bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantum logic as motivated by quantum computing
Authors: Dunn J. Michael , Hagge Tobias J. , Moss Lawrence S. , Wang Zhenghan 
Published: July 2005

§1. Introduction. Our understanding of Nature comes in layers, so should the development of logic. Classic logic is an indispensable part of our knowledge, and its interactions with computer science have recently dramatically changed our life. A new layer of logic has been developing ever since the discovery of quantum mechanics. G. D. Birkhoff and von Neumann introduced quantum logic in a seminal paper in 1936 [1]. But the definition of quantum logic varies among authors (see [2]). How to capture the logic structure inherent in quantum mechanics is very interesting and challenging. Given the close connection between classical logic and theoretical computer science as exemplified by the coincidence of computable functions through Turing machines, recursive function theory, and λ-calculus, we are interested in how to gain some insights about quantum logic from quantum computing. In this note we make some observations about quantum logic as motivated by quantum computing (see [5]) and hope more people will explore this connection.The quantum logic as envisioned by Birkhoff and von Neumann is based on the lattice of closed subspaces of a Hilbert space, usually an infinite dimensional one. The quantum logic of a fixed Hilbert space ℍ in this note is the variety of all the true equations with finitely many variables using the connectives meet, join and negation. Quantum computing is theoretically based on quantum systems with finite dimensional Hilbert spaces, especially the states space of a qubit ℂ2. (Actually the qubit is merely a convenience.
On the type-definability of the binding group in simple theories
Authors: Hart Bradd , Shami Ziv 
Published: July 2005

AbstractLet T be simple, work in Ceq over a boundedly closed set. Let p Є S(∅) be internal in a quasi-stably-embedded type-definable set Q (e.g., Q is definable or stably-embedded) and suppose (p, Q) is ACL-embedded in Q (see definitions below). Then Aut(p/Q) with its action on pc is type-definable in Ceq over ∅. In particular, if p Є S(∅) is internal in a stably-embedded type-definable set Q, and pc ⋃ Q is stably-embedded, then Aut(p/Q) is type-definable with its action on pc.
Expansions of o-minimal structures by fast sequences
Authors: Friedman Harvey , Miller Chris 
Published: July 2005

AbstractLet ℝ be an o-minimal expansion of (ℝ, <, +) and (ϕk)kЄℕbe a sequence of positive real numbers such that limtk→+∞f(ϕk)/ϕk+1 = 0 for everyf: ℝ → ℝ definable in ℜ (Such sequences always exist under some reasonable extra assumptions on ℜ, in particular, if ℜ is exponentially bounded or if the language is countable.) Then (ℜ, (S)) is d-minimal. whereSranges over all subsets of cartesian powers of the range ofϕ.
Randomness, relativization and Turing degrees
Authors: Nies André , Stephan Frank , Terwijn Sebastiaan A. 
Published: July 2005

AbstractWe compare various notions of algorithmic randomness. First we consider relativized randomness. A set is n-random if it is Martin-Löf random relative to ∅(n − 1). We show that a set is 2-random if and only if there is a constant c such that infinitely many initial segments x of the set are c-incompressible: C(x) ≥ ∣x∣ − c. The ‘only if’ direction was obtained independently by Joseph Miller. This characterization can be extended to the case of time-bounded C-complexity.Next we prove some results on lowness. Among other things, we characterize the 2-random sets as those l-random sets that are low for Chaitin's Ω. Also, 2-random sets form minimal pairs with 2-generic sets. The r.e. low for Ω. sets coincide with the r.e. K-trivial ones.Finally we show that the notions of Martin-Löf randomness, recursive randomness, and Schnorr randomness can be separated in every high degree while the same notions coincide in every non-high degree. We make some remarks about hyperimmune-free and PA-complete degrees.
The Church-Rosser property in symmetric combinatory logic
Authors: Bimbó Katalin 
Published: July 2005

