Outward Influence and Cascade Size Estimation in Billion-scale Networks

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes $S$ will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence, and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is $\Omega(\log^4 n)$ times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201

When can we reconstruct the ancestral state? Beyond Brownian motion

Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology. Therefore, it is crucial to understand when we can estimate the ancestral state accurately. Previous works provide a necessary and sufficient condition, called the big bang condition, for the existence of an accurate reconstruction method under discrete trait evolution models and the Brownian motion model. In this paper, we extend this result to a wide range of continuous trait evolution models. In particular, we consider a general setting where continuous traits evolve along the tree according to stochastic processes that satisfy some regularity conditions. We verify these conditions for popular continuous trait evolution models including Ornstein-Uhlenbeck, reflected Brownian Motion, and Cox-Ingersoll-Ross

Differential diagnosis of dna viruses related to reproductive disorder on sows by multiplex-pcr technique

The newly emerged diseases caused by ASFV and PCV3 and their confirmed prevelance in Vietnam whereas most of available common commercial methods such as ELISA or realtime PCR designed for detecting single pathogen per reaction, highlighted a necessity for another diagnostic method to simultaneously detect and differentiate DNA viruses that are related to reproductive failures in sow herds including PCV2, PCV3, PPV, ASFV. In this communication, a diagnostic multiplex-PCR (mPCR) was established with pathogen-specific primers selected from previous studies and another set of primers designed for COX1 gene serving as an internal amplification control (IAC). The predicted products of PCV2, PCV3, PPV, ASFV and IAC were 702 bp, 223 bp, 380 bp, 278 bp and 463 bp, respectively. After optimization, the mPCR functioned specifically at 62°C. Results revealed the consistent detection limit at 100 copies/gene/reaction. In application, 185 serum samples from sows were used to examine the presence of the related pathogens. mPCR results showed that the mono-infection rate of PCV2, PCV3, PPV, and ASFV was 0% (0/185), 40% (74/185), 28.1% (52/185), and 48.1% (89/185), respectively. Regarding coinfection rate, the data indicated that coinfections of 2, 3 and 4 pathogens were 20%, 8.1% and 0% accordingly. In conclusion, the mPCR assay was successfully established and ready to serve for diagnosis of PCV2, PCV3, PPV and ASFV infection in reality with high specificity and sensitivity. It is a good contribution to a better understanding of the epidemiology of these diseases in swine

Accounting-based variables as an early warning indicator of financial distress in crisis and non-crisis periods

Financial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in-sample and out-of-sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre- and post-crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market-based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region

A knowledge-based framework for developing smart interfaces for smart service systems

Nowadays, smart service systems are value co-creating configurations of people, technologies, organisations,and information that are capable of in-dependent learning, adaptation, and decision-making. They are propelled byunprecedented advancements in connectivity, sensors, data storage, computation, and artificial intelligence. One of thekey challenges faced by those systems is how to provide smart interfaces, which can assist business users with limitedknowledge in business analytics in gaining business insights from business data. For this reason, this paper proposes aknowledge-based framework for developing smart interfaces for smart service systems, which will assist business usersin exploring business data to gain business in-sights and subsequently make better business decisions to promote valueco-creation. A prototype with simulation data has been developed and presented as a running example to illustrate how theproposed framework can be applied to create an effective smart interface for a typical smart service system: a customer intelligence system

Methane emission factors from vietnamese rice production: Pooling data of 36 field sites for meta-analysis

Rice production is a significant source of greenhouse gas (GHG) emissions in the national budget of many Asian countries, but the extent of emissions varies strongly across agro-environmental zones. It is important to understand these differences in order to improve the national GHG inventory and effectively target mitigation options. This study presents a meta-analysis of CH4 database emission factors (EFs) from 36 field sites across the rice growing areas of Vietnam and covering 73 cropping seasons. The EFs were developed from field measurements using the closed chamber technique. The analysis for calculating baseline EFs in North, Central and South Vietnam in line with the Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology was specified for the three cropping seasons being early-(E), mid-(M) and late-year (L) seasons. Calculated average CH$_{4}$ EFs are given in kg ha$^{-1}$ d$^{-1}$ and reflect the distinct seasons in North (E: 2.21; L: 3.89), Central (E: 2.84; M+L: 3.13) and South Vietnam (E: 1.72; M: 2.80; L: 3.58). Derived from the available data of the edapho-hydrological zones of the Mekong River Delta, season-based EFs are more useful than zone-based EFs. In totality, these average EFs indicate an enormous variability of GHG emissions in Vietnamese rice production and represent much higher values than the IPCC default. Seasonal EFs from Vietnam exceeded IPCC defaults given for Southeast Asia corresponding to 160% (E), 240% (M) and 290% (L) of the medium value, respectively

Towards a service-oriented architecture for knowledge management in big data era

Nowadays, big data is a revolution that transforms conventional enterprises into data-driven organizations in which knowledge discovered from big data will be integrated into traditional knowledge to improve decision-making and to facilitate organizational learning. Consequently, a major concern is how to evolve current knowledge management systems, which are confronted with a various and unprecedented amount of data, resulting from different data sources. Therefore, a new generation of knowledge management systems is required for exploring and exploiting big data as well as for facilitating the knowledge co-creation between the society and its business environment to foster innovation. This article proposes a service-oriented architecture for elaborating a new generation of big data-driven knowledge management systems to help enterprises to promote knowledge co-creation and to obtain more business value from big data. The proposed architecture is presented based on the principles of design science research and its evaluation uses the analytical evaluation method

Accumulation and distribution of zinc in the leaves and roots of the hyperaccumulator Noccaea caerulescens

Understanding the uptake mechanisms of heavy metals by hyperaccumulators is necessary for improving phytoextraction options to reduce metal toxicities in contaminated soils. In this study, the capacity of Zn uptake by the hyperaccumulator Noccaea caerulescens was investigated and compared to the non-hyperaccumulator Thlaspi arvense. The plants were grown under hydroponic conditions in a glasshouse. The distribution of Zn in the roots and leaves of these species was investigated by scanning electron microscopy with energy-dispersive X-ray analysis. Compared with the control with no Zn added, it was shown that prolonged Zn treatments decreased the biomass of both N. caerulescens and T. arvense. Since N. caerulescens requires Zn for growth, no Zn toxicity symptoms were observed, even when the Zn concentration in shoots reached 2.5% dry mass. T. arvense showed serious Zn toxicity only after two weeks of Zn treatment. Zn uptake by N. caerulescens was mainly translocated to the leaves while almost all of the Zn taken-up by T. arvense was retained in the roots. In N. caerulescens, increasing concentration of Zn in the supply decreased Ca and P concentrations in the shoots by up to 50 and 35%, respectively. Zn-containing crystals were abundant in both the upper and lower epidermal cells of the leaves and in the cortex of the roots during the later growth phase. Co-localization of Ca and Zn, P and S were found in leaf and root tissues. The results suggest that Zn-rich crystals with an abundance of the Zn ligand in the roots and shoots, and co-localization and interaction between Zn and other ions, may have functional significance with respect to conferring particular attributes to N. caerulescens that are not present in the non-hyperaccumulator counterpart. An understanding of these species-specific differences has relevance from the perspective of offering some insight into how particular species could contribute to a strategy for the detoxification of Zn-contaminated sites