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Lilian Salinas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

TCS Journal 2025 Journal Article

Dynamically equivalent disjunctive networks

  • Julio Aracena
  • Luis Cabrera-Crot
  • Adrien Richard
  • Lilian Salinas

The study of the dynamical behavior of Boolean networks with different update schedules has so far focused primarily on the possible dynamics and equivalent networks that can be obtained. However, few studies have been done about which networks can be obtained from another network with a non-parallel schedule. In this article, we define the problem of finding a Boolean network that is dynamically equivalent to another network. For the general case, it is shown that the problem is NP-Hard. However, if the problem is restricted to disjunctive Boolean networks, it can be solved in polynomial time.

Details DOI

I&C Journal 2020 Journal Article

Fixing monotone Boolean networks asynchronously

  • Julio Aracena
  • Maximilien Gadouleau
  • Adrien Richard
  • Lilian Salinas

The asynchronous automaton associated with a Boolean network f: { 0, 1 } n → { 0, 1 } n is considered in many applications. It is the finite deterministic automaton with set of states { 0, 1 } n, alphabet { 1, …, n }, where the action of letter i on a state x consists in switching the ith component if f i ( x ) ≠ x i or doing nothing otherwise. This action is extended to words in the natural way. We then say that a word w fixes f if, for all states x, the result of the action of w on x is a fixed point of f. In this paper, we ask for the existence of fixing words, and their minimal length. Firstly, our main results concern the minimal length of words that fix monotone networks. We prove that there exists a monotone network f with n components such that any word fixing f has length Ω ( n 2 ). Conversely, we construct a word of length O ( n 3 ) that fixes all monotone networks with n components. Secondly, we refine and extend our results to different classes of networks.

Details DOI
v2026.09.13