7.1.1 SageMath

• Name:
  SageMath
• Keywords:
  Combinatorics, Graph algorithmics, Number theory, Cryptography, Algebra
• 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, 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/
• News of the Year:
  We have pursued the maintenance of the graph module (fix bug, improve behavior, improve the performances of many methods). We have also added new methods such as the computation of the longest (induced) cycle, a uniform generator of random proper interval graphs, a method to enumerate cographs, etc.
• URL:
  http://­www.­sagemath.­org/
• Contact:
  David Coudert
• Participants:
  David Coudert, Xavier Caruso

7.1.2 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.

• URL:
  https://­github.­com/­worlddynamics/­WorldDynamics.­jl
• Publication:
  hal-04117509
• Contact:
  Emanuele Natale
• Participants:
  Pierluigi Crescenzi, Paulo Bruno De Sousa Serafim, Aurora Rossi, Hicham Lesfari, Emanuele Natale
• Partner:
  Gran Sasso Science Institute

7.1.3 Earth4All.jl

• Name:
  Earth4All.jl
• Keywords:
  Julia programming language, Integrated assessment modeling, Scientific computing
• Functional Description:
  Integrated assessment models are critical tools for scientists to understand the complex interactions between human and natural systems, and for policymakers to design effective policies to address global social and environmental challenges. However, the lack of transparency and reproducibility of models hinders their use and confidence in their results. This software is an open source implementation of the recent Earth4All integrated assessment model in the Julia programming language, developed according to best coding practices using a modular modeling framework (WorldDynamics.jl), and is publicly available in a GitHub repository for community inspection and use under an open source license.
• URL:
  https://­github.­com/­worlddynamics/­Earth4All.­jl
• Publication:
  hal-04780536
• Contact:
  Emanuele Natale
• Partner:
  Gran Sasso Science Institute

7.1.4 Idawi

• Keyword:
  Middleware
• Functional Description:
  Idawi is a middleware for the development and experimentation of distributed applications for multi-hop dynamic networks, like the IoT, the Edge, Mobile Ad hoc Networks, etc. The development of Idawi was initially motivated by the need of the COATI Research group 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, but in our Research context we were led to introduce new ideas not found in other middleware solutions for distributed computing - such as a fully decentralized network model, and a by-default collective communication/computation model (both naturally matching the very nature of mobile multi-hop networks), the use of digital twins at the core of its network management model, as well as new features making it usable as a Research platform for the experimentation of middleware-level techniques. Idawi defines application elements as components organized into a multi-hop overlay network on top of agnostic transport layers such as TCP, UDP and SSH (SSH being employed to enable component deployment and communication 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 (optionally typed) endpoints, which can be triggered remotely from anywhere in the component overlay. Idawi features a multi-paradigm programming model. Messaging (and hence remote code invocation) 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 for 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.
• News of the Year:
  Improvement of the library and addition of new methods.
• URL:
  https://­github.­com/­lhogie/­idawi
• Publications:
  hal-04878642, hal-03863333, hal-03886521, hal-03562184
• Contact:
  Luc Hogie

8.1.1 Structural graph and digraph theory

Participants: Frédéric Havet, Nicolas Nisse, Lucas Picasarri-Arrieta, Clément Rambaud.

One of our goals is to establish structural results on graphs and digraphs that can be used to design efficient algorithms. In particular, we are looking for substructures with certain properties or ways to represent or approximate efficiently graphs and digraphs.

8.1.2 Partitioning, colouring and labelling graphs and digraphs

Participants: Julien Bensmail, Frédéric Havet, Nicolas Nisse, Lucas Picasarri-Arrieta, Clément Rambaud.

8.1.3 Miscellaneous digraph problems

Participants: Frédéric Havet, Lucas Picasarri-Arrieta, Clément Rambaud.

8.2.1 Complexity of graph problems

Participants: Jean-Claude Bermond, Caroline Brosse, Michel Cosnard, David Coudert, Thomas Dissaux, Frédéric Havet, Nicolas Nisse, André Nusser, Lucas Picasarri-Arrieta.

8.2.2 Combinatorial games in graphs

Participants: Julien Bensmail, Jean-Claude Bermond, Michel Cosnard, Caroline Brosse, Frédéric Havet, Nicolas Almeida Martins, Nicolas Nisse.

8.2.3 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.

8.3.1 Complexity of distributed systems

Participants: Davide Ferré, Nicolas Nisse.

8.3.2 Opinion dynamics

Participants: Niccolo D'Archivio, Emanuele Natale.

8.4.1 New results on the Random Subset Sum problem and the Strong Lottery Ticket Hypothesis

Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains sparse subnetworks that can be trained efficiently to achieve performances comparable to that of training the entire network $N$. Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks. Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem. Informally, the RSS Problem asks how large a random i.i.d. sample $\Omega$ should be so that we are able to approximate any number in $[-1,1]$, up to an error of $ϵ$, as the sum of a suitable subset of $\Omega$.

In collaboration with Giordano Giambartolomei and Frederik Mallmann-Trenn [King's College London, UK], we provide in 52 the first proof of the SLTH in classical settings, such as dense and equivariant networks, with guarantees on the sparsity of the subnetworks. Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest.

8.4.2 Temporal graph neural networks.

We collaborated with Carlo Lucibello [Bocconi University, Italy] to contribute to the Julia open source libraries GraphNeuralNetworks.jl and MLDatasets.jl. Our project’s objective was to extend the support of temporal graph neural networks in GraphNeuralNetworks.jl by creating several layers, known as temporal graph convolutional layers. These layers were designed specifically for a type of graph called TemporalSnapshotsGNNGraph. Particular emphasis was placed on layers combining graph convolutions with recurrent cells, using the Flux.jl machine learning framework as a reference for implementing the latter. Some implemented layers were the DCRNN layer for traffic prediction, the GConvGRU and GConvLSTM layers, and the EGCN-O layer for tasks such as node and edge classification as well as link prediction. Additionally, we adapted all the implemented temporal layers to work seamlessly with another Julia machine learning framework, Lux.jl, ensuring compatibility across frameworks. Beyond enhancing existing tools, we expanded the range of datasets available in MLDatasets.jl, a Julia package for machine learning datasets, by contributing new datasets. We restructured the repository into a multi-repository setup, created and deployed the multi-package documentation, as detailled in 89.

