learn the overall architecture and feature design of the KubeEdge platform.
3学习KubeEdge在多个行业的典型云边协同的生产案例
3. Study typical cloud-edge collaboration production cases of KubeEdge in multiple industries.
4. 基于KubeEdge平台开发部署一个简单的云边协同应用案例
4. Develop and deploy a simple cloud-edge collaborative application case based on the KubeEdge platform.
KubeEdge简介
KubeEdge 是业界首个云原生边缘计算框架，云原生计算基金会（CNCF）唯一孵化级边缘计算开源项目。KubeEdge 将Kubernetes 原生的容器编排和调度能力拓展到边缘，为边缘应用部署、云与边缘间的元数据同步、边缘设备管理等提供基础架构支持，实现云边协同、计算下沉、海量边缘设备管理、边缘自治等能力，完整的打通了边缘计算中云、边、设备协同的场景，为用户提供一体化的云边端协同解决方案。
KubeEdge is the industry's first cloud-native edge computing framework and the only incubating-level edge computing open-source project under the Cloud Native Computing Foundation (CNCF). KubeEdge extends the native container orchestration and scheduling capabilities of Kubernetes to the edge, providing infrastructure support for edge application deployment, metadata synchronization between the cloud and edge, and edge device management. It enables cloud-edge collaboration, computing sinking, massive edge device management, edge autonomy, and seamlessly connects cloud, edge, and device collaboration scenarios in edge computing, providing users with an integrated cloud-edge-end collaborative solution.
华为创立于1987年，是全球领先的ICT（信息与通信）基础设施和智能终端提供商。华为致力于把数字世界带入每个人、每个家庭、每个组织，构建万物互联的智能世界。
Huawei, founded in 1987, is a global leader in ICT (Information and Communication Technology) infrastructure and intelligent terminals. Huawei is committed to bringing the digital world to every person, home, and organization, and building an intelligent world of interconnected things.
上海道客网络科技有限公司(DaoCloud)，云原生领域的创新领导者，成立于2014年底，拥有自主知识产权的核心技术，致力于打造开放的云操作系统为企业数字化转型赋能。产品能力覆盖云原生应用的开发、交付、运维全生命周期，并提供公有云、私有云和混合云等多种交付方式。
Shanghai DaoCloud Network Technology Co., Ltd. (DaoCloud) is an innovative leader in the field of cloud-native. Established at the end of 2014, DaoCloud has core technologies with independent intellectual property rights and is dedicated to building an open cloud operating system to empower enterprise digital transformation. Its product capabilities cover the entire lifecycle of cloud-native application development, delivery, and operation, and provide various delivery methods such as public cloud, private cloud, and hybrid cloud.
方向五：全英文深度学习与TPU平台实践方向
Direction 5: Deep Learning and TPU Platform Practice in English
深度神经网络模型能很快地被训练、测试，然后被产业界部署从而有效完成现实世界中的任务，在小体积、低功耗的 AI 计算平台上部署这类系统很受产业界欢迎。本课程以实践驱动的方式带领大家直观学习、实战和掌握深度神经网络的知识和技术。
Deep neural network models can be trained, tested, and deployed by the industry quickly to efficiently perform tasks in the real world. Deploying such systems on small-sized, low-power AI computing platforms is highly favored by the industry. This course takes a practical approach to help you intuitively learn, practice, and master the knowledge and techniques of deep neural networks.
本课程主要包括三部分，第一部分介绍深度学习的基础知识，以及CPU、GPU和TPU的架构，相比我们熟知的 CPU、GPU通用计算平台，TPU是专用于加速神经网络计算的芯片，因此更适于深度学习计算；第二部分介绍了一款TPU芯片BM1684，这是算能研制的一款专用于加速神经网络运行的微型系统级芯片，课程主要介绍了这款芯片的架构和性能，以及配套的工具链；第三部分是实践部分，涵盖第三章至第十章，主要介绍了模型的训练，以及如何在BM1684平台上运行的流程。
The course consists of three main parts. The first part introduces the fundamentals of deep learning and the architectures of CPUs, GPUs, and TPUs. Compared to the widely known general-purpose CPU and GPU computing platforms, TPUs are specialized chips designed for accelerating neural network computations, making them more suitable for deep learning. The second part introduces the TPU chip BM1684, a microsystem-level chip developed by SOPHGO specifically for accelerating neural network operations. The course covers the architecture, performance, and associated toolchains of this chip. The third part is the practical section, covering Chapters Three to Ten, which primarily focuses on model training and the process of running models on the BM1684 platform.
