Discovery of Core-Nodes in Event-Based Social Networks

Most previous actor-node ranking algorithms for event-based social networks only consider how many events an actor participates in. However in event-based social networks, we should also consider the influence of events when we rank actor-nodes. In this paper we formally define event-based social networks and related concepts, then we propose rules to construct an event-based social network. Algorithms are presented to discover the activity and importance of each actor-node. We test the algorithms by analysing the DBLP data set. In the experiment actors in DBLP data set are ranked based on their activity, importance, and combination of activity and importance, respectively

Engineering diverse fatty acid compositions of phospholipids in Escherichia coli

Bacterial fatty acids (FAs) are an essential component of the cellular membrane and are an important source of renewable chemicals as they can be converted to fatty alcohols, esters, ketones, and alkanes, and used as biofuels, detergents, lubricants, and commodity chemicals. Most prior FA bioconversions have been performed on the carboxylic acid group. Modification of the FA hydrocarbon chain could substantially expand the structural and functional diversity of FA-derived products. Additionally, the effects of such modified FAs on the growth and metabolic state of their producing cells are not well understood. Here we engineer novel Escherichia coli phospholipid biosynthetic pathways, creating strains with distinct FA profiles enriched in ω7-unsaturated FAs (ω7-UFAs, 75%), Δ5-unsaturated FAs (Δ5-UFAs, 60%), cyclopropane FAs (CFAs, 55%), internally-branched FAs (IBFAs, 40%), and Δ5,ω7-double unsaturated FAs (DUFAs, 46%). Although bearing drastically different FA profiles in phospholipids, UFA, CFA, and IBFA enriched strains display wild-type-like phenotypic profiling and growth. Transcriptomic analysis reveals DUFA production drives increased differential expression and the induction of the fur iron starvation transcriptional cascade, but higher TCA cycle activation compared to the UFA producing strain. This likely reflects a slight cost imparted for DUFA production, which resulted in lower maximum growth in some, but not all, environmental conditions. The IBFA-enriched strain was further engineered to produce free IBFAs, releasing 96 mg/L free IBFAs from 154 mg/L of the total cellular IBFA pool. This work has resulted in significantly altered FA profiles of membrane lipids in E. coli, greatly increasing our understanding of the effects of FA structure diversity on the transcriptome, growth, and ability to react to stress

AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy

For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis. However, existing LDP-based range query approaches are limited by their properties, i.e., collecting user data according to a pre-defined structure. These static frameworks would incur excessive noise added to the aggregated data especially in the low privacy budget setting. In this work, we propose an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility. Furthermore, we derive a guideline for properly choosing parameters for AHEAD so that the overall utility can be consistently competitive while rigorously satisfying LDP. Leveraging multiple real and synthetic datasets, we extensively show the effectiveness of AHEAD in both low and high dimensional range query scenarios, as well as its advantages over the state-of-the-art methods. In addition, we provide a series of useful observations for deploying AHEAD in practice

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties