中低纬电离层暴的统计分析与经验建模

As a key issue of ionospheric weather study, systemic studies on ionospheric storms can not only further improve our understanding of the response of the ionosphere to solar and geomagnetic disturbances, but also help us to reveal the chemical, dynamic and electro-dynamic mechanisms during storms. Empirical modelling for regional ionospheric storm is also very useful, because it can provide us with tools and references for the forecasting and further practical application of ionospheric activity. In this thesis, we focus on describing and forecasting of ionospheric storms at middle and low latitudes. The main points of my investigations are listed as follows. (1) By using magnetic storms during the period over 50 years, the dependence of the type, onset time and time delay of the ionospheric storms on magnetic latitude, season and local time at middle and low latitudes in the East-Asian sector are studied. The results show that the occurrences of the types of ionospheric disturbances differ in latitude and season. The onset of the ionospheric storms depends on local time. At middle latitudes, most negative phase onsets are within the local time interval from night to early morning, and they rarely occurred in the local noon and afternoon sectors. At low latitudes, positive phases commence most frequently in the daytime sector as well as pre-midnight sector. The average time delays for both the positive and negative ionospheric storms increase with descending latitudes. The time delay has significant dependence on the local time of main phase onset (MPO). The time delay of positive response is shorter for daytime MPO and longer for night-time MPO, whereas the opposite applies for negative response. (2) Based on some previous researches, a primary empirical model for mid-latitude ionospheric disturbance is set up. By fitting to the observed data, we get a high accuracy with a mean RMSE of only 12-14% in summer and equinox. The model output has been compared with the output of STORM model, and the results show that, our model is much better than STORM in summer and a little better for some mid-latitude stations at equinox. Especially, for the type of two-step geomagnetic storm, our model can present twice descending of foF2 very well. In addition, our model can forecast positive ionospheric storms.

基于Arc Hydro Tools的流域特征自动提取——以贵州省内乌江流域为例

简要回顾和讨论了基于栅格数字高程模型(DEM)自动提取流域特征的原理和方法，介绍了一个新的基于ArcGIS开发的提取流域特征的工具Arc Hydro Tools，基于Arc Hydro Tools 提取流域特征包括5 个流程：DEM 的预处理、水流流向的确定、汇流栅格图的生成、河网的自动生成、子流域边界的划分。最后以贵州省内乌江流域为研究区进行了试验，试验结果表明：提取结果的精度在总体上是符合要求的，但在地势平坦区或人类活动干扰较大的地区，提取的结果与实际相差较大。从提取的效率和试验结果的精度两方面来看，基于Arc Hydro Tools 的流域特征自动提取是切实可行的。

Minimizing Statistical Bias with Queries

I describe an exploration criterion that attempts to minimize the error of a learner by minimizing its estimated squared bias. I describe experiments with locally-weighted regression on two simple kinematics problems, and observe that this "bias-only" approach outperforms the more common "variance-only" exploration approach, even in the presence of noise.

Detecting Digital Forgeries Using Bispectral Analysis

With the rapid increase in low-cost and sophisticated digital technology the need for techniques to authenticate digital material will become more urgent. In this paper we address the problem of authenticating digital signals assuming no explicit prior knowledge of the original. The basic approach that we take is to assume that in the frequency domain a "natural" signal has weak higher-order statistical correlations. We then show that "un-natural" correlations are introduced if this signal is passed through a non-linearity (which would almost surely occur in the creation of a forgery). Techniques from polyspectral analysis are then used to detect the presence of these correlations. We review the basics of polyspectral analysis, show how and why these tools can be used in detecting forgeries and show their effectiveness in analyzing human speech.

Inferring 3D Structure with a Statistical Image-Based Shape Model

We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001

Toivonen, H., Srinivasan, A., King, R. D., Kramer, S. and Helma, C. (2003) Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinformatics 19: 1183-1193

NHS Wales user needs study including knowledgebase tools report. Report for Informing Healthcare Strategy implementation programme.