8.4.3 Attribute Inference Attacks for Federated Regression Tasks.

Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized. However, recent studies have revealed that the training phase of FL is vulnerable to reconstruction attacks, such as attribute inference attacks (AIA), where adversaries exploit exchanged messages and auxiliary public information to uncover sensitive attributes of targeted clients. While these attacks have been extensively studied in the context of classification tasks, their impact on regression tasks remains largely unexplored. In 75, we address this gap by proposing novel model-based AIAs specifically designed for regression tasks in FL environments. Our approach considers scenarios where adversaries can either eavesdrop on exchanged messages or directly interfere with the training process. We benchmark our proposed attacks against state-of-the-art methods using real-world datasets. The results demonstrate a significant increase in reconstruction accuracy, particularly in heterogeneous client datasets, a common scenario in FL. The efficacy of our model-based AIAs makes them better candidates for empirically quantifying privacy leakage for federated regression tasks.

This work was done in collaboration with Othmane Marfoq [Meta], Giovanni Neglia [NEO], and Eoin Thomas [Amadeus].

8.4.4 Link Inference Attacks in Vertical Federated Graph Learning.

Vertical Federated Graph Learning (VFGL) is a novel privacy-preserving technology that enables entities to collaborate on training Machine Learning (ML) models without exchanging their raw data. In VFGL, some of the entities hold a graph dataset capturing sensitive user relations, as in the case of social networks. This collaborative effort aims to leverage diverse features from each entity about shared users to enhance predictive models or recommendation systems, while safeguarding data privacy in the process. Despite these advantages, recent studies have revealed a critical vulnerability that appears in intermediate data representations, which may inadvertently expose link information in the graph. In 56, we propose a novel Link Inference Attack (LIA) that exploits gradients as a new source of link information leakage. Assuming a semi-honest adversary, we demonstrate through extensive experiments on seven real-world datasets that our LIA outperforms state-of-the-art attacks, achieving over 10% higher Area Under the Curve in some instances, thereby highlighting a significant risk of link information leakage through gradients. We analytically derive our Label-based LIA's accuracy using graph characteristics, assessing target graph vulnerability.

This work was done in collaboration with Oualid Zari [Eurecom], Ayşe Ünsal [Eurecom], Melek Önen [Eurecom] and Javier Parra-Arnau [Universitat Politècnica de Catalunya, Barcelona, Spain].

8.4.5 A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses

Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility. Most existing defenses against membership inference attacks assume access to reference data, defined as an additional dataset coming from the same (or a similar) underlying distribution as training data. Despite the common use of reference data, previous works are notably reticent about defining and evaluating reference data privacy. As gains in model utility and/or training data privacy may come at the expense of reference data privacy, it is essential that all three aspects are duly considered. In 50, we conduct the first comprehensive analysis of empirical privacy defenses. First, we examine the availability of reference data and its privacy treatment in previous works and demonstrate its necessity for fairly comparing defenses. Second, we propose a baseline defense that enables the utility-privacy tradeoff with respect to both training and reference data to be easily understood. Our method is formulated as an empirical risk minimization with a constraint on the generalization error, which, in practice, can be evaluated as a weighted empirical risk minimization (WERM) over the training and reference datasets. Although we conceived of WERM as a simple baseline, our experiments show that, surprisingly, it outperforms the most well-studied and current state-of-the-art empirical privacy defenses using reference data for nearly all relative privacy levels of reference and training data. Our investigation also reveals that these existing methods are unable to trade off reference data privacy for model utility and/or training data privacy, and thus fail to operate outside of the high reference data privacy case. Overall, our work highlights the need for a proper evaluation of the triad "model utility / training data privacy / reference data privacy" when comparing privacy defenses.

This work was done in collaboration with Caelin G. Kaplan [NEO], Othmane Marfoq [NEO], Giovanni Neglia [NEO], and Anderson Santana de Oliveira [NEO].

8.5.1 Scheduling ML compressed models

With the advent and the growing usage of Machine Learning as a Service (MLaaS), cloud and network systems are now offering the possibility to deploy ML tasks on heterogeneous clusters. Then, network and cloud operators have to schedule these tasks, determining both when and on which devices to execute them. In parallel, several solutions, such as neural network compression, were proposed to build small models which can run on limited hardware. These solutions allow choosing the model size at inference time for any targeted processing time without having to re-train the network. In collaboration with Ramon Aparicio PArdo [I3S, UniCA ], we propose in 54, 55, 60 approximation algorithms with proven guarantees to solve a novel scheduling problem with task deadlines where the tasks can be compressed.

8.5.2 Enhancing Energy Efficient Task Caching and Offloading in Multi-Access Edge Computing

In recent years, Multi-Access Edge Computing (MEC) has emerged as a promising solution for enhancing energy efficiency in network applications. As a matter of fact, by offloading computational tasks to a server deployed near the base station and by caching both the outputs and the related code for completed tasks, it is possible to effectively reduce the energy consumption of mobile devices while ensuring adherence to their latency constraints. Although there have been some previous works on task caching and task offloading on the cloud, most of them focus on only one of these two strategies or formulate optimization problems that are hard to solve and propose a suboptimal solution.

In collaboration with Fabiano Lorusso [I3S, UniCA ] and Guillaume Urvoy-Keller [I3S, UniCA ], we propose in 88 a linear model for the joint task caching and offloading optimization problem. Moreover, we present two efficient heuristics which provide close-to-optimal results in terms of energy efficiency with a low execution time. We further prove that the offloading sub-problem can be solved with an optimal algorithm. Finally, we demonstrate the performance and scalability of our propositions by extensive simulations on a large number of 100 000 mobile devices.

8.5.3 Optimizing Low Power Wide Area Networks

LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. LoRaWAN is a widely used wireless communication standard that enables the collection of measurement data from numerous monitoring applications (such as smart metering, pollution, and asset tracking). Most research work has been focused on the performance of LoRaWAN as a function of the uplink traffic, as this is the most common scenario. Nevertheless, downlink traffic is a fundamental building block of the LoRaWAN standard, and a crucial part of applications such as smart healthcare, where reliability is extremely important. In collaboration with Alexandre Guitton [Université Clermont Auvergne], Oana Iova [AGORA, Lyon], and Fabrice Valois [AGORA, Lyon], we study in 44 the impact of the downlink traffic and different scheduling policies on the performance of LoRaWAN. We investigate: (i) the impact of an optimal schedule for the downlink traffic, (ii) the choice of the physical layer parameters used to send downlinks to end-devices during the second reception window, and (iii) the choice of the gateway that sends the downlinks. Our results show that even when using an optimal schedule, the presence of downlink traffic reduces the capacity of a LoRaWAN network up to 20%. The most limiting factor is the gateway, due to its duty cycle and half-duplex characteristics.

LoRaWAN mainly uses frequency bands for which network usage is limited to meet the requirements of mass deployment of several thousand connected objects. For example, in the European Union, a device cannot use frequency channels for more than 1% of the time in its duty cycle. This leads to a scheduling problem with waiting times. In collaboration with Teiki Rigaud [ENS, Paris], we formalize in 45 the LoRaWAN data scheduling optimization problem with waiting times using linear programming. We derive an optimal algorithm for the special case of equivalent spreading factors (SF) and investigate approximation algorithms for specific and general cases. We finally discuss about the general problem's complexity.

8.6.1 Analysis of temporal brain networks

Participants: Emanuele Natale, Aurora Rossi.

8.6.2 Integrated Assessment Modeling

Participants: Emanuele Natale, Aurora Rossi.

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

9.1.2 Participation in other International Programs

9.2.1 Visits of international scientists

9.2.2 Visits to international teams

9.3.1 Horizon Europe

DGA/Inria BioSwarm, 2013-2026

Participants: Niccolò D’Archivio, Emanuele Natale.

• Program:
  DGA/Inria
• Project acronym:
  BioSwarm
• Project title:
  Bio-inspired algorithms for collective search and decision-making in drone swarms
• Duration:
  2023 - 2026
• Coordinator:
  Emanuele Natale
• Other partners:
  Inria EP CHROMA
• Summary:
  The BioSwarm project focuses on decentralized algorithms inspired by the collective behavior of biological systems. It aims to enhance research strategies in unknown environments and improve collective decision-making through consensus among agents. The project will explore computational dynamics, particularly consensus algorithms that model decision-making processes observed in natural systems. It seeks to advance the understanding of how parameters, such as the majority threshold (k), influence the robustness and efficiency of consensus processes. Additionally, BioSwarm will investigate Lévy walks, a stochastic process relevant to collective behavior in multi-agent systems, through theoretical analyses and practical simulations.

ANR-19-CE48-0013 Digraphs, 2020-2024

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

• Program:
  ANR
• Project acronym:
  Digraphs
• Project title:
  Digraphs
• Duration:
  January 2020 - December 2024
• Coordinator:
  Frédéric Havet
• Other partners:
  LIRMM, Montpellier; LIP, Lyon
• Summary:
  The objectives of the project are to make some advances on digraph theory in order to get a better understanding of important aspects of digraphs and to have more insight into 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 other 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, FUN], Christophe Biernacki [vice-head, Inria, 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), whose 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: Francesco Diana, Frédéric Giroire, Chuan Xu.

• Project acronym:
  Fed-Malin
• Project title:
  Federated machine Learning over the internet
• Duration:
  2022 - 2026
• Coordinators:
  Aurélien Bellet [PREMEDICAL], Giovanni Neglia [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.

Défi Inria-Hive Alvearium, 2022-2026

Participants: Frédéric Giroire, Stéphane Pérennes.

• Project acronym:
  Alvearium
• Project title:
  Large Scale Secure and Reliable Peer-to-Peer Cloud Storage: towards a shared sovereign cloud that respects its users' data
• Duration:
  2022 - 2026
• Coordinator:
  Claudia-Lavinia Ignat
• Inria participants:
  Inria project-teams COAST, COATI, MYRIADS, WIDE
• Other partners:
  HIVE (www.hivenet.com)
• Summary:
  The project aims to propose an alternative peer-to-peer cloud which provides both computing and data storage via a peer-to-peer network rather than from a centralized set of data centers. HIVE proposes to exploit the unused capacity of computers and to incentivize users to contribute their computer resources to the network in exchange for similar capacity from the network and/or monetary compensation. By exchanging similar computer resources and network capacity users can benefit from all cloud services. Peers store encrypted fragments of the data of other peers. This proposed peer-to-peer cloud solution addresses users concerns about the privacy of their data and the dependency on centralized cloud providers. In this collaboration with HIVE, we will apply our work on replication mechanisms for sharded encrypted data, data placement, Byzantine fault tolerance and security mechanisms in peer-to-peer environments.
• Web:
  project.inria.fr/alvearium/

Défi Inria-Nokia LearnNet, 2024-2027

Participants: Frédéric Giroire, Chuan Xu.

• Project acronym:
  LearnNet
• Project title:
  Learning Networks
• Duration:
  2024 - 2027
• Coordinator:
  Jean-Marie Gorce
• Inria participants:
  Inria project-teams COATI, EPIONE, MARACAS, NEO, PREMEDICAL, STATIFY, TOTH and TRIBE
• Other partners:
  Nokia
• Summary:
  The LearnNet challenge explores new avenues of research at the intersection of the fields of networks and learning with two complementary objectives: rethinking the design of network protocols to serve machine learning applications, and exploring how learning can improve network management. Thus, the LearnNet challenge studies the growing entanglement between the challenges of large-scale learning and network design.
• Web:
  www.inria.fr/en/learnnet

Défi Inria-Nokia SmartNet, 2024-2027

Participants: Frédéric Giroire, Joanna Moulierac.

• Project acronym:
  SmartNet
• Project title:
  AI Methods for Smart Network Management
• Duration:
  2024 - 2027
• Coordinator:
  Yassine Hadjadj-Aoul
• Inria participants:
  Inria project-teams COATI, ERMINE, NEO, SPADES and STACK
• Other partners:
  Nokia
• Summary:
  The challenge is dedicated to exploring the transformative potential of AI methods in enabling smart network management. The project strategically focuses on two key areas: slice provisioning and causal analysis of network malfunctions.
• Web:
  www.inria.fr/en/smartnet

PEPR NF (Networks of the Future 2023-2030, 65M€)

Participants: Jamil Abou Ltaif, Yanis Achaichia, Christelle Caillouet, David Coudert, Frédéric Giroire, Joanna Moulierac.

• Project acronym:
  NF
• Project title:
  Networks of the Future
• Duration:
  2023 - 2030
• Coordinators:
  Dmitri Kténas [CEA], Serge Verdeyme [CNRS], Daniel Koffman [IMT]
• Inria participants:
  Inria project-teams AGORA, AIO, COATI, DIANA, DYOGENE, ERMINE, FUN, MARACAS, NEO, RESIST, TRIBE
• Summary of PEPR NF:

  The 5G network and the networks of the future represent a key issue for French and European industry, society and digital sovereignty. This is why the French government has decided to launch a dedicated national strategy. One of this strategy's priority ambitions is to produce significant public research efforts so the national scientific community contributes fully to making progress that clearly responds to the challenges of 5G and the networks of the future. In this context, the CNRS, the CEA and the Institut Mines-Télécom (ITM) are co-leading the '5G' acceleration PEPR to support upstream research into the development of advanced technologies for 5G and the networks of the future.

  Inria is involved into 8 research projects over the 10 supported by the program, with the participation of 11 project-teams of the theme "Networks and Telecommunications" and the coordination of the PC9-Founds.

  COATI is involed in PC1 NF-MUST (End-to-end multi domain services management), coordinated by Djamal Zeghlache (IMT), which involves Inria project-teams COATI, DIANA and ERMINE.

PEPR Cloud, 2023-2030

Participants: Davide Ferré, Frédéric Giroire, Joanna Moulierac.

• Project acronym:
  Cloud
• Project title:
  Développement de technologies avancées de cloud
• Duration:
  2023 - 2030
• Coordinators:
  CEA, INRIA
• Inria participants:
  AVALON, COATI, SPIRALS
• Summary:

  PC CARECLoud - Understanding, improving, reducing the environmental impacts of Cloud Computing.

  Cloud computing and its many variations offer users considerable computing and storage capacities. The maturity of virtualization techniques has enabled the emergence of complex virtualized infrastructures, capable of rapidly deploying and reconfiguring virtual and elastic resources in increasingly distributed infrastructures. This resource management, transparent to users, gives the illusion of access to flexible, unlimited and almost immaterial resources. However, the power consumption of these clouds is very real and worrying, as are their overall greenhouse gas (GHG) emissions and the consumption of critical raw materials used in their manufacture. In a context where climate change is becoming more visible and impressive every year, with serious consequences for people and the planet, all sectors (transport, building, agriculture, industry, etc.) must contribute to the effort to reduce GHG emissions. Despite their ability to optimize processes in other sectors (transport, energy, agriculture), clouds are not immune to this observation: the increasing slope of their greenhouse gas emissions must be reversed, otherwise their potential benefits in other sectors will be erased. This is why the CARECloud project (understanding, improving, reducing the environmental impacts of Cloud Computing) aims to drastically reduce the environmental impacts of cloud infrastructures.

  Cloud infrastructures are becoming more and more complex: both in width, with more and more distributed infrastructures, whose resources are scattered as close as possible to the user (edge, fog, continuum computing) and in depth, with an increasing software stacking between the hardware and the user's application (operating system, virtual machines, containers, orchestrators, micro- services, etc.) The first objective of the project is to understand how these infrastructures consume energy in order to identify sources of waste and to design new models and metrics to qualify energy efficiency. The second objective focuses on the energy efficiency of cloud infrastructures, i.e., optimizing their consumption during the usage phase. In particular, this involves designing resource allocation and energy lever orchestration strategies: mechanisms that optimize energy consumption (sleep modes, dynamic adjustment of the size of virtual resources, optimization of processor frequency, etc.). Finally, the third objective targets digital sobriety in order to sustainably reduce the environmental impact of clouds. Indeed, current clouds offer high availability and very high fault tolerance, at the cost of significant energy expenditure, particularly due to redundancy and oversizing. This third objective aims to design infrastructures that are more energy and IT resource efficient, resilient to electrical intermittency, adaptable to the production of electricity from renewable energy sources and tolerant of the disconnection of a highly decentralized part of the infrastructure.

PEPR MOBIDEC - Mob Sci-Data Factory project, 2023-2030

Participants: David Coudert.

• Project acronym:
  MOBIDEC
• Project title:
  Digitalisation et décarbonation des mobilités
• Duration:
  2023 - 2030
• Coordinators:
  IFP Energies nouvelles (IFPEN) and Université Gustave Eiffel (UGE)
• Participants Mob Sci-Data Factory:
  INRIA (coordinator), UGE, IFPEN, IGN, CEREMA
• Inria participants:
  AGORA, ASCII, COATI, FUN, TRIBE
• Summary:

  The PEPR Data Technology for Mobility in the Territories (MOBIDEC) is in line with France 2030's objective of developing sober, sovereign and resilient mobility, by placing the collection, analysis and processing of mobility data at the heart of its work. It aims to understand and anticipate the mobility behaviors of goods and people, to facilitate the interpretation and processing of data, and to offer decision-making tools to simulate the impact of public policies in advance, or to assess the relevance of a new transport offer.

  COATI is involed in project “Mob Sci-Data Factory”, one of the three projects composing the PEPR MOBIDEC. It shares the PEPR's primary goal of contributing to developing more sustainable mobility strategies by providing decision-making support methodology and a digital toolbox fed by appropriately selecting and processing mobility data and by a deeper understanding of the involved transport uses and behaviors in mobility. It aims at clarifying and extracting the elements determining and explaining the characteristics of mobility data, which also raise the following challenging questions: (1) What data and what are their availability, accessibility, quality, and representativeness? (2) Which methods and digital tools are necessary for processing, calibrating, understanding, and enriching data while dealing with missing data and new acquisition ? (3) What are the specifications of the decision-support platform required for standard tools and data research sharing?

  Mob Sci-Data Factory will make available in a secure and privacy-compliant cloud-based infrastructure different sources of mobility data together with open-source libraries and methods designed to be unified, modular, and interoperable from conception. Mob Sci-Dat Factory outcomes will facilitate data sovereignty and open-source development interoperability across multiple scientific actors in France, while accelerating research focused on mobility by offering privacy-compliant and secure data accessibility.

BPI SIRCAPASS, 2024-2028

Participants: Christelle Caillouet, David Coudert.

• Project acronym:
  SIRCAPASS
• Project title:
  Monitoring road infrastructure using passive sensors
• Duration:
  2024 - 2028
• Coordinator:
  SilMach
• Inria participants:
  Inria project-team FUN
• Other partners:
  SilMach, AIA Ingénierie, CEREMA, with the support of Vinci Autoroutes
• Summary:
  This project aims to provide an operational response to the challenges associated with the preventive monitoring of bridges and the planning of their maintenance. SIRCAPASS will propose an innovation that breaks with current practices and concepts, based on the use of energy-free sensors.

9.4.1 GDR Actions

10.1.1 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.
• Nicolas Nisse :
  • member of the steering committee of the Workshop on GRAph Searching, Theory and Applications (GRASTA), since 2014.

10.1.2 Scientific events: organisation

10.1.3 Scientific events: selection

10.1.4 Conferences

• Emanuele Natale :
  • Conference on Neural Information Processing Systems (NeurIPS) 2024
  • International Conference on Machine Learning (ICML) 24
• Chuan Xu :
  • International Conference on Learning Representations (ICLR) 2024

10.1.5 Journal

10.1.6 Invited talks

• Julien Bensmail :
  • Introducing and Extracting Irregularity in Graphs. Journées Graphes et Algorithmes 2024 (JGA'24), Université de Bourgogne, November 2024.
• Frédéric Giroire :
  • Opinion Dynamics, Seminar of the D2S/Coord: Dependable Distributed Systems and Coordination Lab, Tokyo, Tokyo Tech, Japan, July 17, 2024.
  • Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression, Japan-France Joint Workshop 2024, NICT - National Institute of Information and Communications, July 10, 2024.
• Emanuele Natale :
  • -
    Algorithmic Principles in Neuroscience, keynote at the 10th workshop on Biological Distributed Algorithms (BDA'24), Nantes, June 17, 2024. (slides)
  • -
    Trevisan's Contributions to Distributed Computing, invited talk at the LucaFest Workshop at the Simons Institute for the Theory of Computing, Berkeley, USA, October 8, 2024. (slides)
  • -
    Dynamics and Community Structure in Networks, invited talk at the Workshop on Computational Aspects of Complex Networks (CACN 2024), University of Rome Tor Vergata, Italy, December 6, 2024. (slides)
• Nicolas Nisse :
  • -
    On some positional games in graphs. 11th Latin American Workshop on Cliques in Graphs (LAWCG), October 20-23, 2024, Fortaleza, Brazil.
• André Nusser :
  • Approximating Klee's Measure Problem and a Lower Bound for Union Volume Estimation, Geometry and Computing Workshop, October 21–25, 2024, CIRM, France.
• Aurora Rossi :
  • -
    Characterizing Dynamic Functional Connectivity Subnetwork Contributions in Narrative Classification with Shapley Values. Invited Talk at the Department of Psychological Science, Birkbeck University, June 5, 2024 (slides).
  • -
    An Overview of Graph Neural Networks with GraphNeuralNetworks.jl. Invited talk at the Julia and Optimization Days, October 8, 2024 (slides, gist).
  • -
    Que se passe-t-il dans le cerveau quand nous écoutons des histoires ? Invited talk at M2AI 2024, November 28, 2024 (slides, video).

10.1.7 Leadership within the scientific community

• Christelle Caillouet :
  • Member of the steering committee of the GDR RSD and also chair of the Networking axis of the GDR RSD since July 2022.
  • Member of the mentorship actions committee of the GDR RSD.
• David Coudert :
  • Member of the steering committee of seminar Forum Numerica of Academy 1 RISE of UCA${}^{jedi}$ since 2018;
• 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 IFM of CNRS since 2005;
  • Member of the Ramon Lull PhD prize committee (Spain).
• Joanna Moulierac :
  • Elected member of the “Conseil d'administration” of SPECIF CAMPUS (Société Professionnelle des Enseignants et Chercheurs en Informatique de France) since 2021.
• Nicolas Nisse :
  • Member of the steering committee of GT CoA of the GDR IFM of CNRS since 2018;
  • Member of the committee for the Ph.D. Prix "Charles Delorme" (GDR IFM, GT Graphe).

10.1.8 Scientific expertise

• Julien Bensmail :
  • External reviewer for the PRELUDIUM-23 program by the National Science Center (NSC) of Poland.
• Jean-Claude Bermond :
  • Expert for DRTT-MESR “Crédit impôt recherche” (CIR et agréments).
• Christelle Caillouet :
  • Member of the scientific advisory committee of the call for projects on « Intelligence Artificielle pour les futurs réseaux » of ANR « Thématiques Spécifiques en Intelligence Artificielle » (TSIA), June, 2024.
  • Member of the scientific committee for the best paper award of the national conference ROADEF 2024, February 2024.
• David Coudert :
  • Member of the Inria selection committe for “Action Exploratoire”, 2004;
  • Member of the scientific advisory committee of LIRMM (Montpellier, France), February 1-2, 2024.
• Frédéric Havet :
  • Expert for FNRS (Fonds de la recherche scientifique), Belgium
• Emanuele Natale :
  • Project reviewer for the Idées 2024 call of the Académie d’Excellence Réseaux, Information et Société numérique of Université Côte d'Azur, January-February 2024.

10.1.9 Research administration

• Christelle Caillouet :
  • Elected member of Conseil de Laboratoire I3S since 2017;
• 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;
  • Member of the “Bureau du comité des équipe-projets” of the Inria Centre at Université Côte d'Azur since 2018, head since September 2022;
  • Member of the Inria Evaluation Committee, since September 2022;
  • Member of the Inria committee for researchers promotions (CRHC, CRHC-8, DR1, DRCE, DRCE-2), 2024;
  • Member of the Inria selection committee for Senior Researchers (DR2), 2024;
  • Vice-President of the Inria selection committee for CRCN and ISFP in Sophia Antipolis, 2024;
  • Member of the Inria admission committee for ISFP in Sophia Antipolis, 2024;
  • Member of the Inria committee RIPEC C3, 2024.
• 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 :
  • Coordinator of ANR project Digraphs;
  • Co-head of Terra Numerica.
• Joanna Moulierac :
  • Member of the I3S CO2 group since 2019 (www.i3s.unice.fr/co2/);
  • Member of the CSPT Terra Numerica, since 2022.
• Emanuele Natale :
  • External member of University of Rome Tor Vergata's PhD School in Data Science (Italy).
• Nicolas Nisse :
  • 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 CSPT Terra Numerica, since 2020.
• Luc Hogie :
  • Elected member of Conseil de Laboratoire I3S.

10.2.1 Teaching responsibilities

• Julien Bensmail :
  • In charge of the whole course schedules of Département QLIO of IUT Nice Côte d’Azur.
• Christelle Caillouet :
  • Member of the “Conseil de Département Informatique” of IUT Nice Côte d'Azur (since September 2022).
  • “Directrice d'études” for the 1st-year students "en alternance" of “Département Informatique” of IUT Nice Côte d'Azur (since September 2024);
• 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 :
  • 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);
  • 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 - Numérique", Université Côte d'Azur, Rectorat et Région PACA. See Section 10.3.1 for more details.

10.2.2 Teaching

Members of COATI have taught for more that 1200 hours (ETD) this year:

• PeiP: Clément Rambaud , Programmation impérative, 24h ETD, Level L1 (prépa), Polytech'Nice;
• BUT: Julien Bensmail , Algorithmique et programmation avancées, 64h ETD, Level L2, Département QLIO of IUT, Université Côte d'Azur, France;
• BUT: Julien Bensmail , Amélioration des systèmes d’information, 40h ETD, Level L3, Département QLIO of IUT Nice Côte d’Azur.
• BUT: Julien Bensmail , Recherche Opérationnelle pour les systèmes de production, 40h ETD, Level L3, Département QLIO of IUT Nice Côte d’Azur.
• BUT: Julien Bensmail , Modélisation des systèmes d’information, 40h ETD, Level L3, Département QLIO of IUT Nice Côte d’Azur.
• BUT: Julien Bensmail , Fondamentaux de la Recherche Opérationnelle, 40h ETD, Level L3, Département QLIO of IUT Nice Côte d’Azur.
• BUT: Julien Bensmail , Bases de données, 90h ETD, Level L2, Département QLIO of IUT Nice Côte d’Azur.
• BUT: Christelle Caillouet , Automates et langages, 12h ETD, Level L2, IUT, Université Côte d'Azur, France;
• BUT: Christelle Caillouet , Programmation orientée objet, 48h ETD, Level L1, IUT, Université Côte d'Azur, France;
• BUT: Christelle Caillouet , Méthodes d'optimisation pour l'aide à la décision, 21h ETD, Level L3, IUT, Université Côte d'Azur, France;
• BUT: Luc Hogie , System programming, 24h ETD, Level L2, IUT, Université Côte d'Azur, France;
• BUT: Joanna Moulierac , Réseaux avancés, 30h ETD, Level L2, IUT, Université Côte d'Azur, France;
• BUT: Lucas Picasarri-Arrieta , Automates et langages, 12h ETD, Level L2, IUT, Université Côte d'Azur, France;
• BUT: Lucas Picasarri-Arrieta , Introduction à la programmation, 52h ETD, Level L1, IUT, Université Côte d'Azur, France;
• BUT: Lucas Picasarri-Arrieta , Méthodes d'optimisation pour l'aide à la décision, 12h ETD, Level L1, IUT, Université Côte d'Azur, France;
• BUT: Lucas Picasarri-Arrieta , Introduction aux Réseaux, 40h ETD, Level L1, IUT, 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 , Database systems, 25h ETD, Level L3, MIAGE - Université Côte d’Azur, France;
• Licence: Michel Syska , Heuristic search, 21h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• Licence: Pierre Pereira , Base de l'IA, 26h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• Licence: Pierre Pereira , Apprentissage, 40h ETD, Level L3, parcours IA Science & Technologie - Université Côte d’Azur, France;
• Licence: Chuan Xu , Programmation fonctionnelle, 36h ETD, Level L3, Université Côte d’Azur, France;
• Licence: Chuan Xu , Python pour l'IA, 30h ETD, Level L3, - Université Côte d’Azur, France;
• Master: Christelle Caillouet , Introduction Algorithmic and Programming, 60h ETD, MAM3, Polytech Nice Sophia Antipolis, France;
• Master: Alexandre Caminada , Radio location systems, 20h ETD, Master 2 (in english), Polytech Nice Sophia Antipolis, 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 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, 34.5 h 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: 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: Clément Rambaud , Programmation en C++, Polytech Nice, 16h ETD, 2nd year (M1), Engineer School, Polytech Nice.
• Master: Michel Syska , Databases for big data, 20h ETD, M1 MIAGE - Université Côte d’Azur, France;
• Master: Michel Syska , Cloud computing, 45h ETD, M2 MIAGE MBDS - Université Côte d’Azur, France.
• Master: Michel Syska , Complex problem and heuristic search, 27h ETD, M2 MIAGE IA2 - Université Côte d’Azur, France.
• Master: Chuan Xu , Distributed-memory parallel programming and its applications, 24h ETD, M1 Computer Science - Université Côte d’Azur, France.
• Master: Chuan Xu , Federated Learning, 24h ETD, M2 IF - Université Côte d’Azur, France.

10.2.3 Supervision

10.2.4 Juries

• Christelle Caillouet :
  • Referee and member of PhD committee of Simon Pichot, Toulouse INP, December 19, 2024;
  • Member of PhD committee of Gwendoline Hochet Derevianckine, INSA Lyon, December 17, 2024;
  • Member of PhD committee of Masoud Taghavian, IMT Atlantique, September 25, 2024;
  • Referee and member of PhD thesis of Dinesh Tamang, University of Pisa, Italy, June 20, 2024;
• Frédéric Giroire :
  • President of PhD committe of Younes Ben Mazziane, Centre Inria d’Université Côte d’Azur, équipe NEO, May 4, 2024;
  • President of PhD committe of Aymeric Picard Marchetto, Université Côte d’Azur, October 22, 2024;
• Frédéric Havet :
  • Referee and member of HDR committe of Edouard Bonnet, Ecole Normale Supérieure de Lyon, de Montpellier, April 15, 2024;
  • Referee and member of HDR committe of Alantha Newman, Grenoble Alpes Université, December 4, 2024;
• Emanuele Natale :
  • MSc thesis reviewer (2 thesis) for the MSc Modeling for Neuronal and Cognitive Systems at Université Côte d'Azur, 2024.
  • Jury member for the PhD scholarship selection of the Neuromod Institute, Université Côte d'Azur, 2024.
• Nicolas Nisse :
  • Member of Ph.D. committe of Mikhael Carmona, École Centrale Méditerranée, Marseille, December 20, 2024;
• Chuan Xu :
  • Member of Ph.D. committe of Fabiola Espinoza Castellon, Université Paris-Saclay, Paris, February 6, 2024

10.3.1 Specific official responsibilities in science outreach structures

• Frédéric Havet is co-head of Terra Numerica and one of the person responsible of the “Comité Scientifique, Pédagogique et Technique” ; Nicolas Nisse is a member of this committee ; 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.
• Michel Syska is Head of "Campus des Métiers et des Qualifications (CMQ)- Numérique" (Université Côte d'Azur, Rectorat et Région PACA). The CMQ brings together educational institutions to address national and regional economic needs in partnership with local authorities and businesses. In the PACA region, several studies reveal significant tension in digital professions. To address this gap, the PACA Digital CMQ aims to: 1) Make digital training programs more attractive, 2) Support the evolution of profession by offering comprehensive training opportunities across all qualification levels.

10.3.2 Productions (articles, videos, podcasts, serious games, ...)

Press articles related to Terra Numerica can be found at terra-numerica.org/presse/. Members of COATI have contributed to several of them.

10.3.3 Education

Most of the members of COATI are involved in Terra Numerica. During the year 2024, more than 300 events held, Terra Numerica has been visited by 290 classes (about 8000 primary school/college/highschool student, for 2 hours in average). We have trained about 360 persons (including 250 teachers) and touched more than 15000 people during events such as Fête de la science, etc.

10.3.4 Participation in Live events

Many members of COATI (Caroline Brosse , Michel Cosnard , Frédéric Giroire , Frédéric Havet , Joanna Moulierac , Nicolas Nisse , Lucas Picasarri-Arrieta , Stéphane Pérennes , Clément Rambaud , Michel Syska ) participated some general audience science fairs, such as the Fête de la science in October 2024 (we were present on the “Village des Sciences” in Antibes-Juan-les-Pins, Valbonne, Villeneuve-Loubet, Vinon-sur Verdon) or some stages of the Science Tour Terra Numerica (20 days in several places : Digne-les-Bains, Forcalquier, Manosque, Aups, La Seyne sur Mer, Brignoles, Draguignan, Salernes, Roquebillière, Saint Martin de Vésubie, Breil sur Roya, Tende). They also occasionally act as scientific facilitator at Terra Numerica.

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

On November 28, 2024, Aurora Rossi delivered an invited talk titled "Que se passe-t-il dans le cerveau quand nous écoutons des histoires ?" during the Mois des Mathématiques Appliquées et Industrielles (M2AI) 2024 to an audience of 100 preparatory class students (BAC+1) and their teachers.

