Verónica Becher
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Verónica Becher has a total of 97 co-authors. Here are the top co-authors by number of shared publications:
- Olivier Carton
Institut universitaire de France et IRIF, Université Paris-Cité, France
12 shared publications - Theodore A. Slaman
Department of Mathematics, University of California, Berkeley, Berkeley, California 94720-3840
10 shared publications - Pablo Ariel Heiber
Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Computat, RA-1428 Buenos Aires, DF, Argentina
7 shared publications - Michael Benedikt
University of Oxford
5 shared publications Pablo Ariel Heiber
Universidad de Buenos Aires & CONICET
5 shared publications- Christoph Aistleitner
Graz University of Technology Institute of Analysis and Number Theory Steyrergasse 30 8010 Graz Austria
4 shared publications
Latest Publications
- 23 January 2026
- Published by Taylor & Francis in The American Mathematical Monthly
- p. 1-19
The Discrepancy of the Champernowne Constant
- 23 January 2026
- Published by Taylor & Francis in The American Mathematical Monthly
- p. 1-19
The Discrepancy of the Champernowne Constant
- 22 January 2026
- Published by Elsevier in Journal of Computer and System Sciences
- Vol. 158
Automata for the commutative closure of regular languages
- 22 January 2026
- Published by Elsevier in Journal of Computer and System Sciences
- Vol. 158
Automata for the commutative closure of regular languages
- 18 November 2025
- Published by American Mathematical Society (AMS) in Transactions of the American Mathematical Society
Poisson genericity in numeration systems with exponentially mixing probabilities
We define Poisson genericity for infinite sequences in any countable alphabet with an invariant exponentially ψ \psi -mixing probability measure. A sequence is Poisson generic if the number of occurrences of blocks of symbols asymptotically follows a Poisson law as the block length increases. We prove that almost all sequences are Poisson generic. Our result generalizes Peres and Weiss’ theorem about Poisson genericity of integer bases numeration systems. In particular, we obtain that the continued fraction expansions of almost all real numbers are Poisson generic.
- 18 November 2025
- Published by American Mathematical Society (AMS) in Transactions of the American Mathematical Society
Poisson genericity in numeration systems with exponentially mixing probabilities
We define Poisson genericity for infinite sequences in any countable alphabet with an invariant exponentially ψ \psi -mixing probability measure. A sequence is Poisson generic if the number of occurrences of blocks of symbols asymptotically follows a Poisson law as the block length increases. We prove that almost all sequences are Poisson generic. Our result generalizes Peres and Weiss’ theorem about Poisson genericity of integer bases numeration systems. In particular, we obtain that the continued fraction expansions of almost all real numbers are Poisson generic.
- 16 July 2025
- Published by Elsevier in Information and Computation
- Vol. 306
Rauzy complexity and block entropy
- 16 July 2025
- Published by Elsevier in Information and Computation
- Vol. 306
Rauzy complexity and block entropy
- Open Access
Automata for the commutative closure of regular sets
Consider $ A^* $, the free monoid generated by the finite alphabet $A$ with the concatenation operation. Two words have the same commutative image when one is a permutation of the symbols of the other. The commutative closure of a set $ L \subseteq A^* $ is the set $ {C}(L) \subseteq A^* $ of words whose commutative image coincides with that of some word in $ L $. We provide an algorithm that, given a regular set $ L $, produces a finite state automaton that accepts the commutative closure $ {C}(L) $, provided that this closure set is regular. The problem of deciding whether $ {C}(L) $ is regular was solved by Ginsburg and Spanier in 1966 using the decidability of Presburger sentences, and by Gohon in 1985 via formal power series. The problem of constructing an automaton that accepts $ {C}(L) $ has already been studied in the literature. We give a simpler algorithm using an algebraic approach.Open AccessAutomata for the commutative closure of regular sets
Consider $ A^* $, the free monoid generated by the finite alphabet $A$ with the concatenation operation. Two words have the same commutative image when one is a permutation of the symbols of the other. The commutative closure of a set $ L \subseteq A^* $ is the set $ {C}(L) \subseteq A^* $ of words whose commutative image coincides with that of some word in $ L $. We provide an algorithm that, given a regular set $ L $, produces a finite state automaton that accepts the commutative closure $ {C}(L) $, provided that this closure set is regular. The problem of deciding whether $ {C}(L) $ is regular was solved by Ginsburg and Spanier in 1966 using the decidability of Presburger sentences, and by Gohon in 1985 via formal power series. The problem of constructing an automaton that accepts $ {C}(L) $ has already been studied in the literature. We give a simpler algorithm using an algebraic approach. - 22 December 2024
- Published by Mathematical Sciences Publishers in Combinatorics and Number Theory
- Vol. 13 (4) , 361-375
Lyndon pairs and the lexicographically greatest perfect necklace
- 22 December 2024
- Published by Mathematical Sciences Publishers in Combinatorics and Number Theory
- Vol. 13 (4) , 361-375
Lyndon pairs and the lexicographically greatest perfect necklace
- 16 August 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 70 (10) , 7463-7471
Nested Perfect Arrays
We introduce two-dimensional periodic arrays that are a variant of the de Bruijn tori. We call them nested perfect arrays. Instead of asking that every array of a given size has exactly one occurrence, we partition the positions in congruence classes and we ask exactly one occurrence in each congruence class. We also ask that this property applies recursively to each of the subarrays. We give a method to construct nested perfect arrays based on Pascal triangle matrix modulo 2. For the two-symbol alphabet, and for n being a power of 2, we partition the positions of the arrays in n 2 many congruence classes by taking the row number modulo n and the column number modulo n . We construct arrays where each possible n × n array occurs n 2 times, once in each congruence class. Our method yields exponentially many (in n 2 ) different nested perfect arrays.
