Barbara Langfeld’s research while affiliated with Kiel University and other places

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Publications (14)


Fig. 2 Construction of the transformation for I = (6; 1, 1, 2, 3, 4, 7). I is a yes-instance of Strong Partition since, for N = {3, 4, 5}, we have i∈N ν i =
(a–d) Illustration of the algorithm in the proof of Theorem 3.2 for G=Z\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb {G}}={\mathbb {Z}}$$\end{document}. (a) The weights of the vertices are given below the nodes in the boxes. (b–d) Successively, the edges connected to the previously satisfied layer are set to their final value (unframed). (e) The same procedure is described as a successive construction of an integer 3×4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3\times 4$$\end{document} matrix with the prescr ibed row and column sums. Positions that are never touched are marked with “×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}”. The other positions correspond to the edges of the tree and are updated step by step
Construction of the transformation for I=(6;1,1,2,3,4,7)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {I}}=(6;1,1,2,3,4,7)$$\end{document}. I\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {I}}$$\end{document} is a yes-instance of Strong Partition since, for N={3,4,5}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=\{3,4,5\}$$\end{document}, we have ∑i∈Nνi=∑i∈[n]\Nνi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sum _{i\in N}\nu _{i}=\sum _{i\in [n]{\setminus } N}\nu _{i}$$\end{document}. (a) Corresponding instance J\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {J}}$$\end{document} of COLRYSc(Z,Z)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textsc {ColRys}}_c({\mathscr {Z}},{\mathscr {Z}})$$\end{document}; here c=4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c=4$$\end{document}, r∗=3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r^{*}=3$$\end{document}, n=6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=6$$\end{document}, ν=∑i=1nνi=18\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\nu =\sum _{i=1}^{n}\nu _{i}=18$$\end{document}. (b) From left to right, four matrices having the given row and column sums (R(ℓ),S(ℓ))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(R^{(\ell )},S^{(\ell )})$$\end{document} (ℓ∈[4]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell \in [4]$$\end{document}). The nonzero entries constructed in the proof are highlighted
On Polyatomic Tomography over Abelian Groups: Some Remarks on Consistency, Tree Packings and Complexity
  • Article
  • Full-text available

September 2020

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36 Reads

Discrete & Computational Geometry

Peter Gritzmann

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Barbara Langfeld

The paper deals with an inverse problem of reconstructing matrices from their marginal sums. More precisely, we are interested in the existence of r×s matrices for which only the following information is available: The entries belong to known subsets of c distinguishable abelian groups, and the row and column sums of all entries from each group are given. This generalizes Ryser’s classical problem of characterizing the set of all 0–1-matrices with given row and column sums and is a basic problem in (polyatomic) discrete tomography. We show that the problem is closely related to packings of trees in bipartite graphs, prove consistency results, give algorithms and determine its complexity. In particular, we find a somewhat unusual complexity behavior: the problem is hard for “small” but easy for “large” matrices.

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Homometry and Direct-Sum Decompositions of Lattice-Convex Sets

July 2016

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146 Reads

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1 Citation

Discrete & Computational Geometry

Two sets in Rd\mathbb{R}^d are called homometric if they have the same covariogram, where the covariogram of a finite subset K of Rd\mathbb{R}^d is the function associating to each uRdu \in \mathbb{R}^d the cardinality of K(K+u)K \cap (K+u). Understanding the structure of homometric sets is important for a number of areas of mathematics and applications. If two sets are homometric but do not coincide up to translations and point reflections, we call them nontrivially homometric. We study nontrivially homometric pairs of lattice-convex sets, where a set K is called lattice-convex with respect to a lattice MRd\mathbb{M} \subseteq \mathbb{R}^d if K is the intersection of M\mathbb{M} and a convex subset of Rd\mathbb{R}^d. This line of research was initiated in 2005 by Daurat, G\'erard and Nivat and, independently, by Gardner, Gronchi and Zong. All pairs of nontrivially homometric lattice-convex sets that have been known so far can be written as direct sums STS \oplus T and S(T)S \oplus (-T), where T is lattice-convex and the underlying lattice is the direct sum of T and some sublattice. We study pairs of nontrivially homometric lattice-convex sets assuming this particular form and establish a necessary and a sufficient condition for the lattice-convexity of STS \oplus T. This allows us to explicitly describe all nontrivially homometric pairs in dimension two, under the above assumption, and to construct examples of nontrivially homometric pairs of lattice-convex sets for each d3d \ge 3.


