The Experts below are selected from a list of 12552 Experts worldwide ranked by ideXlab platform
Richard Saumarez Smith - One of the best experts on this subject based on the ideXlab platform.
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between local tax and Global Statistic the census as local record
Contributions to Indian Sociology, 2000Co-Authors: Richard Saumarez SmithAbstract:The enumeration of caste and religion by the all-India Census was emblematic of a particular kind of imperial rule. Might a different procedure of compilation and record on an alternative epistemological basis have developed? Censuses had provided governments in India with Statistical information at least from the 1820s, and been used for local taxa tion from much earlier. The possibility of a continually updated civil registry had been mooted from time to time but not put into effect. Another alternative, in which a census register formed part of the village record similar to the land registers, existed for a brief period in the 1850s in the Panjab. But this 'rule by record' was itself based on a theory of positive legislation and the preservation of social difference, pannomian if not panoptic. A different kind of census could only have belonged to another kind of imperial rule.
Fabrice P Cordelieres - One of the best experts on this subject based on the ideXlab platform.
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a guided tour into subcellular colocalization analysis in light microscopy
Journal of Microscopy, 2006Co-Authors: Susanne Bolte, Fabrice P CordelieresAbstract:Summary It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, imageanalysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under Image J, named JACoP, that integrates current Global Statistic methods and a novel object-based approach.
Vidhya Ganesan - One of the best experts on this subject based on the ideXlab platform.
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minkowski tensors in two dimensions probing the morphology and isotropy of the matter and galaxy density fields
The Astrophysical Journal, 2018Co-Authors: Stephen Appleby, Pravabati Chingangbam, Changbom Park, Sungwook E Hong, Juhan Kim, Vidhya GanesanAbstract:We apply the Minkowski tensor Statistics to two-dimensional slices of the three-dimensional matter density field. The Minkowski tensors are a set of functions that are sensitive to directionally dependent signals in the data and, furthermore, can be used to quantify the mean shape of density fields. We begin by reviewing the definition of Minkowski tensors and introducing a method of calculating them from a discretely sampled field. Focusing on the Statistic W-2(1,1)-a 2 x 2 matrix-we calculate its value for both the entire excursion set and individual connected regions and holes within the set. To study the morphology of structures within the excursion set, we calculate the eigenvalues lambda(1), lambda(2) for the matrix W-2(1,1) of each distinct connected region and hole and measure their mean shape using the ratio beta equivalent to . We compare both W-2(1,1) and beta for a Gaussian field and a smoothed density field generated from the latest Horizon Run 4 cosmological simulation to study the effect of gravitational collapse on these functions. The Global Statistic W-2(1,1) is essentially independent of gravitational collapse, as the process maintains Statistical isotropy. However, beta is modified significantly, with overdensities becoming relatively more circular compared to underdensities at low redshifts. When applying the Statistics to a redshift-space distorted density field, the matrix W-2(1,1) is no longer proportional to the identity matrix, and measurements of its diagonal elements can be used to probe the large-scale velocity field.
Ali H Sayed - One of the best experts on this subject based on the ideXlab platform.
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optimal linear fusion for distributed detection via semidefinite programming
IEEE Transactions on Signal Processing, 2010Co-Authors: Zhi Quan, Shuguang Cui, Ali H SayedAbstract:Consider the problem of signal detection via multiple distributed noisy sensors. We study a linear decision fusion rule of [Z. Quan, S. Cui, and A. H. Sayed, ?Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,? IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28-40, Feb. 2008] to combine the local Statistics from individual sensors into a Global Statistic for binary hypothesis testing. The objective is to maximize the probability of detection subject to an upper limit on the probability of false alarm. We propose a more efficient solution that employs a divide-and-conquer strategy to divide the decision optimization problem into two subproblems. Each subproblem is a nonconvex program with a quadratic constraint. Through a judicious reformulation and by employing a special matrix decomposition technique, we show that the two nonconvex subproblems can be solved by semidefinite programs in a Globally optimal fashion. Hence, we can obtain the optimal linear fusion rule for the distributed detection problem. Compared with the likelihood-ratio test approach, optimal linear fusion can achieve comparable performance with considerable design flexibility and reduced complexity.
