bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conformal mapping
Authors: Priestley H A 
Published: November 2023

Abstract
This chapter concerns angle-preserving mappings between regions in the complex plane. As we shall see, every holomorphic function whose derivative is non-zero defines such a mapping. These mappings are of intrinsic geometric interest and of importance in advanced complex analysis (Chapter 16 hints at this). They are also worth studying because of their usefulness in solving certain physical problems, for example, problems about two-dimensional fluid flow, the idea being to transform a given problem into an equivalent one which is easier to solve. So we wish to consider the problem of mapping a given region G onto a geometrically simpler region G’, for example the open unit disc or the open upper half-plane. We concentrate in this basic track chapter on presenting and illustrating the principles of conformal mapping. We therefore restrict attention to mapping a region whose boundary is a circline or is a pair of arcs (that is, lines, rays, circular arcs, line segments).
Power series representation
Authors: Priestley H A 
Published: November 2023

Abstract
In Chapter 7 we proved the major result that a convergent power series defines a holomorphic function. This chapter reveals in full the symbiotic relationship that exists between holomorphic functions and power series. We begin with some technical results, needed for manipulating integrals and series.
If the finite sum here is replaced by an infinite sum, the corresponding interchange of summation and integration may well not be valid. For integrals along paths, a systematic study of sufficient conditions for interchange to be valid can be based either on a basic treatment of uniform convergence or on more sophisticated techniques. Since we need only to be able to handle the integration of series closely related to power series, taken round quite special contours, we elect on the basic track to avoid uniform convergence and to adopt an ad hoc approach.
Cauchy’s residue theorem
Authors: Priestley H A 
Published: November 2023

Abstract
This chapter continues the study of singularities and extends in a significant way the techniques stemming from Cauchy’s theorem for evaluating the integral of a function round a contour.
The following lemma comes directly out of Laurent’s theorem and the Deformation theorem: it gives an integral formula for the Laurent coefficient c_1. The result is of sufficient importance for us to record it explicitly.
Ecology: living apart together
Authors: Schoonhoven Louis M , Loon Joop J A van , Dicke Marcel 
Published: November 2023

Abstract
Central themes in ecology are how organisms interact with the biotic and abiotic environment and how these interactions can explain the composition and dynamics of communities.117,141 These aspects can be investigated at different levels of biological organization, but an important challenge for ecologists is to link these. For instance, community ecologists analyse how species composition of communities fluctuates in the context of the underlying population dynamics.
MR proton spectroscopy
Authors: Marenco Stefano , Weinberger Daniel R , Bertolino Alessandro 
Published: November 2023

Abstract
Spectroscopy allows determination of the relative concentrations of certain chemicals in the brain through a non-invasive procedure based on a nuclear magnetic resonance (NMR) scan. The technique exploits the properties of magnetic nuclei such as hydrogen (1H) and phosphorus (31P). The magnetic property of these and other nuclei is manifested with their ‘spin’. When a nucleus has spin, it creates a small magnetic field, with a specific strength and orientation..
Bayesian and Frequentist Multiple Testing
Authors: Genovese Christopher , Wasserman Larry 
Published: November 2023

Abstract
We introduce a Bayesian approach to multiple testing. The method is an extension of the false discovery rate (FDR) method due to Benjamini and Hochberg (1995). We also examine the empirical Bayes approach to simultaneous inference proposed by Efron et al. (2001). We show that, in contrast to the single hypothesis case—where Bayes and frequentist tests do not agree even asymptotically—in the multiple testing case we do have asymptotic agreement There has been renewed interest in simultaneous inference due to the abundance of large, complex data sets. As a motivating example, consider genetic microarrays.
New Tools for Consistency in Bayesian Nonparametrics
Authors: Salinetti Gabriella 
Published: November 2023

Abstract
Consistency in Bayesian nonparametric statistical problems continues to register increasing attention, motivated by various and easy to share reasons, widely illustrated in the rich literature on the subject starting from Diaconis and Freedman (1986) to the recent Ghosh and Ramamoorthi (2002). The difficulty in nonparametrics is in the dimension of the problem: the “parameter” itself is typically a probability measure, the prior is a probability measure on a space of probability measures, and the resulting posterior, after seeing the data, is a random probability measure.
Multi-scale Modelling of 1-D Permeability Fields
Authors: Ferreira Marco A R , West Mike , Bi Herbert K H Lee David Higdon Zhuoxin 
Published: November 2023

