Adaptive State-Dependent Diffusion for Derivative-Free Optimization

This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples.A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing.It can otherwise be compared to annealing with state-dependent temperature.

面向数据权利、数据定价和隐私计算的数据驱动学习

近年来,数据已成为数字经济中最重要的生产要素之一。与传统生产要素不同,数据的数字化性质使其难以合同和交易。因此,建立一个高效和标准的数据交易市场体系将有利于降低成本,提高行业各方的生产力。尽管许多研究致力于数据法规和其他数据交易问题,如隐私和定价,但很少有工作对机器学习和数据科学领域的这些研究进行全面回顾。为了提供对这个主题的完整和最新的理解,本文涵盖了数据交易过程中的三个关键问题:数据权利、数据定价和隐私计算。通过厘清这些主题之间的关系,本文提供了一个数据生态系统的全貌,其中数据由个人、研究机构和政府等数据主体生成,而数据处理者出于创新或运营目的获取数据,并通过适当的定价机制根据数据主体各自的所有权分配收益。为了使人工智能(AI)能够长期有益于人类社会的发展,人工智能算法需要通过数据保护法规(即隐私保护法规)进行评估,以帮助构建日常生活中值得信赖的人工智能系统。

Adversarial Attacks and Defenses in Deep Learning

With the rapid developments of artificial intelligence(AI)and deep learning(DL)techniques,it is critical to ensure the security and robustness of the deployed algorithms.Recently,the security vulnerability of DL algorithms to adversarial samples has been widely recognized.The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans.Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality.Hence,adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years.In this paper,we first introduce the theoretical foundations,algorithms,and applications of adversarial attack techniques.We then describe a few research efforts on the defense techniques,which cover the broad frontier in the field.Several open problems and challenges are subsequently discussed,which we hope will provoke further research efforts in this critical area.

区块链与科学数据治理

数据已经成为继土地、劳动力、资本、技术之后的第五大生产要素.作为大数据的一个分支,科学大数据正在成为科学发现的新型驱动力[1].与其他数据相比,科学数据具有价值密度高、多学科交叉、供需分布广、伦理规范要求严格等特点,是国家创新发展的基础性战略资源.欧美等发达国家和地区对科学数据的管理和利用均出台了相应的法规.

Toward Privacy-Preserving Personalized Recommendation Services

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, hooks, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying per- sonalized recommendation services typically requires the collection of users＇ personal data for processing and anafytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

The New Frontiers of Cybersecurity

Security technology is a special kind of companion technology that is developed for the underlying applications it serves. It is becoming increasingly critical in today＇s society, as these underlying applications become more and more interconnected, pervasive, and intelligent. In recent years, we have witnessed the prolifera- tion of cutting-edge computing and information technologies in a wide range of emerging areas, such as cloud computing.

Spatial distribution and sources of soil heavy metals in the outskirts of Yixing City,Jiangsu Province,China

City outskirts serve as the concentration centers for the pollutants discharged from various sources such as industry,agriculture and transportation.With the acceleration of industrialization and urbanization,the eco-environment of the city outskirts has become a hot spot of public concern.An analysis was conducted in this paper on the soil in the outskirts of Yixing City using frequency distribution functions.The heavy metal sources in this region had been divided into two components,i.e.natural background component and strong human disturbance component.The corresponding interpretation of the distribution pattern and features of heavy metal elements was presented by spatial analysis.The results showed that the strong human disturbance components of Hg,Pb,and Cu accounted for 36.9%, 26.7%,and 23.3%in their contents respectively,which indicated directly the serious effect of human activities on heavy metal contents.Hg and Pb,because of the human disturbance,showed the greatest spatial variability,and human activities intensified the heterogeneity of the spatial distribution.The anisotropic analysis showed the higher organisation of Hg and Zn in the direction of urban-rural transition,which indicated their spatial characteristics with urban-rural transition.Pb displayed distinctive structure in the vertical direction of urban-rural transition,which was largely controlled by highway distribution.Cu content in paddy fields was significantly higher than those of other land-use-types,and the agricultural non-point source pollution played an important role in the distribution pattern of Cu.

Survey of design and security evaluation of authenticated encryption algorithms in the CAESAR competition

The Competition for Authenticated Encryption: Security, Applicability, and Robustness(CAESAR)supported by the National Institute of Standards and Technology(NIST) is an ongoing project calling for submissions of authenticated encryption(AE) schemes. The competition itself aims at enhancing both the design of AE schemes and related analysis. The design goal is to pursue new AE schemes that are more secure than advanced encryption standard with Galois/counter mode(AES-GCM) and can simultaneously achieve three design aspects: security,applicability, and robustness. The competition has a total of three rounds and the last round is approaching the end in 2018. In this survey paper, we first introduce the requirements of the proposed design and the progress of candidate screening in the CAESAR competition. Second, the candidate AE schemes in the final round are classified according to their design structures and encryption modes. Third, comprehensive performance and security evaluations are conducted on these candidates. Finally, the research trends of design and analysis of AE for the future are discussed.

Recent Developments in Numerical Techniques for Transport-Based Medical Imaging Methods

The objective of this paper is to review recent developments in numerical reconstruction methods for inverse transport problems in imaging applications,mainly optical tomography,fluorescence tomography and bioluminescence tomography.In those inverse problems,one aims at reconstructing physical parameters,such as the absorption coefficient,the scattering coefficient and the fluorescence light source,inside heterogeneous media,from partial knowledge of transport solutions on the boundaries of the media.The physical parameters recovered can be used for diagnostic purpose.Numerical reconstruction techniques for those inverse transport problems can be roughly classified into two categories:linear reconstruction methods and nonlinear reconstruction methods.In the first type of methods,the inverse problems are linearized around some known background to obtain linear inverse problems.Classical regularization techniques are then applied to solve those inverse problems.The second type of methods are either based on regularized nonlinear least-square techniques or based on gradient-driven iterative methods for nonlinear operator equations.In either case,the unknown parameters are iteratively updated until the solutions of the transport equations with the those parameters match the measurements to a certain extent.We review linear and nonlinear reconstruction methods for inverse transport problems in medical imaging with stationary,frequency-domain and time-dependent data.The materials presented include both existing and new results.Meanwhile,we attempt to present similar algorithms for different problems in the same framework to make it more straightforward to generalize those algorithms to other inverse(transport)problems.