Camellia和CLEFIA的自动化中间相遇攻击

中间相遇(MitM)是一种高效构造哈希函数原像和碰撞攻击的策略.近年来,基于混合整数线性规划(MILP)的MitM原像、碰撞和密钥恢复攻击被广泛应用于AES-like密码学原语.在ASIACRYPT 2023上,Hou等人将自动化MitM攻击扩展到具有Feistel或广义Feistel结构的哈希函数中,其关注的(广义)Feistel结构轮函数的线性变换层往往是基于MDS矩阵构造.在实际密码设计中,轮函数线性变换可以有多种构造方式,而不一定基于MDS矩阵设计.本文将针对一般化的轮函数线性变换进行自动化MitM攻击建模,提出了n-XOR的模型,将输出字描述为任意数目输入字的异或,并应用在一般化的布尔矩阵中.结合Hou等人的模型,对ISO标准分组密码Camellia和CLEFIA哈希模式原像进行分析.对于省略了白化层和FL/FL^(−1)变换的弱化Camellia-MMO,最高可以搜索到14轮的中间相遇原像特征;对于CLEFIA-MMO,在适当增加存储的代价下,最高搜索到13轮的中间相遇原像特征.两个攻击的时间复杂度均为2^(120),相较于之前的最优结果(Sasaki,ACNS 2013),攻击轮数均有1轮的提高.

密码运行安全体系与关键技术研究

当前大国间网络空间控制权争夺日益激烈,密码作为保护网络空间安全的核心技术,正成为各方博弈的焦点和控制权争夺的胜负手.随着网络和密码攻击技术的发展,仅依赖密码设计安全和工程安全等静态安全已无法满足日益紧迫的密码应用安全需求,亟需对密码运行时的安全开展系统性研究.本文基于密码设计安全与工程安全的基本原理,以重要网络与信息系统密码保障为中心,以密码应用在网络与信息系统中面对的威胁锋面为焦点,提出密码运行安全概念,分析其内涵与演进特征,构建密码运行安全需求模型、保障体系和工程系统等架构.基于此,梳理归纳密码运行安全相关关键技术,形成密码运行安全技术体系.最后,总结分析密码运行安全面临的机遇和挑战,展望需要持续深入研究的重大问题.希望借此推动密码安全领域的科学研究与现代化治理进程.

隐私集合运算中的关键数据结构研究

隐私集合运算(privatesetoperation,PSO)是安全多方计算领域的热点问题,它允许两个参与方对各自私有集合进行安全计算,同时避免额外信息泄露.常见的PSO协议包括隐私集合求交和隐私集合求并.高效的隐私集合运算协议的设计与多种高级的数据结构密切相关.然而,目前隐私集合运算中各种数据结构缺乏系统梳理且无同一平台上的效率对比结果.本文将PSO中的关键数据结构分为三类,分别是哈希表、过滤器和不经意键值存储.在明确各类数据结构的基本定义与构造方式的基础上,本文梳理各数据结构的主要功能作用、总结它们在不同协议中的典型应用、探讨它们在PSO中的研究现状与主要进展,并提供各数据结构的性能对比分析与基准测试结果.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual‑Olfactory Crossmodal Perception

The crossmodal interaction of different senses,which is an important basis for learning and memory in the human brain,is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception,but related researches are scarce.Here,we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus(VP)van der Waals heterojunctions.Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene,the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude,reaching up to 7.7 A W^(−1).Excited by ultraviolet light,multiple synaptic functions,including excitatory postsynaptic currents,pairedpulse facilitation,short/long-term plasticity and“learning-experience”behavior,were demonstrated with a low power consumption.Furthermore,the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments,enabling it to simulate the interaction of visual and olfactory information for crossmodal perception.This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Bidirectional rectifier with gate voltage control based on Bi_(2)O_(2)Se/WSe_(2)heterojunction

