Communication Complexity and Intrinsic Universality in Cellular Automata

The notions of universality and completeness are central in the theories of computation and computational complexity. However, proving lower bounds and necessary conditions remains hard in most of the cases. In this article, we introduce necessary conditions for a cellular automaton to be "universal", according to a precise notion of simulation, related both to the dynamics of cellular automata and to their computational power. This notion of simulation relies on simple operations of space-time rescaling and it is intrinsic to the model of cellular automata. Intrinsinc universality, the derived notion, is stronger than Turing universality, but more uniform, and easier to define and study. Our approach builds upon the notion of communication complexity, which was primarily designed to study parallel programs, and thus is, as we show in this article, particulary well suited to the study of cellular automata: it allowed to show, by studying natural problems on the dynamics of cellular automata, that several classes of cellular automata, as well as many natural (elementary) examples, could not be intrinsically universal

Type-3 Secretion System-induced pyroptosis protects Pseudomonas against cell-autonomous immunity

Inflammasome-induced pyroptosis comprises a key cell-autonomous immune process against intracellular bacteria, namely the generation of dying cell structures. These so-called pore-induced intracellular traps (PITs) entrap and weaken intracellular microbes. However, the immune importance of pyroptosis against extracellular pathogens remains unclear. Here, we report that Type-3 secretion system (T3SS)-expressing Pseudomonas aeruginosa ( P. aeruginosa ) escaped PIT immunity by inducing a NLRC4 inflammasome-dependent macrophage pyroptosis response in the extracellular environment. To the contrary, phagocytosis of Salmonella Typhimurium promoted NLRC4-dependent PIT formation and the subsequent bacterial caging. Remarkably, T3SS-deficient Pseudomonas were efficiently sequestered within PIT-dependent caging, which favored exposure to neutrophils. Conversely, both NLRC4 and caspase-11 deficient mice presented increased susceptibility to T3SS-deficient P. aeruginosa challenge, but not to T3SS-expressing P. aeruginosa. Overall, our results uncovered that P. aeruginosa uses its T3SS to overcome inflammasome-triggered pyroptosis, which is primarily effective against intracellular invaders. Importance Although innate immune components confer host protection against infections, the opportunistic bacterial pathogen Pseudomonas aeruginosa ( P. aeruginosa ) exploits the inflammatory reaction to thrive. Specifically the NLRC4 inflammasome, a crucial immune complex, triggers an Interleukin (IL)-1β and -18 deleterious host response to P. aeruginosa . Here, we provide evidence that, in addition to IL-1 cytokines, P. aeruginosa also exploits the NLRC4 inflammasome-induced pro-inflammatory cell death, namely pyroptosis, to avoid efficient uptake and killing by macrophages. Therefore, our study reveals that pyroptosis-driven immune effectiveness mainly depends on P. aeruginosa localization. This paves the way toward our comprehension of the mechanistic requirements for pyroptosis effectiveness upon microbial infections and may initiate targeted approaches in order to ameliorate the innate immune functions to infections. Graphical abstract Macrophages infected with T3SS-expressing P. aeruginosa die in a NLRC4-dependent manner, which allows bacterial escape from PIT-mediated cell-autonomous immunity and neutrophil efferocytosis. However, T3SS-deficient P. aeruginosa is detected by both NLRC4 and caspase-11 inflammasomes, which promotes bacterial trapping and subsequent efferocytosis of P. aeruginosa -containing-PITs by neutrophils

Communications in cellular automata

The goal of this paper is to show why the framework of communication complexity seems suitable for the study of cellular automata. Researchers have tackled different algorithmic problems ranging from the complexity of predicting to the decidability of different dynamical properties of cellular automata. But the difference here is that we look for communication protocols arising in the dynamics itself. Our work is guided by the following idea: if we are able to give a protocol describing a cellular automaton, then we can understand its behavior

Cellular automata as a model of parallel complexities

The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics�� such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation.The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics, such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation

Les automates cellulaires en tant que modèle de complexités parallèles

The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics, such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation.The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics�� such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation

Directed non-cooperative tile assembly is decidable

International audienceWe provide a complete characterisation of all final states of a model called directed non-cooperative tile self-assembly, also called directed temperature 1 tile assembly, which proves that this model cannot possibly perform Turing computation. This model is a deterministic version of the more general undirected model, whose computational power is still open. Our result uses recent results in the domain, and solves a conjecture formalised in 2011. We believe that this is a major step towards understanding the full model. Temperature 1 tile assembly can be seen as a two-dimensional extension of finite automata, where geometry provides a form of memory and synchronisation, yet the full power of these "geometric blockings" was still largely unknown until recently (note that nontrivial algorithms which are able to build larger structures than the naive constructions have been found)

The structure of communication problems in cellular automata

Studying cellular automata with methods from communication complexity appears to be a promising approach. In the past, interesting connections between communication complexity and intrinsic universality in cellular automata were shown. One of the last extensions of this theory was its generalization to various “communication problems”, or “questions ” one might ask about the dynamics of cellular automata. In this article, we aim at structuring these problems, and find what makes them interesting for the study of intrinsic universality and quasi-orders induced by simulation relations

Clandestine Simulations in Cellular Automata

18 pagesThis paper studies two kinds of simulation between cellular automata: simulations based on factor and simulations based on sub-automaton. We show that these two kinds of simulation behave in two opposite ways with respect to the complexity of attractors and factor subshifts. On the one hand, the factor simulation preserves the complexity of limits sets or column factors (the simulator CA must have a higher complexity than the simulated CA). On the other hand, we show that any CA is the sub-automaton of some CA with a simple limit set (NL-recognizable) and the sub-automaton of some CA with a simple column factor (finite type). As a corollary, we get intrinsically universal CA with simple limit sets or simple column factors. Hence we are able to 'hide' the simulation power of any CA under simple dynamical indicators

Les automates cellulaires en tant que modèle de complexités parallèles

The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation.The intended goal of this manuscript is to build bridges between two definitions of complexity. One of them, called the algorithmic complexity is well-known to any computer scientist as the difficulty of performing some task such as sorting or optimizing the outcome of some system. The other one, etymologically closer from the word "complexity" is about what happens when many parts of a system are interacting together. Just as cells in a living body, producers and consumers in some non-planned economies or mathematicians exchanging ideas to prove theorems. On the algorithmic side, the main objects that we are going to use are two models of computations, one called communication protocols, and the other one circuits. Communication protocols are found everywhere in our world, they are the basic stone of almost any human collaboration and achievement. The definition we are going to use of communication reflects exactly this idea of collaboration. Our other model, circuits, are basically combinations of logical gates put together with electrical wires carrying binary values, They are ubiquitous in our everyday life, they are how computers compute, how cell phones make calls, yet the most basic questions about them remain widely open, how to build the most efficient circuits computing a given function, How to prove that some function does not have a circuit of a given size, For all but the most basic computations, the question of whether they can be computed by a very small circuit is still open. On the other hand, our main object of study, cellular automata, is a prototype of our second definition of complexity. What "does" a cellular automaton is exactly this definition, making simple agents evolve with interaction with a small neighborhood. The theory of cellular automata is related to other fields of mathematics, such as dynamical systems, symbolic dynamics, and topology. Several uses of cellular automata have been suggested, ranging from the simple application of them as a model of other biological or physical phenomena, to the more general study in the theory of computation.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF