7.1.1 Graph Hyperbolicity

• Keywords:
  Graph algorithmics, Algorithm engineering, Gromov hyperbolicity
• Scientific Description:
  Hyperbolicity is a graph parameter related to how much a graph resembles a tree with respect to distances. Its computation is challenging as the main approaches consist in scanning all quadruples of the graph or using fast matrix multiplication as building block, both are not practical for large graphs. This software implements state-of-the-art algorithms and in particular new algorithms enabling to compute the hyperbolicity of graphs with unprecedented size (up to a million nodes) and speeds up the computation of previously attainable graphs.
• Functional Description:
  Implementation in C++ of algorithms for computing the hyperbolicity of graphs.
• URL:
  https://­gitlab.­inria.­fr/­dcoudert/­hyperbolicity
• Publications:
  hal-03431155, hal-03837023, hal-03438325
• Contact:
  David Coudert
• Participants:
  David Coudert, André Nusser, Laurent Viennot

7.1.2 k-shortest simple paths

• Name:
  k-shortest simple paths
• Keywords:
  Graph, Graph algorithmics
• Functional Description:
  Implementation in C++ of algorithms for computing the k shortest simple paths from a source to a destination in a weighted directed graph.
• Release Contributions:
  This version implements the standard algorithm proposed by Yen (Yen), Node Classification algorithm proposed by Feng (NC), the Sidetrack Based algorithm proposed by Kurz and Mutzel (SB), and variants of SB proposed by Al Zoobi, Coudert and Nisse to reduce running time (SB*) and memory usage (PSB). It also implements the PNC algorithm proposed by Al Zoobi, Coudert and Nisse to reduce the execution time of the NC algorithm.
• URL:
  https://­gitlab.­inria.­fr/­dcoudert/­k-shortest-simple-paths
• Publications:
  hal-02865918, hal-03196830, hal-03438331
• Contact:
  David Coudert
• Participants:
  David Coudert, Nicolas Nisse, Ali Al Zoobi

7.1.3 SageMath

• Name:
  SageMath
• Keywords:
  Graph algorithmics, Graph, Combinatorics, Probability, Matroids, Geometry, Numerical optimization
• Scientific Description:
  SageMath is a free open-source mathematics software system. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers.
• Functional Description:

  SageMath is a free mathematics software system written in Python and combining a large number of mathematical libraries under a common interface.

  INRIA teams contribute in different ways to the software collection. COATI adds new graph algorithms along with their documentations and contributes the improvement and maintenance of the graph module and its underlying data structures. LFANT contributes through libraries such as ARB and PARI/GP, and directly through SageMath code for algebras and ring and field extensions.

• Release Contributions:
  See http://­www.­sagemath.­org/­changelogs/
• URL:
  http://­www.­sagemath.­org/
• Contact:
  David Coudert
• Participants:
  David Coudert, Xavier Caruso

7.1.4 WorldDynamics.jl

• Name:
  WorldDynamics.jl
• Keywords:
  Integrated assessment modeling, Scientific computing, Julia programming language
• Scientific Description:
  The World Dynamics project aims to provide a modern framework to investigate integrated assessment models of sustainable development, based on current software engineering and scientific machine learning techniques. Our group is developing a Julia library to allow scientists to easily use and adapt different world models, from Meadows et al.'s World3 to recent proposals. By enabling an open, interdisciplinary, and consistent comparative approach to scientific model development, our goal is to inform global policy makers on environmental and economic issues.
• Functional Description:

  Integrated Assessment Modeling is a research area that focuses on developing and applying integrated models of human and earth systems to help understand how key aspects of society may evolve in the future and how they might interact with the natural environment and with a changing climate.

  Despite the importance of the field, there has been no general software framework which allows scientists to investigate integrated assessment models of sustainable development, by using and adapting the most famous world models, from Meadows et al.'s World3 to recent proposals.

  WorldDynamics.jl is a new software library written by members of the COATI Team, that aims to fill such crucial gap, by providing a modern framework in the Julia programming language based on current software engineering and scientific machine learning techniques.

• News of the Year:
  The first version of WorldDynamics.jl has been released on September 2022. It includes all versions of the World models by Forrester and Meadows et al.
• URL:
  https://­github.­com/­worlddynamics/­WorldDynamics.­jl
• Contact:
  Emanuele Natale
• Participants:
  Pierluigi Crescenzi, Paulo Bruno De Sousa Serafim, Aurora Rossi, Hicham Lesfari, Emanuele Natale
• Partner:
  Gran Sasso Science Institute

7.1.5 Idawi

• Keywords:
  Java, Distributed computing, Web Services, Parallel computing, Component models, Software Components, P2P, Dynamic components, Internet of things, Distributed Applications
• Functional Description:

  Idawi is a middleware for the development and experimentation of distributed applications for mobile multi-hop networks, such as the IoT, the Edge, Mobile Ad hoc Networks, etc. Its development was initially motivated by our need to deploy scientific applications in clusters of computers, in order to run large experimentation campaigns of graph algorithms.

  Idawi is an innovative arrangement of many features found in existing tools into a fresh Open Source Java reference implementation.

  Idawi defines applications elements as components organized into a multi-hop overlay network on top of TCP/UDP and SSH, to be able to communicate even in the presence of NATs and firewalls. In the usual use case, there will be only one component per device. But, in order to enable the simulation/emulation of large systems, components can deploy other components in their Java Virtual Machine (JVM) or in another JVM(s) in the same device.

  Idawi proposes a structuring model of distributed applications, which then must conform to a specific Object-Oriented model in the style of SOA: it defines that components expose their functionality via services. Services hold data and implement functionality about the specific concern they are about. Functionality is then exposed via operations, which can be triggered remotely from anywhere in the component overlay.

  The decentralized communication model of Idawi matches the very nature of mobile multi-hop networks. It defines that components communicate with each other via messages of bounded size. Messaging can be both synchronous (imperative) and asynchronous (reactive/event-driven). It is powered by a default routing scheme and APIs that are tailored to collective communication, so as to offer native support of parallel processing.

  Idawi comes with a set of built-in fully decentralized services for automatized quick deployment/bootstrapping of components through SSH, interoperability through a REST-based web interface, service provisioning and discovery, overlay management, and many other system-level functionality.

• URL:
  https://­github.­com/­lhogie/­idawi
• Publications:
  hal-03863333, hal-03886521, hal-03562184
• Contact:
  Luc Hogie

7.1.6 OnlineGraph

• Keywords:
  Java, Distributed computing, Graph algorithmics
• Functional Description:

  OnlineGraph is a decentralized mixed graph library. OnlineGraph is designed and developed as a specific application-level service of the Idawi middleware. It implements a decentralized application distributed over a multi-hop overlay network of components. In this application dedicated to the storage, edition and graphical rendering of graphs, graphs are partitioned over the set of components. The application imposes no allocation strategy: it is left to the application. A graph may be entirely allocated on one specific component, or a subset of the components forming the application, or all of them.

  Any component in the application can expose graphs to the Web—not just its component-local part, but all the parts it has access to in the overlay networks. Graphs are then accessible to clients through a set of Web servers exposing services.

  A component-local part of a partitioned graph consists of three correlated sets: the vertex set, the arc set, and the edge set. These sets can store the elements they contain in RAM or on disk, thereby enabling persistence. In these sets, graph elements are represented by a 64-bit numerical ID, and they can be associated to any data. This graph data structures can be used out of the distributed infrastructure, just any graph library.

  OnlineGraph is developed within the COATI Research group at Inria Sophia Antipolis and I3S Computer Science Laboratory of Université Côte d'Azur](https://univ-cotedazur.eu/). Its development team is composed of:

  Luc Hogie (project leader) Khadidiatou Dieye (Master's degree intern) Safouane Ouazri (Master's degree intern) Antonin Lacomme (Master's degree intern) Fedi Ghalloussi (Bachelor's degree intern)

• URL:
  https://­github.­com/­lhogie/­OnlineGraph
• Contact:
  Luc Hogie

8.1.1 Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning

Participants: Redha Alliche, Ramon Aparicio, Frédéric Giroire, Hicham Lesfari, Joanna Moulierac.

The emerging 5G induces a great diversity of use cases, a multiplication of the number of connections, an increase in throughput as well as stronger constraints in terms of quality of service such as low latency and isolation of requests. To support these new constraints, Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies have been coupled to introduce the network slicing paradigm. Due to the high dynamicity of the demands, it is crucial to regularly reconfigure the network slices in order to maintain an efficient provisioning of the network. A major concern is to find the best frequency to carry out these reconfigurations, as there is a trade-off between a reduced network congestion and the additional costs induced by the reconfiguration.

In 59, we tackle the problem of deciding the best moment to reconfigure by taking into account this trade-off. By coupling Deep Reinforcement Learning for decision and a Column Generation algorithm to compute the reconfiguration, we propose Deep-REC and show that choosing the best time during the day to reconfigure allows to maximize the profit of the network operator while minimizing the use of network resources and the congestion of the network. Moreover, by selecting the best moment to reconfigure, our approach allows to decrease the number of needed reconfigurations compared to an algorithm doing periodic reconfigurations during the day.

8.1.2 Learning-based Packet Routing

Participants: Ramon Aparicio, Redha Alliche, Tiago da Silva Barros.

8.1.3 Anomaly detection in traffic data

Participants: Frédéric Giroire, Hicham Lesfari.