Uniqueness in Discrete Tomography: Three Remarks and a Corollary

January 2011

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30 Reads

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35 Citations

SIAM Journal on Discrete Mathematics

Discrete tomography is concerned with the retrieval of finite point sets in some ℝ d from their X-rays in a given number m of directions u 1 ,...,u m . In the present paper we focus on uniqueness issues. The first remark gives a uniform treatment and extension of known uniqueness results. In particular, we introduce the concept of J-additivity and give conditions when a subset J of possible positions is already determined by the given data. As a by-product, we settle a conjecture of Brunetti and Daurat on planar lattice convex sets. Remark 2 resolves a problem of Kuba posed in 1997 on the uniqueness in the case d=m=3 with u 1 ,u 2 ,u 3 being the standard unit vectors. Remark 3 determines the computational complexity of finding a smallest set J of positions whose disclosure yields uniqueness. As a corollary, we obtain a hardness result for 0-1-polytopes.


Figure 3: Illustration of the proof of Lemma 3.3, Case 1. The boundary of K and its translations are drawn with a solid line. The boundary of L and its translations are drawn with a dashed line.
Figure 4: Illustration of the proof of Lemma 3.5, Case 2. The relevant parts of the boundaries of K and L are drawn with solid and dashed lines, respectively. In the figure, the arcs A 1 and B 1 are nondegenerate; collapsing these arcs to points, one obtains an illustration for the degenerate case.
On the Reconstruction of Planar Lattice-Convex Sets from the Covariogram

November 2010

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32 Reads

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1 Citation

Discrete & Computational Geometry

A finite subset K of ℤd is said to be lattice-convex if K is the intersection of ℤd with a convex set. The covariogram g K of K⊆ℤd is the function associating to each u∈ℤd the cardinality of K∩(K+u). Daurat, Gérard, and Nivat and independently Gardner, Gronchi, and Zong raised the problem of the reconstruction of lattice-convex sets K from g K . We provide a partial positive answer to this problem by showing that for d=2 and under mild extra assumptions, g K determines K up to translations and reflections. As a complement to the theorem on reconstruction we also extend the known counterexamples (i.e., planar lattice-convex sets which are not reconstructible, up to translations and reflections) to an infinite family of counterexamples.




Discrete Tomography on Modules: Decomposition, Separation, and Uniqueness

January 2008

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15 Reads

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1 Citation

Die Arbeit untersucht drei grundlegende Fragen der Diskreten Tomographie auf Moduln. Im ersten Teil wird charakterisiert, unter welchen Bedingungen das vollständige tomographische Grid in endlich viele Translate des zu Grunde liegenden Moduls zerfällt. Im zweiten Teil wird ein geometrisches Separationsproblem studiert, das in natürlicher Weise bei der Rekonstruktion quasikristalliner Punktmengen aus X-Ray-Daten auftritt. Wir zeigen, wie sich das Separationsproblem in einem semialgebraischen Kontext algorithmisch effizient lösen lässt. Im dritten Teil untersuchen wir das Problem, eine minimale Anzahl an Gridpunkten zu finden, sodass die Fixierung dieser Punkte als (Nicht-)Positionen die eindeutige Rekonstruktion eines gegebenen Musters aus den X-Ray-Daten garantiert. Wir beweisen die NP-Schwere dieses Problems und leiten verwandte Eindeutigkeitsresultate für Polytope ab.


Discrete tomography of planar model sets

October 2006

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58 Reads

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19 Citations

Michael Baake

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Katja Lord

Discrete tomography is a well-established method to investigate finite point sets, in particular finite subsets of periodic systems. Here, we start to develop an efficient approach for the treatment of finite subsets of mathematical quasicrystals. To this end, the class of cyclotomic model sets is introduced, and the corresponding consistency, reconstruction and uniqueness problems of the discrete tomography of these sets are discussed.


Discrete Tomography of Planar Model Sets

September 2006

Discrete tomography is a well-established method to investigate finite point sets, in particular finite subsets of periodic systems. Here, we start to develop an efficient approach for the treatment of finite subsets of mathematical quasicrystals. To this end, the class of cyclotomic model sets is introduced, and the corresponding consistency, reconstruction and uniqueness problems of the discrete tomography of these sets are discussed.


