Wiley Online Library hosts a multidisciplinary collection of online resources covering life, health and physical sciences, social science, and the humanities. The Wellcome Library offers access to selected titles in this collection.

Wiley Online Library hosts a multidisciplinary collection of online resources covering life, health and physical sciences, social science, and the humanities. The Wellcome Library offers access to selected titles in this collection.

British newspapers from the 19th century selected by the British Library's editorial board. Includes both national and regional newspapers. All newspapers are full text and fully searchable. Full runs are available where possible.

London : British Library, [1983]

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/GraKeL.

Dan J. Spitzner.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 861--893.

Abstract:
This article proposes a calibration scheme for Bayesian testing that coordinates analytically-derived statistical performance considerations with expert opinion. In other words, the scheme is effective and meaningful for incorporating objective elements into subjective Bayesian inference. It explores a novel role for default priors as anchors for calibration rather than substitutes for prior knowledge. Ideas are developed for use with multiplicity adjustments in multiple-model contexts, and to address the issue of prior sensitivity of Bayes factors. Along the way, the performance properties of an existing multiplicity adjustment related to the Poisson distribution are clarified theoretically. Connections of the overall calibration scheme to the Schwarz criterion are also explored. The proposed framework is examined and illustrated on a number of existing data sets related to problems in clinical trials, forensic pattern matching, and log-linear models methodology.

Manifold optimization appears in a wide variety of computational problems in the applied sciences. In recent statistical methodologies such as sufficient dimension reduction and regression envelopes, estimation relies on the optimization of likelihood functions over spaces of matrices such as the Stiefel or Grassmann manifolds. Recently, Huang, Absil, Gallivan, and Hand (2016) have introduced the library ROPTLIB, which provides a framework and state of the art algorithms to optimize real-valued objective functions over commonly used matrix-valued Riemannian manifolds. This article presents ManifoldOptim, an R package that wraps the C++ library ROPTLIB. ManifoldOptim enables users to access functionality in ROPTLIB through R so that optimization problems can easily be constructed, solved, and integrated into larger R codes. Computationally intensive problems can be programmed with Rcpp and RcppArmadillo, and otherwise accessed through R. We illustrate the practical use of ManifoldOptim through several motivating examples involving dimension reduction and envelope methods in regression.

It’s goodbye from the Wellcome Library blog. The blog is closing and will no longer be updated. Thank you to those that have read the blog, shared it and posted comments. I hope all our readers have enjoyed being able… Continue reading

Carolyn M. Rutter, Jonathan Ozik, Maria DeYoreo, Nicholson Collier.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2189--2212.

Abstract:
Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.

Ahmed Aziz Ezzat, Mikyoung Jun, Yu Ding.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1484--1510.

Abstract:
Accurate short-term forecasts are indispensable for the integration of wind energy in power grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal resolution. Existing statistical models may capture the in-sample variations in wind behavior, but are often shortsighted to those occurring in the near future, that is, in the forecast horizon. The calibrated regime-switching method proposed in this paper introduces an action of regime dependent calibration on the predictand (here the wind speed variable), which helps correct the bias resulting from out-of-sample variations in wind behavior. This is achieved by modeling the calibration as a function of two elements: the wind regime at the time of the forecast (and the calibration is therefore regime dependent), and the runlength, which is the time elapsed since the last observed regime change. In addition to regime-switching dynamics, the proposed model also accounts for other features of wind fields: spatio-temporal dependencies, transport effect of wind and nonstationarity. Using one year of turbine-specific wind data, we show that the calibrated regime-switching method can offer a wide margin of improvement over existing forecasting methods in terms of both wind speed and power.

Jeong Eun Lee, Geoff K. Nicholls, Robin J. Ryder.

Source: Bayesian Analysis, Volume 14, Number 4, 1245--1269.

Abstract:
We develop and apply two calibration procedures for checking the coverage of approximate Bayesian credible sets, including intervals estimated using Monte Carlo methods. The user has an ideal prior and likelihood, but generates a credible set for an approximate posterior based on some approximate prior and likelihood. We estimate the realised posterior coverage achieved by the approximate credible set. This is the coverage of the unknown “true” parameter if the data are a realisation of the user’s ideal observation model conditioned on the parameter, and the parameter is a draw from the user’s ideal prior. In one approach we estimate the posterior coverage at the data by making a semi-parametric logistic regression of binary coverage outcomes on simulated data against summary statistics evaluated on simulated data. In another we use Importance Sampling from the approximate posterior, windowing simulated data to fall close to the observed data. We illustrate our methods on four examples.

