bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical Models for Ranking and for Identifying Extremes, with Applications¹
Authors: Morris C N , Christiansen C L 
Published: November 2023

Abstract
We address the problem of ranking and of identifying the best or worst of several individuals or groups, e.g., athletic teams or medical practitioners (called “profiling” in medicine, an increasingly important topic in US health policy). Satisfactory statistical approaches must account for covariates that might indicate that the degree of difficulty differs for the individuals being compared, and they also must account for random variation, especially for regression to the mean. Hierarchical Bayesian (multi-level) methods permit one to model and then to account for differing amounts of sampling information, usually due mainly to different sample sizes, in the groups being compared. This can lead to “crossover” effects, which do not occur in standard ranking and selection theory. We review here the relevant theories and compare existing and new methods and criteria for doing this in several applications.
Bayes Posteriors for Scalar Interest Parameters
Authors: Fraser D A S , Reid N 
Published: November 2023

Abstract
The posterior distribution for an interest parameter is usually obtained by integrating the nuisance parameters out of a full posterior distribution. For this, considerable attention has focused on finding a prior that is noninforrnative for the interest parameter and yet allows the effective elimination of the nuisance parameters; of course this needs model structure beyond the likelihood function. As a paradigm for generalizing this, we take a location model for the interest parameter and a separate model for the nuisance parameter. Then for a general asymptotic model we use recent third order asymptotics to determine location model structure relative to a scalar interest parameter; we thus obtain a posterior for the interest parameter based on a flat or diffuse prior for only the interest parameter.
Protoplasmic pantheism
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
At the beginning of the 1890s, support for Golgi’s diffuse neural net was confined to the circle of his direct or indirect pupils. Gerlach’s version of the reticular theory continued to be much more popular in Italy and elsewhere. There were also other reticular theories, sometimes touching on the bizarre. The Lithuanian, Alexander Stanislavovich Dogiel, for example, wishing to mix oil and water, maintained that in the retina there existed a double intercellular reticulum; one was inter-cylindraxial (similar to that of Golgi); the other inter-dendritical (similar to that of Gerlach). And then there was even a suggestion that there might be three reticula, with the third consisting of a connection between dendrites and axons.1 The most important scientists, however, were embracing the theory of the neuron with more and more conviction.
Working unto death
Authors: Mazzarello Paolo , Buchtel Henry A , Badiani Aldo 
Published: November 2023

Abstract
The last years of Golgi’s life were set in a period of dramatic events for Italy and Europe. On 28 June 1914 Archduke Francis Ferdinand of Austria-Este and his morganatic wife, the Duchess of Hohenberg, were assassinated by a Serbian nationalist in Sarajevo. After a century of optimistic faith in technical, scientific, and moral progress, Europe plunged into the abyss of the First World War; a war that was expected to be over in a few months and instead lasted five years, devastating the youth of the continent.
Stem Cells and their Gliogenic Potential
Authors: Brustle O , Dubois-Dalcq M 
Published: November 2023

Abstract
A detailed understanding of the lineage relationships between the three principal cell types of the central nervous system (CNS)—neurons, astrocytes and oligodendrocytes-is a central issue in the study of CNS development and repair. The discovery that in vitro type 2 astrocytes and oligodendrocytes can develop from a common, self-renewing progenitor established the existence of a bipotential precursor for CNS glia (Raff et al., 1983; Bögler et al., 1990). These experiments also showed that the decision of a progenitor cell to differentiate into an oligodendrocyte or a type 2 astrocyte is influenced by the culture medium, indicating that the fate of neural precursor cells can be modulated by extrinsic factors. While type 2 astrocytes are rarely encountered in vivo, subsequent in vivo lineage analyses with retroviral vectors established the presence of a common progenitor for type-I astrocytes and oligodendrocytes (Levison and Goldman, 1993). These studies also provided evidence for the existence of neuron-astrocyte and neuron-oligodendrocyte precursors (Galileo et al., 1990; Williams et al., 1991). However, it was the discovery of a multipotential neural precursor cell for neurons, astrocytes and oligodendrocytes which established the neural stem cell (NSC) concept (for reviews, see Weiss et al., 1996b; McKay, 1997).
Introduction
Authors: Diggle Peter J , Heagerty Patrick J , Liang Kung-yee. , Zeger Scott L 
Published: November 2023