AbstractSymmetic combinatory logic with the symmetric analogue of a combinatorially complete base (in the form of symmetric λ-calculus) is known to lack the Church-Rosser property. We prove a muchstrongertheorem that no symmetric combinatory logic that containsat least two proper symmetric combinatoryhas the Church-Rosser property. Although the statement of the result looks similar to an earlier one concerning dual combinatory logic,the proof is differentbecause symmetric combinators may form redexes in both left and right associated terms. Perhaps surprisingly, we are also able to show that certain symmetric combinatory logics that include justone particular constantare not confluent. This result (beyond other differences) clearly sets apart symmetric combinatory logic from dual combinatory logic, since all dual combinatory systems with a single combinator or a single dual combinator are Church-Rosser. Lastly, we prove that a symmetric combinatory logic that contains the fixed point and the one-place identity combinator has the Church-Rosser property.
Asymptotic theory of modules of separably closed fields
Authors: Point Françoise 
Published: July 2005

AbstractWe consider the reduct to the module language of certain theories of fields with a non surjective endomorphism. We show in some cases the existence of a model companion. We apply our results for axiomatizing the reduct to the theory of modules of non principal ultraproducts of separably closed fields of fixed but non zero imperfection degree.
Structured pigeonhole principle, search problems and hard tautologies
Authors: Krajíček Jan 
Published: July 2005

AbstractWe consider exponentially large finite relational structures (with the universe {0, 1}n) whose basic relations are computed by polynomial size (nO(1)) circuits. We study behaviour of such structures when pulled back by P/poly maps to a bigger or to a smaller universe. In particular, we prove that:1. If there exists a P/poly map g: {0, 1}n → {0, 1}m, n < m, iterable for a proof system then a tautology (independent of g) expressing that a particular size n set is dominating in a size 2n tournament is hard for the proof system.2. The search problem WPHP. decoding RSA or finding a collision in a hashing function can be reduced to finding a size m homogeneous subgraph in a size 22m graph.Further we reduce the proof complexity of a concrete tautology (expressing a Ramsey property of a graph) in strong systems to the complexity of implicit proofs of implicit formulas in weak proof systems.
Upward categoricity from a successor cardinal for tame abstract classes with amalgamation
Authors: Lessmann Olivier 
Published: July 2005

AbstractThis paper is devoted to the proof of the following upward categoricity theorem: Let be a tame abstract elementary class with amalgamation, arbitrarily large models, and countable Löwenheim-Skolem number. If is categorical in ℵ then is categorical in every uncountable cardinal. More generally, wc prove that if is categorical in a successor cardinal λ+ then is categorical everywhere above λ+.
Une fonction de Kolchin pour les corps imparfaits de degré d'imperfection fini
Authors: Delon Françoise 
Published: July 2005

AbstractNon-perfect separably closed fields are stable, and not superstable. As a result, not all types can be ranked. We develop here a new tool, a “semi-rank”, which takes values in the non-negative reals, and gives a sufficient condition for forking of types. This semi-rank is built up from a transcendence function, analogous to the one considered by Kolehin in the context of differentially closed fields. It yields some orthogonality and stratification results.
Blinding or Masking
Authors: Day Simon J. 
Published: July 2005

Abstract
The need for blinding in clinical trials and epidemiological studies is reviewed and its importance stressed. Various reasons for blinding include the potential for patient bias, bias in patient management and evaluation, bias in data management and study management. Blinding and unblinding interim analyses need special attention, and procedures for emergency unblinding need to be available.
Four levels of blinding are described from the slightly ambiguous term “single‐blind” through to quadruple‐blinding. Methods to maintain the blind at various stages such as use of placebos, “double‐dummy” designs, and sham procedures are discussed.
Finally, some of the difficulties of blinding are highlighted. Often, it may be ineffective and reporting of the achieved quality of blinding is recommended. There are special issues with regard to equivalence studies, and regulatory guidance exists discussing many issues included in this article.
Intention to Treat Analysis
Authors: Ellenberg Jonas H. 
Published: July 2005

