bluuebunny/crossref_metadata_2025_split huggingface dataset:
Admission controls for loss networks with diverse routing
Authors: MacPhee lain , Ziedins Ilze 
Published: November 2023

Abstract
This paper studies control policies for large symmetric star-shaped loss networks, in which arrivals request capacity on one or two links. We formulate the optimization problem of interest for the network, and consider the limit as the number of links in the network becomes large while the arrival rate and capacities at any single link are held fixed. The optimal solution of the time-independent version of the deterministic problem obtained in this limit is to use a threshold policy, also known as trunk reservation. This work complements that of Hunt and Laws for fully connected networks.
Flexible Discriminant and Mixture Models
Authors: Hastie Trevor , Tibshirani Robert , Buja Andreas 
Published: November 2023

Abstract
Fisher’s linear discriminant analysis is a useful and graphical tool for multi-group classification. With a large number of predictors, one can find a reduced number of discriminant coordinate functions that are ‘optimal’ for separating the groups. With two such functions one can produce a classification map that partitions the reduced space into regions that are identified with group membership, and the decision boundaries are linear.
Developmental Origins of Astrocytes
Authors: Goldman J E 
Published: November 2023

Abstract
Astrocytes, the delicate, multiprocessed glial cells of the central nervous system (CNS), were first visualized over a century ago (Andriezen, 1893), and at that time were noted for their complex shapes, in which thicker processes branch into ever-thinner ones. Although many astrocytes are indeed ‘star-shaped’, they in fact display a wide variety of morphologies, among them the spherical or ovoid protoplasmic glia of gray matter; the more irregular and less complex fibrous astrocytes of white matter; the Bergmann glia of the cerebellar molecular layer, whose processes are oriented radially from the Purkinje cell layer to the pial surface; the velate astrocytes of the cerebellar granule cell layer, whose sheet-like processes wrap mossy fiber terminals; and the radially oriented glia that populate the periventricular regions of the diencephalon and brainstem. All share certain salient characteristics, such as numerous branching processes, intermediate filaments in the cell cytoplasm and intimate contact with mesenchymal elements of the blood vessels and pial surfaces and with neurons.
Analysis of Longitudinal Data
Authors: Diggle Peter J , Heagerty Patrick J , Liang Kung-yee. , Zeger Scott L 
Published: November 2023

Abstract
The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This new edition of Analysis for Longitudinal Data provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.
Plant structure: the solidity of anti-herbivore protection
Authors: Schoonhoven Louis M , Loon Joop J A van , Dicke Marcel 
Published: November 2023

Abstract
A clear understanding of insect-plant relationships requires fairly detailed knowledge of plant structures and chemicals involved in resistance to most insects and susceptibility to others. The present Chapter deals with the morphological (physical) factors that interfere with feeding or oviposition, and Chapter 4 considers plant chemicals that affect insect behaviour and physiology.
Plant chemistry: endless variety
Authors: Schoonhoven Louis M , Loon Joop J A van , Dicke Marcel 
Published: November 2023

Abstract
The subject of this Chapter-plants as food for herbivorous insects-touches the heart of insect plant biology. Its theme can be captured in two basic questions. First, what do plants offer to insects by way of nutrition? And second, what do insects need for optimal growth and reproduction? Answering these two questions isseriously hindered by the fact that (1) the chemical composition of plants, as noted before, varies among species in both space and time, and (2) the nutritional requirements of insects vary between species and with developmental stage and environmental conditions.
Insects and flowers: mutualism par excellence
Authors: Schoonhoven Louis M , Loon Joop J A van , Dicke Marcel 
Published: November 2023

Abstract
When C. K. Sprengel, rector of a Lutheran school in Germany, became depressed as a result of his duties, his doctor advised him to study Nature and, to facilitate his recovery, taught his patient some elementary botany. Sprengel then gained an in-depth knowledge of flower morphology, nectar secretion and its function, and published in 1793 a treatise under the imaginative title Das entdeckte Geheimniss der Natur im Bau und in der Befruchtung der Blumen (The Secret of Nature revealed in the Structure and Fertilization of Flowers; Fig. 12.1).
Magnetoencephalography
Authors: Rosburg Timm , Sauer Heinrich 
Published: November 2023

