Levels in Arrangements: Linear Relations, the -Matrix, and Applications to Crossing Numbers
Abstract
A long-standing conjecture of Eckhoff, Linhart, and Welzl, which would generalize McMullen’s Upper Bound Theorem for polytopes and refine asymptotic bounds due to Clarkson, asserts that for , the complexity of the -level in a simple arrangement of hemispheres in is maximized for arrangements that are polar duals of neighborly -polytopes. We prove this conjecture in the case . By Gale duality, this implies the following result about crossing numbers: In every spherical arc drawing of in (given by a set of unit vectors connected by spherical arcs), the number of crossings is at least . This lower bound is attained if every open linear halfspace contains at least of the vectors in .
Moreover, we determine the space of all linear and affine relations that hold between the face numbers of levels in simple arrangements of hemispheres in . This completes a long line of research on such relations, answers a question posed by Andrzejak and Welzl in 2003, and generalizes the classical fact that the Dehn–Sommerville relations generate all linear relations between the face numbers of simple polytopes (which correspond to the -level).
To prove these results, we introduce the notion of the -matrix, which encodes the face numbers of levels in an arrangement and generalizes the classical -vector of a polytope.
Keywords and phrases:
Levels in arrangements, -sets, -facets, convex polytopes, -vector, -vector, -vector, Dehn–Sommerville relations, Radon partitions, Gale duality, -matrixCopyright and License:
![[Uncaptioned image]](x1.png)
2012 ACM Subject Classification:
Theory of computation Computational geometry ; Mathematics of computing Discrete mathematicsEditors:
Oswin Aichholzer and Haitao WangSeries and Publisher:

1 Introduction
Levels in arrangements (and the dual notions of -sets and -facets) play a fundamental role in discrete and computational geometry and are a natural generalization of convex polytopes (which correspond to the -level); we refer to [21, 30, 45] for more background.
It is a classical result in polytope theory that the Euler–Poincaré relation is the only linear relation between the face numbers of arbitrary -dimensional convex polytopes, and that the Dehn–Sommerville relations (which we will review below) generate all linear relations between the face numbers of simple (or, dually, simplicial) polytopes [23, Chs. 8–9]. Another central result in polytope theory is McMullen’s Upper Bound Theorem [31], which asserts that cyclic polytopes have the largest possible number of faces among all -dimensional convex polytopes with a given number of vertices.
Here, we are interested in generalizations of these results to levels and sublevels in arrangements. To state our results formally, it will be convenient to work with spherical arrangements in (which can be seen as a “compactification” of arrangements of affine hyperplanes or halfspaces in that avoids technical issues related to unbounded faces).
1.1 Levels in Arrangements, Dissection Patterns, and Polytopes
Throughout this paper, let , and let be the unit sphere in . We denote the standard inner product in by , and write for the sign of a real number . For a sign vector , let , , and denote the subsets of coordinates such that , , and , respectively.
Let be a set of vectors; fixing the labeling of the vectors, we will also view as a matrix with columns . Unless stated otherwise, we assume that is in general position, i.e., that any of the vectors are linearly independent. We refer to as a vector configuration of rank .
Every vector defines a great -sphere in and two open hemispheres
The resulting arrangement of hemispheres in determines a decomposition of into faces of dimensions through , where two points lie in the relative interior of the same face of iff for . Let be the set of all sign vectors , where ranges over all non-zero vectors (equivalently, unit vectors) in ; we can identify each face of with its signature . By general position, the face with signature has dimension (the arrangement is simple, i.e., there are no faces with ). Moreover, we call the level of the face. Equivalently, the elements of correspond bijectively to the partitions of by oriented linear hyperplanes, and we will also call them the dissection patterns of . In what follows, we will pass freely back and forth between a vector configuration and the corresponding arrangement and refer to this correspondence as polar duality (to distinguish it from Gale duality, see below).
Definition 1 (-matrix and -polynomial).
For integers and , let111By general position, unless and .
Thus, counts the -dimensional faces of level in .
Together, these numbers form the -matrix . Equivalently, we can encode this data into the bivariate -polynomial defined by
We call a vector configuration pointed if it is contained in an open linear halfspace , for some , or equivalently, if . The closure of this intersection is then a simple (spherical) polytope , the -level of . By radial projection onto the tangent hyperplane , every pointed configuration corresponds to a point set , see [30, Sec. 5.6]. The convex hull is a simplicial polytope (the polar dual of ), and the elements of correspond to the partitions of by affine hyperplanes; in particular, counts the -dimensional faces of , and counts the -sets of .