Mengyang Gu.

Source: Bayesian Analysis, Volume 14, Number 3, 877--905.

Abstract:
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some objective priors, such as the reference prior, are studied in the context of emulating (approximating) computationally expensive mathematical models. In this work, we introduce a new class of priors, called the jointly robust prior, for both the emulation and calibration. This prior is designed to maintain various advantages from the reference prior. In emulation, the jointly robust prior has an appropriate tail decay rate as the reference prior, and is computationally simpler than the reference prior in parameter estimation. Moreover, the marginal posterior mode estimation with the jointly robust prior can separate the influential and inert inputs in mathematical models, while the reference prior does not have this property. We establish the posterior propriety for a large class of priors in calibration, including the reference prior and jointly robust prior in general scenarios, but the jointly robust prior is preferred because the calibrated mathematical model typically predicts the reality well. The jointly robust prior is used as the default prior in two new R packages, called “RobustGaSP” and “RobustCalibration”, available on CRAN for emulation and calibration, respectively.

Feeding the world’s growing population, which is expected to go beyond 9 billion by 2050, is one of the world’s biggest challenges. Some of the highest rates of population growth are predicted to occur in areas that are highly dependent on the agriculture sector (i.e. crops, livestock, forests and fisheries). Sustainable agricultural growth is one of the most effective means [...]

Every year, FAO publishes a number of major ‘State of the World’ reports containing balanced, science-based reviews of issues related to food, agriculture, forestry, fisheries and natural resources. The reports also cover vital areas such as food production and hunger alleviation, which are at the heart of FAO’s work. With close to 800 million people currently chronically undernourished and an extra [...]

The Internet Archive describes the downloadable collection of more than one million books as a library, but critics call it piracy

Sourdough librarian Karl De Smedt has traveled the world to gather more than 120 jars of starters

The following is a guest post by Katherine Blood, Curator of Fine Prints, Prints & Photographs Division. As the Library of Congress marks its 220th year of serving the nation, the publication of a new guide tells two stories: how staff have for decades worked with art professionals to build the collections and how by […]

Vincent Novara, Head of the Music Division's Acquisitions & Processing Section, describes his section's new teleworking practices during a global pandemic.

There's a wonderfully quirky — and little known — information archive in downtown Winnipeg that predates Google and probably has more hidden secrets than the search engine giant.

Coatzacoalcos, Mexico :: The local mayor boards Logos Hope for a unique arrival and welcomes crew as they settle into a new city.

Coatzacoalcos, Mexico :: A village gets its first library, with books and shelves donated by Logos Hope.

Public libraries are moving deeper into digital learning, often in partnership with schools and other institutions, to help prepare students for the skills they'll need for college and careers.

Two Penn State University Libraries employees at Berks Thun Library, Penn State Berks, have been awarded the American Library Association’s 2020 Emerging Technologies Section 2020 Best Emerging Technology Application (BETA) Award, which recognizes a technology application that directly benefits library users.

Three Berks Thun Library employees received the 2020 Innovation in College Librarianship Award from the College Libraries Section of the Association of College & Research Libraries, a division of the American Library Association. The award honors librarians who demonstrate a capacity for innovation in working with or serving undergraduates or instructors in the areas of programs, services, and operations, or creating innovations for library colleagues that facilitate their ability to better serve the library’s community.

The winning writers of Penn State University Libraries Short Stories’ “April Showers” spring 2020 contest have been announced. Representing six Penn State campuses, the latest writing contest challenged Penn State students, faculty and staff across the commonwealth to unleash their best original short stories or poetry on the theme of weather.

The Kobo Libra H2O sets the bar for non-Amazon ebook readers with flexible format support and a terrific form factor.

Bernadette Lear, Penn State University Libraries behavioral sciences and education librarian and coordinator of library instruction at the Penn State Harrisburg Library, has been elected vice chair/chair elect of the Library History Round Table, a membership group of the American Library Association.

Rebecca Mattson, head of faculty and research services for the H. Laddie Montague Jr. Law Library, Penn State University Libraries, and professor of legal research at Penn State Law at University Park, has been elected vice chair/chair elect of the Research Instruction and Patron Services Special Interest Section of the American Association of Law Libraries.

Penn State University Libraries is offering an Online De-Stress Fest to help students relax and unwind while completing end-of-term reports and studying for final exams. Visit through Friday, May 8, for a curated list of links, tutorials and homegrown videos that showcase the talents of University Libraries’ faculty and staff.