Abstract
Magnetoencephalography (MEG) devices permit the observation of magnetic fields stemming from electrical brain activity. MEG is, therefore, like electroencephalography (EEG), a method to record brain activity with a time resolution of milliseconds. The high temporal resolution is an important feature for the study of human brain function, as recent findings have stressed the importance of high-frequency signals in cortical information processing. Gamma-band activity (in the range of 40 Hz) is thought to play a key role in human information processing (Varela et al. 2001) and some sensory information seems to be processed even at much higher frequencies (~600 Hz) (Curio et al. 1994). Although EEG and MEG share some similarities, there are some considerable differences, which lead to slightly different applications. The differences between the methods are discussed here briefly—the reader who is interested in methodological and practical issues of MEG in more detail is referred to Hämäläinen et al. (1993) or Elbert (1998).
Towards an integrated imaging of schizophrenia
Authors: Lawrie Stephen M , Johnston Eve C , Weinberger Daniel R 
Published: November 2023

Abstract
The preceding chapters in this book demonstrate both the great advances in technology applied to brain imaging and knowledge of schizophrenia gained from these techniques over the past 25 years or so. In this chapter, we aim to synthesize the findings from these studies, in the light of other improvements in our understanding of schizophrenia and related neuropsychiatric conditions—particularly those deriving from genetics, epidemiology, and cognitive neuropsychology. We consider the conceptual and methodological problems of the field, and indicate where we think definite progress is likely in the foreseeable future (e.g. scanning both special and representative populations, multi-modal imaging, employing complementary approaches to study disconnectivity, and using neuroimaging databases).
Hierarchical Bayesian Models for Applications in Information Retrieval
Authors: Jordan David M , Blei. Michael I , Ng Andrew Y 
Published: November 2023

Abstract
The field of information retrieval is broadly concerned with the problem of organizing collections of documents and other media so as to support various information requests on the part of users. The familiar problem of returning a subset of documents in response to a query lies within the scope of information retrieval, as do the problems of analyzing cross-referencing within a document collection, analyzing linguistic structure so as to be able to say who did what to whom, automatic compilation of document summaries, and automatic translation. Another class of problems involve analyzing interactions between users and a collection, including the “collaborative filtering” problem in which suggestions are made to new users of a system based on choices made by previous users.
Bayesian Nonparametric Inference for Mixed Poisson Processes
Authors: Gutiérrez-Pena Eduardo , Nleto-Barajas Luise E 
Published: November 2023

Abstract
In the theory of stochastic processes there are two that are fundamental. One is the Weiner model of Brownian motion; the other is the Poisson process. Both are important instances of exponential families of stochastic processes (Kiichler and Sorensen, 1997). The Poisson process has several interesting properties and characterizations. For one thing, it is the only point process with stationary and independent increments. In addition, Poisson processes are ordinary renewal processes with exponentially distributed inter-occurrence times; they are the only ordinary renewal processes that are also stationary. For a thorough account of the structure and properties of general Poisson processes, the reader is referred to Kingman (1993).
Bayesian Clustering with Variable and Transformation Selections
Authors: Liu Jun S , Zhang JunniL , Palumbo Michael J , Lawrence Charles E 
Published: November 2023

Abstract
The clustering problem has attracted much attention from both statisticians and computer scientists in the past 50 years. Methods such as hierarchical clustering and the K-means method are convenient and competitive first choices off the shelf for the scientist. Gaussian mixture modelling is another popular but computationally expensive clustering strategy, especially when the data are high-dimensional. We propose to first conduct a principal component analysis (PCA) or correspondence analysis (CA) for dimension reduction, and then fit Gaussian mixtures to the data projected to the several major PCA or CA directions. Two technical difficulties of this approach are: (a) the selection of a subset of the PCA factors that are informative for clustering, and (b) the selection of a proper transformation for each factor. We propose a Bayesian formulation and Markov chain Monte Carlo strategies that overcome the two difficulties and examine the performances of the new method by both simulation studies and real applications in molecular imaging analysis and DNA microarray analysis.
A Statistical Approach to Modeling Genomic Aberrations in Cancer Cells
Authors: Michael A , Yang Newton Hyuna , Tomlinson Patricia A Gorman Ian , Roylance Rebecca R 
Published: November 2023

Abstract
Whereas most cells in the body carry the normal complement of 23 chromosome pairs, the cells within a cancerous tumor very often present highly abnormal genomic structure. Deletions, amplifications, rearrangements and mutations are common at various scales and are highly variable amongst tumors, as indicated by molecular technologies which enable ever better measurement. It is an important statistical problem to separate sporadic abnormalities from those that may not be sporadic and that may have some biological significance. We discuss a modelling strategy for genomic aberration data, which allows us to to infer combinations of aberrations that together increase the chance that a precancerous cell will have a descendant tumor lineage. The likelihood component involves a network of pathway structures. Markov chain Monte Carlo is used to sample from the space of these oncogenic networks. We illustrate the methodology with comparative genomic hybridizations from a recent breast cancer study, and we derive a likelihood formula for the larger class of tree-like networks.
Identifying Mixtures of Regression Equations by the SAR procedure
Authors: Rodríguez Daniel Peña Julio , Tiao George C 
Published: November 2023