1.2 Exact Upper Bounds for Sublevels
The following two special vector configurations will play an important role in this paper.
Example 2 (Cyclic and Cocyclic Configurations).
Let be real numbers and define . We call and the cyclic and cocyclic configurations of vectors in , respectively.222The combinatorial types of these configurations are independent of the choice of the parameters .
Cyclic and cocyclic configurations are examples of neighborly and coneighborly configurations, which we define next. Let be a vector configuration in general position. A subset is extremal if there exists a linear hyperplane bounding an open halfspace such that and . In particular, is pointed iff is extremal.
Definition 3 (Neighborly and Coneighborly Configurations).
A vector configuration is coneighborly if every open linear halfspace contains at least vectors of . It is neighborly if every subset of size is extremal.
Cyclic configurations are neighborly, cocyclic configurations are coneighborly [48, Cor. 0.8], and these notions are Gale dual to each other (see Section 2). Every neighborly vector configuration is pointed, hence corresponds to a point set , , and being neighborly means the simplicial -polytope is a neighborly polytope, i.e., every subset of of size at most forms a face. We note that for () neighborliness is the same as being pointed, and for () is neighborly iff the point set is in convex position. By a celebrated result of McMullen [31] neighborly polytopes maximize the number of faces of any dimension:
Theorem 4 (Upper Bound Theorem for Convex Polytopes).
Let be a configuration of vectors in general position. Then
with equality if is neighborly.
Eckhoff [20, Conj. 9.8], Linhart [26], and Welzl [46], independently of one another (and in slightly different forms) conjectured a far-reaching generalization of Theorem 4 for sublevels of arrangements. To state this conjecture, let .
Conjecture 5 (Generalized Upper Bound Conjecture for Sublevels).
Let be a configuration of vectors in general position, and . Then
Equality holds if is neighborly.
A random sampling argument due to Clarkson (see [18]) shows that Conjecture 5 is true asymptotically, for fixed and , up to a constant factor depending on . For the case of vertices at sublevel , Conjecture 5 was proved by Peck [36] and by Alon and Győri [10] for and by Welzl [46] for pointed vector configurations in rank . The second author [44] proved that it is true up to a factor of for arbitrary rank . Here, we prove the conjecture for corank .
Theorem 6.
Let be a configuration of vectors in general position. Then
Equality holds if is neighborly.
Remark 7.
Bounding the maximum number of faces at level exactly is of a rather different flavor. For coneighborly configurations, all vertices of the dual arrangement are concentrated at one or two consecutive levels and . By contrast, determining the maximum number of vertices at level for pointed vector configurations in is a difficult open problem, first studied by Lovász [28] and Erdős, Lovász, Simmons, and Straus [22] in the 1970s (see [45] or [30, Ch. 11] for more details and background); even for (i.e., ) there remains a big gap between the best upper and lower bounds to date, which are and , respectively (due to Dey [19] and Tóth [43]).
1.3 The Spherical Arc Crossing Number of
Determining the crossing number of the complete graph (the minimum number of crossings in any drawing of in the plane, or equivalently in the sphere , with edges represented by arbitrary Jordan arcs) is one of the foundational unsolved problems in geometric graph theory, first studied by Hill in the 1950’s. Hill conjectured the following:
Conjecture 8 (Hill).
(1) |
This is known to hold for , but remains open in general (see [37, Sec. 1.3] or [42] for further background and references). There are several families of drawings showing that for all , but the best lower bound to date [14] is .
We prove Hill’s conjecture for the following class of drawings. Let be a configuration of unit vectors in general position. If we connect every pair of vectors in by the shortest geodesic arc between them in (which is unique, since no two vectors are antipodal, by general position) we obtain a drawing of the complete graph in , which we call a spherical arc drawing. Let denote the number of crossings in this drawing.
Theorem 9.
For every configuration of unit vectors in general position,
Moreover, the lower bound is attained with equality if is coneighborly.