Two-dimensional(2D)WSe_(2)has received increasing attention due to its unique optical properties and bipolar behavior.Several WSe_(2)-based heterojunctions exhibit bidirectional rectification characteristics,but most devices have a lower rectification ratio.In this work,the Bi_(2)O_(2)Se/WSe_(2)heterojunction prepared by us has a typeⅡband alignment,which can vastly suppress the channel current through the interface barrier so that the Bi_(2)O_(2)Se/WSe_(2)heterojunction device has a large rectification ratio of about 10^(5).Meanwhile,under different gate voltage modulation,the current on/off ratio of the device changes by nearly five orders of magnitude,and the maximum current on/off ratio is expected to be achieved 106.The photocurrent measurement reveals the behavior of recombination and space charge confinement,further verifying the bidirectional rectification behavior of heterojunctions,and it also exhibits excellent performance in light response.In the future,Bi_(2)O_(2)Se/WSe_(2)heterojunction field-effect transistors have great potential to reduce the volume of integrated circuits as a bidirectional controlled switching device.

Controllable thermal rectification design for buildings based on phase change composites

Phase-change material(PCM)is widely used in thermal management due to their unique thermal behavior.However,related research in thermal rectifier is mainly focused on exploring the principles at the fundamental device level,which results in a gap to real applications.Here,we propose a controllable thermal rectification design towards building applications through the direct adhesion of composite thermal rectification material(TRM)based on PCM and reduced graphene oxide(rGO)aerogel to ordinary concrete walls(CWs).The design is evaluated in detail by combining experiments and finite element analysis.It is found that,TRM can regulate the temperature difference on both sides of the TRM/CWs system by thermal rectification.The difference in two directions reaches to 13.8 K at the heat flow of 80 W/m^(2).In addition,the larger the change of thermal conductivity before and after phase change of TRM is,the more effective it is for regulating temperature difference in two directions.The stated technology has a wide range of applications for the thermal energy control in buildings with specific temperature requirements.

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.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor

Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sparking significant advancements in electronic devices that utilize 2D TMDs.Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance.This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor.It delves into the impacts of miniaturization,including the reduction of channel length,gate length,source/drain contact length,and dielectric thickness on transistor operation and performance.In addition,this review provides a detailed analysis of performance parameters such as source/drain contact resistance,subthreshold swing,hysteresis loop,carrier mobility,on/off ratio,and the development of p-type and single logic transistors.This review details the two logical expressions of the single 2D-TMD logic transistor,including current and voltage.It also emphasizes the role of 2D TMD-based transistors as memory devices,focusing on enhancing memory operation speed,endurance,data retention,and extinction ratio,as well as reducing energy consumption in memory devices functioning as artificial synapses.This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications.It underscores the anticipated challenges,opportunities,and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

AI审图机器人在消防图审业务运营管理系统中的应用

本文结合清华大学与清大东方智能科技有限公司的最新研究成果,介绍了“图智2.0:施工图消防AI审查系统”与现实图审业务运营管理系统的创新实践。将AI审图功能集成到实际消防图审管理流程中,提出了面向全套施工图的消防审查AI机器人解决方案,并在实际消防图审业务中进行了应用。初步实践结果表明,当前AI审图机器人能够自动接收审图任务、执行全自动审查并提交规范化的审查结果。AI审图机器人已初步应用于幼儿园、学校、住宅等多类民用建筑的消防图审项目,提高了审查的客观性与效率;同时,本文提出的AI消防审查解决方案为人工智能技术在消防审查行业的应用提供了有益参考。

Photonics-assisted THz wireless communication enabled by wide-bandwidth packaged backilluminated modified uni-traveling-carrier photodiode

This paper presents a wide-bandwidth back-illuminated modified uni-traveling-carrier photodiode(MUTC-PD)packaged with standard WR-5 rectangular waveguide for high-speed wireless communications.With optimized epitaxy structure and coplanar waveguide electrodes,the fabricated 4-μm-diameter PD exhibits ultra-flat frequency response and high saturation power.Integrated passive circuits including low-loss bias-tee and E-plane probe are designed to package the PD into a compact module with waveguide output.The packaged PD module has demonstrated a flat frequency response with fluctuations within±2.75 d B over a broadband of 140–220 GHz and a high saturated output power of-7.8 d Bm(166μW)at 140 GHz.For wireless communication applications,the packaged PD is used to implement 1-m free space transmission at carrier frequencies of 150.5 and 210.5 GHz,with transmission rates of 75 and 90 Gbps,respectively.