学生在完成本课程的学习后将能够掌握以下能力：
Upon completing this course, students will be able to acquire the following skills:
对深度学习的基本认知
Fundamental understanding of deep learning
了解CPU，GPU，TPU的区别
Understanding the differences between CPUs, GPUs, and TPUs
掌握算能TPU芯片BM1684架构以及平台的使用，交叉编译环境的搭建和使用
Mastery of the architecture and usage of SOPHGO's TPU chip BM1684, including setting up and using the cross-compilation environment
学习图像分类，目标检测，语义分割，实例分割等神经网络在TPU平台上的实现
Learning the implementation of neural networks such as image classification, object detection, semantic segmentation, and instance segmentation on the TPU platform
具备运用深度学习的知识解决具体问题的基本能力
Basic ability to apply deep learning knowledge to solve specific problems
五、学生个人收获
V. Personal Gains for Students
夏令营将采用“AI+国际化+胜任力+视野拓展”的内容设计模式，“模块式+项目式+体验式+团队式”的实施模式。学生可感受到优质的国际教育资源，听取学科前沿报告、专题讲座，享受计算机领域的国际著名专家执教、充分聆听和感悟“人工智能”的魅力。
The summer camp will adopt a content design model of "AI + internationalization + competence + expanded vision" and an implementation model of "modular + project-based + experiential + team-based." Students will have the opportunity to experience high-quality international educational resources, attend cutting-edge subject presentations, and benefit from internationally renowned experts in the field of computer science teaching, fully immersing themselves in and appreciating the charm of artificial intelligence.
营员活动将以团队的形式完成全部的学习和实践活动。每个小组配备2位中英双语志愿者带领。小组成员来自不同国家、地区、学校、专业。拥有不同文化背景的营员可在此共同学习、生活、成长。
All learning and practical activities during the camp will be completed in teams. Each group will be led by two bilingual volunteers proficient in both Chinese and English. Group members come from different countries, regions, schools, and majors. Campers with diverse cultural backgrounds will have the opportunity to learn, live, and grow together.
成功完成专业及实践课程的同学可获得含金量十足的结业证书！
优秀者有机会获得额外奖励证书！
Students who successfully complete the professional and practical courses will receive highly valued graduation certificates!
Those who perform well have the opportunity to obtain addtional certificates！
六、申请方式及时间
VI. Application Process and Timeline
1. 营员选拔标准
1. Selection Criteria for Campers:
（1）热爱学习和钻研，对人工智能、云计算、大数据、深度学习等计算机专业领域相关技术具有浓厚兴趣；
(1) Passion for learning and research, with a strong interest in computer-related technologies such as artificial intelligence, cloud computing, big data, and deep learning.
（2）具有一定的英语交流能力；
(2) Proficiency in English communication to a certain extent.
（3）专业不限。
(3) No specific major requirements.
2. 截止时间：
2. Deadline:
营员报名截止时间：6月28日。
The deadline for camper applications is June 28th.
3. 报名方式：请有意向参加的同学通过校内联系人报名，QQ群见下方，大家可根据自己感兴趣的项目加群咨询。
3 Application Method: Interested students should apply through the on-campus contact persons. The QQ group information can be found below, and you can join the group for inquiries based on your preferred project.
校内联系人：邵老师 曹老师
On-campus contact persons: Mrs. Shao, Mrs. Cao
联系电话：029-88430215
Contact number: 029-88430215
6月30日前向获得入营资格者发放入营通知（邮件及电话形式），未接到入营通知的同学为未入选，不再另行通知。
Camp admission notifications (via email and phone) will be sent to qualified applicants by June 30th. Students who do not receive admission notifications by that date have not been selected and will not be notified separately.
申请人保证全部申请材料的真实性。
The applicant guarantees the authenticity of all application materials.
七、报名费用
VII. Registration Fees
校内学生1000元/人，校外学生1800元/人。
For on-campus students: 1000 RMB/person.
For off-campus students: 1800 RMB/person.
（含报名费、学费、活动费、住宿费）(Registration fee, tuition, activity fee, accommodation fee included)