The fact that coneighborly configurations yield spherical arc drawings of achieving the number of crossings in Hill’s conjecture, and the connection to the Eckhoff–Linhart–Welzl conjecture were first observed by Wagner [44, 45]. It is known [35] that there are at least combinatorially different coneighborly configurations of vectors in .
Spherical arc drawings generalize the well-studied class of rectilinear drawings of (given by points in general position in connected by straight-line segments), which correspond to spherical arc drawings given by pointed vector configurations .
Theorem 9 complements earlier results of Lovász, Vesztergombi, Wagner, and Welzl [29] and Ábrego and Fernández-Merchant [6], who showed that the rectilinear crossing number (the minimum number of crossings in any rectilinear drawing of ) is at least ; in fact, for some constant [29]. Thus, unlike the spherical arc crossing number, the rectilinear crossing number is strictly larger than in the asymptotically leading term. We refer to [8] for a detailed survey, including a series of subsequent improvements [15, 9, 7, 4] leading to the currently best bound [5] . We remark that the arguments in [29, 6] have been generalized to verify Hill’s conjecture for other classes of drawings, including -page drawings [2], monotone drawings [13], cylindrical, -bounded, and shellable drawings [3], bishellable drawings [1], and seq-shellable drawings [34]. Currently we do not know how spherical arc drawings relate to these other classes of drawings.
1.4 The -Matrix
The central notion of this paper is the -matrix of a pair of vector configurations, which encodes the difference between the -matrices. The geometric definition of the -matrix is given in Sec. 3, based on how the -matrix changes by mutations in the course of generic continuous motion (an idea with a long history, see, e.g., [11]). The -matrix is characterized by the following properties:
Theorem 10.
Let be a pair of vector configurations in general position.
The -matrix of the pair is an -matrix with integer entries , , , which has the following properties:
-
1.
For and , the -matrix satisfies the skew-symmetries
(2) Thus, the -matrix is determined by the submatrix , which we call the small -matrix. Equivalently, the -polynomial satisfies
(3) -
2.
The -polynomial determines the difference of -polynomials by
(4) Equivalently (by comparing coefficients), for and ,
(5) -
3.
, and
Remark 11.
The system of equations (5) yields a linear transformation through which the -matrix of the pair determines the difference of -matrices by . In the presence of the skew-symmetries (2), the transformation is injective (Lemma 25), i.e., is uniquely determined by . Thus, Theorem 10 could be taken as a formal definition of the -matrix.
1.5 Linear Relations
Linear relations between face numbers of levels in simple arrangements have been studied extensively [33, 24, 27, 12, 16].
The first set of linear relations is given by the antipodal symmetry of :
Observation 12.
Let . Then ; equivalently,
(6) |
Moreover, it is well-known [23, Sec. 18.1] that the total number of faces of a given dimension (of any level) in a simple arrangement in depends only on , , and :
Lemma 13.
Let be a simple arrangement of hemispheres in . Then, for , the total number of -dimensional faces (of any level) in equals
(7) |
In terms of the -polynomial, this can be expressed very compactly as
(8) |
Linhart, Yang, and Philipp [27] proved the following result, which generalizes the classical Dehn–Sommerville relations for simple polytopes:
Theorem 14 (Dehn–Sommerville Relations for Levels in Simple Arrangements).
Let be a vector configuration in general position. Then
(9) |
Equivalently (by comparing coefficients), for and ,
(10) |
Remark 15.
The Dehn–Sommerville relations for polytopes correspond to the identity . The coefficients on the right-hand side of (10) are zero unless (and ). This yields, for every , a linear system of equations among the numbers , and , of face numbers of the -sublevel of the arrangement . An equivalent system of equations (expressed in terms of an -matrix that generalizes the -vector of a simple polytope) was proved earlier by Mulmuley [33], under the additional assumption that the -sublevel is contained in an open hemisphere. Related relations have been rediscovered several times (e.g., in the recent work of Biswas et al. [16]).
Remark 16.
Let denote the set of vector configurations in general position. Let
be the affine space spanned by all -matrices, and the linear space spanned by all -matrices of pairs, respectively. Let be the subset of pointed configurations, and let
be the corresponding subspaces of and . Answering a question posed by Andrzejak and Welzl [12], we determine these spaces:
Theorem 17.
Theorem 18.
. More precisely,
for any .
2 Gale Duality and Dependency Patterns
Let be a vector configuration in general position, and let be the set of all sign vectors given by non-trivial linear dependencies (with coefficients , not all of them are zero). We call the dependency patterns of . Both and are invariant under invertible linear transformations of and under positive rescaling (multiplying each vector by some positive scalar ).
If is a pointed configuration corresponding to a point set , , then the elements of encode the sign patterns of affine dependencies of , hence they correspond bijectively to (ordered) Radon partitions , .
Definition 19 (-matrix and -polynomial).
For integers and , define333Note that unless and .
Together, these numbers form the -matrix . Equivalently, we can encode this data into the bivariate -polynomial defined by
Dependency patterns and dissection patterns are, in a precise sense, dual to each other:
Definition 20 (Gale Duality).
Two vector configurations and are called Gale duals of one another if the rows of and the rows of span subspaces of that are orthogonal complements of one another.
Since we always assume that and are in general position and of full rank, and are Gale dual to each other iff .
It is well-known that Gale dual configurations determine each other up to linear isomorphisms of their ambient spaces and , respectively [30, Sec. 5.6]. Thus, we will speak of the Gale dual of , which we denote by . It follows from the definition that .
As another consequence of the definition, , hence for all ; equivalently, .
By the well-known separation theorem for convex sets [30, Sec. 1.2], a vector configuration in general position is either pointed (equivalently, ), or there is a linear dependence of the vectors all of whose coefficients are positive (i.e., ); the same holds for all subsets . It follows from this that and determine each other (see, e.g., [40, Sec. 2] for more details). In particular, a vector configuration is neighborly iff for , i.e., is neighborly iff is coneighborly.
Moreover, one can show that the -matrix and the -matrix of a vector configuration, or equivalently the polynomials and determine each other as well [40, Sec. 2]:
Theorem 21.
Let be a vector configuration in general position. Then
(12) |
and
(13) |
By Gale duality, Theorem 17 immediately gives a complete description of the affine space spanned by the -matrices of vector configurations , and there is an analogous characterization for the subspace spanned by the -matrices of pointed vector configurations in (which count the number of Radon partitions of given types for the corresponding point sets in ), see [40, Sec. 1.2]
We say that two vector configurations have the same combinatorial type if (up to a permutation of the vectors) (equivalently, ). Furthermore, we call and weakly equivalent if they have identical -matrices (equivalently, identical -matrices).
Remark 22.
For readers familiar with oriented matroids (see [48, Ch. 6] or [17]), and are precisely the sets of vectors and covectors, respectively, of the oriented matroid realized by . However, speaking of “(co)vectors of a vector configuration” seems potentially confusing, and we hope that the terminology of dissection and dependency patterns is more descriptive. The Dehn–Sommerville relations hold for (uniform, not necessarily realizable) oriented matroids. The definition of the -matrix via continuous motion does not carry over to the oriented matroid setting, but one can still define the -matrix formally, by the properties described in Theorem 10. We plan to treat this in detail in a future paper.
3 Continuous Motion and the -Matrix
3.1 The -Matrix of a Pair
Any two configurations and of vectors in general position in can be deformed into one another through a continuous family of vector configurations, where describes a continuous path from to in . If we choose this continuous motion sufficiently generically, then there is only a finite set of events , called mutations, during which the combinatorial type of (which is encoded by ), changes, in a controlled way (see Figures 1 and 2 for an illustration in the case ). Specifically, during a mutation, a unique -tuple of vectors in , indexed by some , momentarily becomes linearly dependent and the orientation of this -tuple (i.e., the sign of ) changes, while the orientations of all other -tuples of vectors remain unchanged. Thus, any two vector configurations are connected by a finite sequence such that and differ by a mutation, .
We describe the change from to when and differ by a mutation. Let index the unique -tuple of vectors that become linearly dependent. In terms of the polar dual arrangements, this means that the -tuple of great -spheres , , momentarily intersect in an antipodal pair of points in . Immediately before and immediately after the mutation, these great -spheres bound an antipodal pair of simplicial -faces in and a corresponding pair of simplicial -faces in , respectively. We have iff the face of with signature is contained in or , and iff the face of with signature is contained in or . All other faces are preserved, i.e., they belong to .
Let be the signature of . We define a partition by
Define and . We call the pair the type of the simplicial face . The signature of the corresponding simplicial face of satisfies for and for . Thus, is of type . Analogously, and are of type and , respectively, see Figures 1 and 2.
