Detection of an anomalous cluster in a network

We consider the problem of detecting whether or not, in a given sensor network, there is a cluster of sensors which exhibit an "unusual behavior." Formally, suppose we are given a set of nodes and attach a random variable to each node. We observe a realization of this process and want to decide between the following two hypotheses: under the null, the variables are i.i.d. standard normal; under the alternative, there is a cluster of variables that are i.i.d. normal with positive mean and unit variance, while the rest are i.i.d. standard normal. We also address surveillance settings where each sensor in the network collects information over time. The resulting model is similar, now with a time series attached to each node. We again observe the process over time and want to decide between the null, where all the variables are i.i.d. standard normal, and the alternative, where there is an emerging cluster of i.i.d. normal variables with positive mean and unit variance. The growth models used to represent the emerging cluster are quite general and, in particular, include cellular automata used in modeling epidemics. In both settings, we consider classes of clusters that are quite general, for which we obtain a lower bound on their respective minimax detection rate and show that some form of scan statistic, by far the most popular method in practice, achieves that same rate to within a logarithmic factor. Our results are not limited to the normal location model, but generalize to any one-parameter exponential family when the anomalous clusters are large enough.Comment: Published in at http://dx.doi.org/10.1214/10-AOS839 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism

Testing for the significance of a subset of regression coefficients in a linear model, a staple of statistical analysis, goes back at least to the work of Fisher who introduced the analysis of variance (ANOVA). We study this problem under the assumption that the coefficient vector is sparse, a common situation in modern high-dimensional settings. Suppose we have $p$ covariates and that under the alternative, the response only depends upon the order of $p^{1-\alpha}$ of those, $0\le\alpha\le1$. Under moderate sparsity levels, that is, $0\le\alpha\le1/2$, we show that ANOVA is essentially optimal under some conditions on the design. This is no longer the case under strong sparsity constraints, that is, $\alpha>1/2$. In such settings, a multiple comparison procedure is often preferred and we establish its optimality when $\alpha\geq3/4$. However, these two very popular methods are suboptimal, and sometimes powerless, under moderately strong sparsity where $1/2<\alpha<3/4$. We suggest a method based on the higher criticism that is powerful in the whole range $\alpha>1/2$. This optimality property is true for a variety of designs, including the classical (balanced) multi-way designs and more modern "$p>n$" designs arising in genetics and signal processing. In addition to the standard fixed effects model, we establish similar results for a random effects model where the nonzero coefficients of the regression vector are normally distributed.Comment: Published in at http://dx.doi.org/10.1214/11-AOS910 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Searching for a trail of evidence in a maze

Consider a graph with a set of vertices and oriented edges connecting pairs of vertices. Each vertex is associated with a random variable and these are assumed to be independent. In this setting, suppose we wish to solve the following hypothesis testing problem: under the null, the random variables have common distribution N(0,1) while under the alternative, there is an unknown path along which random variables have distribution $N(\mu,1)$, $\mu> 0$, and distribution N(0,1) away from it. For which values of the mean shift $\mu$ can one reliably detect and for which values is this impossible? Consider, for example, the usual regular lattice with vertices of the form $$\{(i,j):0\le i,-i\le j\le i and j has the parity of i\}$$ and oriented edges $(i,j)\to (i+1,j+s)$, where $s=\pm1$. We show that for paths of length $m$ starting at the origin, the hypotheses become distinguishable (in a minimax sense) if $\mu_m\gg1/\sqrt{\log m}$, while they are not if $\mu_m\ll1/\log m$. We derive equivalent results in a Bayesian setting where one assumes that all paths are equally likely; there, the asymptotic threshold is $\mu_m\approx m^{-1/4}$. We obtain corresponding results for trees (where the threshold is of order 1 and independent of the size of the tree), for distributions other than the Gaussian and for other graphs. The concept of the predictability profile, first introduced by Benjamini, Pemantle and Peres, plays a crucial role in our analysis.Comment: Published in at http://dx.doi.org/10.1214/07-AOS526 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Entrepreneurship Development Model for Migrant Workers (Case Study of Migrant Workers in Hongkong)