面向计算机视觉系统的对抗样本攻击综述

对抗样本攻击是近年来计算机视觉领域的热点研究方向,通过对图像添加细微的噪声,对抗样本使计算机视觉系统做出错误判断.对抗样本攻击的研究起初重点关注于图像分类任务,随着研究的深入逐步拓展到目标检测、人脸识别等更加复杂的计算机视觉任务中.然而,现有的对抗样本综述缺乏对新兴图像分类攻击方案的梳理总结以及针对目标检测、人脸识别等复杂任务攻击的分析总结.本论文聚焦于计算机视觉系统中的对抗样本攻击,对其理论与前沿技术进行了系统性的综述研究.首先,本论文介绍了对抗样本的关键概念与敌手模型.其次,分类总结和对比分析了对抗样本存在原因的三大类相关假设.再次,根据数字域与物理域两大应用场景,分类概述和对比分析图像分类系统中的对抗样本攻击技术.根据不同的敌手模型,我们进一步地将图像分类任务数字域的攻击方案划分为白盒和黑盒两种场景,并重点总结梳理了新兴的攻击类别.同时,在目标检测、人脸识别、语义分割、图像检索、视觉跟踪五类复杂计算机视觉任务上,根据适用场景分类总结各类任务中的对抗样本攻击方案.进一步地,从攻击场景、攻击目标、攻击效果等方面对于不同攻击方案进行详细地对比分析.最后,基于现有对抗样本攻击方法的总结,我们分析与展望了计算机视觉系统中对抗样本的未来研究方向.

基于BIM的设计成果自动审查的实现方法研究

随着建筑信息模型(BIM)在智慧城市领域应用需求的不断提出,政府机构和各企事业单位为提高设计管控质量,对BIM模型的合标性及合规性提出了更具体的要求,传统人工审查的方式已经远远不能满足数据审查的需要。本文基于BIM的设计成果自动审查系统的研发与实践工作,研究了国内外BIM自动审查技术路线,并对其在国内的适用性展开分析,探讨了标准数字化和模型自动审查的技术路线,并对具体的实施步骤进行了规划,系统地阐述了BIM的设计成果自动审查实现方法,在某地产企业的落地应用实践过程,重点介绍了标准量化、标准规则化、模型检查和设计审查四个主要的过程,并对于模型审查过程中用到的模板、算法进行了较为详细的介绍,为建筑行业设计成果自动审查研发工作提供参考。

基于铁电晶体管的存储与存算一体电路

近年来,物联网和人工智能等技术的发展对片上存储与智能计算的能效、密度以及性能提出了更高的要求。面对传统CMOS处理器的能效与密度瓶颈,以及传统冯·诺伊曼架构的“存储墙”瓶颈,以铁电晶体管(FeFET)为代表的新型非易失存储器(NVM)提供了新的机遇。FeFET具有非易失、高能效、高开关比等特点,非常适合低功耗、高密度场景下的存储与存算一体(CiM)应用,为数据密集型应用在边缘端的部署提供支持。该文回顾了FeFET的发展历程、结构、特性以及建模相关的工作,概述了FeFET存储器在电路结构和访存机制上的探索与优化。进一步地,该文还探讨了FeFET CiM在非易失计算、存内逻辑计算、矩阵向量乘法以及内容可寻址存储器上的应用。最后,该文从不同方面分析并展望了基于FeFET的存储与CiM电路的前景与挑战。

自注入锁定微腔克尔频梳的锁定态研究

当前自注入锁定频梳理论研究在不同参数对于自启动效果的影响的探究尚不完善,用于解释锁定机理的工作点理论与数值仿真的结果差异较大且无法很好地解释部分实验现象。本文利用包含微腔背散射效应的LLE (Lugiato-Lefever Equation)方程,构建了自启动克尔微梳模型,使用基于分步傅里叶算法的数值仿真并结合各参数的物理内涵,探究了模型关键参数对于频梳自启动锁定态的影响;在此基础上,针对现有自注入锁定微腔频梳锁定理论不足的问题,从模型基本公式出发,修正了工作点锁定理论,提升了对自启动数值仿真中锁定态预测的准确性,提高了理论预测与数值仿真和实验结果的契合度。

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.