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Soit Y un sous-ensemble algébrique de codimension 1 de R'. Une distribution globale (resp. partielle) de signes sur Rn est une application qui associe un signe a chaque composante connexe (resp. à certaines parmi les composantes connexes) de Rn – Y.
This paper is concerned with convex subsets of finite dimensional vtctor spaces, over the field of real scalars. As in [10, p. 244] and [20] we say that a compact convex set A, symmetric about the origin, is reducible, if there is a nonsymmetric closed convex set B for which A = B - B. The latter term denotes the set of all differences, {x-y: x, y є B}. Equivalently, A is reducible if, and only if, A =1/2 (B - B) for some B which is not a translate of A. If the identity A = B - B is only possible when B is centrally symmetric, then A is irreducible. If A is symmetric about a point other than the origin, we can say that it is (ir)reducible when it is the translate of an (ir)reducible set which is symmetric about the origin. It is well known that a parallelotope of any dimension is irreducible [6, Hilfssatz 3], that any 2-dimensional convex body other than a parallelogram is reducible [9, p. 217], and that euclidean balls of any dimension (other than one) are reducible [4, Ch. 7]. For more general convex bodies, the determination of reducibility is not a simple problem. Shephard [20] showed that a set is reducible if, and only if, it has an asymmetric summand, and he used this to study reducibility of polytopes. The main purpose of this paper is to give a new condition, necessary and sufficient for a symmetric polytope to be reducible. This condition may be expressed in the form: does a certain finite family of linear equations have a nontrivial solution? Thus, to determine the reducibility of a given polytope, it suffices to find the rank of some matrix. Using our criterion, we are able to describe some large families of irreducible polytopes. For example, every n- dimensional symmetric polytope with 4n – 2 or fewer vertices is irreducible (unless n = 2). We also establish the existence of irreducible, smooth, strictly convex bodies.
In the part (16-3) of his extensive study on measurability in Banach spaces, Talagrand [12] considered the Banach space C(K) of continuous functions on a dyadic topological space K. He proved that C(K) is realcompact in its weak topology, if, and only if, the topological weight of K is not a twomeasurable cardinal (Theorem 16-3-1). Then he asked for an alternative to a rather complicated proof presented there (p. 214) and posed the problem whether C(K) is measure-compact whenever the weight of K is not a realmeasurable cardinal (Problem 16-3-2).
We improve W. Schmidt's lower bound for the slice (intersection of two halfspheres) discrepancy of point distributions on spheres and show that this estimate is up to a logarithmic factor best possible. It is shown that the slice and spherical cap discrepancies are equivalent for the definition of uniformly distributed sequences on spheres.
Almost seventy years ago Jeffery [1] showed that a finite velocity can result at infinity when the biharmonic equation is solved for the titled problem. Here, we extend his calculations to show that finite vorticity is the more general conclusion, and then indicate a resolution of the apparent paradox.
One may perhaps doubt whether in the geometry of numbers any particular family of lattices deserves such an attention as, for example, BCH codes receive in coding theory. However, only recently a quite interesting family has emerged. The general case of these lattices considered by Rosenbloom and Tsfasman [5, Section 2] parallels Goppa's construction of codes from algebraic curves. Here we shall take a closer look at the case of genus zero where some special features of Goppa's early codes will show up again: There is a lattice Λ (L, g) in n-dimensional euclidean space associated with a subset L of the field, and a polynomial g satisfying g(є) ≠ for all λ є L. For g = zd previously known sphere packings are recovered and generalized. A nonconstructive argument shows that for n → ∞ and some irreducible polynomials g Minkowski's lower packing bound is met (this being not achieved in [5] where q is fixed, but the genus grows; cf. also [4]).
A right S-system over a monoid S is a set A on which S acts unitarily on the right. That is, there is a function A such that (φ,1)φ and (a, st)φ = ((a, s) t)φ for all a є A and for all s, t є S. We shall refer to right S-systems simply as S-systems. It is clear what is meant by S-homomorphism, S-subsystem etc.; further details of the terms used in this Introduction are given in Section 2.
For satellite knots there is a well-known formula which relates the Alexander polynomial of the satellite to those of a companion knot and the corresponding pattern. If &s, &C and &P are the Alexander polynomials of a satellite, companion and pattern respectively then
where is the linking number of P with a meridian of the companion torus (see [BZ], p. 118). Analogous relationships do not exist for other knot polynomials [MS]. This suggests that the existence of the above formula depends more on the geometry underlying the polynomial than on the geometry of the satellite construction.
In this paper two expansions are obtained by contour integration methods for the velocity potential describing two-dimensional time-harmonic surface waves due to a free-surface wave source on water of infinite depth in the presence of surface tension. First the series expansion at r = 0 is found and then the asymptotic expansion as Kr®¥, where K is the wave number for progressive waves and r the radial distance from the source. The corresponding expansions for the more important submerged wave source in terms of the radial distance from the image source in the free surface may then easily be deduced. The latter are required in a number of surface wave problems, particularly those of a short-wave asymptotic nature, and are also relevant in obtaining expansions for finite constant depth.
This article considers the effect of more than one quotient and improves a theorem of Tong which is a generalization of a theorem of Segre on asymmetric approximation.
The purpose of this work is to investigate the relationship between Radon transforms and centrally symmetric convex bodies. Because of the injectivity properties of the Radon transform it is natural to consider transforms on the sphere separately from those on the higher order Grassmannians. Here we shall concentrate on the latter, whilst the former will be the subject of another article presently in preparation, Goodey and Weil [1991].
The problem concerning the distribution of the fractional parts of the sequence ank (k an integer exceeding one) was first considered by Hardy and Littlewood [6] and Weyl [20] earlier this century. This work was developed, with the focus on small fractional parts of the sequence, by Vinogradov [17], Heilbronn [13] and Danicic [2] (see [1]). Recently Heath-Brown [12] has improved the unlocalized versions of these results for k ≥ 6 (a slightly stronger result than Heath-Brown's for K = 8 is given on page 24 of [8]. The method mentioned there can, after some numerical calculation, improve Heath-Brown's result for 8 ≤ k ≤ 20, but still stronger results have recently been obtained by Dr. T. D. Wooley). The cognate problem regarding the sequence apk, where p denotes a prime, has also received some attention. In this situation even the case k = 1 proves to be difficult (see [9] and [14]). The first results in this field were given by Vinogradov (see Chapter 11 of [19] for the case k = 1, [18] for k ≥ 2). For k = 2 the best result to date has been supplied by Ghosh [5], and for ≥, by Harman (Theorem 3 in [9], building on the work in [7] and [8]). In this paper we shall improve the known results for 2 ≤ k ≤ 12. For larger k, Theorem 3 in [8] is more efficient. The theorem we prove is as follows.
Using several transformation formulae from Ramanujan's second Notebook we achieve distribution results on random variables related to dynamic data structures (so-called “tries”). This continues research of Knuth, Flajolet and others via an approach that is completely new in this subject.
The only symbolic logic used in this book is a small part of propositional logic, also called sentential logic or Boolean logic. In this appendix, I review the relevant part of this simple area of logic and clarify some notation and terminology. This appendix is not an introduction to logic; various important fine points and distinctions are not be mentioned. But I hope this will suffice as an introduction to the basic ideas in the elementary part of logic used in this book, and as a clarification of the logical terminology and symbols used in this book.
The basic entities of the formal propositional calculus are usually called the propositions and the propositional connectives (and the language of propositional logic usually includes punctuation marks, usually parentheses, that are used to avoid ambiguity of grouping when “propositions” are “connected” in complex ways).
In this book, it is factors (or properties, or types) that play the role of the so-called propositions of propositional logic. The abstract and formal propositional calculus can be interpreted as applying to propositions in a number of ways in which the term “proposition” could be understood. For example, we could think of propositions as sentences (which may be understood as concrete linguistic entities such as utterances or inscriptions). Or we could think of them as statements (understood in such a way that many sentences can all be used to “make” the same statement, and the same sentence, if used in different contexts, would make different statements).
The first main qualification of the basic probability-increase idea of probabilistic causation, explained in Chapter 1, is the relativity of the causal relation to a given token population, considered to be of a given (appropriate) kind that the population exemplifies. The second main qualification of the basic probability-increase idea, to be explored in this chapter, involves the possibility of what has been called “spurious correlation.” Of course, what is meant by saying that a factor X raises the probability of a factor X is that Pr(Y/X) > Pr(Y) – equivalently, Pr(Y/X) > Pr(Y/∼X) Another way of expressing this relation is to say that Y is positively probabilistically correlated with X. It is famous that “correlation is no proof of causation,” and it is also true that causation does imply correlation. The possibility of spurious correlation is one reason why.
In this book, I will actually explore in detail three general ways in which probability increase may fail to coincide with causation, and I will show how the probability-increase idea of causation should be adjusted to accommodate these three possibilities. After briefly describing the three possibilities below, this chapter will concentrate on one of them, the one called “spurious correlation.” The other two will be dealt with in subsequent chapters.
One simple way to see that probability increase does not imply causation is to notice that the relation of positive correlation is symmetric. If X raises the probability of Y, then Y raises the probability of X.
For the examples of spurious correlation discussed in Chapter 2, it sufficed to hold fixed all (independent) positive, negative, and mixed causes of the candidate effect factor, in order for the probability-increase idea to deliver the right answers about what caused what. For these examples, only factors that were causally relevant to the candidate effect factor needed to be held fixed. In this chapter, I will argue that other kinds of factors, which may be causally irrelevant to (neutral for) the effect factor in question, must be held fixed as well, if the probability-increase theory is to deliver the right answers in other kinds of cases.
For example, if the right answer in Dupré's example, discussed in Chapter 2, is that smoking has a mixed causal role (not positive, negative, or neutral) for lung cancer, then it will be necessary to hold fixed the factor of that rare physiological condition. Otherwise, causal relevance would go by average probabilistic impact of smoking on lung cancer, across the presence and absence of that condition, and this cannot give the correct answer of mixed causal relevance. However, as noted in Chapter 2 and explained more fully in this chapter, that physiological condition need not itself be a positive, negative, or mixed cause of lung cancer.
At the beginning of Chapter 2, the possibility of there being such factors as that physiological condition in Dupré's example was called the problem of causal interaction.
We turn finally to the role of time in the theory of proper tylevel probabilistic causation. This will be dealt with in Sections 5.1 and 5.2, and this will complete the theory of property level probabilistic causation offered in this book. Section 5.3 offers some comparisons and contrasts between this theory and several others.
First, recall the problem that, at the beginning of Chapter 2, I called “the problem of temporal priority of the cause to the effect.” This problem arises from the fact that probabilistic correlation is symmetric. Leaving aside qualifications having to do with causal background contexts, if a factor X is a genuine probabilistic cause of a factor Y in a population, then X raises the probability of Y in that population. This implies that Y raises the probability of X in the same population. But we cannot infer that Y is a cause of X in the population, for while correlation is symmetric, causation is not.
Second, if we agree that property-level probabilistic causation is asymmetric, then we may want to capture this by saying that one factor can only be a cause of “later” factors, and that it can only be caused by “earlier” factors. But what is it for one factor itself–that is, one event type or one property – to be earlier or later than another? How can we make sense of the idea that such abstract things as factors (or types, or properties) enter into temporal relations among themselves?
Type-level probabilistic causation is sometimes called “population-level” probabilistic causation and sometimes “property-level” probabilistic causation. And the items that enter into type-level probabilistic causal relations are called “factors,” or “properties,” or “event types.” The basic idea in the theory of type-level probabilistic causation is that causes raise the probabilities of their effects. A factor C is a property-level probabilistic cause of – or a positive causal factor for – a factor E, if the probability of E is higher in the presence of C than it is in the absence of C. And C is causally negative or causally neutral for E if the presence of C lowers or leaves unchanged the probability of JS, respectively. But this basic idea needs several clarifications and qualifications.
In this chapter, I explain the importance of the idea of a population to type-level probabilistic causal connection. I argue that type-level probabilistic causation is a relation among four things: a cause factor, an effect factor, a token population within which the first is some kind of cause of the second, and, finally, a kind (of population) that is associated with the given token population. Subsequent chapters reveal how important relativity to populations is for the versatility of the probabilistic theory and how it renders the theory immune to a number of criticisms that have recently been advanced.
Of course, some kind of clarification of the idea of probability is in order.