Abstract
The definning characteristic of a longitudinal study is that individuals are measured repeatedly through time. Longitudinal studies are in contrast to cross-sectional studies, in which a single outcome is measured for each individual. While it is often possible to address the same scientfic questions with a longitudinal or cross-sectional study, the major advantage of the former is its capacity to separate what in the context of population studies are called cohort and age effects. The idea is illustrated in Fig. 1.1. In Fig. 1.1(a), reading ability is plotted against age for a hypothetical cross-sectional study of children. Reading ability appears to be poorer among older children; little else can be said. In Fig. 1.1(b), we suppose the same data were obtained in a longitudinal study in which each individual was measured twice. Now it is clear that while younger children began at a higher reading level, everyone improved with time. Such a pattern might have resulted from introducing elementary education into a poor rural community beginning with the younger children. If the data set were as in Fig. 1.1(c), a different explanation would be required. The cross-sectional and longitudinal patterns now tell the same unusual story – that reading ability deteriorates with age.
Paths
Authors: Priestley H A 
Published: November 2023

Abstract
In this chapter we start systematically to study curves in the complex plane. To regard a curve, such as a circle, simply as a subset of the plane will not suffice for our purposes. Instead, we adopt a dynamic approach and think of a curve as the route traced out by a moving point, the route being specified by a suitable function of some real parameter.
We shall need a few of the ideas from this chapter when we consider elementary conformal mapping in Chapter 8 and multifunctions in Chapter 9. Readers seeking the quickest possible route to the theory at the heart of complex analysis, in particular to Cauchy’s theorem, may wish to proceed straight from this chapter to Chapter 10, in which we discuss integrals along paths.
Applications of contour integration
Authors: Priestley H A 
Published: November 2023

Abstract
This chapter is devoted to applications of Cauchy’s residue theorem to the evaluation of definite integrals and the summation of series. The method will handle quite baroque examples. If we seem to have included some examples of this sort, especially in the final section (advanced track), it is because these give valuable technical experience. Very few of the integrals we consider can be evaluated by more elementary methods such as substitution.
Markov Random Field Extensions using State Space Models
Authors: Dethlefsen Claus 
Published: November 2023

Abstract
The class of state space models is very broad and comprises structural time series models, ARIMA models, cubic spline models, and, as demonstrated by Lavine (1999), also Markov random field models. The Kalman filter techniques are powerful tools for inference in such sequential models. Basic references on state space model methodology are Harvey (1989), West and Harrison (1997) and Durbin and Koopman (2001). In the past decade, there has been a certain interest in developing Markov chain Monte Carlo (MCMC) methods for the analysis of complex state state space models (see Carlin et al., 1992; Carter and Kohn, 1994; Friihwirth- Schnatter, 1994; de Jong and Shephard, 1996). Our approach is not based on MCMC but on iterated extended Kalman smoothing, which may be combined with importance sampling for exact simulation (see Durbin and Koopman, 2001). Using this method, we avoid the MCMC problems of ensuring that the Markov chain is mixing well and assessing whether the chain has converged. Writing Markov random field models as state space models, following Lavine (1999), makes it possible to use Kalman filter techniques to extend and analyze more complex Markov random field models. We show how to analyze such extensions and we also deliver an illustration, formulating a model for restoring digital images with focus on finding edges in the image. However, the new class of models also have applications within agricultural experiments (see, e.g., Besag and Higdon, 1999) and disease mapping (see, e.g., Knorr-Held and Rue, 2002).
State Space Models for Density Dependence in Population Ecology
Authors: Brooks Lara E Jamieson Stephen P 
Published: November 2023

