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We describe a new algorithm, Minesweeper, that is able to satisfy stronger runtime guarantees than previous join algorithms (colloquially, 'beyond worst-case guarantees') for data in indexed search trees. Our first contribution is developing a framework to measure this stronger notion of complexity, which we call certificate complexity, that extends notions of Barbay et al. and Demaine et al.; a certificate is a set of propositional formulae that certifies that the output is correct. This notion captures a natural class of join algorithms. In addition, the certificate allows us to define a strictly stronger notion of runtime complexity than traditional worst-case guarantees. Our second contribution is to develop a dichotomy theorem for the certificate-based notion of complexity. Roughly, we show that Minesweeper evaluates β-acyclic queries in time linear in the certificate plus the output size, while for any β-cyclic query there is some instance that takes superlinear time in the certificate (and for which the output is no larger than the certificate size). We also extend our certificate-complexity analysis to queries with bounded treewidth and the triangle query. We present empirical results that certificates can be much smaller than the input size, which suggests that ideas in minesweeper might lead to faster algorithms in practice. Categories and Subject Descriptors H.2.4 [Database Management]: Systems-Relational databasesWe thank LogicBlox, Mahmoud Abo Khamis, Semih Salihoglu and Dan Suciu for many helpful conversations. We thank Jérémy Barbay for bringing helpful references on set intersection to our attention.

The Asian countries chronically infected with avian influenza A H5N1 are 'global hotspots' for biodiversity conservation in terms of species diversity, endemism and levels of threat. Since 2003, avian influenza A H5N1 viruses have naturally infected and killed a range of wild bird species, four felid species and a mustelid. Here, we report fatal disseminated H5N1 infection in a globally threatened viverrid, the Owston's civet, in Vietnam, highlighting the risk that avian influenza H5N1 poses to mammalian and avian biodiversity across its expanding geographic range.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceE-learning is an inevitable trend of education in the future. Although there are several researches about E-learning based on cloud computing, not many researches on the cloud computing adoption model, on the other hand, there are not many studies on the adoption of cloud-based E-learning in Vietnam and in the World. This study adapts the extended of Unified Theory of Acceptance and Use of Technology (UTAUT2) [48] to research the acceptance and use of E-learning based on cloud computing in Vietnam. These elements, namely facilitating condition, performance expectancy, effort expectancy, social influence, hedonic motivation, price value and habit influence on the intention and use of cloud-based E-Learning, the results show that seven out of eleven hypotheses are supported. The results will help implementing E-learning based on cloud and learning strategies to be more successful

Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines. Recently, new join algorithms have been discovered that achieve optimal worst-case run times for any join or even so-called beyond worst-case (or instance optimal) run time guarantees for specialized classes of joins. These new algorithms match or improve on those used in specialized graph-processing systems. This paper asks can these new join algorithms allow relational engines to close the performance gap with graph engines?We examine this question for graph-pattern queries or join queries. We find that classical relational databases like Postgres and MonetDB or newer graph databases/stores like Virtuoso and Neo4j may be orders of magnitude slower than these new approaches compared to a fully featured RDBMS, LogicBlox, using these new ideas. Our results demonstrate that an RDBMS with such new algorithms can perform as well as specialized engines like GraphLabwhile retaining a high-level interface. We hope our work adds to the ongoing debate of the role of graph accelerators, new graph systems, and relational systems in modern workloads.

Surveillance and outbreak reporting systems in Vietnam required improvements to function effectively as early warning and response systems. Accordingly, the Ministry of Health of Vietnam, in collaboration with the US Centers for Disease Control and Prevention, launched a pilot project in 2016 focusing on community and hospital event-based surveillance. The pilot was implemented in 4 of Vietnam's 63 provinces. The pilot demonstrated that event-based surveillance resulted in early detection and reporting of outbreaks, improved collaboration between the healthcare facilities and preventive sectors of the ministry, and increased community participation in surveillance and reporting.

We specify and analyze the conditions under which the MNL market share models are appropriate for equilibrium analysis. Our results show that a linear price response function as is often used in empirical research, in conjunction with the typical concavity assumed in a large range of marketing response functions, would yield an interior equilibrium solution. We then consider the optimal reactions on pricing and marketing spending to entry and potential market expansion. In the context of the MNL models, we demonstrate that the entry of a new brand evokes a decrease in the equilibrium prices of the existing brands as a defensive reaction. This is true in both an expanding market as well as a fixed market. However, while new entry into a fixed market would trigger the incumbents to lower the marketing expenditure, we show that firms tend to raise marketing activities as they experience market expansion. Consequently, there exist distinct possibilities that marketing efforts for the existing brands increase in view of entry in an expanding market. Further managerial and marketing implications for endogeneity of the number of firms are explored.MNL Market Attraction Models, Equilibrium Analysis, Entry and Market Expansion, Competitive Pricing and Advertising, Free-Entry Equilibrium