AbstractIn a randomized clinical trial design for the comparison of treatments, subjects are randomly assigned to different treatments. The intention‐to‐treat principle requires that any comparison of the treatments be based upon comparison of all patients in the treatment groups to which they were randomly assigned. This approach maintains the benefits of randomization, one of which is that the characteristics of the group of patients receiving the different treatments should be roughly equivalent at the onset of the trial.Several conditions can influence the actual versus intended protocol under which subjects are studied including: nonadherence to the assigned treatment regimen, or termination of participation prior to outcome measurement. Investigators may attempt to compare groups defined by factors that might be influenced by the treatments under test, for example, the comparison of only those patients in the trial that actually complied with the prescribed treatments. Any observed differences among treatment groups constructed in this manner may be due not to treatment, but to factors associated with compliance.
Actuarial Methods
Authors: Haberman Steven 
Published: July 2005

AbstractActuarial methods overlap with biostatistics in a number of important areas including the development and application of the survival model, and its extensions—the multiple decrement and multiple state models; projections of populations that involve extrapolations of demographic and biostatistical rates and probabilities; parametric and nonparametric methods of smoothing and curve fitting (known as graduation); and risk classification. In each of these areas, actuarial science has followed the innovations in statistical and biostatistical theory and application and developed these further for specific actuarial applications.
Standardization Methods
Authors: Inskip Hazel 
Published: July 2005

Abstract
Standardization methods are used to adjust for the effects of age and sex, and possibly other factors, in the comparison of mortality or disease rates between two or more populations. Such methods have a long history, particularly for the comparison of mortality rates. The two methods that are most widely used are known as direct and indirect standardization and the mortality ratios that are derived from these are known respectively as the Comparative Mortality Figure and the Standardized Mortality Ratio. When the populations at risk are hard to define, methods involving the proportions of deaths from specific causes can be used. More recently, different approaches involving modeling have been developed to adjust rates for the effects of age and other confounding factors.
Cancer Registries
Authors: Hankey B. , Fritz April 
Published: July 2005

Abstract
We review the history of cancer surveillance and distinguish hospital‐based from population‐based cancer registries. Registry operations include case ascertainment, abstracting and coding, data collection, quality control, and producing statistics on health outcomes. The characteristics of the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) program are described, and we give web site addresses that describe SEER procedures, facilities for making data and analytical tools available, and a strategic plan for cancer surveillance research.
Case–Control Study, Sequential
Authors: O'Neill Robert T. 
Published: July 2005

AbstractRecruitment of cases and controls for a case–control study can take an appreciable amount of time. There is potential for shortening the duration of a case–control study and reducing the numbers of required cases and controls by analyzing the accruing data sequentially and perhaps by stopping accrual early if a definitive early decision can be reached. We discuss the Wald sequential probability ratio test and group sequential tests for 1:1 matched case–control designs and tabulate the average reductions in sample sizes obtained by sequential procedures, compared with fixed sample size designs.
Confounder
Authors: Gail Mitchell H. 
Published: July 2005

Abstract
A confounder is a variable that alters the association between an exposure and a disease. A confounder is associated with disease risk in unexposed subjects, is not affected by exposure or disease, and is associated with exposure in the source population. Methods to control confounding are mentioned, as is a definition of confounding based on a counterfactual theory of causal effects.
Confounder Summary Score
Authors: Greenland Sander 
Published: July 2005

Abstract

Consider an observational study of the effect of an “exposure” variable
X
on an outcome variable
Y
in which multiple confounders must be controlled. Simultaneous stratification on all observed confounder combinations may lead to many uninformative strata (i.e. strata in which there is either no variation in the exposure or no variation in the outcome). The usual method of coping with this problem is to estimate exposure effects from coefficients in a regression model for the dependence of the outcome on the exposure and confounders. Confounder summary scores are alternatives that use fitted models to define strata.
Ecologic Study
Authors: Morgenstern Hal 
Published: July 2005