Abstract
In this paper, we focus upon the detection and estimation of density dependence in two species of North American ducks on the basis of annual census counts taken over a 47-year period. The data comprise the counts themselves, plus an estimate of the associated standard errors. Classical approaches to investigating density dependence in population ecology from data of this sort tend to suffer from (often extreme) sensitivity to sampling errors. Here, we adopt a Bayesian approach and demonstrate how a state space model can be used to account for both measurement and process error within the analysis. We use (reversible jump) MCMC to both fit and discriminate between the competing models and investigate goodness-of-fit using both cross-validation and Bayesian p-values. We conclude that one of these species of duck appears to exhibit density dependence, whilst the other does not and discuss the implications that this new result has for the future management of these duck populations.
A Marginal Ergodic Theorem
Authors: Lavine Michael 
Published: November 2023

Abstract
In recent years, several papers including Gelfand and Sahu (1999), Hobert (2001), Liu and Wu (1999), Meng and van Dyk (1999) and van Dyk and Meng (2001) have appeared which use MCMC samplers based on improper posteriors to make valid inference about a proper target posterior. The following example is in Hobert (2001). It or a close relative has appeared in Liu and Wu (1999), Meng and van Dyk (1997, 1999) and van Dyk and Meng (2001).
Outlier Robust Estimation of a Finite Population Total
Authors: Pettit Lawrence I , Sugden Roger A 
Published: November 2023

Abstract
We discuss a Bayesian approach to estimating a finite population total when the mean of the observed sample values is assumed to be proportional to a known covariate. Observations have one of two possible variances, a standard form or an inflated form. Observations with the larger variance are termed outliers. We learn about the proportion of outliers and parameters of the model from the sample. Inference about the mean and variance of the population total is made using the predictive distribution. The frequentist properties of the procedure are analyzed for a particular finite population by examining the bias of the estimator and coverage of the confidence interval. The performance of the estimator is shown to be superior to the Horvitz-Thompson estimator and a simple Bayes estimator which allows only the standard variance.
A Method for Sequential Optimization in Bayesian Analysis
Authors: Virto Miguel , Martín Jacinto , Insua David Ríos , Díaz Arminda Moreno 
Published: November 2023

Abstract
Bayesian analysis frequently leads to sequential optimization problems under uncertainty. Some examples include optimal sequential design, the analysis of influence diagrams and decision trees and many Operations Research related problems in areas like maintenance or reliability. We introduce here a method to deal with such problems, within a stochastic dynamic programming context. The basic approach is a Metropolis augmented probability simulation algorithm. Once introduced, we describe how to exploit the dynamic structure of the problem in a Bellman-like approach and describe our basic algorithm. Implementation details are discussed together with an illustrative example in preventive maintenance. An appendix includes theoretical results.
Fundamentals of the finite element method
Authors: Elishakoff Isaac , Ren Yongjian 
Published: November 2023

Abstract
Everybody nowadays knows what finite elements are: they are the methods for solving boundary value problems in which one divides the domain of the problem into little pieces over which the solution is approximated using polynomials. The little pieces are finite elements, and the polynomials are called shape functions—J. T. Oden (1990). … I wanted to have a better name for the method because ‘direct stiffness ‘ did not seem to convey the essence of the idea. That name is suitable to describe the procedure for assembly of discrete elements of any type, but it does not imply anything about stress analysis. So thinking of the work we had done at Boeing in comparing discrete element assembly results with finite different results, the name ‘finite element method ‘ seemed to be appropriate … it seems to have been a good choice because it has stood the test of time—R. W. Clough (1994).
Epilogue
Authors: Elishakoff Isaac , Ren Yongjian 
Published: November 2023

Abstract
‘It is this author ‘s belief that with the large scale introduction of computers in the practice and lately also at many technical schools, the teaching of and the approach to solving technical problems has been shifting in the wrong direction … the insight of how structures behave under loading of the older generation is being more and more replaced by the nearly religious faith of the younger ones in the predictions of their favorite computer codes. ‘
The big-bang models
Authors: Rowan-Robinson Michael 
Published: November 2023

Abstract
To describe the properties of a gas we do not need to study the behaviour of individual atoms and molecules. Instead we define various macroscopic quantities-density, pressure, temperature-and study the relations between these. In the same way, we make no attempt to incorporate individual galaxies, or clusters of galaxies, into our description of the universe as a whole. We imagine the matter in the universe smeared out into an idealized, smooth fluid, which we call the substratum .
Observational cosmology
Authors: Rowan-Robinson Michael 
Published: November 2023

Abstract
The isotropic 2.7 K blackbody radiation, the 24 per cent cosmic helium abundance, and the similarity between the ages of galaxies and the age of the universe all support the big-bang models derived from general relativity assuming the cosmological principle. Can we test these models in more detail? In particular, can we determine the current value of the deceleration parameter q (Section 4.7) and thereby deduce what the future of the universe will be? In this chapter we look at a variety of cosmological tests, using discrete sources of radiation and the microwave background radiation fluctuations, that have been applied to try to answer this question.
Algebraic sets and algebraic groups
Authors: Geck Meinolf 
Published: November 2023

Abstract
In this first chapter, we introduce the basic objects: algebraic sets in affine space, the corresponding affine algebras, and algebraic groups. We proceed in a completely elementary way and assume only some very basic knowledge about groups, polynomial rings, quotient rings, and fields. The general theory will be mainly illustrated with examples concerning hypersurfaces and algebraic groups.
The Neural Bases of Subliminal Priming
Authors: Dehaene Stanislas 
Published: November 2023

Abstract
Psychologists have long reported that words that are made invisible by forward and backward masking can nevertheless cause behavioral priming effects. Functional neuroimaging can now be used to explore the neural bases of masked priming. Subliminal priming causes reduced activation in multiple areas (fusiform gyrus, intraparietal sulcus, and motor cortex), in direct correspondence with behavioral manifestations of priming at the orthographic, semantic, and motor level. This implies that a whole stream of processors can operate unconsciously. The neural code in each area can be assessed by varying prime-target relations. A simple mathematical framework is proposed that tentatively relates priming at the voxel level with the shape of the tuning curves of single neurons in the underlying tissue.
The Influence of Scene Organization on Attention: Psychophysics and Electrophysiology
Authors: Valdes-Sosa Mitchell , Bobes Maria A , Rodriguez Valia , Acosta Yanelis , Pérez Alejandro , Iglesias Jorge , Borrego Mayelin 
Published: November 2023

Abstract
Many studies have examined the neural mechanisms of visual attention by recording event-related potentials (ERPs). In most of these studies, objects are abruptly presented to the observer, often in a rapid succession of events. Similar stimuli are used in the psychophysical technique known as rapid serial visual presentation (RSVP). We argue that in RSVP (and related ERP paradigms) it is difficult to evince object-based attentional effects and to study the influence of scene organization. Rapid serial object transformation (RSOT) is offered as an alternative.
Introduction
Authors: Gibson David J 
Published: November 2023

Abstract
Each grass-covered hillside is an open book for those who care to read. Upon its pages are written the conditions of the present, the events of the past, and a forecast of those of the future. Some see without understanding; but let us look closely and understandingly, and act wisely, and in time bring our methods of land use and conservation activities into close harmony with the dictates of Nature. The purpose of this first chapter is to introduce the grassland biome. Grasslands are the most extensive, arguably the most useful to human society, yet the most threatened biome on the planet. Nevertheless, it is surprisingly difficult to unambiguously define grassland. Here we seek such a definition (§1.1); discuss where grasslands occur (§1.2); how they are being lost, fragmented, and degraded (§1.3); and summarize their outstanding and immense value (§1.4). The chapter finishes with a brief biography of John Bews and John Weaver, two of the early pioneers of grassland ecology (§1.5).
Order, Lattices, And Boolean Algebras
Authors: Dunn J Michael , Hardegree Gary M 
Published: November 2023

