Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Naive Bayes classifier based on memristor nonlinear conductance

Edge computing, as a new computing paradigm, brings cloud computing’s computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constitute <italic>edge storage systems</italic> (ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this article, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel <italic>Erasure Coding based Edge Data Placement</italic> (EC-EDP) problem as an integer linear programming problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="jin-ieq1-3152849.gif"/></alternatives></inline-formula>-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

https://ieeexplore.ieee.org/ielx7/4629386/4629387/09721137.pdf

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Industrial Internet of Things (IIoT), as the integration of industrial network and IoT, has attracted a lot of attention from both academia and industry. However, with such integration dee...

A security‐driven network architecture for routing in industrial Internet of Things

Maximal clique enumeration (MCE) is a fundamental problem in graph theory and is used in many applications, such as social network analysis, bioinformatics, intelligent agent systems, cyber security. Most existing MCE algorithms focus on improving the efficiency rather than reducing the size of the output, which could consist of a large number of maximal cliques. In this paper, we study how to report a summary of less overlapping maximal cliques. The problem was studied before, however, after examining the pioneer approach, we consider it still not satisfactory. To advance the research along this line, this paper attempts to make two contributions: (a) We propose a more effective sampling strategy, which produces a much smaller summary but still ensures that the summary can somehow witness all the maximal cliques and the expectation of each maximal clique witnessed by the summary is above a predefined threshold. (b) To verify experimentally, we tested ten real benchmark datasets that have a variety of graph characteristics. The results show that our new sampling strategy consistently outperforms the state-of-the-art method by producing smaller summaries and running faster on all the datasets.

Qiang He

Papers

Naive Bayes classifier based on memristor nonlinear conductance

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

A security‐driven network architecture for routing in industrial Internet of Things

Mining Maximal Clique Summary with Effective Sampling

Enrichment and specific quantification of Methanocalculus in anaerobic digestion.