A Faster kk-means++ Algorithm

K-means++ is an important algorithm to choose initial cluster centers for the k-means clustering algorithm. In this work, we present a new algorithm that can solve the $k$-means++ problem with near optimal running time. Given $n$ data points in $\mathbb{R}^d$, the current state-of-the-art algorithm runs in $\widetilde{O}(k )$ iterations, and each iteration takes $\widetilde{O}(nd k)$ time. The overall running time is thus $\widetilde{O}(n d k^2)$. We propose a new algorithm \textsc{FastKmeans++} that only takes in $\widetilde{O}(nd + nk^2)$ time, in total

Measuring sustainability:Development and application of the Inclusive Wealth Index in China

It is increasingly common to use the Inclusive Wealth Index (IWI) to evaluate national sustainability; however, IWI's highly aggregated components and limited regional cases restrict its further application in achieving the Sustainable Development Goals (SDGs). This study extends the traditional three-component IWI framework into six disaggregated components, namely male/female human capital, advanced/ordinary produced capital, and renewable/non-renewable natural capital. We apply the modified framework to China and evaluate the sustainability performance at the provincial level. The results show that China continues to develop with an annual IWI per capita increase rate of 2.3%. Gender inequality is found to hinder the growth of IWI, whereas advanced product features benefit the growth of IWI. The results also suggest significant heterogeneity in provincial IWI primarily due to differences in economic development stages, geographic locations, and uneven IWI growth. IWI growth is largely driven by wealth accumulation resulting from human capital and advanced produced capital. In contrast, insufficient IWI growth is caused by a substantial amount of ordinary produced capital or a continued decline in natural capital. The study provides a basis for tracking progress toward the SDGs and measuring the heterogeneity of regional socio-economic development in China

KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

KNN-Based outlier detection over IoT streaming data is a fundamental problem, which has many applications. However, due to its computational complexity, existing efforts cannot efficiently work in the IoT streaming data. In this paper, we propose a novel framework named GAAOD(Grid-based Approximate Average Outlier Detection) to support KNN-Based outlier detection over IoT streaming data. Firstly, GAAOD introduces a grid-based index to manage summary information of streaming data. It can self-adaptively adjust the resolution of cells, and achieve the goal of efficiently filtering objects that almost cannot become outliers. Secondly, GAAOD uses a min-heap-based algorithm to compute the distance upper-/lower-bound between objects and their k-th nearest neighbors respectively. Thirdly, GAAOD utilizes a k-skyband based algorithm to maintain outliers and candidate outliers. Theoretical analysis and experimental results verify the efficiency and accuracy of GAAOD. INDEX TERMS IoT streaming data, KNN-based outliers, indexes, error guarantee

MicroRNA-322 inhibits inflammatory cytokine expression and promotes cell proliferation in LPS-stimulated murine macrophages by targeting NF-κB1 (p50)

Correspondence : Hanchuan Dai ([email protected]) Inflammation is the body's normal self-protection mechanism to eliminate pathogens and resist pathogen invasion. The excessive inflammatory response may lead to inflammatory lesions. The mechanisms accounting for inflammation remain hazy. miRNAs have been proposed to have crucial effects on inflammation. In the present study, we reported that lipopolysaccharide (LPS)-stimulation increased the expression levels of inflammatory cytokines and the cell-cycle progression was suppressed in RAW264.7 cells. Meanwhile, the expression of miR-322 was significantly down-regulated after LPS treatment. Bioinformatics predictions revealed a potential binding site of miR-322 in 3 -UTR of NF-κB1 (p50) and it was further confirmed by luciferase assay. Moreover, both the mRNA and protein levels of NF-κB1 (p50) were down-regulated by miR-322 in RAW264.7 cells. Subsequently, we demonstrated that miR-322 mimics decrease in the expression levels of inflammatory cytokines and cell-cycle repression can be rescued following LPS treatment in RAW264.7 cells. The anti-inflammatory cytokines expression including IL-4 and IL-10 were significantly up-regulated. Furthermore, miR-322 could also promote RAW264.7 cells proliferation. These results demonstrate that miR-322 is a negative regulator of inflammatory response by targeting NF-κB1 (p50)

Factors associated with distant metastasis in pediatric thyroid cancer: evaluation of the SEER database

Objectives: Controversies regarding factors associated with distant metastasis in pediatric thyroid cancer remain among the scientific community. The aim of this study was to investigate factors influencing distant metastasis in pediatric thyroid cancer. Methods: We reviewed 1376 patients (aged 2 to 18 years) with thyroid cancer treated between 2003 and 2014. Data collected and analyzed included sex, race, age at diagnosis, year of diagnosis, pathological type, number of tumor foci, tumor extension, T-stage, N-stage, surgical procedure and radiation. Univariate and multivariate analyses were conducted to evaluate factors influencing distant metastasis of pediatric thyroid cancer. Results: In the univariate analysis, factors influencing distant metastasis of thyroid cancer were age at diagnosis (P 0.05). Furthermore, according to chi-squared test, younger pediatric thyroid cancer patients with higher T- and N-stages are more likely to have distant metastasis. Conclusion: Age at diagnosis, T-stage and N-stage influence distant metastasis of thyroid cancer patients aged 2 to 18 years; accordingly, more radical treatments may need to be used for patients with those risk elements