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SummaryViral diseases severely affect crop yield and quality, thereby threatening global food security. Genetic improvement of plant virus resistance is essential for sustainable agriculture. In the last decades, several modern technologies were applied in plant antiviral engineering. Here we summarized breakthroughs of the two major antiviral strategies, RNA silencing and genome editing. RNA silencing strategy has been used in antiviral breeding for more than thirty years, and many crops engineered to stably express small RNAs targeting various viruses have been approved for commercial release. Genome editing technology has emerged in the past decade, especially CRISPR/Cas, which provides new methods for genetic improvement of plant virus resistance and accelerates resistance breeding. Finally, we discuss the potential of these technologies for breeding crops, and the challenges and solutions they may face in the future.

Introduction 1 1.1. Main result 1 1.2. Idea of the proof 2 2. Preliminaries 3 2.1. The reduction process 3 2.2. Numerical inequalities 4 3. Relative Noether inequality 6 4. Asymptotic behavior of cohomological dimensions 9 5. Proof of Theorem 1.1 11 References 12

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The rise of crowdsourcing brings new types of malpractices in Internet advertising. One can easily hire web workers through malicious crowdsourcing platforms to attack other advertisers. Such human generated crowd frauds are hard to detect by conventional fraud detection methods. In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity. Then we propose an effective crowd fraud detection method for search engine advertising based on these patterns, which consists of a constructing stage, a clustering stage and a filtering stage. At the constructing stage, we remove irrelevant data and reorganize the click logs into a surferadvertiser inverted list; At the clustering stage, we define the sync-similarity between surfers' click histories and transform the coalition detection to a clustering problem, solved by a nonparametric algorithm; and finally we build a dispersity filter to remove false alarm clusters. The nonparametric nature of our method ensures that we can find an unbounded number of coalitions with nearly no human interaction. We also provide a parallel solution to make the method scalable to Web data and conduct extensive experiments. The empirical results demonstrate that our method is accurate and scalable.

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