With the continuous growing level of dynamicity, heterogeneity, and complexity of traffic data, anomaly detection remains one of the most critical tasks to ensure an efficient and flexible management of a network. Recently, driven by their empirical success in many domains, especially bioinformatics and computer vision, graph kernels have attracted increasing attention. Our work aims at investigating their discrimination power for detecting vulnerabilities and distilling traffic in the field of networking. In 63, we propose Nadege, a new graph-based learning framework which aims at preventing anomalies from disrupting the network while providing assistance for traffic monitoring. Specifically, we design a graph kernel tailored for network profiling by leveraging propagation schemes which regularly adapt to contextual patterns. Moreover, we provide provably efficient algorithms and consider both offline and online detection policies. Finally, we demonstrate the potential of kernel-based models by conducting extensive experiments on a wide variety of network environments. Under different usage scenarios, Nadege significantly outperforms all baseline approaches.

8.1.4 Modeling LoRa networks for the IoT

Participants: Christelle Caillouet.

LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. These devices are often deployed in remote areas where the end-to-end connectivity provided through one or more gateways may be limited. In collaboration with Martin Heusse and Andrzej Duda (Drakkar, LIG, Grenoble), we propose a model fitting the unslotted ALOHA protocol for channel access in a LoRaWAN cell 60, 68. It combines the effects of collisions with channel fading, by which reception may get buried in noise. Unlike the existing models of LoRaWAN, our model takes into account the frame arrival timing: it distinguishes, on the one hand, the interference created by earlier transmissions with respect to the frame of interest, and on the other hand, the interference by the frames arriving later on. From the results of the model, we draw three observations regarding the improvement of Packet Delivery Ratio (PDR). First, it puts back under the spotlight the often overlooked fact that repeating frames is always beneficial when the desired PDR is above 60%, even though the extra packet transmissions create more collisions. Second, as soon as the node density becomes notable and collisions have a similar impact on losses as attenuation, adding a smaller spreading factor SF6 modulation into the cell list of transmission parameters allows increasing the coverage range. Third, the model shows that cell capacity sometimes grows with the distance to the gateway or with decreased node transmission power, a trend seldom observed in wireless networks.

8.1.5 Aerial networks for sensor coverage and energy harvesting

Participants: Christelle Caillouet, Igor Dias da Silva.

The use of autonomous unmanned aerial vehicles (UAVs) or drones has emerged to efficiently collect data from mobile sensors when there is no infrastructure available. The drones can form a flying ad-hoc network through which the sensors can send their data to a base station at any time. In 56, we present a mixed integer linear program to find the drones’ optimal trajectories to form and maintain this network through time while minimizing their movements and energy consumption. Furthermore we analyze the trade-off between distance and energy, where increasing the drones’ mobility can reduce their energy consumption, and derive a fair trade-off optimal solution to balance the two opposite objectives.

The problem of the lifetime of connected objects, in most use cases (Industrial Internet of Things (IIoT), disaster management, etc.) is an essential element of the proposed solutions. Radio frequency (RF) harvesting of sensor batteries is an attractive solution, however, it does not scale up if it has to be done by human operators, and becomes impossible if the objects are located in unreachable places. An innovative solution consists of using fleets of drones to take care of this regular recharge. In collaboration with Yann Busnel (IMT Atlantique) 55, we focus on the self-organised deployment of a fleet of drones to solve this problem, taking into account the multiple constraints involved. We propose a two-step optimization framework based on an optimal orchestration solution to reduce the recharging time of a complete sensor system, by optimizing the number of drones, the overall flight time and their energy consumption. We illustrate the performance of our framework that ensures the drones avoid conflicts to guarantee a higher energy harvesting efficiency (establishment of optimal drone positions and planning of the global flight plan).

8.1.6 A middleware for the experimentation on IoT Mobile Networks

Participant: Luc Hogie [.

In the context of studies on decentralized algorithms for mobile dynamic networks, we investigated the state of the art of the experimentation tools. We discovered that existing solutions, either coming from labs or companies, do not match the requirements of experimentation as it is usually done by researchers. Indeed commercial products focus on reliability and interoperability at the expense of versatility, while lab tools most often serve as proof of concepts. The experimental study of algorithms requires the availability, in a single solution, of the following features: support for both synchronous and asynchronous communication, simulation/emulation of large systems, fast deployment, Web interoperability, and because less is more, full decentralization, just to name a few. Idawi was designed and implemented to the very purpose of providing the research community with a tool tailored to its needs. The resulting middleware, called Idawi is released via the MAVEN global distribution platform for Java software. MAVEN Central statistics indicate that Idawi binaries have been downloaded (for integration) 350 times in the year 2022. In addition to this, Idawi and its satellite tools are all Open Source. They are released under the Apache V2 license. For the sake of Open Science, their source code are fully available on GitHub. See 61, 62, 85

8.2.1 Complexity of graph problems

Participants: Thomas Dissaux, Foivos Fioravantes, Nicolas Nisse.

8.2.2 Combinatorial games in graphs

Participants: Julien Bensmail, Jean-Claude Bermond, Michel Cosnard, Foivos Fioravantes, Frédéric Havet, Nicolas Nisse.

8.2.3 Optimal stopping algorithms

Participants: Malgorzata Sulkowska.

The optimal stopping theory is the art of choosing a proper time to take a particular action in order to maximize gain or minimize cost. In the series of articles we undertake several optimal stopping problems and come up with some tools that help to estimate the winning probabilities.

In 41 together with Fabrício Siqueira Benevides (UFC, Ceará, Brazil) we obtain a surprising result about a certain family of optimal stopping problems defined on graphs. The vertices of a graph $G$ are revealed one by one, in a random order, to a selector. He aims to stop this process at a time $t$ that maximizes the expected number of connected components in the graph ${\tilde{G}}_t$, induced by the currently revealed vertices. The selector knows $G$ in advance, but different versions of the game are considered depending on the information that he gets about ${\tilde{G}}_t$. We show that when $G$ has $N$ vertices and maximum degree of order $o\left(\sqrt{N}\right)$, then the number of components of ${\tilde{G}}_t$ is concentrated around its mean, which implies that playing the optimal strategy the selector does not benefit much by receiving more information about ${\tilde{G}}_t$. Results of similar nature were previously obtained by M. Lasoń for the case where $G$ is a $k$-tree (for constant $k$). We also consider the particular cases where $G$ is a square, triangular or hexagonal lattice, showing that an optimal selector gains $cN$ components and we compute $c$ with an error less than $0.005$ in each case.

Next, we turn to a particular problem defined classically for uniform random variables. A decision maker observes a sequence of $n$ independent realizations from the uniform distribution on the unit interval. However, he does not observe the precise values of these realizations, but only their ranks relative to those that have appeared previously. The goal of the decision maker is to select the realization whose value is closest to $\frac{1}{2}$. A realization can only be selected at the moment of its appearance. In 43, in collaboration with Ewa Kubicka, Grzegorz Kubicki (University of Louisville, USA) and Małgorzata Kuchta (WUST, Wrocław, Poland), we derive a stopping rule which maximizes the probability of achieving this goal, together with the asymptotic probability of success.

In cooperation with Alexander Gnedin (QMUL, London, UK) and Patryk Kozieł (WUST, Wrocław, Poland) we investigate much more general, full-information, models in 42. The full-information best choice problem asks one to find a strategy maximizing the probability of stopping at the minimum (or maximum) of a sequence $X_1,\cdots ,X_n$ of i.i.d. random variables with continuous distribution. We approach more general problem, where independent $X_j$'s may have different distributions, discrete or continuous. A central role in our study is played by the running minimum process, which we first employ to re-visit the classic problem and its limit Poisson counterpart. The approach is further applied to two explicitly solvable models: in the first the distribution of the $j$th variable is uniform on $\{j,\cdots ,n\}$, and in the second it is uniform on $\{1,\cdots ,n\}$.

Finally, we come up with some tools that may be helpful by estimating winning probabilities in optimal stopping problems defined for tree-like graphs or partially ordered sets (posets). When deriving the optimal stopping rule, one needs to compare the probability of success by stopping the process at the current moment with the probability of success if one decided to play further. Then one is often forced to derive the number of embeddings of a currently generated structure (e.g. graph or poset) in the whole underlying structure. More precisely, the number of embeddings of a partially ordered set $S$ in a partially ordered set $T$ is the number of subposets of $T$ isomorphic to $S$. If both, $S$ and $T$, have only one unique maximal element, we define good embeddings as those in which the maximal elements of $S$ and $T$ overlap. In 38 (together with Bernhard Gittenberger, Isabella Larcher, TU Wien, Austria; and Zbigniew Gołębiewski, WUST, Wrocław, Poland) we investigate the number of good and all embeddings of a rooted poset $S$ in the family of all binary trees on $n$ elements considering two cases: plane (when the order of descendants matters) and non-plane. Furthermore, we study the number of embeddings of a rooted poset $S$ in the family of all planted plane trees of size $n$. We derive the asymptotic behaviour of good and all embeddings in all cases and we prove that the ratio of good embeddings to all is of the order $\Theta (1/\sqrt{n})$ in all cases, where we provide the exact constants. Furthermore, we show that this ratio is asymptotically non-decreasing with $S$. Finally, we comment on the case when $S$ is disconnected.