Thomas, R., Spink, S., Durbin, J. & Urquhart, C. (2005). NHS Wales user needs study including knowledgebase tools report. Report for Informing Healthcare Strategy implementation programme. Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: Informing Healthcare, NHS Wales

Ewaluacja splotowa InfoKultury. Skala dwuważonych ocen

Pakiet oprogramowania InfoCult™ Analyser 1.4 stanowiącego obudowę książki do pobrania ze strony: ewaluacja.amu.edu.pl

Human papillomavirus detection in women with and without human immunodeficiency virus infection in Colombia

Background: HIV infection leads to a decreasing immune response, thereby facilitating the appearance of other infections, one of the most important ones being HPV. However, studies are needed for determining associations between immunodeficiency caused by HIV and/or the presence of HPV during the course of cervical lesions and their degree of malignancy. This study describes the cytological findings revealed by the Papanicolaou test, laboratory characteristics and HPV molecular profile in women with and without HIV infection. Methods: A total of 216 HIV-positive and 1,159 HIV-negative women were invited to participate in the study; PCR was used for the molecular detection of HPV in cervical samples. Statistical analysis (such as percentages, Chi-square test and Fisher's exact test when applicable) determined human papillomavirus (HPV) infection frequency (single and multiple) and the distribution of six types of high-risk-HPV in women with and without HIV infection. Likewise, a logistic regression model was run to evaluate the relationship between HIV-HPV infection and different risk factors. Results: An association was found between the frequency of HPV infection and infection involving 2 or more HPV types (also known as multiple HPV infection) in HIV-positive women (69.0% and 54.2%, respectively); such frequency was greater than that found in HIV-negative women (44.3% and 22.7%, respectively). Statistically significant differences were observed between both groups (p = 0.001) regarding HPV presence (both in infection and multiple HPV infection). HPV-16 was the most prevalent type in the population being studied (p = 0.001); other viral types had variable distribution in both groups (HIV-positive and HIV-negative). HPV detection was associated with <500 cell/mm(3) CD4-count (p = 0.004) and higher HIV-viral-load (p = 0.001). HPV-DNA detection, <200 cell/mm(3) CD4-count (p = 0.001), and higher HIV-viral-load (p = 0.001) were associated with abnormal cytological findings. Conclusions: The HIV-1 positive population in this study had high multiple HPV infection prevalence. The results for this population group also suggested a greater association between HPV-DNA presence and cytological findings. HPV detection, together with low CD4 count, could represent useful tools for identifying HIV-positive women at risk of developing cervical lesions.

Men and missions / by William T. Ellis ; with a foreword by John B. Sleman, Jr. ; and with a statistical and historical appendix compiled by Abigail J. Davies.

http://www.archive.org/details/menandmissions003181mbp

The Encyclopaedia of missions : descriptive, historical, biographical, statistical : with a full assortment of maps, a complete bibliography, and lists of Bible versions, missionary societies, mission stations, and a general index / edited by Edwin Munsell Bliss.

http://www.archive.org/details/encyclopaediamis02unknuoft

SIEGE: Smoking Induced Epithelial Gene Expression Database

The SIEGE (Smoking Induced Epithelial Gene Expression) database is a clinical resource for compiling and analyzing gene expression data from epithelial cells of the human intra-thoracic airway. This database supports a translational research study whose goal is to profile the changes in airway gene expression that are induced by cigarette smoke. RNA is isolated from airway epithelium obtained at bronchoscopy from current-, former- and never-smoker subjects, and hybridized to Affymetrix HG-U133A Genechips, which measure the level of expression of ~22 500 human transcripts. The microarray data generated along with relevant patient information is uploaded to SIEGE by study administrators using the database's web interface, found at http://pulm.bumc.bu.edu/siegeDB. PERL-coded scripts integrated with SIEGE perform various quality control functions including the processing, filtering and formatting of stored data. The R statistical package is used to import database expression values and execute a number of statistical analyses including t-tests, correlation coefficients and hierarchical clustering. Values from all statistical analyses can be queried through CGI-based tools and web forms found on the �Search� section of the database website. Query results are embedded with graphical capabilities as well as with links to other databases containing valuable gene resources, including Entrez Gene, GO, Biocarta, GeneCards, dbSNP and the NCBI Map Viewer.

On the Convergence of Statistical Techniques for Inferring Network Traffic Demands

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study strategies to improve the convergence of a powerful statistical technique based on an Expectation-Maximization iterative algorithm. First we analyze modeling approaches to generating starting points. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we study the convergence characteristics of our EM algorithm and compare it against a recently proposed Weighted Least Squares approach.