Migrant workers have became a phenomenon for the government. They have been considered as foreign exchange heroes for the government but on the contrary there is something untouchable thing after they have done their contracts. Do they will decide to continue their contracts or cut off their contracts and go back to Indonesia with all the problems embedded to them. Entreprenuership is one of the solution for them as their provision skill after they go back to Indonesia and not working as migrant workers anymore. Based on the problem, an alternative model must be found for the problem solver. The Objectives of this research is to make an entrepreneurship development model for the migrant workers. The specifif objectives for the research is to implement the entrepreneurship development model for the migrant worker that can be used by migrant workers. The Research method used for the research is multiple case study. The collecting data method used for the research is combinating approach (triangulation). There are survey, observation, field study PRA (Participatory rural appraisal) and action research. Research level used for the research is explorative and analysis method used for the research is qualtitative

Perancangan Program Aplikasi Sistem Informasi Penjualan Barang Berbasis Website Pada JPS Teknik

JPS TEKNIK adalah sebuah Perusahaan yang bergerak dibidang penjualan handtools specialist. Dalam pendataan penjualan produk pada JPS TEKNIK masih dilakukan secara manual dengan cara ditulis ke dalam pembukuan dan seringnya terjadi kesalahan perhitungan dalam pembukuan tersebut. Pembuatan program aplikasi website JPS TEKNIK dibutuhkan untuk memudahkan penjualan, serta mempermudah pelanggan untuk mendapatkan informasi produk-produk dan melakukan pembelian produk.Metode yang digunakan dalam pembuatan website ini dengan menggunakan metode SDLC (System Development Life Cycle), database yang digunakan adalah MySQL, bahasa pemrograman PHP, CSS, XAMPP, Dreamweaver untuk pembuatan website. Hasil uji aplikasi dengan cara User Acceptance Test dan dari hasil kuisione

A Pediatric Infectious Disease Perspective of SARS-CoV-2 and COVID-19 in Children.

Understanding the role that children play in the clinical burden and propagation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for novel coronavirus (COVID-19) infections is emerging. While the severe manifestations and acute clinical burden of COVID-19 has largely spared children compared to adults, understanding the epidemiology, clinical presentation, diagnostics, management, and prevention opportunities as well as the social and behavioral impacts on child health is vital. Foremost is clarifying the contribution of asymptomatic and mild infections to transmission within the household and community and the clinical and epidemiologic significance of uncommon severe post-infectious complications. Herein we summarize the current knowledge, identify useful resources, and outline research opportunities. Pediatric infectious disease clinicians have a unique opportunity to advocate for the inclusion of children in epidemiological, clinical, treatment and prevention studies to optimize their care, as well as to represent children in the development of guidance and policy during pandemic response

How to fragment peralkaline rhyolites: Observations on pumice using combined multi-scale 2D and 3D imaging

Peralkaline rhyolites are volatile-rich magmas that typically erupt in continental rift settings. The high alkali and halogen content of these magmas results in viscosities two to three orders of magnitude lower than in calc-alkaline rhyolites. Unless extensive microlite crystallisation occurs, the calculated strain rates required for fragmentation are unrealistically high, yet peralkaline pumices from explosive eruptions of varying scales are commonly microlite-free. Here we present a combined 2D scanning electron microscopy and 3D X-ray microtomography study of peralkaline rhyolite vesicle textures designed to investigate fragmentation processes. Microlite-free peralkaline pumice textures from Pantelleria, Italy, strongly resemble those from calc-alkaline rhyolites on both macro and micro scales. These textures imply that the pumices fragmented in a brittle fashion and that their peralkaline chemistry had little direct effect on textural evolution during bubble nucleation and growth. We suggest that the observed pumice textures evolved in response to high decompression rates and that peralkaline rhyolite magmas can fragment when strain localisation and high bubble overpressures develop during rapid ascent