The human geographical environment is a comprehensive setting formed by the interaction between human activities and the geographical environment, characterized by its complexity and vulnerability. Applying the digital twin method to create a new research model in a human geographical environment holds significant academic and practical value. This approach helps avoid disturbances in the real environment, deeply explores complex issues, and optimizes solutions for real-world geographical problems. By reviewing the current state of research, we propose the basic concept of the digital twin human geographical environment and elaborate on its meaning. Additionally, we construct a technical framework for the digital twin human-geographical environment system. We identify the digital twin human-geographical environment as comprising the real human geographical environment, the virtual human geographical environment, and the interaction between the two. This is achieved through the twin construction and interaction of the geographical environment, human activities, and human geographical interaction, facilitating coordination and mutual enhancement between the real and virtual environments. Using the digital twin of Bailudong Academy as a case study, we demonstrate the construction methods, main functions, and result forms of a digital twin human geographical environment. Besides, this paper will provoke thoughts on the coupling of digital twins and human geographical environments, jointly promoting the development of human geography.

The human geographic environment is a comprehensive environment formed by the interaction of human activities and the geographic environment,which is complex and highly vulnerable.Introducing the digital twin method to build a new framework of research on human geography issues has great disciplinary importance and application value because this framework has the characteristics of driving the virtual according to the real,controlling the real according to the virtual,and the coordinated evolution of the virtual and the real.It also helps avoid disturbing the real environment,explores complex problems in depth,and optimizes the control scheme of real geographical problems.Based on the review of relative research in China and abroad,a conceptual model of the digital twin human geographic environment is proposed,and its systematic connotation is elaborated.The major components of this system include the real human geographic environment,the virtual human geographic environment,and the information interaction between them.Through the digital twin construction and simulation of the geographic environment,human activities as well as human-nature interaction,the virtual and real synergy,complementary advantages,and coevolution between the virtual and the real environment can be realized.Furthermore,a technical framework with bilateral technology roadmap for the implementation of digital twin human geographic environments is proposed.One route is to complete the comprehensive perception and twin reproduction of the real human geographic environment through digital accurate mapping to the virtual geography environment,and the other route is to achieve intelligent feedback of the virtual human geographic environment and its manipulation on the real human geographic environment through multichannel virtual-real interaction.On the basis of the above,a hierarchical construction scheme of the digital twin human geographic environment platform,which covers five sub platforms,namely,collaborative monitoring and environmental awareness platform,multimodal full-scale database,intelligent modeling and simulation technology integration,high-performance spatiotemporal computing engine,and cross-domain knowledge application for decision making is designed.Supported by the Nanchang Subcenter of the UNESCO International Natural and Cultural Heritage Space Technology Center,the digital twin of Jiangxi Bailudong Academy,the education base of Chinese Confucian culture,is taken as a typical case to demonstrate the construction method,main function,and result form of the digital twin human geographic environment.The conclusions are drawn from three perspectives:(1)The digital twin human geographic environment should be taken as a creative method for the exploratory learning of human geography.(2)The digital twin human geographic environment is currently the best way to build a geographic metaverse with human activities.(3)The virtual geographic environment can provide theoretical basis for the construction of the digital twin human geographic environment.Lastly,the purpose of this paper is to throw bricks and spark jade to arouse scholars'thinking on the research of coupling digital twins and human geographic environment,and to promote the sustainable development of human geography jointly.

Tropical cyclones frequently disrupt maritime transportation systems, impacting the normal operation of vessels and causing transportation delays. Analyzing the occurrence and degree of vessel transportation delays under disaster conditions is one of the critical aspects of maritime transportation management. This article, based on Automatic Identification System (AIS) data, examined the real behavioral trajectories of vessels and delay characteristics during tropical cyclones. Taking the example of Tropical Cyclone Veronica, which occurred in the waters off northwestern Australia in 2019, we conducted a comprehensive analysis. This involved numerical simulations of the entire disaster process and the cleaning and matching of trajectory data for all affected vessels in the area. We delved deeply into the relationship between vessel behavior characteristics and tropical cyclones, identified factors contributing to delays, and, based on our findings, constructed a Bayesian network inference model for vessel transportation delays under disaster conditions. This model uses information such as tropical cyclone intensity, vessel basic attributes, behavior choices, and port disaster avoidance measures as its primary nodes. The research results indicate that vessel rerouting, vessel loss of control, and waiting for port entry are the three major sources of vessel delays during disasters. Key influencing factors contributing to these consequences include port closure measures, the duration of vessels being adversely affected by strong winds, vessel tendencies toward loss of control, and risk preferences. To control the extent of vessel delays more effectively, it is crucial to prioritize these sensitive factors and strengthen monitoring and management efforts. This model is driven by real data, providing an objective reflection of real-world scenarios, and its effectiveness has been validated through sensitivity analysis. The research perspective and algorithmic framework presented in this paper offer a novel paradigm and fresh perspective for the study of maritime transportation disasters. The research findings have significant implications for bolstering the resilience of maritime transportation networks, enhancing our comprehension and anticipation of delays, and ensuring the continuity, security, and efficiency of the global supply chain for goods.