Abstract
Permeability plays an important role in subsurface fluid flow studies, being one of the most important quantities for the prediction of fluid flow patterns. The estimation of permeability fields is therefore critical and necessary for the prediction of the behavior of contaminant plumes in aquifers and the production of petroleum from oil fields. In general, there are two types of information that can be used in the estimation of the permeability field: static data and dynamic data. Static data can be regarded as measurements of the permeability field, and are available at different levels of resolution as a result of geological studies, well tests, and laboratory measurements. Dynamic data are obtained through the production from a collection of wells, being the production history in the case of a mature oil field or the result of tracer experiments in the case of aquifers. To incorporate the dynamic data in formal statistical analysis, corresponding likelihood functions for the high-dimensional random field parameters representing the permeability field can be computed with the help of a fluid flow simulator (FFS). The FFS can run at different scales of resolution of the permeability field, lower levels providing faster but less accurate results. In this paper, we incorporate the static information available at the different scales of resolution by using a multi-scale time series model as a prior for 1-D permeability fields. Estimation of the multi-scale permeability field is then performed using an MCMC algorithm with an embedded . FFS running at different scales to incorporate the dynamic data. We use simulated data to study the performance of the proposed approach with respect to the recovery of the original permeability field.
Lymphoscintigraphy of Upper Limbs: A Bayesian Framework
Authors: Petr Gebouský , Miroslav Kárný , Anthony Quinn 
Published: November 2023

Abstract
Lymphoscintigraphy is a sensitive diagnostic technique in nuclear medicine. One of its principal applications is the investigation of upper limb lymphedema, a complication of breast cancer. Typically, only two or three snapshots of the distribution of the radioactive tracer in the limb can be obtained, implying a sparse data problem. Hence, traditional inferences of important physiological indicators are completely unreliable. In this paper, the Bayesian paradigm, exploiting available prior information in conjunction with a simplified model of the diffusion dynamics, is used to obtain reliable quantitative evaluations for the first time.
Bayesian Analysis of Matched Pairs in the Presence of Covariates
Authors: Martínez F Javier Girón M Lina , Moreno Elías , Torres Francisco 
Published: November 2023

Abstract
In this paper, we address three main questions connected with the statistical analysis of matched pairs in the presence of covariates, from a Bayesian viewpoint. These sort of data appear when measurements are made on the same individuals before and after a treatment, at possibly different dose levels, is applied. The first question refers to the issue of how to construct statistical models for matched pairs data that reflect the dependency of the post-treatment on the pre-treatment observation, the dose level and the values of the covariates. The second one addresses the problem of seeking suitable statistical models for which inferences on the effect of the treatment only depends on some simple summaries of the data, for instance, the differences or ratios of the corresponding paired data. The third issue is concerned with testing the effectiveness or not of the treatment, the relevance of the different dose levels, and the influence of the covariates. Explicit solutions and formulae are derived for the case of an additive linear normal model. These problems are addressed here from a Bayesian default model choice perspective. Comparison of this novel approach to testing with classical and standard (non informative) Bayesian approaches are given, based on a simulation study.
On the Application of Logistic Regression Modelling in Microarray Studies
Authors: Mertens Bart j. A 
Published: November 2023

Abstract
Microarrays (sometimes also referred to as “DNA chips”) simultaneously record sample responses on a large collection of genes, thus yielding data of extremely high dimensionality. The methodology is to a considerable extent driven by advances in technology. It is envisaged that the chips will eventually be produced in large numbers from standard “matrices” or templates (which are in themselves costly to produce), thus allowing economies of scale. Evaluation of the chip responses is largely automated. At present, microarrays (and sample preparation) are still expensive. Hence, sample sizes from microarray experiments are small, which exacerbates the singularity problems which are encountered with such high-dimensional data.
Bayesian Inference for Derivative Prices
Authors: Polson Nicholas G , Stroud Jonathan R 
Published: November 2023

Abstract
This paper develops a methodology for parameter and state variable inference using both asset and derivative price information. We combine theoretical pricing models and asset dynamics to generate a joint posterior for parameters and state variables and provide an MCMC simulation strategy for inference. There are several advantages of our inferential approach. First, more precise parameter estimates are obtained when both asset and derivative price information are used. Second, we provide a diagnostic tool for model misspecification based on agreement of the state and parameter estimates with and without derivative price information. Furthermore, the time series properties of the state variables can also be used to evaluate model fit. We illustrate our methodology using daily equity index options on the Standard and Poor’s (S&P 500) index from 1998-2002.
Objective Bayesian Comparison of Laplace Samples from Geophysical Data
Authors: Álvarez Abel Rodr íguez Giselle , Sans Bruno 
Published: November 2023