8.2.4 Algorithms for social networks

Participants: Frédéric Giroire, Nicolas Nisse, Stéphane Pérennes, Malgorzata Sulkowska.

8.2.5 Algorithm engineering

Participants: David Coudert, Nicolas Nisse.

Algorithm Engineering is concerned with the design, analysis, implementation, tuning, and experimental evaluation of computer programs for solving algorithmic problems. It provides methodologies and tools for developing and engineering efficient algorithmic codes and aims at integrating and reinforcing traditional theoretical approaches for the design and analysis of algorithms and data structures. This approach is particularly suited when formal analysis pessimistically suggests bounds which are unlikely to appear on inputs of practical interest.

8.2.6 Distributed algorithms

Participants: Francesco d'Amore, Frédéric Giroire, Hicham Lesfari, Emanuele Natale, Stéphane Perennes.

8.3.1 Partitioning and labelling graphs and digraphs

Participants: Julien Bensmail, Foivos Fioravantes, Frédéric Havet, Nicolas Nisse.

8.3.2 Structural digraph theory

Participants: Julien Bensmail, Frédéric Havet, Nicolas Nisse.

One of our goals is to establish structural results on digraphs that can then be used to design efficient algorithms. In particular, we are looking in finding substructures with certain properties or ways to represent or approximate efficently the digraphs.

8.3.3 Metric dimension in oriented graphs

The metric dimension $md\left(G\right)$ of an undirected graph $G$ is the cardinality of a smallest set of vertices that allows, through their distances to all vertices, to distinguish any two vertices of $G$. Many aspects of this notion have been investigated since its introduction in the 70's, including its generalization to digraphs.

In 33, we study, for particular graph families, the maximum metric dimension over all strongly-connected orientations, by exhibiting lower and upper bounds on this value. We first exhibit general bounds for graphs with bounded maximum degree. In particular, we prove that, in the case of subcubic $n$-node graphs, all strongly-connected orientations asymptotically have metric dimension at most $\frac{n}{2}$, and that there are such orientations having metric dimension $\frac{2n}{5}$. We then consider strongly-connected orientations of grids. For a torus with $n$ rows and $m$ columns, we show that the maximum value of the metric dimension of a strongly-connected Eulerian orientation is asymptotically $\frac{nm}{2}$ (the equality holding when $n,m$ are even, which is best possible). For a grid with $n$ rows and $m$ columns, we prove that all strongly-connected orientations asymptotically have metric dimension at most $\frac{2nm}{3}$, and that there are such orientations having metric dimension $\frac{nm}{2}$.

Revisiting and generalizing the Random Subset Sum problem

The average properties of the well-known Subset Sum Problem can be studied by the means of its randomised version, where we are given a target value $z$, random variables $X_1,...,X_n$, and an error parameter $ϵ>0$, and we seek a subset of the Xi's whose sum approximates $z$ up to error $ϵ$. In this setup, it has been shown that, under mild assumptions on the distribution of the random variables, a sample of size $O(log\frac{1}{ϵ})$ suffices to obtain, with high probability, approximations for all values in $[-1/2,1/2]$. Recently, this result has been rediscovered outside the algorithms community, enabling meaningful progress in other fields. In 81, we investigate we present an alternative proof for this theorem, with a more direct approach and resourcing to more elementary tools. An immediate multidimensional generalisation of the problem is to regard the random variables as $d$-dimensional random vectors and seek to approximate any point $z\in {[-1,1]}^d$. In 1998, G. S. Lueker showed that, in the one-dimensional setting, $n=𝒪(log\frac{1}{\epsilon })$ samples guarantee the approximation property with high probability. In 74, we prove that, in $d$ dimensions, $n=𝒪(d^{3}log\frac{1}{\epsilon }·(log\frac{1}{\epsilon }+logd))$ samples suffice for the approximation property to hold with high probability.

Proving the Strong Lottery Ticket Hypothesis for Convolutional Neural Networks.

The lottery ticket hypothesis states that a randomly-initialized neural network contains a small subnetwork which, when trained in isolation, can compete with the performance of the original network. Recent theoretical works proved an even stronger version: every sufficiently overparameterized (dense) neural network contains a subnetwork that, even without training, achieves accuracy comparable to that of the trained large network. These works left as an open problem to extend the result to convolutional neural networks (CNNs). In 54 we provide such generalization by showing that, with high probability, it is possible to approximate any CNN by pruning a random CNN whose size is larger by a logarithmic factor.

Patent deposit FR2210217: “Accurate programmable resistances from few inaccurate resistors”.

The high energy demands of modern Artificial Intelligence not only imply huge costs to run it at scale, but also constrains its deployment on edge devices. While analog computing offers a way to run those algorithms with orders of magnitude more efficiency, most proposals for its actual implementation require very high precision components and would depend on non-standard manufacturing. We propose a new method that uses a few inaccurate components to build accurate and programmable resistors, allowing analog neuromorphic devices to be manufactured with standard processes. It leverages the possibility of approximating any target value by summing a subset of given random values.

Privacy in federated learning

In federated learning, clients such as mobile devices or data silos (e.g. hospitals and banks) collaboratively improve a shared model, while maintaining their data locally. Multiple recent works show that client's private information can still be disclosed to an adversary who just eavesdrops the messages exchanged between the targeted client and the server. In 66, we propose a novel model-based attribute inference attack in federated learning which overcomes the limits of gradient-based ones. Furthermore, we provide an analytical lower-bound for the success of this attack. Empirical results using real world datasets confirm that our attribute inference attack works well for both regression and classification tasks. Moreover, we benchmark our novel attribute inference attack against the state-ofthe-art attacks in federated learning. Our attack results in higher reconstruction accuracy especially when the clients' datasets are heterogeneous (as it is common in federated learning). Most importantly, our model-based fashion of designing powerful and explainable attacks enables an effective quantification of the privacy risk in FL.

8.5.1 Graph algorithms for (low and high) resolution models of large protein assemblies

Participants: Frédéric Havet.

A macromolecular assembly is composed of subunits (e.g. proteins or nucleic acids). We assume that the composition, in terms of individual subunits, of selected complexes of the assembly is known. Indeed, a given assembly can be chemically split into complexes by manipulating chemical conditions, and the composition of these complexes can then be inferred using native mass spectrometry.

We then get a data set that can be represented by a hypergaph: a node represents a subunit, and the hyperedges represent the different complexes. The problem is then to find a graph $G$ with the same vertex set as $H$ whose edges represent the contacts between subunits, and satisfying (i) some local properties for every complex (i.e. hyperedge), and (ii) some other global properties. In 40, the considered local constraints is that $G$ |it $F$-overlays $H$, that is the subgraph induced by $G$ on every hyper-edge of $H$ must have $F$ as a spanning subgraph (for some fixed given $F$). The global contrainst is that $\Delta \left(G\right)$, the maximum degree of $G$, is at most $k$. We consider the computational complexity of a decision problem and a maximisation problem. The decision problem, called $\Delta \le k)$-$F$-Overlay problem, consists in deciding whether there is a graph with maximum degree at most $k$ that $F$-overlays a given hypergraph. It is a particular case of the maximisation problem Max $\Delta \le k)$-$F$-Overlay, which takes a hypergraph $H$ and an integer $s$ as input, and consists in deciding whether there is a graph with maximum degree at most $k$ that $F$-overlays at least $s$ hyperedges of $H$. We give a complete polynomial/NP-complete dichotomy for the Max $\Delta \le k)$-$F$-Overlay problems depending on the pairs $(F,k)$, and establish the complexity of $\Delta \le k)$-$F$-Overlay for many pairs $(F,k)$.

Once the contact graph is obtained by solving the abiove problem, it serves as input graph for a second problem, called the domino problem, that consists in determining the high resolution structure of a given assembly. A configuration of a node is so a conformation of the corresponding protein (that is a position of each of its atoms in ${ℝ}^3$) and is obtained by X-ray cristallography. The domino problem can be modelled as a conflict-colouring problem. We are given a contact graph $G=(V,E)$, a list $C\left(v\right)$ of possible colours (configurations) for every node $v\in V$ and a bipartite graph between colour sets $C\left(u\right)$ and $C\left(v\right)$ for every edge $uv\in E$ which repersent the incompatible conformations of the two subunits. Then Conflict colouring consists in deciding whether there exists a conflict colouring, that is a colouring in which $c\left(u\right)c\left(v\right)$ is not an edge of the bipartite graph. In 84, we first establish the complexity dichotomies (polynomial vs NP-complete) for Conflict colouring and its variants. We provide some experiments in which we build instances of Conflict colouring associated to Voronoi diagram in the plane, and we then analyse the existences of a solution related to parameters used in our experimental setup. The domino problem consists in computing a configuration for each node maximizing some objective functions. Again, see the bioinformatics context below.

8.5.2 Analysis of temporal brain networks

Participants: Emanuele Natale, Aurora Rossi.

8.5.3 Planning with Biological Neurons and Synapses

Participants: Francesco d'Amore, Emanuele Natale.

In collaboration with Daniel Mitropolsky and Christos Papadimitriou (Columbia University, New York) and Pierluigi Crescenzi (GSSI, L'Aquila), we revisit in 88, 64 the planning problem in the blocks world, and we implement a known heuristic for this task. The program is written in the Assembly Calculus, a recently proposed computational framework meant to model computation in the brain by bridging the gap between neural activity and cognitive function. Its elementary objects are assemblies of neurons (stable sets of neurons whose simultaneous firing signifies that the subject is thinking of an object, concept, word, etc.), its commands include project and merge, and its execution model is based on widely accepted tenets of neuroscience. A program in this framework essentially sets up a dynamical system of neurons and synapses that eventually, with high probability, accomplishes the task. The purpose of this work is to establish empirically that reasonably large programs in the Assembly Calculus can execute correctly and reliably; and that rather realistic — if idealized — higher cognitive functions, such as planning in the blocks world, can be implemented successfully by such programs.

Amadeus, 2022-2024

Participants: Ilias Driouich, Frédéric Giroire, Chuan Xu.

• Collaboration with Amadeus funded by ANR in the context of "plan de relance". It supports the PhD thesis of Ilias Driouich under the co-supervision of Frédéric Giroire and Chuan Xu.

10.1.1 Inria associate team not involved in an IIL or an international program

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

DGA/Inria Brainside, 2019-2023

Participants: Francesco D'Amore, Arthur Carvalho Walraven Da Cunha, Emanuele Natale.

• Program:
  DGA/Inria
• Project acronym:
  Brainside
• Project title:
  Algorithms for simplifying neural networks
• Duration:
  October 2019 - March 2023
• Coordinator:
  Emanuele Natale
• Other partners:
  • Inria Paris, EP GANG
• Summary:
  The widespread use of neural networks on devices with computationally-low capabilities, demands for lightweight and energy-efficient networks. Despite such need, and despite the strategies employed to prevent overfitting by removing a substantial part of their edges, the question of how to reduce their size in terms of the number of neurons appears largely unexplored. The aim of the project is to investigate algorithmic procedures to reduce the size of neural networks, in order to improve the speed with which they can be evaluated and to shed light on how much information about the computational problem at hand can be encoded within neural networks of small size.

ANR-17-CE22-0016 MultiMod, 2018-2023

Participants: Ali Al Zoobi, David Coudert, Nicolas Nisse, Michel Syska.

• Program:
  ANR
• Project acronym:
  MultiMod
• Project title:
  Scalable routing in Multi Modal transportation networks
• Duration:
  January 2018 - June 2023
• Coordinator:
  David Coudert
• Other partners:
  • Inria Paris, EP GANG
  • team CeP, I3S laboratory
  • SME Instant-System
  • SME Benomad
• Summary:
  The MultiMod project addresses key algorithmic challenges to enable the fast computation of personalized itineraries in large-scale multi-modal public transportation (PT) networks (bus, tram, metro, bicycle, etc.) combined with dynamic car-pooling. We will use real-time data to propose itineraries with close to real travel-time, and handle user-constraints to propose personalized itineraries. Our main challenge is to overcome the scalability of existing solutions in terms of query processing time and data-structures space requirements, while including unplanned transportation means (car-pooling), real-time data, and personalized user constraints. The combination of car-pooling and PT network will open-up areas with low PT coverage enable faster itineraries and so foster the adoption of car-pooling. We envision that the outcome of this project will dramatically enhanced the mobility and daily life of citizens in urban areas.
• Web:
  project.inria.fr/multimod/

ANR-19-CE48-0013 Digraphs, 2020-2023

Participants: Julien Bensmail, David Coudert, Frédéric Havet, Nicolas Nisse, Stéphane Pérennes.

• Program:
  ANR
• Project acronym:
  Digraphs
• Project title:
  Digraphs
• Duration:
  January 2020 - December 2023
• Coordinator:
  Frédéric Havet
• Other partners:
  • LIRMM, Montpellier
  • LIP, Lyon
• Summary:
  The objectives of the project is to make some advances on digraph theory in order to get a better understanding of important aspects of digraphs and to have more insight on the differences and the similarities between graphs and digraphs. Our methodology is two-fold. On the one hand, we will focus on the tools. Indeed we believe that many proof techniques have been too rarely used or adapted to digraphs and can be developed to obtain many more results.On the second hand, we will consider many results on graphs, find their (possibly many) formulations in terms of digraphs and see if and how they can be extended. Studying such extensions has been occasionally done, but the point here is to do it in a kind of systematic way. Moreover we shall push even further the study by considering classes of digraphs: if a result does not extend to the whole class of digraphs, for which classes does it extend ? if a result extends, can we get better results for some restricted classes of digraphs ?
• Web:
  project.inria.fr/anrdigraphs/

Défi Inria-Cerema ROAD-AI, 2021-2024

Participants: Christelle Caillouet, David Coudert.

• Project acronym:
  ROAD-AI
• Project title:
  Routes et Ouvrages d'Art Diversiformes, Augmentés & intégrés
• Duration:
  July 2021 - June 2024
• Coordinators:
  Nathalie Mitton (head, Inria, EP FUN), Christophe Biernacki (vice-head, Inria, EP MODAL), Pierre Marchand (Cerema, DTEC ITM), André Orcési (Cerema, DTEC ITM)
• Inria participants:
  Inria project-teams ACENTAURI, COATI, FUN, MODAL, STATIFY, MODAL
• Other partners:
  Cerema
• Summary:
  Integrated management of infrastructure assets is an approach which aims at reconciling long-term issues with short-term constraints and operational logic. The main objective is to enjoy more sustainable, safer and more resilient transport infrastructure through effective, efficient and responsible management. To achieve this, CEREMA and Inria are joining forces in this Inria Challenge (DEFI) which main goals are to overcome scientific and technical barriers that lead to the asset management of tomorrow for the benefit of road operators: (i) build a “digital twin” of the road and its environment at the scale of a complete network; (ii) define “laws” of pavement behavior; (iii) instrument system-wide bridges and tunnels and use the data in real time; (iv) define methods for strategic planning of investments and maintenance.

Défi Inria Fed-Malin, 2022-2026

Participants: Ilias Driouich, Frédéric Giroire, Chuan Xu.

• Project acronym:
  Fed-Malin
• Project title:
  Federated machine Learning over the internet
• Duration:
  2022 - 2026
• Coordinators:
  Aurélien Bellet (Inria, EP MAGNET), Giovanni Neglia (Inria, EP NEO)
• Inria participants:
  Inria project-teams ARGO, COATI, COMETE, EPIONE, MAGNET, MARACAS, NEO, SPIRALS, TRIBE, WIDE
• Summary:
  In many use-cases of Machine Learning (ML), data is naturally decentralized: medical data is collected and stored by different hospitals, crowdsensed data is generated by personal devices, etc. Federated Learning (FL) has recently emerged as a novel paradigm where a set of entities with local datasets collaboratively train ML models while keeping their data decentralized. Fed-Malin is a research project that spans 10 Inria research teams and aims to push FL research and concrete use-cases through a multidisciplinary consortium involving expertise in ML, distributed systems, privacy and security, networks, and medicine. We propose to address a number of challenges that arise when FL is deployed over the Internet, including privacy & fairness, energy consumption, personalization, and location/time dependencies. Fed-Malin will also contribute to the development of open-source tools for FL experimentation and real-world deployments, and use them for concrete applications in medicine and crowdsensing.

10.3.1 GDR Actions

11.1.1 Scientific events: Steering Committees

• David Coudert :
  • member of the steering committee of the Symposium on Experimental Algorithms, since September 2022.
• Emanuele Natale :
  • member of the steering committee of the Symposium on Experimental Algorithms, since September 2022.

11.1.2 Scientific events: organisation

11.1.3 Scientific events: selection

11.1.4 Journal

11.1.5 Invited talks

• David Coudert :
  • On the $k$-shortest simple paths problem: time and space trade-offs. Keynote speaker at the working group session of axis CAGDO and GT GT2L, ROADEF'22, February 25, 2022.
  • A brief introduction to Sagemath, tutorial at the Python working group seminar of the LINCS, March 23, 2022.
• Francesco d'Amore
  • Search via Parallel Lévy Walks on ${\mathbb{Z}}^2$, invited talk at the Wide team, Inria Rennes, Rennes, France, May 2022;
  • Dynamics for Multi-Agent System Coordination in Noisy and Stochastic Environments, invited talk at University of Hamburg, online seminar, May 2022;
  • Phase transition of the 3-Majority dynamics with Uniform Communication Noise, invited talk at the Third Italian Meeting on Probability and Mathematical Statistics 2022, Bologna, Italy, June 2022;
  • On the Multidimensional Random Subset Sum Problem, invited talk at Aalto University, Department of Computer Science, Espoo, Finland, September 2022.
• Frédéric Havet
  • Substructures in digraphs, School on graph theory, Murol, France, June 7-10, 2022.
• Nicolas Nisse
  • Tree and Path decompositions with small diameter bags in subclasses of planar graphs . Seminar of the ParGO team, Universidade Federal do Ceara, Fortaleza, Brazil. November 18th, 2022.
  • Minimum lethal sets in grids and tori under 3-neighbour bootstrap percolation, Workshop on Mobile Agents and Computing, Pisa, Italy, September, 21th, 2022.
• Małgorzata Sulkowska
  • Modularity of minor-free graphs, TCS seminar at Jagiellonian University, November 30, 2022

11.1.6 Leadership within the scientific community

• Christelle Caillouet :
  • Co-chair of Pôle RESCOM of GDR RSD of CNRS since July 2022.
• David Coudert :
  • Co-chair of Pôle RESCOM of GDR RSD of CNRS from 2017 till June 2022.
• Frédéric Giroire :
  • Member of the steering committee of GT Energy of the GDR RSD of CNRS.
• Frédéric Havet :
  • Member of the steering committee of GT Graphes of the GDR IM of CNRS since 2005;
  • President of the PhD prize committee ; Graphes "Charles Delorme".

