Multi-view shaker detection: Insights from a noise-immune influence analysis Perspective

Entities whose changes will significantly affect others in a networked system are called shakers. In recent years, some models have been proposed to detect such shaker from evolving entities. However, limited work has focused on shaker detection in very short term, which has many real-world applications. For example, in financial market, it can enable both investors and governors to quickly respond to rapid changes. Under the short-term setting, conventional methods may suffer from limited data sample problems and are sensitive to cynical manipulations, leading to unreliable results. Fortunately, there are multi-attribute evolution records available, which can provide compatible and complementary information. In this paper, we investigate how to learn reliable influence results from the short-term multi-attribute evolution records. We call entities with consistent influence among different views in short term as multi-view shakers and study the new problem of multi-view shaker detection. We identify the challenges as follows: (1) how to jointly detect short-term shakers and model conflicting influence results among different views? (2) how to filter spurious influence relation in each individual view for robust influence inference? In response, a novel solution, called Robust Influence Network from a noise-immune influence analysis perspective is proposed, where the possible outliers are well modelled jointly with multi-view shaker detection task. More specifically, we learn the influence relation from each view and transform influence relation from different views into an intermediate representation. In the meantime, we uncover both the inconsistent and spurious outliers.Comment: 14 pages, 4 figure

Prospecting Community Development Strength based on Economic Graph: From Categorization to Scoring

Recent years have witnessed a growing number of researches on community characterization. In contrast to the large body of researches on the categorical measures (rise or decline) for evaluating the community development, we propose to estimate the community development strength (to which degree the rise or decline is). More specifically, given already known categorical information of community development, we are attempting to quantify the community development strength, which is of great interest. Motivated by the increasing availability of large-scale data on the network between entities among communities, we investigate how to score the the community's development strength. We formally define our task as prospecting community development strength from categorization based on multi-relational network information and identify two challenges as follows: (1) limited guidance for integrating entity multi-relational network in quantifying the community development strength; (2) the existence of selection effect that the community development strength has on network formation. Aiming at these challenges, we start by a hybrid of discriminative and generative approaches on multi-relational network-based community development strength quantification. Then a network generation process is exploited to debias the selection process. In the end, we empirically evaluate the proposed model by applying it to quantify enterprise business development strength. Experimental results demonstrate the effectiveness of the proposed method.Comment: 12 pages, 3 figure

Flux enhancements in cross-flow microfiltration

Two-phase flow microfiltration successfully reduced the fouling problem for several microfiltration processes. Two-phase flow, created by introducing air into the fluid, increased the permeate flux 120%, 45%, and 40% for three different fermented biomass solutions at one hour operating time. For cheese whey microfiltration, the two-phase flow method successfully improved the permeate flux approximately 50% with only 5% air. Without the two-phase flow method, the permeate flux increased 20% when the liquid flow rate was doubled. Intermittent use of air was less effective than continual addition. Operating parameters of two-phase flow microfiltration, such as liquid flow rate and air percentage, were optimized based on permeate flux and energy requirements. The two-phase flow technique saved more energy and processing time than simply increasing the liquid flow rate. An economic analysis was performed to estimate the annual costs for scale-up of a cheese whey microfiltration process