Title: Self-organizing Robot Swarms
Abstract: Robot swarms that operate in a fully self-organized manner, without any central coordinating unit, have been widely demonstrated. These systems rely on decentralized architectures where collective behavior emerges from local interactions. This design provides key advantages such as scalability, fault tolerance, and the absence of single points of failure. However, it also introduces challenges in terms of system-level control and manageability.
In contrast, centralized systems are easier to design and control but lack scalability and are more vulnerable to failures at critical nodes.
In this talk, I will present a novel swarm architecture that enables self-organized hierarchy, combining the resilience of decentralized systems with the controllability of centralized ones. Using a heterogeneous team of ground and aerial robots, I will demonstrate how the swarm can self-organize into a dynamic hierarchical control structure through local asymmetric communication. I will present the results of experiments that illustrate capabilities such as autonomous sub-swarm splitting and merging, dynamic replacement of failed robots, and real-time adaptation of collective behavior, while preserving the key benefits of self-organization, including scalability and interchangeability of individual robots.

Title: Decoding functional signals in proteins: from natural codes to synthetic designs
Abstract: Functional classification based on protein sequences has become a critical challenge due to the exponential growth of protein data in biological databases. The remarkable diversity within homologous sequences often obscures an array of distinct functional activities, many of which cannot be reliably inferred through conventional methods. Accurate identification and characterization of these functions are essential, both for unraveling fundamental evolutionary processes in living organisms and for harnessing biotechnological potential. I will discuss how deep learning approaches, by re-envisioning sequence space, facilitate the accurate functional classification of proteins directly from their sequences. Such computational strategies not only enhance our understanding of protein evolution but also open avenues for designing innovative functional switches and generating novel, high-efficiency artificial proteins.

Title: Harnessing Random Molecular Motion to Ratchet Up Information in Self-Assembled Structures
Abstract: Thermal energy fuels random molecular motion which causes the molecules in a solution to mix and interact with each other. When interactions among those molecules, or subsets of them, allow them to bind together, self-assembly may occur. This process leads to the formation of intricate structures in nature, such as snowflakes, and also to increasingly complex structures in rationally designed, artificial self-assembling systems. In this talk, we discuss various methods by which molecular components composed of synthetic DNA can be designed so that, under the correct experimental conditions, the random thermal motion and interactions of those components yields the growth of structures that encode the results of targeted computations, which in turn can direct the shapes of the self-assembling structures. We show how these systems, fueled by randomness, are computationally universal in theory, and discuss limitations and errors that occur in practice and various methods for mitigating them that will hopefully lead to systems whose complexity eventually approaches the theoretical powers.

Title: Towards non-abelian scenarios for additive CA over finite groups
Abstract: Cellular Automata (CA) are formal models for complex systems that have been widely studied and find application in a number of disciplines. However many properties of the temporal evolution of general CA are undecidable and this can be a severe problem in applications. The undecidability issue can be tackled by imposing constraints on the model. Indeed, we equip the alphabet with a group structure and we require that the global map is additive, giving rise to Additive CA over a finite group.
First, we present the abelian situation in which easy-to-check algebraic characterizations of the main dynamical properties in terms of the CA local rule have been provided.
Then, we move to the non-abelian scenario. We will consider the dynamical behavior of Additive CA on a number of classes of specific finite groups and for each of those classes, we focus on non-abelian scenarios, providing exact characterizations for some of the dynamical properties. Some results are quite surprising because they show that the non-abelianness of the group imposes strong limitations on defining the local rule of the cellular automaton, making the class of group cellular automata very constrained.

Title: Deciphering 3D DNA crystals
Abstract: Bottom-up assembly of DNA nanostructures have been proposed for a variety of biotech uses ranging from targeted drug delivery to scaffolding of new materials. Units based on the rationally-designed 3D DNA motif, the tensegrity triangle, provide a wide range of DNA crystallographic assemblies. The sequence design possibilities of these building blocks give ever-increasing geometric complexities to form vast arrays of three-dimensional structures. We show an experimentally verified mathematical model that explains occurrences of diverse chiral topologies within the crystals. We also present methods based on periodic graphs and topological graph theory that enable design and analysis of various crystallographic constructs.