11.1.7 Scientific expertise

• Ramon Aparicio:
  • Expert for ANR.
• Julien Bensmail
  • External reviewer for the SONATA-17 program by the National Science Center (NSC) of Poland.
• Jean-Claude Bermond :
  • Expert for DRTT-MESR Crédit impôt recherche (CIR et agréments).
• David Coudert :
  • Expert for ANR;
• Frédéric Havet :
  • Expert for FNRS (Belgium).
• Emanuele Natale :
  • Expert reviewer for ANR.
  • Member of the Expert Panel “ST6 – Computer science and informatics" of the National Science Centre, Poland (NCN).
• Nicolas Nisse :
  • Expert for European Science Foundation (ESF)
• Michel Syska :
  • Expert for DRTT-MESR Crédit impôt recherche (CIR et agréments).

11.1.8 Research administration

• Ramon Aparicio:
  • Principal investigator of ARTIC project (ARTificial Intelligence-based Cloud network control), 220 k€ over 42 months funded by ANR JCJC 2019, March 2020 - Sept. 2024;
  • Representative of the I3S laboratory at the steering committee of Institut Fédératif Quantique Azuréen (IF QuantAzur) at Université Côte d'Azur, since February 2022;
  • Elected member of Conseil de Laboratoire du Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis (I3S) since March 2022.
• Christelle Caillouet
  • Elected member of Conseil de Laboratoire I3S since 2017;
  • Member of selection committee PRAG 134 and 135, Université Côte d'Azur, 2022.
• Alexandre Caminada
  • Member of the executive board of the Sophia Interdisciplinary Institute of Artificial Intelligence started in 2019;
  • Manager of the research committee for the Polytech network national academic Foundation.
• David Coudert :
  • Head of Science of the Inria Centre at Université Côte d'Azur, since September 2022;
  • Nominated member for Inria at the board of doctoral school STIC, September 2017 - August 2022;
  • Head of the “Comité de Suivi Doctoral” of Inria, December 2019 - August 2022;
  • Nominated member for Inria at the steering committee of Academy 1 RISE (Networks, Information, Digital Society) of UCA${}^{\text{JEDI}}$, February 2018 - August 2022;
  • Nominated member for Inria at the steering committee of EUR DS4H, February 2018 - August 2022;
  • Nominated member for Inria at the steering committee of Labex UCN@Sophia, February 2018 - August 2022;
  • Member of the “Bureau du comité des équipe-projets” of Inria research center Sophia Antipolis - Méditerranée since 2018;
  • Member of the steering committee of seminar Forum Numerica of Academy 1 RISE of UCA${}^{\text{JEDI}}$ since 2018;
  • Representative of Inria at the steering committee of Institut Fédératif Quantique Azuréen (QuantAzur) at Université Côte d'Azur, since February 2022.
• Frédéric Giroire :
  • Head of COMRED team of I3S laboratory, since April 2022;
  • In charge of the internships of stream UbiNet of Master 2 IFI, Université Côte d'Azur.
• Frédéric Havet
  • Head of COMRED team of I3S laboratory, till March 2022;
  • Coordinator of ANR project Digraphs.
  • President of selection committee PR 854, Université Côte d'Azur, 2022;
  • Co-head of Terra Numerica.
• Joanna Moulierac:
  • Member of selection committee MCF27 0105, Université Côte d'Azur, 2022;
  • Member of the CA of SPECIF-CAMPUS (Société Professionnelle des Enseignants et Chercheurs en Informatique de France);
  • Member of the I3S CO2 group since 2019 (www.i3s.unice.fr/co2/).
• Emanuele Natale:
  • External member of University of Rome Tor Vergata's PhD School in Data Science (Italy).
• Nicolas Nisse :
  • Elected member for the "Comité de centre", Inria Sophia Antipolis - Méditerranée, since 2017;
  • Elected member for Inria at the CoSP of EUR DS4H since October 2020;
  • Nominated member for Inria at the board of doctoral school STIC, since September 2022;
  • Member of the “Comité de Suivi Doctoral” of Inria, since September 2022;
  • Member of the CoSP Terra Numerica, since 2020.

11.2.1 Teaching Responsibilities

• Julien Bensmail :
  • Head of the Licence Professionnelle “Managements des Processus Logistiques” (MPL) of Université Côte d'Azur, since September 2019.
• Christelle Caillouet :
  • Member of the “Conseil de Département Informatique” of IUT Nice Côte d'Azur (since September 2022).
• Alexandre Caminada
  • Head of the graduate school of engineering Polytech Nice Sophia (1500 master grade students, 100 faculty members, 50 staffs);
  • Member of the executive board of the Polytech network, national network of public graduate school of engineering;
  • Member of the executive board of Université Côte d'Azur.
• Joanna Moulierac :
  • “Directrice d'études” for the 1st-year students of “Département Informatique” of IUT Nice Côte d'Azur (Sep. 2017 - March 2022);
  • Member of the “Conseil de Département Informatique” of IUT Nice Côte d'Azur (since September 2017).
• Michel Syska :
  • Head of the MIAGE (IT methods applied to business management) Master’s degree MBDS (Mobiquity, Big Data and Systems integration), of Université Côte d'Azur (since September 2022);
  • Co Head of the Bachelor’s degree in Artificial Intelligence (Licence Sciences et Technologies parcours IA), of Université Côte d'Azur;
  • Head of "Campus des Métiers et des Qualifications - défi du numérique", Université Côte d'Azur, Rectorat et Région PACA.

11.2.2 Teaching

Members of Coati have taught for more that 1400 hours (ETD) this year:

• DUT: Julien Bensmail, Recherche opérationnelle, 90h ETD, Level L2, Département QLIO of IUT, Université Côte d'Azur, France;
• DUT: Julien Bensmail, Algorithmique et programmation avancées, 64h ETD, Level L2, Département QLIO of IUT, Université Côte d'Azur, France;
• DUT: Francesco d'Amore, Exploitation d'une base de données avancée, 56h ETD, Level L1, Département Informatique of IUT Nice Côte d'Azur, UCA, France.
• DUT: Thomas Dissaux, Communication et fonctionnement bas niveau, 20h ETD, Level L1, Département Informatique of IUT, Université Côte d’Azur, France;
• DUT: Thomas Dissaux, Introduction aux services réseaux, 14h ETD, Level L1, Département Informatique of IUT, Université Côte d’Azur, France;
• DUT: Thomas Dissaux, Initiation au développement, 64h ETD, Level L1, Département Informatique of IUT, Université Côte d’Azur, France;
• DUT: Foivos Fioravantes, Introduction aux bases de données, 44h ETD, Level L1, Département Informatique of IUT, Université Côte d'Azur, France;
• DUT: Foivos Fioravantes, Théorie des graphes, 20h ETD, Level L1, Département Informatique of IUT, Université Côte d'Azur, France;
• DUT: Luc Hogie, Distributed programming, 24h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Luc Hogie, System programming, 24h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Hicham Lesfari, Réseaux d'opérateurs et réseaux d'accès, 48h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Michel Syska, Data Structures and Algorithms, 44h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Michel Syska, Introduction to Artificial Intelligence, 48h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Michel Syska, Algorithmics, 30h ETD, Level L2, IUT, Université Côte d'Azur, France;
• DUT: Michel Syska, Distributed programming, 84h ETD, Level L2, IUT, Université Côte d'Azur, France;
• BUT: Joanna Moulierac, Introduction aux Réseaux, 56h ETD, Level L1, IUT, Université Côte d'Azur, France;
• BUT: Joanna Moulierac, Réseaux avancés, 30h ETD, Level L2, IUT, Université Côte d'Azur, France;
• PeiP: Joanna Moulierac, Programmation Objet, 30h ETD, Level L1, Polytech, Université Côte d'Azur, France;
• LP: Julien Bensmail, Sécurité des échanges de données inter-entreprises, 30h ETD, Level LP, LP MPL of IUT, Université Côte d'Azur, France;
• LP: Joanna Moulierac, Programmation C#, 30h ETD, Level L3, IUT, Université Côte d'Azur, France;
• Licence: Redha Abderrahmane Alliche, Systèmes et Réseaux, 24h ETD, Level L3, Polytech Nice-Sophia, France;
• Licence: Ali Al Zoobi, Programmation et structures en C, 24h ETD, Level L2, Faculté des sciences, Université Côte D'Azur, France;
• Licence: Michel Syska, Networks, 31h ETD, Level L3, MIAGE - Université Côte d’Azur, France;
• Licence: Michel Syska, Algorithmics, 33h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• Licence: Michel Syska, Heuristic search, 25h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• Licence: Luc Hogie, Programmation et Conception Orientée Objet, 24h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• License: Chuan Xu, Programmation fonctionnelle, 36h ETD, Level L3, Université Côte d’Azur, France;
• License: Chuan Xu, Python pour l'IA, 30h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• License: Emanuele Natale, Elements of Computational Modeling in Julia, 3 Italian CFU (20h), bachelor's degree in Computer Science, University of Rome Tor Vergata, Italy (natema.github.io/ECMJ-it-2022/);
• Master: Christelle Caillouet, Introduction Algorithmic and Programming, 30h ETD, MAM3, Polytech Nice Sophia Antipolis, France;
• Master: Alexandre Caminada, Radio location systems, 20h ETD, Master 2 (in english), Polytech Nice Sophia, France;
• Master: Alexandre Caminada, Artificial intelligence, 40h ETD, Master 2 (in english), Polytech Nice Sophia, France;
• Master: Alexandre Caminada, Master grade student's internship supervision and assesment, 10h ETD, Master 2, Polytech Nice Sophia, France;
• Master: David Coudert, Algorithms for Telecoms, 36h ETD, M2 Ubinet, Université Nice Sophia Antipolis, France;
• Master: Frédéric Giroire, Graph Algorithms, 18h ETD, Master 2, International Track Ubinet, Université Côte d'Azur, France;
• Master: Frédéric Giroire, Machine learning for networks, 24h ETD, Master 2, International Track Ubinet, Université Côte d'Azur, France;
• Master: Frédéric Giroire, ICT and Environment, Green algorithm design, 4.5h ETD, Master 2, minor, Université Côte d'Azur, France;
• Master: Hicham Lesfari, Data mining for networks, 12h ETD, Master 2, International Track Ubinet, Université Côte d'Azur, France;
• Master: Nicolas Nisse, Graphs, 36h ETD, M1 Informatique et Interaction, Université Côte d'Azur, France;
• Master: Nicolas Nisse, Algorithms for Telecoms, 15h ETD, M2 Ubinet, Université Côte d'Azur, France;
• Master: Nicolas Nisse, Advanced Graphs, 36h ETD, M2 Informatique et Interaction, Université Côte d'Azur, France;
• PhD: Emanuele Natale, Elements of Computational Modeling in Julia, 8h, PhD in Computer Science, Gran Sasso Science Institute, Italy (natema.github.io/ECMJ-GSSI-2022/).

11.2.3 Supervision

11.2.4 Juries

• Ramon Aparicio:
  • Referee of PhD thesis of Fatemehsadat Tabatabaeimehr, Université Polytechnique de Catalogne, Barcelone, Espagne, June 2, 2022;
  • Referee of PhD thesis of Sima Barzegar, Université Polytechnique de Catalogne, Barcelone, Espagne, June 2, 2022.
• Julien Bensmail
  • Reviewer of the Ph.D. thesis of Fabien Jacques on Homomorphismes de graphes signés et complexité de quelques problèmes d’homomorphismes, Université de Montpellier, France, July 11, 2022.
• Christelle Caillouet :
  • Member of the PhD committee of Mohamed Hamnache on Performances et Gestion de l'itinérance dans les réseaux LoRaWAN, Toulouse INP, France, Nov 25, 2022;
  • Member of the PhD committee of Jean-Philippe Abegg on Sécurité et efficacité des technologies Blockchain appliquées à l'internet des objets, Université de Strasbourg, France, Oct 21, 2022;
  • Member of the PhD committee of Amal Boubaker on Performances des protocoles de transport dans les constellations de satellites, Toulouse INP, France, May 4, 2022.
• David Coudert :
  • Member (and president) of the PhD committee of Arthur Finkelstein on Recherche de plus court chemin multimodal de point à point dépendant du temps, Université Côte d'Azur, France, September 22, 2022.
• Frédéric Havet :
  • Member (and president) of PhD committe of Julien Devreton, Université Côte d'Azur, France, Avril 6, 2022.
• Nicolas Nisse :
  • Referee and member of PhD committe of Tobias Castanet, Université de Bordeaux, September 12, 2022.

11.3.1 Internal or external Inria responsibilities

Frédéric Havet is co-head of Terra Numerica and one of the responsible of the “Comité Scientifique, Pédagogique et Technique” ; Nicolas Nisse is a member of this comité ; Joanna Moulierac is the referent of Terra Numerica for higher education ; Luc Hogie is in charge of hardware and software development.

Frédéric Havet is member of the editorial board of 1024, le bulletin de la SIF (Société Informatique de France, in which he draws cartoons to illustrate some articles.

11.3.2 Articles and contents

Coati members have participated in the development of numerous mediation devices for Terra Numerica. We only list below the most important ones.

• Galejade: Frédéric Havet, Joanna Moulierac, and Nicolas Misse importantly participate in the Galejade project whose aim is to provide funny and interactive activities to approach mathematics and computer science by getting out of pencil paper and computers. A first handbook has been made available on Hal  93. Since its release, it has been downloaded several thousands of times.
• Two-Player games in graphs: Nicolas Nisse co-supervised with Dorian Mazauric (ABS) Yannis Belkhiter (intern, first year engineering school, Mines d'Ales), who designed a popularization software about the surveillance game, related to prefetching problems.
• Green Networks: Frédéric Giroire and Joanna Moulierac created of a scientific popularization activity on the energy efficiency in networks on a 2m50 by 3m20 model representing a city. This activity takes place in the main hall of Terra Numerica, Valbonne and is largely appreciated by the public.
• Walk of ants: Frédéric Havet and Luc Hogie created an activity to explain how a simple decentralized algorithm allows ants to find shortest paths. They supervised the internships of Thimotée Juillet (2nd year IUT) to develop a numerical application and the internships of Matthis Kuhl, Quentin Scordo and Lucas Lyon (DS4H) to program robots behaving like ants.
• Hanoi towers: Frédéric Havet co-supervised with Dorian Mazauric (ABS) Antoine Agré (intern, second year engineering school) and Mael Rivière, who designed a popularization software about Hanoi towers.

Frédéric Havet and Luc Hogie co-supervised with Dorian Mazauric (ABS team) five TER of EUR DS4H to develop various popularization softwares (Lucas Lyon, Quentin Scordo, David Prigodin, Milena Kostov, and Chahan Movsessian).

Nicolas Nisse participated to the WECAM (WeekEnd Création d'Activité de Médiation) Türing, Lyon, February 5-6th, 2022.

11.3.3 Education

Members of Coati participated to the receptions of 3ème trainees:

• 12 trainees, January 31–February 4, 2022 (T. Dissaux, F. Havet, J. Moulierac, N. Nisse, L. Picasarri-Arrieta).
• 15 trainees, December 12–16, 2022 (F. Havet, N. Nisse)

Members of Coati participated to the training of teachers:

• Training for 25 secondary school teachers in Toulon, March 2022. Algorithms, by F. Havet and N. Nisse.
• Training for 40 primary school teachers in Rians, May 2022. Galejade method, by F. Havet and N. Nisse.
• Training for 25 secondary and high school teachers in Valbonne, March 2022. Tilings, by F. Havet.
• Training for 25 primary school teachers in Valbonne, October 2022. Galejade method, by F. Havet.

Many members of Coati (J.-C. Bermond, M. Cosnard, T. Dissaux, F. Havet, H. Lesfari, J. Moulierac, L. Picasarri-Arrieta) ran numerous popularization activities in front of schoolgirls and schoolboys, either at TerraNumerica@Sophia, at MIA (Maison de l'Intelligence Artificielle), or in their schools. Some were part of some national programmes such as “Chiche !”, “Cordées de la réussite”, or “Maths en Jeans”.

11.3.4 Interventions

Many members of Coati (T. Dissaux, F. Giroire, F. Havet, H. Lesfari, J. Moulierac, L. Picasarri-Arrieta, S. Pérennes, C. Rambaud, M. Syska) participated some general audience science fairs like the inauguration of TerraNumerica@Sophia on June 11th 2022, and Fête de la science in October 2022 (we were present on the “Village des Sciences” in Antibes-Juan-les-Pins, Valbonne, Villeneuve-Loubet, Vinon-sur Verdon).

Frédéric Havet also gave general audience conferences in several cities (Biot, Bonson, Brignoles, Falicon, Rians, Vinon-sur-Verdon) for Esope 21, Science pour Tous 06, and Terra Numerica.

International journals

• 16 articleP.Pierre Aboulker, F.Frédéric Havet, K.Kolja Knauer and C.Clément Rambaud. On the dichromatic number of surfaces.The Electronic Journal of Combinatorics291January 2022
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• 17 articleJ.Jørgen Bang-Jensen, F.Frédéric Havet, M.Matthias Kriesell and A.A. Yeo. Low chromatic spanning sub(di)graphs with prescribed degree or connectivity properties.Journal of Graph Theory994April 2022, 615-636
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• 18 articleJ.Jørgen Bang-Jensen, F.Frédéric Havet and A.Anders Yeo. Spanning eulerian subdigraphs in semicomplete digraphs.Journal of Graph Theory1002August 2022, 294-314
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• 19 articleJ.Julien Bensmail. A σ3 condition for arbitrarily partitionable graphs.Discussiones Mathematicae Graph Theory2022, 1-22
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• 20 articleJ.Julien Bensmail, T.Tapas Das, D.Dimitri Lajou, S.Soumen Nandi and S.Sagnik Sen. On the pushable chromatic number of various types of grids.Discrete Applied Mathematics2023
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• 21 articleJ.Julien Bensmail, S.Sandip Das, S.Soumen Nandi, T.Théo Pierron, S.Sagnik Sen and E.Eric Sopena. On the signed chromatic number of some classes of graphs.Discrete Mathematics3452022, 112664
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• 22 articleJ.Julien Bensmail, F.François Dross, N.Nacim Oijid and É.Éric Sopena. Generalising the achromatic number to Zaslavsky's colourings of signed graphs.Theoretical Computer Science9232022, 196-221
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• 23 articleJ.Julien Bensmail, F.Foivos Fioravantes, F.Fionn Mc Inerney and N.Nicolas Nisse. The Largest Connected Subgraph Game.Algorithmica8492022, 1030-1063
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• 24 articleJ.Julien Bensmail, F.Foivos Fioravantes and N.Nicolas Nisse. On Proper Labellings of Graphs with Minimum Label Sum.Algorithmica844January 2022, 1030-1063
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• 25 articleJ.Julien Bensmail and F.Foivos Fioravantes. On proper 2-labellings distinguishing by sums, multisets or products.Discussiones Mathematicae Graph Theory2022, 1-16
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• 26 articleJ.Julien Bensmail, H.Hervé Hocquard and D.Dimitri Lajou. On the algorithmic complexity of determining the AVD and NSD chromatic indices of graphs.Theoretical Computer Science9392023, 105-118
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• 27 articleJ.Julien Bensmail, H.Hervé Hocquard, D.Dimitri Lajou and É.Éric Sopena. A proof of the Multiplicative 1-2-3 Conjecture.Combinatorica2022
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• 28 articleJ.Julien Bensmail, H.Hervé Hocquard, D.Dimitri Lajou and E.Eric Sopena. Further Evidence Towards the Multiplicative 1-2-3 Conjecture.Discrete Applied Mathematics3072022, 135-144
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• 29 articleJ.Julien Bensmail, H.Hervé Hocquard, D.Dimitri Lajou and E.Eric Sopena. On a List Variant of the Multiplicative 1-2-3 Conjecture.Graphs and Combinatorics3832022, 88
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• 30 articleJ.Julien Bensmail, H.Hervé Hocquard and P.-M.Pierre-Marie Marcille. Going Wide with the 1-2-3 Conjecture.Discrete Applied Mathematics3202022, 15-32
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• 31 articleJ.Julien Bensmail and B.Binlong Li. More Aspects of Arbitrarily Partitionable Graphs.Discussiones Mathematicae Graph Theory4242022, 1237-1261
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• 32 articleJ.Julien Bensmail, F.Fionn Mc Inerney and K.Kasper Lyngsie. On {a,b}-edge-weightings of bipartite graphs with odd a,b.Discussiones Mathematicae Graph Theory4212022, 159-185
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• 33 articleJ.Julien Bensmail, F.Fionn Mc Inerney and N.Nicolas Nisse. Metric Dimension: from Graphs to Oriented Graphs.Discrete Applied Mathematics3232022, 28-42
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• 34 articleJ.Julien Bensmail and F.Fionn Mc Inerney. On a Vertex-Capturing Game.Theoretical Computer Science9232022, 27-46
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• 35 articleJ.Julien Bensmail. On the hardness of determining the irregularity strength of graphs.Theoretical Computer Science9372022, 96-107
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• 36 articleD.David Coudert, A.André Nusser and L.Laurent Viennot. Enumeration of far-apart pairs by decreasing distance for faster hyperbolicity computation.ACM Journal of Experimental Algorithmics271.15December 2022, 29
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• 37 articleF.Frédéric Giroire, S.Stéphane Pérennes and T.Thibaud Trolliet. A Random Growth Model with any Real or Theoretical Degree Distribution.Theoretical Computer Science940Part AJanuary 2023, 36-51
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• 38 articleB.Bernhard Gittenberger, Z.Zbigniew Gołębiewski, I.Isabella Larcher and M.Małgorzata Sulkowska. Counting embeddings of rooted trees into families of rooted trees.The Electronic Journal of Combinatorics293September 2022, 1-34
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• 39 articleF.Frédéric Havet, A.Allen Ibiapina and L.Leonardo Rocha. On the Nash number and the diminishing Grundy number of a graph.Discrete Applied Mathematics314June 2022, 1-16
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• 40 articleF.Frédéric Havet, D.Dorian Mazauric, V.-H.Viet-Ha Nguyen and R.Rémi Watrigant. Overlaying a hypergraph with a graph with bounded maximum degree.Discrete Applied Mathematics3192022, 394-406
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• 41 articleM.Małgorzata Sulkowska and F. S.Fabrício Siqueira Benevides. Maximizing the expected number of components in an online search of a graph.Discrete Mathematics3451January 2022, 112668
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• 42 articleM.Małgorzata Sulkowska, A.Alexander Gnedin and P.Patryk Kozieł. Running minimum in the best-choice problem.ExtremesNovember 2022
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• 43 articleM.Małgorzata Sulkowska, E.Ewa Kubicka, G.Grzegorz Kubicki and M.Małgorzata Kuchta. An optimal algorithm for stopping on the element closest to the center of an interval.Advances in Applied Mathematics133February 2022, 102281
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• 44 articleM.Małgorzata Sulkowska and M.Michał Lasoń. Modularity of minor-free graphs.Journal of Graph TheoryOctober 2022
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• 45 articleF.Francesco d'Amore, A.Andrea Clementi and E.Emanuele Natale. Phase Transition of a Non-Linear Opinion Dynamics with Noisy Interactions.Swarm Intelligence16November 2022, 261–304
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International peer-reviewed conferences

• 46 inproceedingsA.Ali Al-Zoobi, D.David Coudert, A.Arthur Finkelstein and J.-C.Jean-Charles Régin. On Finding k Earliest Arrival Time Journeys in Public Transit Networks.ICORES 2022 - 11th International Conference on Operations Research and Enterprise SystemsVirtual event, FranceSciTePressFebruary 2022, 314-325
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• 47 inproceedingsR. A.Redha A. Alliche, T.Tiago da Silva Barros, R.Ramon Aparicio-Pardo and L.Lucile Sassatelli. Impact Evaluation of Control Signalling onto Distributed Learning-based Packet Routing.ITC -34 - Teletraffic Engineering for Smart Networking conferenceShenzen, ChinaSeptember 2022
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• 48 inproceedingsR. A.Redha A. Alliche, T.Tiago da Silva Barros, R.Ramon Aparicio-Pardo and L.Lucile Sassatelli. PRISMA: A Packet Routing Simulator for Multi-Agent Reinforcement Learning.4th Intl Workshop on Network Intelligence collocated with IFIP Networking 2022Proceedings IFIP Networking Conferance 2022Catania, ItalyJune 2022
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• 49 inproceedingsJ.Julien Bensmail, F.Foivos Fioravantes, F.Fionn Mc Inerney, N.Nicolas Nisse and N.Nacim Oijid. Une version Maker-Breaker du jeu du plus grand sous-graphe connexe.AlgoTel 2022 - 24èmes Rencontres Francophones sur les Aspects Algorithmiques des TélécommunicationsSaint-Rémy-Lès-Chevreuse, FranceMay 2022
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• 50 inproceedingsJ.Julien Bensmail, H.Hervé Hocquard and P.-M.Pierre-Marie Marcille. The Weak (2,2)-Labelling Problem for graphs with forbidden induced structures.Lecture Notes in Computer ScienceCALDAM 2023 - 9th Annual International Conference on Algorithms and Discrete Applied MathematicsGandhinagar, IndiaSpringerFebruary 2023
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• 51 inproceedingsJ.-C.Jean-Claude Bermond, M.Michel Cosnard and F.Frédéric Havet. Grabbing Olives on Linear Pizzas and Pissaladières.The Eleventh International Conference on Fun with Algorithms (FUN 2022)Island of Favignana, ItalyMay 2022
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• 52 inproceedingsD.David Coudert, A.André Nusser and L.Laurent Viennot. Dominer pour calculer l'hyperbolicité des graphes.AlgoTel 2022 - 24èmes Rencontres Francophones sur les Aspects Algorithmiques des TélécommunicationsSaint-Rémy-Lès-Chevreuse, FranceMay 2022
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• 53 inproceedingsD.David Coudert, A.André Nusser and L.Laurent Viennot. Hyperbolicity Computation through Dominating Sets.ALENEX 2022 - SIAM Symposium on Algorithm Engineering and ExperimentsAlexandria, VA, United StatesJanuary 2022, 78-90
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• 54 inproceedingsA.Arthur da Cunha, E.Emanuele Natale and L.Laurent Viennot. Proving the Strong Lottery Ticket Hypothesis for Convolutional Neural Networks.ICLR 2022 - 10th International Conference on Learning RepresentationsVirtual, FranceApril 2022
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• 55 inproceedingsI.Igor Dias da Silva, Y.Yann Busnel and C.Christelle Caillouet. Trajectory Optimization for Fast Sensor Energy Replenishment using UAVs as RF sources.GLOBECOM 2022 - IEEE Global Communications ConferenceRio de Janeiro / Hybrid, BrazilIEEEDecember 2022, 1-6
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• 56 inproceedingsI.Igor Dias da Silva and C.Christelle Caillouet. Quand l'immobilisme coûte plus cher que l'échange.AlgoTel 2022 - 24èmes Rencontres Francophones sur les Aspects Algorithmiques des TélécommunicationsSaint-Rémy-Lès-Chevreuse, FranceMay 2022
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• 57 inproceedingsT.Thomas Dissaux and N.Nicolas Nisse. Longueur linéaire des graphes planaires extérieurs.AlgoTel 2022 - 24èmes Rencontres Francophones sur les Aspects Algorithmiques des TélécommunicationsSaint-Rémy-Lès-Chevreuse, FranceMay 2022
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• 58 inproceedingsT.Thomas Dissaux and N.Nicolas Nisse. Pathlength of Outerplanar graphs.LATIN 2022: Theoretical Informatics : 15th Latin American Symposium, Guanajuato, Mexico, November 7–11, 2022, ProceedingsLATIN 2022 - 15th Latin American Theoretical Informatics SymposiumLNCS3568Lecture notes in computer scienceGuanajuato, Mexico2022, 172-187
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• 59 inproceedingsA.Adrien Gausseran, R. A.Redha A. Alliche, H.Hicham Lesfari, R.Ramon Aparicio-Pardo, F.Frédéric Giroire and J.Joanna Moulierac. Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning.CloudNet 2022 - IEEE International Conference on Cloud NetworkingParis, FranceNovember 2022
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• 60 inproceedingsM.Martin Heusse, C.Christelle Caillouet and A.Andrzej Duda. Frame Arrival Timing in LoRaWAN: Capacity Increase With Repeated Transmissions and More Channel Attenuation.PIMRC 2022 - IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio CommunicationsVirtual Conference, Japan2022
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• 61 inproceedingsL.Luc Hogie. A Service-Oriented Middleware Enabling Decentralised Deployment in Mobile Multihop Networks.3rd International Workshop on Architectures for Future Mobile Computing and Internet of Things (FMCIoT 2022), collocated with the 20th International Conference on Service-Oriented Computing (ICSOC 2022)Sevilla, SpainOctober 2022
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• 62 inproceedingsL.Luc Hogie. Idawi: a decentralized middleware for achieving the full potential of the IoT, the fog, and other difficult computing environments.Proceedings of the 1st Workshop on Middleware for the EdgeQuebec City, CanadaACM; ACMNovember 2022, 1-5
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• 63 inproceedingsH.Hicham Lesfari and F.Frédéric Giroire. Nadege: When Graph Kernels meet Network Anomaly Detection.IEEE International Conference on Computer Communications (INFOCOM)London, United KingdomMay 2022
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• 64 inproceedingsF.Francesco d'Amore, D.Daniel Mitropolsky, P.Pierluigi Crescenzi, E.Emanuele Natale and C.Christos Papadimitriou. Planning with Biological Neurons and Synapses.Proceedings of the AAAI Conference on Artificial Intelligence 2022Vol. 36 No. 1: AAAI-22 Technical Tracks 1Vancouver, CanadaDecember 2021
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Conferences without proceedings