Abstract
We consider a Bayesian reference analysis for comparing two samples which follow Laplace distributions as well as the analysis of the performance of a Bayesian classification procedure for such a model. The methodology is motivated by an oil exploration problem where the geological structure of a reservoir has to be described. The aim is to characterize the lithology of the reservoir using well registers and seismic traces. We use a continuous wavelet transformation (CWT) of the original data and estimate the power of the transformation that links the energy of the CWT to its scale. For both well logs and seismic traces, we found that such an estimate follows a Laplace distribution with location depending on whether the lithology corresponds to gravel or sand. In this work we explore the comparison problem from an objective Bayesian viewpoint through the use of the Intrinsic Bayes Factor.
Bayesian Object Matching with Hierarchical Priors and Markov Chain Monte Carlo
Authors: Tamminen Toni , Lampinen Jouko 
Published: November 2023

Abstract
We present an object matching system where Bayesian inference is used for estimating the probability distribution of object feature locations in a digital image. The representation of the object contains two parts: the likelihood part which defines the probability of perceiving a pixel image corresponding to the object detail, and a hierarchical prior part which defines the distributions of the feature locations relative to the other features. In this study the objects are human faces. The likelihood part is based on Gabor filters, which are a type of optimal bandpass digital filters. A distortion-tolerant measure of feature similarity is produced by performing statistical analysis on the filter response vectors, and the actual likelihood of observing an image detail given the feature location is computed by analyzing the distribution of the similarities. The prior part defines a priori distributions for the relative locations of the features, which correspond to their average locations on the object. The prior is hierarchical in nature, that is, a hyperprior is set for the width of the prior distribution, which allows the system to automatically determine the feature location variance appropriate for the image at hand. The object matching is carried out as Bayesian inference: the goal is to find the posterior probability distribution of the possible matches given the image and the hierarchical prior. Markov Chain Monte Carlo methods, especially Gibbs sampling, are used to draw samples from the posterior distributions of the feature locations, allowing simple estimation of the essential distribution statistics.
Lucky for some-miscellanea
Authors: Haigh John 
Published: November 2023

Abstract
There are 52C13 = 635013 559 600 ways of selecting 13 objects from 52, so that is the total number of different bridge or whist hands that you could hold. From the work in Chapter 7, you would need to hold about a million hands for a 50% chance of being dealt a repeat, and it is most unlikely that you would notice! Effectively, every hand is a new challenge, even without the help of your ‘partner’, the polite name applied to the person opposite. The number of different deals is much larger.
Progress in five parts
Authors: Fulford K W M , Thornton Tim , Graham George 
Published: November 2023

Abstract
Things have changed a lot since Anthony Quinton, a British philosopher, formerly at Oxford and then a member of the House of Lords, made this observation in a lecture to the Royal Institute of Philosophy (Quinton, 1985). Philosophers are nowadays actively interested in a wide range of topics from mental health practice and research. Conversely, mental health practitioners (including both professionals and users of services) are actively interested in a wide range of philosophical topics. Indeed, the explosive growth in cross-disciplinary contact in the last few years (see Reading Guide) has been as remarkable as the earlier long period of mutual neglect.
Introduction
Authors: Ockendon John , Howison Sam , Lacey Andrew , Movchan Alexander 
Published: November 2023

Abstract
Partial differential equations are central to mathematics, whether pure or applied. They arise in mathematical models whose dependent variables vary continuously as functions of several independent variables, usually space and time. Their most striking attribute is their universality, a property which has enabled us to motivate every mathematical idea in this book by real-world examples from fluid or solid mechanics, electromagnetism, probability, finance and a host of other areas of application. Moreover, this applicability is growing day by day because of the flexibility and power of modern software tailored to suitable discretised approximations of the equations. Equally dramatic is the way in which the equations that arise in all these areas of application can so easily motivate the study of fundamental mathematical questions of great depth and significance and, conversely, benefit from the results of such investigations.
First-order scalar quasilinear equations
Authors: Ockendon John , Howison Sam , Lacey Andrew , Movchan Alexander 
Published: November 2023

Abstract
Even though this chapter deals only with the simplest category of partial differential equations, the theory that emerges is relevant to many important and fascinating practical situations. An example is the flow of a thin coat of paint down a wall, as illustrated in Fig. 1.l(a). Because the layer is thin, the velocity, say u(x, y, t), is approximately unidirectional down the wall.
Parabolic equations
Authors: Ockendon John , Howison Sam , Lacey Andrew , Movchan Alexander 
Published: November 2023

Abstract
As we have already mentioned, and as this chapter emphasises, parabolic equations probably occur more commonly than any other type of partial differential equation in applied science. However, that is not the only reason why this chapter is one of the longest of the book. It also reflects the vast amount of knowledge that has been gained about many parabolic problems over recent decades. This has come about as the result of the stimulus of the many practical applications combined with the applicability of a wide variety of mathematical techniques.
An introduction to functional magnetic resonance imaging of the brain
Authors: Matthews Paul M 
Published: November 2023