Let us define , where we use the notation to indicate that the sum ranges over all corresponding to faces of contained in . The polynomials , , and are defined analogously. These four polynomials have a simple form:
We say that the mutation is of Type . The reverse mutation is of Type . We can summarize the discussion as follows:
Lemma 23.
Let be a mutation of Type between configurations of vectors in . Then
(14) |
Note that the right-hand side of (14) is zero if or .
We are now ready to define the -matrix of a pair of vector configurations.
Definition 24 (-Matrix of a pair).
Let be configurations of vectors in .
If is a single mutation of Type then we define the -matrix , and , as follows:
If or , then for all . If and , then
More generally, if and are connected by a sequence , where and differ by a single mutation, then we define
Proof of Thm 10.
A priori, it may seem that the definition of the -matrix depends on the choice of a particular sequence of mutations transforming to . However, this is not the case:
Lemma 25.
Let and be polynomials (with real coefficients and that are zero unless and , respectively and ). Suppose that and satisfy the identity
(15) |
Then, for every fixed , the numbers , , are linear combinations of the numbers , and , with coefficients given inductively by the polynomial equations
Proof.
The coefficient of in equals (which is zero unless ). Thus, fixing and collecting terms in (15) according to , we get
Moving the terms with to the other side yields
The result follows by a change of variable from to (inductively, the numbers , , are determined by the numbers , .)
By Theorem 21, the -polynomial and the -polynomial of a vector configuration determine each other. This yields the following analogue of Theorem 10 (which can also proved directly, by studying how changes during mutations):
Theorem 26.
Let be configurations of vectors in . Then
(16) |
Corollary 27.
Let be vector configurations, and let be their Gale duals. Then .
Using the above results, one can show [40, Sec. 3.1] that any pair of neighborly configurations of vectors in have the same -matrix and the same -matrix (equivalently, the -matrix of the pair is identically zero); analogously for coneighborly configurations.
Moreover, an inductive argument based on deletions and contractions [40, Sec. 3.2] yields the following result. We say that a vector configuration is -neighborly if every subset of of size is extremal, and we call is -coneighborly if for , i.e., if every open linear halfspace contains at least vectors from .
Lemma 28.
Let , , and let be vector configurations such that is -coneighborly and is -neighborly. Then
(17) |
3.2 The -Matrix and the -Matrix
We are now ready to define the -matrix and the -matrix of a vector configuration.
Definition 29 (-matrix and -matrix).
Let be a configuration of vectors in . Set
for and . We call and the -matrix and the -matrix of , respectively. By the skew-symmetries (2), both matrices are determined by their “upper left” quadrants indexed by and , which we call the small -matrix and the small -matrix, respectively.
By Gale duality, we get , i.e., the -matrix of is the transpose of the -matrix of the Gale dual . Moreover, by Lemma 28,
(18) |
for and .
The -th column of the -matrix corresponds to the classical -vector of a simple polytope. The Generalized Lower Bound Theorem (first proved by Stanley [39], as part of a full characterization of -vectors, and hence -vectors of simple polytopes, see [48, Sec. 8.6]), asserts that for .
The -th row corresponds to a Gale dual version of the -vector of convex polytopes studied by Lee [25] and Welzl [46]. The following is implicit in [46, Sec. 4] (and implies McMullen’s Upper Bound Theorem):
Theorem 30.
Let be a configuration of vectors in general position. Then
Equivalently, by (18), for . Equality holds if is coneighborly.
As a common generalization of the Upper Bound Theorem, the Generalized Lower Bound Theorem, and the Eckhoff–Linhart–Welzl Conjecture (Conj. 5), we propose the following:
Conjecture 31 (Nonnegativity of the Small -Matrix).
Let be a configuration of vectors in general position. Then for and .
Theorem 32.
Let be a configuration of vectors in general position. Then
Equivalently, for . Equality holds if is coneighborly.
4 The Space Spanned by -Matrices
Here, we prove Theorem 17. (The proof of the corresponding result for pointed configurations – Theorem 18 – uses similar ideas but is more involved, see [40]). By Theorem 10, the description of the space follows from the description of the space , so it remains to prove the latter. Recall that is the set of all vector configurations in general position.
