Les lundis, à partir de 14h - UPEC CMC - Salle P2-131
It seems pretty obvious to add random choice to your
preferred higher-order functional language.
One can then give it an operational semantics in the form
of an infinite Markov chain whose states are
machine configurations, and with easily understandable
Denotational semantics provide helpful invariants to
reason about programs, and Jones and Plotkin’s
probabilistic powerdomain (1990) models random choice
One can then interpret higher-order probabilistic
programs in the category of dcpos (directed-complete
partial orders), and that works perfectly well…
except that some dcpos are rather pathological,
and that prevents us from proving all the theorems
we would like to have. As a case in point, it
is unknown whether Fubini’s theorem holds on all
dcpos, which means that drawing x then y at random
is not known to be equivalent with drawing y then x.
Such problems do not occur with so-called continuous
dcpos, but then we must face the Jung-Tix problem (1998):
we do not know of any category of continuous dcpos
that can handle both higher-order features and
the probabilistic powerdomain.
We will show that there is a simple way of getting
around the Jung-Tix problem, relying on a variant
of Levy’s call-by-push-value paradigm (2003), and provided
we also include a form of demonic non-deterministic
choice (related to must-termination, operationally).
We will argue that the language satisfies adequacy:
on programs of base type (int), the denotational semantics
computes a subprobability distribution whose
mass at any given number n is exactly the minimal probability
that the output of the program will be n.
If we have time, we will then study full abstraction,
namely the relation between denotational (in)equality
and the observational preorder. Our language is
not fully abstract. One reason is expected: the absence
of parallel conditionals. Another has surfaced
more recently, and that is the absence of so-called
statistical termination testers. With both added,
however, our language is fully abstract.
In 1941, Claude Shannon introduced a continuous-time analog model of
computation, namely the General Purpose Analog Computer (GPAC). The
GPAC is a physically feasible model in the sense that it can be
implemented in practice through the use of analog electronics or
mechanical devices. It can be proved that the functions computed by a
GPAC are precisely the solutions of a special class of differential
equations where the right-hand side is a polynomial. Analog computers
have since been replaced by digital counterpart. Nevertheless, one can
wonder how the GPAC could be compared to Turing
A few years ago, it was shown that Turing-based paradigms and
the GPAC have the same computational power. However, this result did
not shed any light on what happens at a computational complexity
level. In other words, analog computers do not make a difference about
what can be computed; but maybe they could compute faster than a
digital computer. A fundamental difficulty of continuous-time model is
to define a proper notion of complexity. Indeed, a troubling problem is
that many models exhibit the so-called Zeno’s phenomenon, also known as
In this talk, I will present results from my thesis that give several
fundamental contributions to these questions. We show that the GPAC has
the same computational power as the Turing machine, at the complexity
level. We also provide as a side effect a purely analog, machine-
independent characterization of P and Computable Analysis.
I will present some recent work on the universality of polynomial
differential equations. We show that when we impose no restrictions at
all on the system, it is possible to build a fixed equation that
is universal in the sense it can approximate arbitrarily well any
continuous curve over R, simply by changing the initial condition of
If time allows, I will also mention some recent application of this
work to show that chemical reaction networks are strongly Turing
complete with the differential semantics.
La conjecture de Nivat dit que toute configuration (coloration de Z^2) de faible complexité (le nombre de motifs qui y apparaissent est “faible”) est nécessairement périodique.
En 2015, Michal Szabados et Jarkko Kari ont publié leur premier article utilisant une nouvelle approche pour s’attaquer à cette conjecture: une approche algébrique.
Leur idée est de représenter une configuration comme une série formelle, et en étudiant la structures de certains objets qui lui sont liés (tels que des idéaux polynomiaux), ils parviennent à utiliser des théorèmes d’algèbre pour se rapprocher de la conjecture de Nivat.
Dans cet exposé, je présenterai les travaux que j’ai effectué avec Jarkko Kari dans le continuation de la thèse de Michal Szabados. Je présenterai deux théorèmes utilisant ces outils algébriques pour se rapprocher encore une fois de la conjecture de Nivat, dans deux sens différents: Le premier montre que la conjecture de Nivat est vraie pour une certaine classe de sous-shifts, tandis que le second prouve la décidabilité du problème du domino pour les sous-shift de faible complexité (résultat que la conjecture de Nivat impliquerait de manière presque immédiate).
The computational model of programs over monoids, introduced by Barrington and Thérien in the late 1980s, gives a way to generalise the notion of (classical) recognition through morphisms into monoids in such a way that almost all open questions about the internal structure of the complexity class NC^1 can be reformulated as understanding what languages (and, in fact, even regular languages) can be program-recognised by monoids taken from some given variety of finite monoids. Unfortunately, for the moment, this finite semigroup theoretical approach did not help to prove any new result about the internal structure of NC^1 and, even worse, any attempt to reprove well-known results about this internal structure (like the fact that the language of words over the binary alphabet containing a number of 1s not divisible by some fixed integer greater than 1 is not in AC^0) using techniques stemming from algebraic automata theory failed.
In this talk, I shall present the model of programs over monoids, explain how it relates to “small” circuit complexity classes and present some of the contributions I made during my Ph.D. thesis to the understanding of the computational power of programs over monoids, focusing on the well-known varieties of finite monoids DA and J (giving rise to “small” circuit complexity classes well within AC^0). I shall conclude with a word about ongoing work and future research directions.