Bias correction and confidence intervals following sequential tests

An important statistical inference problem in sequential analysis is the construction of confidence intervals following sequential tests, to which Michael Woodroofe has made fundamental contributions. This paper reviews Woodroofe's method and other approaches in the literature. In particular it shows how a bias-corrected pivot originally introduced by Woodroofe can be used as an improved root for sequential bootstrap confidence intervals.Comment: Published at http://dx.doi.org/10.1214/074921706000000590 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Travelling Wave Solutions of Nonlinear Dynamical Equations in a Double-Chain Model of DNA

We consider the nonlinear dynamics in a double-chain model of DNA which consists of two long elastic homogeneous strands connected with each other by an elastic membrane. By using the method of dynamical systems, the bounded traveling wave solutions such as bell-shaped solitary waves and periodic waves for the coupled nonlinear dynamical equations of DNA model are obtained and simulated numerically. For the same wave speed, bell-shaped solitary waves of different heights are found to coexist

Extract human mobility patterns powered by City Semantic Diagram

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Distributed Load Balancing in Peer-to-Peer Computing

In this paper, we address the load balancing problem in the context of peer-to-peer computing environments. The key challenge to employ peer-to-peer networks for distributed computing is to exploit the heterogeneous processing capability of the participating hosts as well as the diverse network conditions. The contribution of our work is twofold. First, we model the load balance problem as an optimization problem with the objective of minimizing the system response time. This modeling considers not only the current loading of hosts, but also the fluctuation of network delay, which completely captures the characteristics of the P2P systems. Second, we propose a gradient projection algorithm to solve the optimization problem, which is fully distributed and easy for implementation. Simulation results demonstrate that our scheme has satisfied performance in terms of convergence, response time and load distribution

T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing

Nonlinear Frechet derivative and its De Wolf approximation

We introduce and derive the nonlinear Frechet derivative for the acoustic wave equation. It turns out that the high order Frechet derivatives can be realized by consecutive applications of the scattering operator and a zero-order propagator to the source. We prove that the higher order Frechet derivatives are not negligible and the linear Frechet derivative may not be appropriate in many cases, especially when forward scattering is involved for large scale perturbations. Then we derive the De Wolf approximation (multiple forescattering and single backscattering approximation) for the nonlinear Frechet derivative. We split the linear derivative operator (i.e. the scattering operator) onto forward and backward derivatives, and then reorder and renormalize the nonlinear derivative series before making the approximation by dropping the multiple backscattering terms. Numerical simulations for a Gaussian ball model show significant difference between the linear and nonlinear Frechet derivatives.University of California, Santa Cruz (Wavelet Transform on Propagation and Imaging for seismic exploration Research Consortium); Massachusetts Institute of Technology. Earth Resources Laborator

Phoebe's orbit from ground-based and space-based observations

The ephemeris of Phoebe, the ninth satellite of Saturn, is not very accurate. Previous dynamical models were usually too simplified, the astrometry is heterogeneous and, the Saturn's ephemeris itself is an additionnal source of error. The aim is to improve Phoebe's ephemeris by using a large set of observations, correcting some systematic errors and updating the dynamical model. The dynamical model makes use of the most recent ephemeris of planets and Saturnian satellites. The astrometry of Phoebe is improved by using a compilation of ground-based and space-based observations and by correcting the bias in stellar catalogues used for the reduction. We present an accurate ephemeris of Phoebe with residuals of 0.45 arcsec and with an estimated accuracy of Phoebe's position of less that 100 km on 1990-2020 period.Comment: 10 pages, 9 figures, accepted by A&