Using quasi-Newton update and acceleration scheme, a new Dai-Liao conjugate gradient method that does not need computing or storing any approximate Hessian matrix of the objective function is developed for unconstrained optimization. It is shown that the search direction derived from a modified Perry matrix not only possesses sufficient descent condition but also fulfills Dai-Liao conjugacy condition at each iteration. Under certain assumptions, we establish the global convergence of the proposed method for uniformly convex function and general function, respectively. The numerical results illustrate that the presented method can effectively improve the numerical performance and successfully solve the test problems with a maximum dimension of 100000.

SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for the theory of Linear Integer Arithmetic and implement them on top of Spacer solver in Z3. Our empirical results show that GSpacer, Spacer extended with global guidance, is significantly more effective than both Spacer and sole global reasoning, and, furthermore, is insensitive to interpolation.

自20世纪60年代初以来,地理信息系统引领了地理信息的分析与服务.然而,由于地理学家对于地理过程开展多维模拟的需求,同时许多大型工程需要对于不同方案的后果事先开展模拟评估,为此中国学者们在世纪相交之际开始了对于虚拟地理环境的探索.本文从地理信息系统到虚拟地理环境的认识上的转变着手,分析了虚拟地理环境的演进过程、当前的阶段定位与挑战,着重探讨了虚拟地理环境研究的新态势:基于虚拟地理环境的地理空间认知研究、虚拟地理环境与实验地理学新方向、大数据背景下的虚拟地理认知实验方法和虚拟地理环境与地理知识工程,并给出最新研究思路.

In this paper, we present a new conjugate gradient method using an acceleration scheme for solving large-scale unconstrained optimization. The generated search direction satisfies both the sufficient descent condition and the Dai–Liao conjugacy condition independent of line search. Moreover, the value of the parameter contains more useful information without adding more computational cost and storage requirements, which can improve the numerical performance. Under proper assumptions, the global convergence result of the proposed method with a Wolfe line search is established. Numerical experiments show that the given method is competitive for unconstrained optimization problems, with a maximum dimension of 100,000.

Program verifiers are not exempt from the bugs that affect nearly every piece of software. In addition, they often exhibit brittle behavior: their performance changes considerably with details of how the input program is expressed-details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool's idiosyncrasies before they can use it effectively. This paper introduces a technique to detect lack of robustness of program verifiers; the technique is lightweight and fully automated, as it is based on testing methods (such as mutation testing and metamorphic testing). The key idea is to generate many simple variants of a program that initially passes verification. All variants are, by construction, equivalent to the original program; thus, any variant that fails verification indicates lack of robustness in the verifier. We implemented our technique in a tool called "mugie", which operates on programs written in the popular Boogie language for verification-used as intermediate representation in numerous program verifiers. Experiments targeting 135 Boogie programs indicate that brittle behavior occurs fairly frequently (16 programs) and is not hard to trigger. Based on these results, the paper discusses the main sources of brittle behavior and suggests means of improving robustness.

为解决虚拟地理环境缺乏有效组织、规划、管理群体协同的问题,研究并构建角色模型及角色支持下的虚拟地理环境群体协同方法.构建了包含角色扮演、角色权限、角色感知、角色思维、角色行为和角色表达等模块的角色模型,设计了基于角色的虚拟地理环境群体协同框架,提出了多角色群体协同规划方法,建立了角色支撑下的群体协同冲突检测与消除策略.针对人类活动与全球变化相互影响问题,构建多领域人员参与下虚拟地理环境群体协同模拟与评估原型系统.经系统测试发现,通过角色模型能够较好地实现协同参与者与虚拟地理环境之间的耦合,同时能够为虚拟地理环境的群体协同提供系统性方法支撑.

Spectral clustering receives much attention as a competitive clustering algorithms emerging in recent years, which has achieved excellent efficiency. Outlier detection shows its increasingly high practical value in many application areas such as intrusion detection, fraud detection, discovery of criminal activities in electronic commerce and so on. In this paper, we proposed a new outlier detection method inspired by spectral clustering. Our algorithm combines k-Nearest Neighbor and statistical techniques to acquire and use the information of eigenvalues and eigenvectors of feature space and finally digs the abnormal data as outliers and have achieved the method to directly compute k-neighbors spectral clustering. In this way, it not only effectively reduces the storage cost required for clustering, but also provides important reference values in terms of dimension disaster on the high-dimensional space based on distance and density-based outlier detection. We compare the performance of our method with distance-based outlier detection methods and density-based outlier detection methods. Experimental results show that the algorithm has higher accuracy and better applicability in outlier detection.