AbstractAn ecologic study focuses on the comparison of groups rather than individuals; thus, individual‐level data are missing on the joint distribution of variables within groups. The purpose of an ecologic analysis may be to make biologic inferences about effects on individual risks or to make ecologic inferences about effects on group rates. Ecologic study designs may be classified on two dimensions: (a) whether or not the primary exposure is measured; and (b) whether subjects are grouped by place, time, or place and time. Despite practical advantages of ecologic studies, there are several methodologic problems that severely limit causal inference, including ecologic and cross‐level bias, problems of confounder control, within‐group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups. Even when the study objective is to estimate ecologic effects, individual‐level data are generally needed to make valid causal inferences, preferably using multilevel analysis.
Effect Modification
Authors: McKnight Barbara 
Published: July 2005

Abstract

Effect modification occurs when a measure of the association between an exposure and the risk of disease is modified, or changes, with values of a third variable called the
effect modifier
. For a given measure of association between exposure and disease risk, there is usually a corresponding regression model scale on which the presence of effect modification corresponds to the presence of statistical interaction between exposure and the effect modifying variable. This correspondence is described for several additive and multiplicative measures of association.
Geographic Epidemiology
Authors: Bithell John F. 
Published: July 2005

AbstractGeographical analysis of epidemiologic data may be useful in modeling risk as a function of geographic covariates, testing hypotheses, mapping disease patterns, identifying areas of high risk, or detecting clusters. Distinctions are drawn between case dependence and independence (the latter not requiring a spatially correlated error structure), and between areal and continuous data. Various models are described, and attention is given to disease mapping (involving data smoothing) and clustering.
Decision Analysis in Diagnosis and Treatment Choice
Authors: Tosteson Anna N. A. 
Published: July 2005

AbstractDecision analysis, a quantitative approach to decision making under conditions of uncertainty, is introduced and its use in clinical decision making is described. Decision analysis identifies the optimal course of action as that which maximizes (or minimizes) the expected value of the outcome as specified by the decision maker. Through sensitivity analysis, the parameters that affect the optimal course of action are identified. The expected value of clinical information and perfect information are defined in the context of a clinical example.
Dose‐response in Pharmacoepidemiology
Authors: Murphy J. R. 
Published: July 2005

Abstract
Pharmacoepidemiology deals with the action of drugs within a population. Because of differences in the method of data collection, estimation of pharmacoepidemiologic dose–response parameters is somewhat different from that in biological assay or clinical trials. Pharmacodynamic models to estimate these parameters are reviewed, with a specification of appropriate dose–response variables. Parameter estimation is described, together with mention of appropriate software to do so.
Drug Approval and Regulation
Authors: Lewis John A. , Jones David R. 
Published: July 2005

Abstract
Before it can be marketed, any new medicinal product generally requires the regulatory approval of the appropriate governmental agency. This article describes the contribution of statistics and statisticians to the regulatory process. It addresses the role of statisticians within regulatory agencies worldwide. It provides an explanation of the various ways in which statistical assessment is carried out within the major regulatory agencies. It concludes by considering the statistical aspects of the regulatory application written by the applicant company to support their claims for their medicinal product.
Incubation Period of Infectious Diseases
Authors: Brookmeyer Ron 
Published: July 2005

AbstractThe incubation period is the time interval between exposure to a disease‐causing agent and the onset of symptomatic disease. For example, the incubation period of an infectious disease refers to the time interval between infection or exposure to a viral or bacterial agent and the onset of symptomatic (clinical) disease. The incubation period is also called the clinical latency period. The focus of this article is on modeling and estimating the incubation period of infectious diseases. However, some of the ideas may also be applicable to the incubation period of noninfectious disease, for example, the incubation period of radiation‐induced cancer that refers to the time interval from radiation exposure to cancer diagnosis. Alternative designs for the estimation of an incubation period are reviewed, and methods to synthesize studies on incubation periods are mentioned.
Natural History Study of Prognosis
Authors: Cnaan Avital 
Published: July 2005