Abstract
In the current chapter, we present a very special and useful class of mathematical structures, which play an important role in the algebraic description of logic. These structures, called lattices, can be characterized both as relational structures (pertaining to the notion of implication), and as algebraic structures (pertaining to the notions of conjunction and disjunction)..
Semantics
Authors: Dunn J Michael , Hardegree Gary M 
Published: November 2023

Abstract
We have now discussed the mathematical theory of propositions, which we formulated in the framework of lattice theory, and we have discussed the mathematical theory of sentences (formal syntax), which we formulated in the theory of universally free algebras. In the present chapter, we discuss the relation between sentences and propositions, which is the central focus of algebraic formal semantics.
Classical Propositional Logic
Authors: Dunn J Michael , Hardegree Gary M 
Published: November 2023

Abstract
Classical propositional logic has associated with it a number of different algebraic log ics, which we will call Frege logic, Boolean logic, and unital Boolean logic. Although these logics are characterized by different classes of logical matrices, as we will see later, they are all strongly equivalent, which is to say that they validate exactly the same asymmetric consequences. However, they do not validate all the same symmetric con sequences.
Codes and Curves
Authors: Hirschfeld J W P 
Published: November 2023

Abstract
In 1981 Gappa [5] gave a construction of linear codes beginning from an algebraic curve over the finite field GF(q).The parameters of these codes are restricted by the Riemann-Roch theorem. They also give asymptotic codes better than those previously known. Here we describe the construction and give some of the subsequent results for curves of genus zero and one. Foe othe, smvey,, see [2], [7], [10], [16], [29], [30], [32], [35].
The Homogeneous Linear Equation and Wronskians
Authors: Collins Peter J 
Published: November 2023

Abstract
The main aim of this chapter will be to use Theorem 4 of Chapter 2 – and specifically both existence and uniqueness of solutions – to develop a theory that will describe the solutions of the homogeneous linear second-order equation where p 0; p 1; p 2 are continuous real-valued functions on [a, b] and p 2(x) &lt; 0 for each x in [a, b]. (‘Homogeneous’ here reflects the zero on the right-hand side of the equation which allows λy to be a solution (for any real constant λ) whenever y is a given solution.) The language of elementary linear algebra will be used and the theory of simultaneous linear equations will be presumed. Central to our discussion will be the Wronskian, or Wronskian determinant: if y 1 : [a, b] → ℝ and y 2 : [a, b] → ℝ are differentiable functions on the closed interval [a, b], the Wronskian of y 1 and y 2, W(y 1; y 2) : [a, b] → ℝ, is defined, for x ∈ [a, b], by If y 1 and y 2 are solutions of (1), it will turn out that either W(y 1, y 2) is identically zero or never zero in [a, b].
The Non-Homogeneous Linear Equation
Authors: Collins Peter J 
Published: November 2023

Abstract
In this chapter we shall consider the non-homogeneous second-order linear equation where f : [a, b] → ℝ and each p i: [a, b] → ℝ are continuous, and p 2(x) &gt; 0, for each x in [a, b]. Any particular solution of (1) (or, indeed, of any differential equation) is called a particular integral of the equation, whereas the general solution c 1 y 1 + c 2 y 2 of the corresponding homogeneous equation given by Proposition 6 of Chapter 3 (where y 1, y 2 are linearly independent solutions of (2) and c 1, c 2 are arbitrary real constants) is called the complementary function of (1). If y P is any particular integral and y C denotes the complementary function of (1), y C + y P is called the general solution of (1). We justify this terminology by the following proposition, which shows that, once two linearly independent solutions of (2) are found, all that remains to be done in solving (1) is to find one particular integral.