Abstract
This chapter is intended to provide an overview of fMRI methods and applications. Like the rest of the book, it is intended to highlight key concepts or strategies rather than to provide a review of the literature. It also should provide a guide to the chapters that follow. We hope that it will help to place them in context. In doing so, this chapter makes later repetition inevitable, but we have embraced this in the belief that learning can be made easier by having ideas presented more than one time and in more than one way.
Reciprocity
Authors: Villegas Fernando Rodriguez 
Published: November 2023

Abstract
We saw in §2.1 how the quadratic reciprocity law can be interpreted as describing the variation of the number of solutions N(p) of a quadratic equation modulo primes p. We will now consider the situation for polynomials still in one variable but of higher degree. Let us fix a prime p. In order to formulate the general reciprocity law (of Artin) it turns out to be necessary to consider not just the number of solutions to an equation modulo p but the result of other related countings as well.
Remarks On Selected Exercises
Authors: Villegas Fernando Rodriguez 
Published: November 2023

Abstract
Ex. 1.13 The connection is in the general spirit of reciprocity as in §3.2.2 but goes beyond the scope of this book. See for example [26], (4.24) and chapter III, 14 C. Ex. 1.26 Here is a simple GP script that will plot the ratio of Rk(n) and the volume of the corresponding sphere for fixed k and n 1 through a given bound
An Introduction to Algebraic Geometry and Algebraic Groups
Authors: Geck Meinolf 
Published: November 2023

Abstract
An accessible text introducing algebraic geometries and algebraic groups at advanced undergraduate and early graduate level, this book develops the language of algebraic geometry from scratch and uses it to set up the theory of affine algebraic groups from first principles. Building on the background material from algebraic geometry and algebraic groups, the text provides an introduction to more advanced and specialised material. An example is the representation theory of finite groups of Lie type. The text covers the conjugacy of Borel subgroups and maximal tori, the theory of algebraic groups with a BN-pair, a thorough treatment of Frobenius maps on affine varieties and algebraic groups, zeta functions and Lefschetz numbers for varieties over finite fields. Experts in the field will enjoy some of the new approaches to classical results. The text uses algebraic groups as the main examples, including worked out examples, instructive exercises, as well as bibliographical and historical remarks.
Neuropsychological perspectives on sensorimotor integration: Eye-hand coordination and visually-guided reaching
Authors: Carey David P 
Published: November 2023

Abstract
Neuroimaging work has done much to confirm hypotheses of a role for the posterior parietal cortex (PPC) in sensorimotor integration. Nevertheless, in the future such studies will need to test more specific models of the processes in which the PPC participates. A recurrent theme cutting across neuropsychology, physiology, and computational modeling is the idea of transformations in reference frames from retinal- to arm- (or hand-) centered coordinates. Understanding sensorimotor control processes at this level with neuroimaging will always be difficult. Evidence from neurophysiology suggests that any populations of cells related to encoding target location, preparing, and executing saccadic eye movements, and preparing, executing, and monitoring arm movements overlap considerably.
The achievement of brain imaging: Past and future1
Authors: Posner Michael I 
Published: November 2023

Abstract
Brain imaging has forged an impressive link between psychology and neuroscience. Many studies show consistent activation of a small number of widely separated brain areas that differ among tasks. Do these results imply a modular organization of the human brain? One view of modularity requires that there be no top down or cognitive input from other systems. This view has almost no support even for primary sensory cortex, which can be influenced by attention. A less restrictive version of modularity requires the ability to manipulate one area of activation independently from another. The studies in the sections of this volume on visual pathways and on orienting of attention, provide support for this form of modularity, but require specification of the time course and the connections involved.
Population ecology
Authors: Gibson David J 
Published: November 2023

Abstract
Population ecology seeks to understand the relationship between groups of individuals of a single species in an area (a population) and their environment. The ‘father’ of plant population ecology was John L. Harper, following the publication of his Population Biology of Plants in 1977, and, indeed, much of his work was undertaken in a Lolium per- enne–Trifolium repens sheep pasture in North Wales (e.g. Sackville Hamilton and Harper 1989; Sarukhán and Harper 1973; Turkington and Harper 1979). Many topics in the population ecology of grasses and grasslands are covered in the book edited by G.P. Cheplick (1998a) (see review in Gibson 1998). In this chapter, some of these important topics are explored. First, reproduction and population growth of grasses is discussed (§5.1), followed by a discussion of how fungi infecting grasses either above or below ground affect their population ecology (§ 5.2), and finally, ecotypes, polyploidy, hybridization, and genetic structure are covered under the heading of genecology (§5.3).