AbstractPrognosis refers to the probable outcome of an attack of disease. The traditional definition would describe the evolution of disease in the absence of medical intervention, but nowadays we may also include the term “clinical course” to describe the natural history of a disease that has been affected by medical intervention. Various design considerations are reviewed for the study of natural history, including the use of cohort studies, the various biases that may affect such studies, and the definition of time zero as a clearly defined starting point. Other topics include the analysis of dichotomous outcomes, survival analysis and longitudinal data analysis. Combination of data from several studies, and situations involving multiple outcomes are also mentioned.
Normal Clinical Values, Design of a Study
Authors: Harris Eugene K. 
Published: July 2005

AbstractThis entry describes the estimation of normal ranges for clinical variables. A key issue is the identification of the appropriately healthy segment of the general population, and ways in which it can be sampled without bias. Techniques such as convenience sampling often do involve potential bias. Topics such as exclusion and partitioning criteria for the sampling are described. Sample size determination for establishing normal limits is reviewed.
Observer Reliability and Agreement
Authors: De Vet H. 
Published: July 2005

AbstractThe termsobserver reliabilityandobserver agreementrepresent different concepts. Reliability coefficients express the ability to differentiate between subjects. Agreement parameters determine whether the same value is achieved if a measurement is repeated. Parameters for reliability are ICCs for measurements on an interval scale. Parameters for agreement are kappa for measurements on a nominal scale, weighted kappa for an ordinal scale, and levels of agreement for measurements on an interval scale.Assessing observer reliability and agreement is essential for the interpretation of clinical observations in research and medical practice. Moreover, only if the sources of measurement errors are known, can they be anticipated, thereby improving the quality of measurements.
Screening, Models of
Authors: Stevenson Chris 
Published: July 2005

AbstractThis article describes models for disease screening and how they have developed in recent years. The discussion focuses on screening for cancer, because most of the methodological advances in screening design and evaluation have concerned cancer screening. The first part of the article describes the characteristics of these models and illustrates them with a discussion of a simple screening model. The second part describes the development, strengths, and weaknesses of the two main types of screening model—analytic and simulation models—with a particular focus on microsimulation models. The third part discusses model fitting and validation, and the final part briefly describes models for diseases other than cancer—in particular, models for screening for infectious diseases—and discusses the current state and possible future directions for models of disease screening.
Age‐of‐Onset Estimation
Authors: Siegmund K. 
Published: July 2005

Abstract
Age of onset estimates are crucial to clinical management and designing disease prevention studies. For many diseases, age is the primary risk factor. Age of onset is censored if a person is unaffected. Two different modeling philosophies are possible: to assume a mixture of susceptible and nonsusceptible individuals in the population, or to assume that all individuals are susceptible and would become affected if only they lived long enough. Variability of age of onset as a function of exposures can be modeled through the hazard. Designs possible to estimate age of onset parameters are: case—control family studies, kin—cohort studies, and the study of high‐risk families. Age‐specific penetrances can be estimated using a likelihood, pseudolikelihood, marginal likelihood, weighted score, or the method of moments.
Ascertainment
Authors: Hodge S. E. 
Published: July 2005

Abstract
For most disorders of genetic interest, families are not selected for study at random but rather because they are segregating for the disorder. This biases the estimate of the segregation ratio if not properly allowed for in the analysis. The classical ascertainment model assumes families are ascertained if they contain at least one proband, an affected person who at any time was detected independently of the other members of the family. Ascertainment can be complete (“truncate selection”), multiple, or single; in the last situation, the probability of that a sibship will be ascertained is proportional to the number of affected sibs it contains. In practice, this independent “proband” model may not be appropriate and other models have been proposed. However, some situations still remain difficult or intractable.