Title: DNA for massive data storage
Abstract: The rapid and exponential growth of digital data - 90% of which has been generated in the last two years - poses a significant challenge for long-term storage due to limited resources, energy consumption, and the short lifespan of conventional storage media. Moreover, about 70% of this data is “cold,” rarely accessed and needing preservation for 10 years or more, further highlighting the need for durable and scalable storage solutions.

Recent advances identify DNA as a highly promising medium for next-generation data storage, offering an extraordinary theoretical capacity of up to 215 petabytes per gram and the potential for data stability over centuries through synthetic DNA encapsulated in specialized microcapsules. Retrieval is enabled by advanced sequencing technologies.
This presentation will review the current state of the art in DNA-based data storage, with a particular focus on the efficient compression and encoding of digital data into the quaternary code of DNA’s four nucleotides -Adenine (A), Thymine (T), Cytosine (C), and Guanine (G). We will introduce JPEG DNA, an emerging standard specifically designed for image compression and coding tailored to DNA’s unique biochemical constraints.
We will also talk about the French initiative, the PEPR MoleculArXiv, which aims to bring together all French academic laboratories working in the field to build a DNA data storage proof of concept capable of writing 10 GB of data per day.

Title: Strengths and weaknesses of quantum algorithms
Abstract: There are quite a few computational problems for which a quantum algorithm is known that is substantially better than a classical algorithm. Despite this, large sums of money are being invested in the development of quantum computers around the world.
In this overview, we will compare classical and quantum computing, and present reasons to invest and not to invest in quantum computing.

Title: Nanoscopic Origins of Quantum Decoherence
Abstract: Quantum decoherence is a critical issue for quantum computation as it affects qubit superposition and output fidelity and is typically due to the coupling of qubits to external fields and environmental variables (i.e., thermal fluctuations, molecular vibrations, and electromagnetic fields). In this presentation, we will discuss the nanoscopic origins of quantum decoherence by examining how atoms and qubits interact through electron interactions and entanglement. Furthermore, we will explore how external fields (magnetic and electric) and thermal fluctuations affect these interactions, leading to a breaking of quantum coherence. We will conclude by discussing potential mechanisms for enhancing qubit interactions to achieve more robust information fidelity.

Title: Reaction System analysis with BioReSolve
Abstract: BioResolve (http://www.di.unipi.it/~bruni/LTSRS/) is a Prolog interpreter for Reaction Systems that offers a flexible playground for their analysis. BioReSolve has been designed by exploiting a process algebraic version of Reaction Systems, which allowed to seamlessly enhance basic Reaction Systems with additional features, like delays, duration, monitoring, dynamic slicing and various kinds of contexts (guarded, nondeterministic and recursive). The talk will provide an overview of the many facets of BioReSolve related to the simulation, analysis and verification of Reaction Systems.

Title: Model checking for temporal and epistemic properties of Reaction Systems
Abstract: In this talk, we address the problem of model checking temporal and epistemic properties in reaction systems — a computational model inspired by the biochemistry of living cells. We introduce two dedicated temporal logics: rsCTL and rsLTL, counterparts of Computation Tree Logic (CTL) and Linear Temporal Logic (LTL), and discuss the complexity of their model checking procedures. We also present a synthesis method for rsLTL applied to partially defined reaction systems. To incorporate reasoning about knowledge and interaction, we extend the formalism toward distributed reaction systems with agency. This leads to the development of rsCTLK, an epistemic extension of rsCTL, and its corresponding model checking algorithm. We report on experimental results using a biological benchmark and demonstrate the effectiveness of our ReactICS toolkit, comparing it with the MCMAS model checker for multi-agent systems.