- 16 August 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 70 (10) , 7463-7471
Nested Perfect Arrays
We introduce two-dimensional periodic arrays that are a variant of the de Bruijn tori. We call them nested perfect arrays. Instead of asking that every array of a given size has exactly one occurrence, we partition the positions in congruence classes and we ask exactly one occurrence in each congruence class. We also ask that this property applies recursively to each of the subarrays. We give a method to construct nested perfect arrays based on Pascal triangle matrix modulo 2. For the two-symbol alphabet, and for n being a power of 2, we partition the positions of the arrays in n 2 many congruence classes by taking the row number modulo n and the column number modulo n . We construct arrays where each possible n × n array occurs n 2 times, once in each congruence class. Our method yields exponentially many (in n 2 ) different nested perfect arrays.
- Open Access
De Bruijn Sequences with Minimum Discrepancy
The discrepancy of a binary string is the maximum (absolute) difference between the number of ones and the number of zeroes over all possible substrings of the given binary string. In this note we determine the minimal discrepancy that a binary de Bruijn sequence of order $n$ can achieve, which is $n$. This was an open problem until now. We give an algorithm that constructs a binary de Bruijn sequence with minimal discrepancy. A slight modification of this algorithm deals with arbitrary alphabets and yields de Bruijn sequences of order $n$ with discrepancy at most $1$ above the trivial lower bound $n$.Open AccessDe Bruijn Sequences with Minimum Discrepancy
The discrepancy of a binary string is the maximum (absolute) difference between the number of ones and the number of zeroes over all possible substrings of the given binary string. In this note we determine the minimal discrepancy that a binary de Bruijn sequence of order $n$ can achieve, which is $n$. This was an open problem until now. We give an algorithm that constructs a binary de Bruijn sequence with minimal discrepancy. A slight modification of this algorithm deals with arbitrary alphabets and yields de Bruijn sequences of order $n$ with discrepancy at most $1$ above the trivial lower bound $n$. - Open Access
The discrepancy of the Champernowne constant
A number is normal in base $b$ if, in its base $b$ expansion, all blocks of digits of equal length have the same asymptotic frequency. The rate at which a number approaches normality is quantified by the classical notion of discrepancy, which indicates how far the scaling of the number by powers of $b$ is from being equidistributed modulo 1. This rate is known as the discrepancy of a normal number. The Champernowne constant $c_{10} = 0.12345678910111213141516\ldots$ is the most well-known example of a normal number. In 1986, Schiffer provided the discrepancy of numbers in a family that includes the Champernowne constant. His proof relies on exponential sums. Here, we present a discrete and elementary proof specifically for the discrepancy of the Champernowne constant.Open AccessThe discrepancy of the Champernowne constant
A number is normal in base $b$ if, in its base $b$ expansion, all blocks of digits of equal length have the same asymptotic frequency. The rate at which a number approaches normality is quantified by the classical notion of discrepancy, which indicates how far the scaling of the number by powers of $b$ is from being equidistributed modulo 1. This rate is known as the discrepancy of a normal number. The Champernowne constant $c_{10} = 0.12345678910111213141516\ldots$ is the most well-known example of a normal number. In 1986, Schiffer provided the discrepancy of numbers in a family that includes the Champernowne constant. His proof relies on exponential sums. Here, we present a discrete and elementary proof specifically for the discrepancy of the Champernowne constant. - 29 June 2024
- Published by Elsevier in Discrete Applied Mathematics
- Vol. 357, 352-364
On extremal factors of de Bruijn-like graphs
- 29 June 2024
- Published by Elsevier in Discrete Applied Mathematics
- Vol. 357, 352-364
On extremal factors of de Bruijn-like graphs