International journals

• 19 articleP.Pierre Aboulker, T.Thomas Bellitto, F.Frédéric Havet and C.Clément Rambaud. On the minimum number of arcs in $k$-dicritical oriented graphs.SIAM Journal on Discrete Mathematics382June 2024, 1863-1901HALDOIback to text
• 20 articleJ.Jørgen Bang-Jensen, S.Stéphane Bessy and L.Lucas Picasarri-Arrieta. Constrained Flows in Networks.Theoretical Computer Science1010September 2024, 114702HALDOIback to text
• 21 articleO.Olivier Baudon, J.Julien Bensmail and M.Morgan Boivin. Interplays between variations of arbitrarily partitionable graphs under minimality constraints.Applied Mathematics and Computation4752024, 128753HALDOIback to textback to text
• 22 articleF.Fabricio Benevides, J.-C.Jean-Claude Bermond, H.Hicham Lesfari and N.Nicolas Nisse. Minimum lethal sets in grids and tori under 3-neighbour bootstrap percolation.European Journal of Combinatorics119June 2024, 18HALDOIback to text
• 23 articleJ.Julien Bensmail. A notion of vertex equitability for proper labellings.Discrete Applied Mathematics3462024, 228-247HALDOIback to textback to text
• 24 articleJ.Julien Bensmail. A σ3 condition for arbitrarily partitionable graphs.Discussiones Mathematicae Graph Theory4422024, 755-776HALDOIback to textback to text
• 25 articleJ.Julien Bensmail. Closure Results for Arbitrarily Partitionable Graphs.Opuscula Mathematica4462024, 773-788HALback to textback to text
• 26 articleJ.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.Discrete Mathematics347112024, 114174HALDOIback to text
• 27 articleJ.Julien Bensmail and F.Foivos Fioravantes. On proper 2-labellings distinguishing by sums, multisets or products.Discussiones Mathematicae Graph Theory432024, 863-878HALDOIback to textback to text
• 28 articleJ.Julien Bensmail, H.Hervé Hocquard and P.-M.Pierre-Marie Marcille. On inducing degenerate sums through 2-labellings.Graphs and Combinatorics4022024, 23HALDOIback to textback to text
• 29 articleJ.Julien Bensmail. On a graph labelling conjecture involving coloured labels.Discussiones Mathematicae Graph Theory4412024, 231-244HALDOIback to textback to text
• 30 articleN.Nicolas Bousquet, F.Frédéric Havet, N.Nicolas Nisse, L.Lucas Picasarri-Arrieta and A.Amadeus Reinald. Digraph redicolouring.European Journal of Combinatorics116February 2024, 103876HALDOIback to text
• 31 articleC.Caroline Brosse, A.Aurélie Lagoutte, V.Vincent Limouzy, A.Arnaud Mary and L.Lucas Pastor. Efficient enumeration of maximal split subgraphs and induced sub-cographs and related classes.Discrete Applied Mathematics345March 2024, 34-51HALDOIback to text
• 32 articleJ.Jonas Costa Ferreira da Silva and F.Frédéric Havet. On b-greedy colourings and z-colourings.Discrete Applied Mathematics359December 2024, 250-268HALDOIback to text
• 33 articleD.David Coudert, S.Samuel Coulomb and G.Guillaume Ducoffe. Leanness Computation: Small Values and Special Graph Classes.Discrete Mathematics and Theoretical Computer Science2622024, 22HALDOIback to text
• 34 articleP.Pierluigi Crescenzi, G.Giorgio Gambosi, L.Lucia Nasti, A.Aurora Rossi and E.Emanuele Natale. A sensitivity analysis of the Earth for all model: Getting the giant leap scenario with fewer policies.Journal of Industrial EcologyNovember 2024, 1-12HALDOIback to text
• 35 articleP.Pierluigi Crescenzi, E.Emanuele Natale, A.Aurora Rossi and P. B.Paulo Bruno Serafim. WorldDynamics.jl: A Julia Package for Developing and Simulating Integrated Assessment Models.Journal of Open Source Software995March 2024, 5772HALDOIback to text
• 36 articleF.Frédéric Havet, L.Lucas Picasarri-Arrieta and C.Clément Rambaud. On the minimum number of arcs in 4-dicritical oriented graphs.Journal of Graph Theory1074April 2024, 778-809HALDOIback to text
• 37 articleF.Florian Hörsch and L.Lucas Picasarri-Arrieta. Complexity results on the decomposition of a digraph into directed linear forests and out-stars.The Electronic Journal of Combinatorics31P4.26November 2024, 40HALback to text
• 38 articleG.Gwenaël Joret and C.Clément Rambaud. Neighborhood complexity of planar graphs.Combinatorica445June 2024, 1115-1148HALDOIback to text
• 39 articleN.Nicolas Nisse, L.Lucas Picasarri-Arrieta and I.Ignasi Sau. Redicolouring digraphs: directed treewidth and cycle-degeneracy.Discrete Applied Mathematics356October 2024, 191-208HALDOIback to text
• 40 articleL.Lucas Picasarri-Arrieta and C.Clément Rambaud. Subdivisions in dicritical digraphs with large order or digirth.European Journal of Combinatorics122December 2024, 104022HALDOIback to text
• 41 articleL.Lucas Picasarri-Arrieta and M.Michael Stiebitz. Minimum number of arcs in $k$-critical digraphs with order at most $2k-1$.Discrete Mathematics3479September 2024, 114072HALDOIback to text
• 42 articleA.Aurora Rossi, S.Samuel Deslauriers-Gauthier and E.Emanuele Natale. On null models for temporal small-worldness in brain dynamics.Network NeuroscienceJanuary 2024, 1-30HALDOIback to text