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

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polya-urn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.Comment: Full version of ICML 201

Lignin and Cellulose Extraction from Vietnam’s Rice Straw Using Ultrasound-Assisted Alkaline Treatment Method

The process of cellulose and lignin extraction from Vietnam’s rice straw without paraffin pretreatment was proposed to improve economic efficiency and reduce environmental pollution. Treatment of the rice straw with ultrasonic irradiation for 30 min increased yields of lignin separation from 72.8% to 84.7%. In addition, the extraction time was reduced from 2.5 h to 1.5 h when combined with ultrasonic irradiation for the same extraction yields. Results from modern analytical methods of FT-IR, SEM, EDX, TG-DTA, and GC-MS indicated that lignin obtained by ultrasound-assisted alkaline treatment method had a high purity and showed a higher molecular weight than that of lignin extracted from rice straw without ultrasonic irradiation. The lignin and cellulose which were extracted from rice straw showed higher thermal stability with 5% degradation at a temperature of over 230°C. The ultrasonic-assisted alkaline extraction method was recommended for lignin and cellulose extraction from Vietnam’s rice straw

Validating a Scale for Measuring Preschool Teachers’ Competence in Promoting Children’s Language Development in Vietnam: An Exploratory Factor Analysis

The role of preschool teachers in supporting children’s language development is unquestionably substantial. To ensure the quality of preschool teachers’ performance in this specific task, various assessing instruments have been developed and justified in recent years. This study joins such efforts by investigating a new scale based on the “Framework for assessing preschool teacher competence in promoting children’s language development” proposed by a previous research. The scale’s psychometric properties are examined with a sample of 685 Vietnamese preschool teachers. The results supported the four-factor model suggested by the original authors and confirmed its reliability and validity. Finally, further usages of the scale are discussed

A Distributed Heuristic Algorithm for Delay Constrained Energy Efficient Routing in Wireless Sensor Networks

Besides energy restriction, wireless sensor networks (WSNs) should be able to provide bounded end-to-end delay when they are used to support real-time applications such as early forest fire alarm systems. In this paper, we investigate the problem of finding the least energy consumption route subject to a delay constraint with low computational complexity in such networks. Based on the distance-vector routing approach, which has less computational complexity and message overhead, we propose a distributed heuristic algorithm called Delay Constrained Energy Efficient Routing (DCEER) in order to minimize the total energy consumption while meeting the end-to-end delay requirement. DCEER only requires a moderate amount of information at each sensor node and does not suffer from the excessive running time. We prove that our proposed algorithm always finishes within a finite time and the computation complexity is only O(n), where n is a divisor of the number of sensor nodes. By mathematical proof and simulation, we verify that DCEER is suitable for large-scale WSNs because the number of messages exchanged between sensor nodes are represented by a polynomial function. Furthermore, we evaluate our proposal to compare its performance with related protocols

Synthesis of ZnO nanorod for immunosensor application

This paper reported a facile method to synthesize ZnO nanorods for immunosensor application. The ZnO nanorods were synthesized by hydrothermal reaction. Synthesis time affecting on morphology of nanorods was also studied. The immobilization of anti-rotavirus onto ZnO nanorod-deposited sensor was performed via absorption method. The electrochemical responses of the immunosensor were studied by cyclic voltammetry (C-V) method with [Fe(CN)6]3−/4− as redox probe. A linear decreased response in C-V for cell of rotavirus concentration was found in the range of 7.8×105 CFU/mL to 7.8×108 CFU/mL. The detection limit of the immunosensor was 7.8×105 CFU/mL. The results indicated application of ZnO nanorod sensor for label-free real-time detection of a wide dynamics range of biological species