Agenda

Département
Langue
Date
Thématique
2025

December

  • 14:00
    17:00

    In recent years, data production in biology has experienced unprecedented growth, driven by the development of high-throughput sequencing techniques, whose scope of application continues to expand. New sequencing technologies specifically targeting a single cell (``single-cell'') are one example. In oncology, this new data is crucial for improving our understanding of tumor development and heterogeneity by identifying the different cell types (or states) that make up a tumor. At the patient level, comprehensible mapping of this heterogeneity paves the way for new personalized medicine therapies. Characterizing this cellular heterogeneity requires the use of automatic or manual methods to annotate an individual cell (or group of similar cells) based on its gene expression. In this context, the aim of this thesis project is to develop new methods for annotating single-cell data at the intersection of several disciplines, such as bioinformatics and computer science (knowledge representation and visualization, in particular). 

    Amphi LaBRI
  • 10:00
    13:00

    High-performance computing refers to the use of supercomputers to solve complex problems requiring exceptional computing power, particularly in numerical simulations such as weather forecasting or fluid dynamics. These systems, organized into computing clusters, combine system administration, networking, hardware architecture, and software optimization. Supercomputers are composed of multiple computing nodes, each equipped with multi-core processors or even graphics cards, connected in a network that allows data exchange. Rather than executing a task on a single machine, problems are divided and parallelized. This allows simultaneous execution on multiple resources. There are two forms of parallelism: inter-node, where communication between nodes is critical but resources are vast, and intra-node, where processors share memory, facilitating communication but with more limited resources. In this context, streaming applications, particularly software radio, take advantage of intra-node parallelism. Stream computing differs from batch processing. Data is processed as it arrives, without accumulating input data. Processing filters are organized in a pipeline, with each stage being executed by a different computing resource. This mechanism significantly increases throughput, which is essential for applications such as video or radio broadcasting. This thesis aims to optimize the automatic allocation of resources for streaming applications on multicore architectures, first homogeneous and then heterogeneous. The first part of this work focuses on task chain scheduling on homogeneous multicore architectures. The problem is modeled as a pipeline workflow scheduling problem. The objective is to maximize throughput by exploiting pipeline parallelism and task replication. Two algorithms are proposed: a dynamic programming approach to obtain an optimal solution, and OTAC, an optimal greedy algorithm that guarantees high throughput while minimizing resource usage. Experiments show that OTAC quickly produces optimal partitions with reduced resource usage. The emergence of hybrid processors composed of high-performance cores (core-P) and energy-efficient cores (core-E) introduces new challenges: execution times vary depending on the assignment. The objective becomes twofold: to maximize throughput while minimizing energy consumption, favoring the use of efficient cores. The second part therefore focuses on resource allocation for task chains on heterogeneous architectures. Three strategies are developed: two greedy heuristics (FERTAC and 2CATAC) and an optimal solution using dynamic programming (HeRAD).
    The results indicate that heuristics achieve near-optimal performance while consuming very few additional resources. The last part of this work focuses on the management of multiple simultaneous streaming channels. In certain contexts, such as embedded systems, IoT, or the cloud, multiple applications coexist on the same resources. The goal is to intelligently distribute resources among multiple pipelines while satisfying throughput constraints without wasting resources. The last part explores allocation strategies adapted to the management of multiple task chains or task graphs. Thus, this thesis offers several contributions to the optimization of streaming systems on parallel architectures, covering optimal scheduling, adaptation to heterogeneous architectures, and the coexistence of multiple simultaneous streams, with a constant focus on performance and energy efficiency.

    Amphi LaBRI
  • 16:00
    15:48

    The diversification of high-performance computing (HPC) applications and the development of heterogeneous workflow applications involving HPC, artificial intelligence and machine learning, and in situ analysis are challenging the standard architecture of HPC clusters. Modern HPC workflows increasingly rely on cloud-based services and applications, which are often not supported in HPC environments. Cloud computing has historically been the preferred platform for running services, leading to the development of a large ecosystem of software for cloud environments. In addition, it is increasingly supporting HPC applications by offering dedicated hardware and job scheduling via standard HPC tools or cloud tools. The convergence of the needs of both communities has sparked growing interest in an architecture that combines the advantages of both platforms and is capable of supporting both HPC and cloud applications within the same environment, without compromising performance, security, or usability. This type of approach is generally referred to as converged infrastructure computing. This thesis explores several approaches to converging computing to enable the deployment of cloud-based applications in HPC clusters. We study two approaches based on the on-demand deployment of Kubernetes clusters on HPC resources, offering different compromises in terms of flexibility for resource management and supported Kubernetes features.
    Our main contribution lies in the design and implementation of a secure container network that ensures user isolation and provides the flexibility and security necessary for deploying service-type applications, while remaining transparent to end users. We implemented this network in the pcocc container engine used in production at the CEA and measured no significant impact of the container network on MPI application performance. This innovative approach demonstrates that network virtualization can increase the flexibility of HPC clusters and enable the deployment of cloud-based applications without impacting the performance of traditional HPC applications.