Pairing image patches is to decide whether two image patches belong to the same scene but taken from different imaging conditions. It is a key procedure in the applications of unmanned aerial vehicle (UAV) video images. The challenges in pairing UAV image patches derive from the complex imaging conditions on UAV platforms such as jitter, frequent undefined motion, viewpoint changes, and illumination changes. Available popular methods usually follow the flowchart: preprocess images at first, then extract hand-crafted features, and finally match the extracted features through evaluating an independently predefined similarity metric. These methods could only handle part of negative factors from the complex imaging conditions and thus cannot effectively handle the challenges in pairing UAV image patches. This study aims to handle the challenges through automatically and simultaneously learning more representative features and accurate metric. Especially, this study proposes a deep learning method to jointly learn the feature representations and similarity metric over the training samples obtained from various imaging conditions. The model structure of the proposed pairing system consists of three parts: two stream convolutional neural networks (CNNs), one similarity metric layer and one softmax layer. They are jointly trained through the usual back propagation algorithm. Moreover, to further improve the performance, this study develops a transfer learning strategy for the proposed deep model. Two new training datasets from satellite scenes and UAV scenes, respectively, are built to evaluate the proposed pairing system, and the experimental results show that our method outperforms the most recent approaches in pairing UAV video image patches.

SMT solvers have become de rigueur in deductive verification to automatically prove the validity of verification conditions. While these solvers provide an effective support for theories—such as arithmetic—that feature strongly in program verification, they tend to be more limited in dealing with first-order quantification, for which they have to rely on special annotations—known as triggers—to guide the instantiation of quantifiers. Writing effective triggers is necessary to achieve satisfactory performance with SMT solvers, but remains a tricky endeavor—beyond the purview of non-highly trained experts. In this paper, we experiment with the idea of using first-order provers instead of SMT solvers to prove the validity of verification conditions. First-order provers offer a native support for unrestricted quantification, but have been traditionally limited in theory reasoning. By leveraging some recent extensions to narrow this gap in the Vampire first-order prover, we describe a first-order encoding of verification conditions of programs written in the Boogie intermediate verification language. Experiments with a prototype implementation on a variety of Boogie programs suggest that first-order provers can help achieve more flexible and robust performance in program verification, while avoiding the pitfalls of having to manually guide instantiations by means of triggers.

With the information burst in the modern world, especially in the financial field, timeliness and correctness is of great importance for different modules in a distributed system. This thesis describes a reliable messaging middleware using multicast technology to achieve information exchange which is very suitable for financial scenarios. This thesis also explains the design on how to achieve the reliability and provide some serving strategy for data recovery. The middleware adopts publish-subscribe model which supports 4 different communication models to fulfill various purpose of message delivery. Finally this thesis explains the message throttling functionality and provides a sample throttling strategy which is very useful to tackle the traffic jam frequently happened in the financial field.

In this paper, a subspace three-term conjugate gradient method is proposed. The search directions in the method are generated by minimizing a quadratic approximation of the objective function on a subspace. And they satisfy the descent condition and Dai-Liao conjugacy condition. At each iteration, the subspace is spanned by the current negative gradient and the latest two search directions. Thereby, the dimension of the subspace should be 2 or 3. Under some appropriate assumptions, the global convergence result of the proposed method is established. Numerical experiments show the proposed method is competitive for a set of 80 unconstrained optimization test problems.

Comparing Unmanned Aerial Vehicle (UAV) video image patches is a fundamental task in UAV video image processing. The main difficulty lies in the wide variety of appearance changes in images taken under different UAV imaging conditions, such as jitter, changing points of view, frequent undefined motion, illumination changes and etc. The usual algorithms which are based on the hand-craft features and independently predefined similarity metrics, cannot deal with these factors well. Motivated by recent successes on learning deep feature representations and feature similarity metric, a method which jointly models and learns these two objects is proposed here. Especially, comparing UAV video image patches is deemed as a binary classification problem and a Convolutional Neural Network (CNN) based comparing system is developed. It is composed of three parts: (1) two stream CNNs, (2) one similarity metric network, (3) one softmax layer. To jointly learn the CNNs and similarity metric, the available standard natural image datasets are employed and two new datasets representing typical satellite and UAV imaging scenes are built. Furthermore, over the datasets from different imaging scenes, the transfer joint learning of the proposed comparing system is investigated. The primary experimental results show that the proposed method can significantly outperform the recent results of the hand-craft feature based comparing methods.

We describe new extensions of the first-order theorem prover Vampire for supporting program analysis and proving properties of loops with arrays. The common theme of our work is the symbol elimination method for generating loop invariants. In our work, we improve symbol elimination for program analysis in two ways. First, we enhance the program analysis framework of Vampire by simplifying skolemization during consequence finding. Second, we extend symbol elimination with theory-specific reasoning, in particular in the theory of polymorphic arrays, and generate and prove program properties over arrays. We illustrate our approach on a number of challenging examples coming from program analysis and verification. Our experiments show that, thanks to our improvements, programs that could not be analyzed before can now be verified with our method.

In this paper, a spectral-spatial classification method with Gaussian process was proposed for hyperspectral image classification. This method exploits the relationship among adjacent pixels and integrates it into spectral information to obtain spectral-spatial classification. In the proposed approach, the spatial information of a single pixel is weighted by the cosine similarity value between the adjacent pixels in the neighborhood. Experiments were conducted on the AVIRIS Indian Pines data set to evaluate the performance of the proposed approach. And the results demonstrated the effectiveness of the proposed methods to improve the classification performance by consideration of the spatial relationship between adjacent pixels in the hyperspectral image.