Discrete Tomography of Mathematcal Quasicrystals: A Primer

July 2005

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14 Reads

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1 Citation

Electronic Notes in Discrete Mathematics

This text is a report on work progress. We introduce the class of cyclotomic model sets (mathematical quasicrystals) Λ ⊂ Z [ξn], where Z [ξn] is the ring of integers in the nth cyclotomic field Q (ξn), and discuss the corresponding decomposition, consistency and reconstruction problems of the discrete tomography of these sets. Our solution of the so-called decomposition problem also applies to the case of the square lattice Z2 = Z [ξ4], which corresponds to the classical setting of discrete tomography.


Citations (7)


... Underlying geometric construction. There is an underlying simple geometric construction which generalises to R n (see [1] and Figures 9 and 10 below). We say that a finite set X ⊂ R, asymmetric iff it is not invariant under the inversion that swaps the minimum and maximum of X, namely ...

Reference:

Isospectral Configurations in Euclidean and Hyperbolic Geometry
Homometry and Direct-Sum Decompositions of Lattice-Convex Sets

Discrete & Computational Geometry

... This is a very active field of research. See [4,31,75,76,77,78,79,80,81,82,90,91] and the references therein. These reconstruction problems are important in CAT scanner development, electron microscope image reconstruction, and quality control in semiconductor production (see, e.g., [4,75,76] and the references therein). ...

On the index of Siegel grids and its application to the tomography of quasicrystals
  • Citing Article
  • November 2008

European Journal of Combinatorics

... Since the discrete sets consistent with a given set of projections are, in general, a huge number, and these can be In the literature, the connectedness constraints have been primarily investigated, and many meaningful results have been obtained, both concerning the reconstruction and the enumeration of possible solutions in such a restricted space (see [24,25] for an overview). Differently, one can confine the investigation to a given lattice grid A, so looking for uniqueness conditions inside A, or in suitable subregions of A (a non-exhaustive list of useful papers is [9,[14][15][16]22,23,26,28], also including the corresponding reference sections). ...

Uniqueness in Discrete Tomography: Three Remarks and a Corollary
  • Citing Article
  • January 2011

SIAM Journal on Discrete Mathematics

... It seems that generally it is hard to transfer techniques developed for the covariogram problem within the family of convex bodies to the family of M-convex sets. One such attempt was made in our previous publication [4] for two-dimensional M-convex sets K . In [4] it was shown that if K samples conv(K ) well enough, that is, if K is close enough to a compact convex set in a certain sense, then the reconstruction from g K is similar to the reconstruction in the case of compact convex sets. ...

On the Reconstruction of Planar Lattice-Convex Sets from the Covariogram

Discrete & Computational Geometry

... As a consequence, a small number of projection angles (typically less than 5) can already lead to an accurate reconstruction [7,8]. The theoretical properties of the discrete reconstruction problem have been studied extensively with results on algorithm complexity, uniqueness, and stability [4,6,19]. A key drawback of the discrete lattice assumption when considering real-world applications to nanocrystal data is that in many interesting cases the atoms do not lie on a perfect lattice due to defects in the crystal structure or interfaces between different crystal lattices. ...

Discrete tomography of planar model sets

... From a mathematical standpoint, however, the situation is still rather unsatisfying, as there are relatively few algebraic constructions of convolutional codes having provably good distance properties or an accompanying algebraic decoding algorithm. Recent years have seen interesting developments in the algebraic theory of convolutional codes: the papers [5,7,8,12] extend the notion of cyclicity familiar from block code theory to convolutional codes; the papers [3,9] investigate weight enumerators and the existence of a MacWilliams Identity for convolutional codes; the paper [15] uses methods from systems theory to construct convolutional codes having a designed distance; and the papers [4,6,11,14,17] contain results concerning convolutional codes having certain maximal distance properties. Motivated by existence results proved in this last set of papers, we decided to investigate so-called superregular matrices. ...

ON THE ALGEBRAIC PARAMETERS OF CONVOLUTIONAL CODES WITH CYCLIC STRUCTURE

... However, these constructions require a larger field size than the constructions obtained in [6]. Later, Gluesing-Luerssen and Langfeld presented in [8] a novel construction of convolutional codes of rate 1/n with the same field size as the ones obtained in [6] but also with a restriction on the degree of the code. After that, Gluesing-Luerssen, Smarandache and Rosenthal [3] constructed MDS convolutional codes for arbitrary parameters. ...

A CLASS OF ONE-DIMENSIONAL MDS CONVOLUTIONAL CODES