• 65 inproceedingsJ.Julien Bensmail, H.Hervé Hocquard, D.Dimitri Lajou and E.Eric Sopena. A proof of the Multiplicative 1-2-3 Conjecture.CALDAM 2022 - 8th Annual International Conference on Algorithms and Discrete Applied MathematicsPuducherry, IndiaFebruary 2022
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• 66 inproceedingsI.Ilias Driouich, C.Chuan Xu, G.Giovanni Neglia, F.Frederic Giroire and E.Eoin Thomas. A Novel Model-Based Attribute Inference Attack in Federated Learning.FL-NeurIPS'22 - Federated Learning: Recent Advances and New Challenges workshop in Conjunction with NeurIPS 2022New orleans, United StatesDecember 2022
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• 67 inproceedingsF.Foivos Fioravantes, N.Nikolaos Melissinos and T.Theofilos Triommatis. Complexity of Finding Maximum Locally Irregular Induced Subgraphs.Scandinavian Symposium and Workshops on Algorithm Theory (SWAT) 2022Torshavn, Faroe IslandsSeptember 2022
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• 68 inproceedings M.Martin Heusse, C.Christelle Caillouet and A.Andrzej Duda. LoRaWAN Reception probability vs. packet arrival timing: please speak quietly and repeat? CORES 2022 – 7ème Rencontres Francophones sur la Conception de Protocoles, l’Évaluation de Performance et l’Expérimentation des Réseaux de Communication Saint-Rémy-Lès-Chevreuse, France May 2022
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• 69 inproceedingsH.Hicham Lesfari, F.Frédéric Giroire and S.Stéphane Pérennes. Biased Majority Opinion Dynamics: Exploiting graph $k$-domination.IJCAI 2022 - International Joint Conference on Artificial IntelligenceVienna, AustriaJuly 2022
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• 70 inproceedingsF.Francesco d'Amore and I.Isabella Ziccardi. Phase Transition of the 3-Majority Dynamics with Uniform Communication Noise.SIROCCO 2022 - Structural information and communication complexityPaderborn, GermanyInria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France; Università degli Studi dell'AquilaDecember 2021
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Doctoral dissertations and habilitation theses

• 71 thesisF.-S.Foivos-Sotirios Fioravantes. Edge-labellings, vertex-colourings and combinatorial games on graphs.Université Côte d'AzurSeptember 2022
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• 72 thesisF.Francesco d'Amore. On the collective behaviors of bio-inspired distributed systems.Université Côte d'AzurOctober 2022
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Reports & preprints

• 73 reportJ.Julio Araujo, F.Frédéric Havet, C. L.Claudia Linhares Sales, N.Nicolas Nisse and K.Karol Suchan. Semi-proper orientations of dense graphs.Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France2022
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• 74 miscL.Luca Becchetti, A.Arthur Carvalho Walraven da Cunha, A.Andrea Clementi, F.Francesco d'Amore, H.Hicham Lesfari, E.Emanuele Natale and L.Luca Trevisan. On the Multidimensional Random Subset Sum Problem.July 2022
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• 75 reportJ.Julien Bensmail, S.Sandip Das, S.Soumen Nandi, A.Ayan Nandy, T.Théo Pierron, S.Swathy Prabhu and S.Sagnik Sen. Oriented Total-Coloring of Oriented Graphs.Université côte d'azur; Université lyon 12022
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• 76 reportJ.Julien Bensmail, H.Hervé Hocquard and P.-M.Pierre-Marie Marcille. Adding Direction Constraints to the 1-2-3 Conjecture.Université côte d'azur; Université de Bordeaux2023
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• 77 reportJ.Julien Bensmail, H.Hervé Hocquard and P.-M.Pierre-Marie Marcille. The Weak (2, 2)-Labelling Problem for graphs with forbidden induced structures.Université côte-d'Azur; Université de Bordeaux, LaBRI, UMR 5800, France2022
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• 78 reportJ.Julien Bensmail. Some Properties of Minimal Arbitrarily Partitionable Graphs.Université côte d'azur2022
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• 79 miscJ.-C.Jean-Claude Bermond, M.Michel Cosnard and F.Frédéric Havet. Grabbing olives on linear pizzas and pissaladières.April 2022
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• 80 miscJ.-C.Jean-Claude Bermond, T.Takako Kodate and J.Joseph Yu. Gossiping with interference in radio ring networks.April 2022
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• 81 miscA.Arthur da Cunha, F.Francesco d'Amore, F.Frédéric Giroire, H.Hicham Lesfari, E.Emanuele Natale and L.Laurent Viennot. Revisiting the Random Subset Sum problem.April 2022
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• 82 reportT.Thomas Dissaux and N.Nicolas Nisse. Pathlength of Outerplanar graphs.Inria & Université Nice Sophia Antipolis, CNRS, I3S, Sophia Antipolis, FranceApril 2022
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• 83 reportF.Frédéric Giroire, N.Nicolas Nisse, K.Kostiantyn Ohulchanskyi, M.Malgorzata Sulkowska and T.Thibaud Trolliet. Preferential attachment hypergraph with vertex deactivation.Inria - Sophia antipolis; UCA, I3S2022
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• 84 miscF.Frédéric Havet, D.Dorian Mazauric, T. V.Thi Viet Ha Nguyen and F.Frédéric Cazals. Conflict coloring problems: (theory, complexity, algorithm), and application to multi-resolution modeling in structural bioinformatics.May 2022
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• 85 reportL.Luc Hogie. Idawi: a middleware for distributed applications in the IOT, the fog and other multihop dynamic networks.CNRS - Centre National de la Recherche Scientifique; Université Côte d'azur; InriaFebruary 2022
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Other scientific publications

• 86 inproceedingsA.Andrea Clementi, G.George Giakkoupis, E.Emanuele Natale and F.Francesco d'Amore. Search via Parallel Lévy Walks on $Z^2$.HALG 2022 - Highlights of AlgorithmsLondon, United KingdomJune 2022, 1-13
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• 87 inproceedingsA.Aurora Rossi, P.Pierluigi Crescenzi, S.Samuel Deslauriers-Gauthier and E.Emanuele Natale. Hyperbolic Model Captures Temporal Small Worldness of Brain Dynamics.NeuroMod meeting 2022Antibes, FranceJune 2022
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• 88 inproceedingsF.Francesco d'Amore, D.Daniel Mitropolsky, P.Pierluigi Crescenzi, E.Emanuele Natale and C. H.Christos H Papadimitriou. Planning with Biological Neurons and Synapses - AAAI22 Poster.AAAI 2022 - 36th AAAI Conference on Artificial IntelligenceVancouver, CanadaFebruary 2022
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