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

2D Proactive Uplink Resource Allocation Algorithm for Event Based MTC Applications

We propose a two dimension (2D) proactive uplink resource allocation (2D-PURA) algorithm that aims to reduce the delay/latency in event-based machine-type communications (MTC) applications. Specifically, when an event of interest occurs at a device, it tends to spread to the neighboring devices. Consequently, when a device has data to send to the base station (BS), its neighbors later are highly likely to transmit. Thus, we propose to cluster devices in the neighborhood around the event, also referred to as the disturbance region, into rings based on the distance from the original event. To reduce the uplink latency, we then proactively allocate resources for these rings. To evaluate the proposed algorithm, we analytically derive the mean uplink delay, the proportion of resource conservation due to successful allocations, and the proportion of uplink resource wastage due to unsuccessful allocations for 2D-PURA algorithm. Numerical results demonstrate that the proposed method can save over 16.5 and 27 percent of mean uplink delay, compared with the 1D algorithm and the standard method, respectively.Comment: 6 pages, 6 figures, Published in 2018 IEEE Wireless Communications and Networking Conference (WCNC

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

Clinical Epidemiological Characteristics and Risk Factors for Severity of SARS-CoV-2 Pneumonia in Pediatric Patients: A Hospital-Based Study in Vietnam

Introduction Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which can cause organ failure in several organs, cardiac problems, or acute respiratory distress syndrome (ARDS). Identifying clinical epidemiological characteristics and risk factors for complications of COVID-19 allows clinicians to diagnose and treat promptly. Objectives This study aims to describe the clinical epidemiological characteristics of COVID-19 and assess risk factors for the severity of SARS-CoV-2 pneumonia in children treated at Haiphong Children\u27s Hospital. Methods A descriptive cross-sectional study was conducted in Haiphong Children\u27s Hospital, Haiphong, Vietnam, for one year, from January 1, 2022, to December 31, 2022. Results In our study, 540 children were evaluated; the male-to-female ratio was 1.48/1; the median age was 23 months (IQR=6-74); Children aged under one year accounted for the highest proportion (n=202; 37.4%); 40 (7.4%) children had underlying illnesses. The number of admitted patients diagnosed with COVID-19 peaked in February 2022. Regarding severity, 380 (70.4%) cases were mild, 136 (25.2%) were moderate, only 24 (4.4%) cases were severe, and no children died. Common symptoms were fever in 483 (89.4%), coughing in 399 (73.9%), and tachypnea in 163 (30.2%) children. Laboratory features: white blood cell count, platelet count, serum CRP, and coagulation test showed little change. Around 116 (21.5%) had lymphopenia and 148 (27.4%) had pneumonia. Patients under one year were approximately 1.64 times more likely to experience pneumonia complications from COVID-19 than those without such a history (OR=1.64, 95%CI = 1.12 - 2.41, p=0.0112). Patients with underlying conditions were approximately 2.08 times more likely to experience pneumonia complications from COVID-19 compared to those without such conditions (OR=2.08, 95%CI =1.08 - 4.02, p=0.0289). Conclusion In COVID-19 pediatric patients, the severity of the disease was mild to moderate without any mortality. Children aged under one year accounted for the highest proportion of all COVID-19 patients. This study found that age under one year and underlying illnesses are related to pneumonia in COVID-19 pediatric patients

n-Gram-based text compression

We propose an efficient method for compressing Vietnamese text using n-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it into n-grams and then encodes them based on n-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Each n-gram is encoded by two to four bytes accordingly based on its corresponding n-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to build n-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.Web of Scienceart. no. 948364