Abstract
A procedure for identifying data heterogeneity when fitting regression models is presented. The method is based on the SAR procedure, developed by Pena and Tiao (2002), and has three steps. First, the sample is cleaned for large outliers by using a discrepancy measure based on predictive ordinates. Second, observations are split into small homogeneous groups by using a link function derived from cross-validation predictive distributions. Third, these small groups are then iteratively enlarged by incorporating observations homogeneous to those in the group. In this way the possible piece-wise regression equations are found. Examples are shown to illustrate the performance of the procedure for finding mixtures of regressions due to the presence of outliers or to switching regression models. A Monte Carlo study of the power of the proposed procedure is presented.
Measures of Incoherence: How not to Gamble if you Must
Authors: Seidenfeld Mark L Schervish. Teddy , Kadane Joseph B 
Published: November 2023

Abstract
The degree of incoherence, when previsions are not made in accordance with a probability measure, is measured by the rate at which an incoherent bookie can be made a sure loser. We consider each bet from three points of view: that of the gambler, that of the bookie, and a neutral viewpoint. From each viewpoint, we define a normalization for each bet, and the sure loss for incoherent previsions is divided by the normalization to determine the rate of incoherence. Several different definitions of normalization are considered in order to determine plausible ranges for the degree of incoherence. We give examples of the measurement of incoherence of of some classical statistical procedures.
A Novel Framework for Tracking Groups of Objects
Authors: Zohar Ron , Geiger Dan 
Published: November 2023

Abstract
We describe a novel framework for group tracking and identification termed flow conservation group tracking. Our framework integrates local kinematic measurements with flow conservation constraints so as to yield a robust estimation of the positions and types of targets across an arena. The framework is based on three components each of which is novel. The measurement-to-track component uses a reduction to minimum cost flow problems. The estimation of the number of objects on each track is based on estimation using flow networks, and the assessment of the types of vehicles is reduced to hierarchy matching algorithms.
Expected Utility Estimation via Cross-Validation
Authors: Vehtari Aki , Lampinen Jouko 
Published: November 2023

Abstract
We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate in order to describe the associated uncertainty. We synthesize and extend the previous work in several ways. We give a unified presentation from the Bayesian viewpoint emphasizing the assumptions made and propose practical methods to obtain the distributions of the expected utility estimates. We discuss the properties of two practical methods, the importance sampling leave-one-out and the A’-fold cross-validation. We propose a quick and generic approach based on the Bayesian bootstrap for obtaining samples from the distributions of the expected utility estimates. These distributions can also be used for model comparison, for example, by computing the probability of one model having a better expected utility than some other model. We discuss how the cross-validation approach differs from other predictive density approaches, and the relationship of cross-validation to information criteria approaches, which can also be used to estimate the expected utilities. We illustrate the discussion with one toy and two real world examples.
Psychoanalysis: An Introduction to the Philosophy of Science (Co-author: Paul Sturdee)
Authors: Fulford K W M , Thornton Tim , Graham George 
Published: November 2023

Abstract
Science or non-sense? Along with other areas of human activity, medicine has reaped enormous rewards from science. Casting off myth and received authority, its history has been a history of progress—Vesalius, Harvey, Morgagni, Sydenham, Pasteur, Jenner, Currie, Fleming … the great names ring down the centuries. Signs and symptoms are defined; diseases discovered; new treat- ments established; causal theories refined; and in this century in particular, all this has culminated in unprecedented improve- ments for many in life expectancy and freedom from disease.
The empirical basis for cosmological theorie
Authors: Rowan-Robinson Michael 
Published: November 2023

Abstract
We can make progress towards answering the questions ‘what is the structure of the universe?’ and ‘how has the universe changed with time?’ only if we believe that the universe has a simple over-all structure. In our immediate locality the universe has an exceedingly complex structure. If we had to construct a mathematical model of the human brain, and had as little observational information about it as we have about the universe, we would hardly know where to start. However, a human brain is a highly atypical region of the universe. For one thing its density is about 1030 times the present average density of matter in the universe.
Early stages of the big bang
Authors: Rowan-Robinson Michael 
Published: November 2023

Abstract
So far we have constructed models of universes filled only with matter. Although radiation contributes less than 1 per cent of the average energy per unit volume in the universe at the present epoch, it plays a crucial and dominant role in the early stages. We can study the evolution of a universe containing matter and radiation by applying the first law of thermodynamics to an element of the substratum. This gives the same answer as applying the full field equations of general relativity.
Separate Modifiability and the Search for Processing Modules
Authors: Sternberg Saul 
Published: November 2023

