Privacy-Preserving Federated Mobility Prediction with Compound Data and Model Perturbation Mechanism

Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

Compressed SNR-and-Channel Estimation for Beam Tracking in 60-GHz WLAN

Signal-to-noise ratio （SNR） and channel estimations are critical for 60-GHz communications to track the optimal trans- mission and reception beam pairs. However, the excessive pilot overhead for the estima- tions severely reduces system throughput in fast-rotation scenarios. In order to address this problem, we firstly demonstrate the potential sparseness property of 60-GHz channel in beam tracking; subsequently, via exploiting this property, we propose a novel compressed SNR-and-channel estimation. The estimation is conducted in a three-stage fashion, includ- ing the unstructured estimation, nonzero-tap detection, and structured estimation with non- zero-tap location. Numerical simulations show that, in the case of substantial reduction of the pilot overhead, the proposed estimator still reveals a significant improvement in terms of estimation performance over the scheme in IEEE 802.1 lad. Furthermore, it is also demon- strated that the proposed SNR and channel estimators can approach the lower bounds in sparse channels so long as SNR exceeds 8 dB.

Hardware/software co-verification platform for EOS design

Ethernet over SDH/SONET (EOS) is a hotspot in today's data transmission technology for it combines the merits of both Ethernet and SDH/SONET. However, implementing an EOS system on a chip is complex and needs full verifications. This paper introduces our design of Hardware/Software co-verification platform for EOS design. The hardware platform contains a microprocessor board and an FPGA (Field Programmable Gate Array)-based verification board, and the corresponding software includes test benches running in FPGAs, controlling programs for the microprocessor and a console program with GUI (Graphical User Interface) interface for configuration, management and supervision. The design is cost-effective and has been successfully employed to verify several IP (Intellectual Property) blocks of our EOS chip. Moreover, it is flexible and can be applied as a general-purpose verification platform.

An integrated self-healing algorithm for resilient packet ring

Resilient Packet Ring (RPR) is a Media Access Control (MAC) layer protocol that operates over a double counter-rotating ring network topology. RPR is designed to enhance Synchronous Digital Hierarchy(SDH) in order to handle data traffic more efficiently. Since Intelligent Protection Switching(IPS) is one of the key technologies in ring networks, RPR provides two intelligent protection algorithms: steering and wrapping. While wrapping in RPR in essence inherits the automatic protection switching(APS) algorithm of SDH, it also wastes the bandwidth on the wrapping ringlets and may result in severe congestion. Whereas steering in RPR provides high bandwidth utilization, its switching speed is low, because it is indeed a high layer's restoration algorithm. In this paper, integrated self-healing(ISH) algorithm as an effective algorithm for RPR is proposed, which synthesizes the merits of the two algorithms by transporting healing signal and computing routing in MAC layer. At last, the performance of ISH algorithm is analyzed and simulated.

0.18μm CMOS, MONOLITHIC MSTP ASIC

A highly integrated monolithic Multi-Service Transport Platform （MSTP） Application Specified Integrated Circuit （AS1C） MSEOSX8-6 has been fabricated with 0.18μm CMOS technology incorporating 26×10^6 transistors. The chip is designed to provide standard framing and mapping of 10/100/1000Mbit/s Ethernet, Resilient Packet Ring （RPR） and E1 traffics into protected Synchronous Digital Hierarchy （SDH） STM-1 transport payloads using hitless rate adaptation for optimum bandwidth utilization. It consumes 4W of power on average and utilizes 756 pin enhanced BGA package.

服务国家战略需求培养拔尖创新人才――中国科学院大学科教融合办学的制度逻辑与发展实践

党的二十大报告把“实施科教兴国战略,强化现代化建设人才支撑”作为专章部署,并特别强调“教育、科技、人才是全面建设社会主义现代化国家的基础性、战略性支撑”。科教融合是实现教育、科技、人才一体化发展的重要途径。文章追溯了科教融合的源起和发展,分析了世界范围内跨组织科教融合的3种具体表现形式,回顾了改革开放以来中国科学院大学科教融合培养高层次创新人才的发展历程、制度逻辑和主要特征,最后展望了中国科学院大学科教融合培养拔尖创新人才的前景。

深化科教融合,培养未来科技领军人才

新时代对创新创业人才在品德素养、知识结构、专业能力等方面提出了新的更高要求。在此背景下,世界范围内创新创业人才培养实践呈现出多学科交叉会聚、培养过程贯通融合、教科产多主体开放协同、数字化转型赋能等新走向。依托中国科学院创新创业优势办学,中国科学院大学深入研究高素质创新创业人才尤其是科技领军人才的培养规律,从培养模式和组织模式创新、课程教学体系改革、完善“大思政”工作体系等方面,积极探索科教融合育人的新模式、新路径,以及为支撑育人目标而强化师资队伍建设的思路举措。