By Theorem 10, the -matrix of any pair satisfies the skew-symmetries in (2). Thus, in order to prove Theorem 17, it remains to show that has dimension . To see this, consider a generic continuous deformation from a coneighborly configuration to a neighborly configuration , and let , , be the intermediate vector configurations, i.e., and differ by a mutation. Thus, the -matrices and differ by the -matrix of a mutation, i.e., their first quadrants (small -matrices) differ in at most one coordinate, by or . Moreover, is identically zero, and every entry of the first quadrant of is strictly positive by Lemma 28. Thus, the proof of Theorem 17 is completed by the following lemma:
Lemma 33.
Let be vectors in such that
-
1.
;
-
2.
and differ in one coordinate, by or ;
-
3.
All coordinates of are non-zero.
Then there is a subset of vectors that form a basis of .
Proof.
For , let be the smallest such that the -th coordinate of is non-zero; the index exists by Properties 1 and 3. Moreover, by Property 2, no two coordinates can become non-zero at the same time, i.e., the indices are pairwise distinct. Up to re-labeling the coordinates, we may assume . Then, for , the vector is linearly independent from the vectors , since all of the latter vectors have -th coordinate zero. Thus, the form a basis.
5 Bounding the Spherical Arc Crossing Number
In this section, we outline the proof of Theorem 32 and explain how it implies Theorems 6 and 9. By Gale duality, Theorem 6 is equivalent to the following:
Theorem 34.
Let be a vector configuration in general position. Then, for all , the numbers and are maximized if is coneighborly.
Lemma 35.
Let be a vector configuration in general position and let . Then there are non-negative integers and , , such that
and
Specifically, and .
Proof of Theorem 34.
Proof of Theorem 9.
Let be a configuration of vectors in general position. There are three combinatorial types of quadruples , which we call Type , Type , and Type , respectively, see Fig. 3. Each quadruple of Type supports exactly two dependency patterns with four non-zero signs, one with negative signs, and the other one with negative signs.
It follows that
Moreover, if all the vectors in have unit length, then the number of crossings in the induced spherical arc drawing of equals . Thus Theorem 9 is equivalent to the statement that , equivalently, , where equality holds in both bounds if is coneighborly. By the special case of Theorem 34,
(19) |
with equality if is coneighborly. By a direct calculation (see [41]), .
Remark 36.
We mention a connection to geometric probability. Let be a probability distribution on ; we assume that is non-degenerate in the sense that every plane through the origin has -measure zero. A beautiful argument due to Wendel [47] shows that if is centrally symmetric and is a set of four independent -random vectors then the probability that is of Type (Fig. 3) equals , where , , and ; it follows that for any set of independent -random vectors, the expected number of quadruples of type equals . In particular, if the vectors in are chosen independently and uniformly at random from then the expected number of crossings in the spherical arc drawing given by is , which was also independently shown by Moon [32]. Theorem 9, together with a well-known limiting argument [38] implies that for an arbitrary (not necessarily centrally symmetric) non-degenerate probability distribution on , the expected number of crossings in the spherical arc drawing given by independent -random points is at least .
To conclude, we outline some of the main ideas for the proof of Theorem 32 (see [41] for the details). Let be a configuration of vectors in general position. We will view as the set of differences between a point set (the tips of the vectors) and another point (the origin).
Choose a line in through in general position. By positively rescaling the vectors , we may assume that is a subset of the cylinder consisting of the points in at Euclidean distance from ; in particular, is a point set in convex position.
The point set corresponds to a neighborly configuration of vectors in , and the origin corresponds to an additional vector . Let be the polar dual arrangement of hemispheres in . The intersection is a spherical arrangment in the equatorial -sphere , which is combinatorially isomorphic to the arrangement .
Let be the great circle in formed by the intersection of two great -spheres of the arrangement , . For , consider the subgraph of consisting of the vertices and edges of contained in and at sublevel . This subgraph cannot cover the entire circle , since contains at least one pair of antipodal vertices at levels . Thus, the connected components of this subgraph form closed intervals, which we call -arcs of . We define as the number of -arcs of that are completely contained in the negative open hemisphere .
Suppose we move the origin continuously along the line , while keeping the set fixed. Consider the corresponding continuous motions of the vector configurations in and in . A careful analysis of how and change during this continuous motion yields
for , which proves Theorem 32.
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