Liu Kun - One of the best experts on this subject based on the ideXlab platform.
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Efficient data integration techniques in some modern applications
Georgia Institute of Technology, 2019Co-Authors: Liu KunAbstract:Data science is changing our society and economy, and complicated data from heterogeneous sources is often collected in various industries such as finance, manufacturing, security, and pharmaceutical industries. The main challenge is often how to analyze these complicated data from heterogeneous sources. One useful data analysis technique is data integration that allows one to extract invaluable information from heterogeneous sources to make intelligent decisions at the Global level. This dissertation aims to develop efficient data integration techniques in some modern real-world applications. We consider four different contexts: (i) online monitoring of large-scale data streams, (ii) consensus sequential detection over distributed networks, (iii) combining different patients' responses to assess the treatment effects of new drugs, and (iv) robust Statistical inference in the presence of contaminated data. Chapter 1 investigates the problem of online monitoring large-scale data streams where an undesired event may occur at some unknown time and affect only a few unknown data streams. Existing research is either Statistical inefficient or computationally infeasible. Motivated by parallel and distributed computing, we propose to develop a new information fusion technique we called the “SUM-Shrinkage” approach that is efficient and scalable. The main idea is to parallel run local detection procedures and to use the sum of the shrinkage transformation of local detection Statistics as a Global Statistic to make a decision. The proposed shrinkage transformation approach is able to automatically filter out the unaffected data streams and only use information from affected data streams to make the decision. The usefulness of our proposed SUM-Shrinkage approach is illustrated in an example of monitoring large-scale independent normally distributed data streams when the local post-change mean shifts are unknown and can be positive or negative. In Chapter 2, we consider the consensus sequential detection problem over distributed sensor networks, in which each local sensor can only communicate local information with its immediate neighborhood sensors at each time step, and the question is how the sensors can work together to make a quick but accurate decision when testing binary hypotheses on the true raw sensor distributions. An interesting data integration technique is based on the weighted local-likelihood-ratio-Statistics, which yields the Consensus-Innovation Sequential Probability Ratio Test (CISPRT) algorithm proposed by Sahu and Kar (IEEE Trans. Signal Process., 2016). Our new contribution is to present improved, non-asymptotic properties of the CISPRT algorithm for Gaussian data in term of network connectivity no matter how large the number of sensors is. Moreover, we also provide sharp upper bounds on the information loss of the CISPRT algorithm as compared to the centralized optimal SPRT algorithm in term of expected sample sizes in the asymptotic regime when Type I and II error probabilities go to 0. Numerical simulations suggest that our results are useful under the practical setting when the number of sensors is moderately large. Chapter 3 aims to develop an efficient method that is able to combine different patients' responses to assess the treatment effects of new drugs. Our research is motivated by Biogen's ongoing Phase 3 clinical trial of a new drug “Aducanumab” for Alzheimer's disease (AD), where the primary outcome is on the change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) scores. The current gold standard method is the so-called responder analysis based on the two-sample proportion test, which only uses information at Month 18 and 0. This might lose detection powers because of two reasons: (i) Not every subject will have these CDR-SB scores at Month 18, due to various reasons such as missing the appointments or dropping out; (ii) it does not take advantage of the longitudinal study design when the CDR-SB scores will be collected multiple times for most subjects (e.g., at Month 0, 6, 12, 18, 24 and 36 after the enrollment of the study). We propose to model the CDR-SB scores by the Beta distribution and to use the mixed-effects Beta regression model combining all observed CDR-SB values together to increase the detection power of the changes in the CDR-SB scores. The usefulness of our proposed models and methods is demonstrated through the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and simulation studies. In Chapter 4 of the dissertation, we investigate the problem of robust Statistical inference in the presence of contaminated data. The corrupted or contaminated data is often a big issue when we integrate data from different sources, and thus it is crucial to have a robust local inference before combining different local information together. We present our research on the robust point estimations in the mixture model. Our main contribution is to consider an exponential loss function that is better to mitigate the effect of outliers and develop an asymptotic theory in a new asymptotic regime when the outlier means go to infinity in a suitable rate as the proportion of outliers goes to 0.Ph.D