Abstract
One approach to understanding a complex process or system starts with an attempt to divide it into modules: parts that are independent in some sense, and functionally distinct. In this chapter I discuss a method for the modular decomposition of neural and mental processes that reflects recent thinking in psychology and cognitive neuroscience. This process- decomposition method, in which the criterion for modularity is separate modifiability, is contrasted with task comparison and its associated subtraction method. Four illustrative applications of process decomposition and one of task comparison are based on the event-related potential (ERP), transcranial magnetic stimulation (TMS), and functional magnetic resonance imaging (fMRI).
Models of Incompressible Fluid Flow
Authors: Elman Howard C , Silvester David J , Wathen Andrew J 
Published: November 2023

Abstract
To set the scene for subsequent chapters, the basic PDE models that are the focus of the book are derived in this chapter. Our presentation is rudimentary. Readers who would like to learn more about the physics underlying fluid flow modelling are advised to consult the books of Acheson [1] or Batchelor [12]. The fundamental principles are conservation of mass and conservation of momentum, or rather, that forces effect a change in momentum as described by Newton’s famous Second Law of Motion.
Probability distributions
Authors: Lindsey J K 
Published: November 2023

Abstract
In Chapter 2, we looked at a family of models, the logistic and log linear models. to describe how the shape of a probability distribution changes in different subgroups of a population. However, because the response in these models is nominal, their multinomial distributions (and their histogram representations) can take any form. (See, for example, Figure 1.2.) These types of models are really the only ones possible for independent observations when the response is nominal (although there are various possibilities for linking the probabilities to the explanatory variables instead of the log or logit).
Identifying neurocognitive phenotypes in autism
Authors: Tager-Flusberg Helen , Joseph ,Robert M 
Published: November 2023

Abstract
Autism is a complex disorder that is heterogeneous both in its phenotypic expression and its ethology. The search for genes associated with autism and the neurobiological mechanisms that underlie its behavioural symptoms has been hampered by this heterogeneity. Recent studies indicate that within autism, there may be distinct subgroups that can be defined based on differences in neurocognitive profiles. This paper presents evidence for two kinds of subtypes in autism that are defined on the basis of language profiles and on the basis of cognitive profiles. The implications for genetic and neurobiological studies of these subgroups are discussed, with special reference to evidence relating these cognitive phenotypes to volumetric studies of brain size and organization in autism.
Babylonian mathematics
Authors: Hodgkin Luke 
Published: November 2023

Abstract
Obviously the pioneers and masters of hydraulic society were singularly well equipped to lay the foundations for two major and interrelated sciences: astronomy and mathematics. (Wittfogel, Oriental Despotism, p. 29, cited Høyrup 1994, p. 47) Based on intensive cereal agriculture and large-scale breeding of small livestock, all in the hands of a centralized power, [this civilization] was quickly caught up in a widespread economy which made necessary the meticulous control of infinite movements, infinitely complicated, of the goods produced and circulated. It was to accomplish this task that writing developed; indeed for several centuries, this was virtually its only use.
Sobolev spaces in a metric Framework
Authors: Ambrosio Luigi , Tilli Paolo 
Published: November 2023

Abstract
In this section we define a class of Sobolev functions between metric spaces, essentially following the original definition of Haj-las1 27] for real-valued functions. We refer to Haj-las1 and Koskela 28] for a complete overview on this quickly developing area see also Cheeger 9], Franchi et al. 20], Shanmugalingam 53], Heinonen 29], and Heinonen et al. 30] .
Gene Genealogies, Variation and Evolution
Authors: Hein Jotun , Schierup Mikkel H , Wiuf Carsten 
Published: November 2023

Abstract
Coalescent theory is a central concept in the study of genetic sequence variation that probabilistically describes the genealogy relating the sampled sequences. In this text, besides fulfilling the glaring need for such a book, the authors present this theory in a straightforward and elementary manner and describe the statistical and computational methods used in modelling and analyzing genetic sequence variation. Rich in examples and illustrations the book covers basic concepts, complications arising from geographical structure and recombination before considering aspects of statistical inference based on these models. The book ends with chapters on Gene Mapping, which combines sequence variation data with phenotypic data (such as disease) to define areas of the genome where genes are responsible for the trait, and Human Evolution, a research area that is experiencing a renaissance due to the enormous amounts of data produced in molecular studies. Authored by leading experts, this seminal text presents a straightforward and elementary account of coalescent theory, which is a central concept in the study of genetic sequence variation observed in a population. It is highly suitable for a graduate course in statistics, population, molecular and medical genetics, bioscience and medicine and students studying the evolution of human population and disease, and will be an invaluable reference for bioscientists and statisticians in the pharmaceutical industry and academia