     

    Salle Ada Lovelace (Inria)
  • 14:30
    18:00

    Determining the conditions for culturing microorganisms is a difficult and recurring problem in microbiology, as it requires identifying nutritional requirements and characterising metabolism. \textit{Genome-scale metabolic networks} (GSMNs) obtained from genomic data enable simulations of the metabolic potential from a predefined environment. We developed methods that solve the inverse problem: predicting nutrient sources, or \textit{seeds}, from a GSMN and a metabolic objective. The methods propose hybrid models, combining a discrete and iterative Boolean approximation of metabolic activity with numerical flux balance analysis (FBA). Applied at the scale of an individual population on GSMNs, the logic modelling method is a good approximation of flux balance constraints. The problem was then extended to communities of microorganisms. At this scale, it is necessary to consider possible transfers between networks, which increases the combinatorial complexity of the problem. We hypothesise that it is relevant to identify in priority minimal sets of nutrients that ensure the functionality of the metabolic network, which led us to consider prioritising optimisations on sets, first seeking to minimise seeds, then transfers. Three algorithms were developed, two of which ensure subset minimality. The application of methods to small communities of network reveals the combinatorial complexity, but also the complementarity of the algorithms.

    Amphi LaBRI
  • 10:00
    14:00

    This thesis comes at a time of unprecedented growth in humanoid robotics, driven by rapid technological advances and growing enthusiasm among private actors and the general public. In this context, recent progress in humanoid robot motor skills and their growing social acceptance seem to herald their imminent integration into real-world environments alongside humans. Faced with this ambition, a major challenge remains: ensuring the robustness and autonomy of locomotion in the dynamic contexts in which these robots will operate. To address this challenge, this work combines physical modeling and reinforcement learning, exploiting the complementary advantages of these two paradigms—the stability guarantees offered by modeling and the adaptability provided by learning. This manuscript begins with a state-of-the-art review of control and learning methods applied to humanoid locomotion, aimed at identifying the most promising approaches for reconciling robustness, adaptability, and dynamic realism. On this foundation, the PlaCo software, dedicated to motion planning and robot control, is developed. It aims to abstract the complexity of the optimization formulations necessary for trajectory generation, while maintaining performance compatible with real-time execution. This framework is then used to design and deploy a walking controller based on the linear inverted pendulum model (LIPM) on the Sigmaban humanoid robot. This development highlights the model's ability to produce consistent trajectories in real time, while revealing the practical limitations encountered on a real platform. In order to overcome these limitations and enable dynamic adaptation to disturbances, a reinforcement learning agent dedicated to fall recovery is developed. Trained in simulation, this agent is successfully transferred to the real robot, demonstrating a significant gain in autonomy. The difficulty of this transfer nevertheless highlights the central issue of the gap between simulated and real environments. This observation leads us to seek ways to minimize this discrepancy by improving the accuracy of the simulation. An in-depth study of friction phenomena in servo-actuators has been conducted, showing how a more detailed consideration of these phenomena improves the quality of the simulation and policy transfers.

    Salle 178 (LaBRI)
  • 14:00
    15:00

    The celebrated Linear Arboricity Conjecture of Akiyama, Exoo, and Harary from 1980 asserts that the edges of every graph with maximum degree Δ can be decomposed into at most ⌈(Δ+1)/2⌉ linear forests, where a linear forest is a collection of vertex-disjoint paths. We show that any graph on n vertices can be decomposed into at most Δ/2 + O(log n) linear forests. This improves the previous best bound for Δ = Ω(log^2 n).

    Along the way, we show that any d-regular graph on n vertices has a spanning linear forest with at most 2 * n/(d+1) paths. This resolves a conjecture of Feige and Fuchs and confirms a well-known conjecture of Magnant and Martin up to a factor of 2. As a corollary, it also follows that every connected d-regular graph on n vertices has a tour of length at most (1+O(1/d)) * n. This is optimal and improves bounds of Vishnoi and of Feige, Ravi, and Singh, but we do not obtain an efficient algorithm for computing such a tour.

    (Lukas Michel) [University of Oxford]
    https://people.maths.ox.ac.uk/michel

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    Remarks / Remarques

    Find all the information of the working group on this [ https://graphesetoptimisation.labri.fr/pmwiki.php/Groupe/GT?userlang=en | web page ] .
    Retrouvez toutes les informations du GT sur cette [ https://graphesetoptimisation.labri.fr/pmwiki.php/Groupe/GT | page web ] .

    English
    LaBRI/178
  • 14:00
    18:00

    Quantum physics is evolving with the emergence of new quantum technologies. These advances open up a range of possibilities for future practical applications. However, developing these applications requires training new generations of students, not only as researchers, but also as engineers and technicians. To achieve this goal, education plays a central role and, in the context of quantum physics, should evolve to meet these needs. The issue of teaching quantum physics is intrinsically linked to the nature of its concepts, which are, in most cases, abstract, counterintuitive, and based on complex mathematical theories. As a result, teaching quantum physics remains a challenge. Among the tools that help students acquire complex knowledge, experimental practices and digital tools have shown benefits for students' conceptual understanding.
    In this thesis, we explore how quantum physics education can leverage digital tools to promote these experimental practices. We propose new approaches to practicing and learning quantum physics, based on real-time digital simulation of quantum optics experimental setups across multiple formats. Thanks to these new tools, the simulation can be controlled using physically manipulable components, thus mimicking real quantum optics experiments. Beyond the practical advantages (cost, implementation, danger), this approach enriches the simulation by adding digital educational information that links the concrete experience to its theoretical foundations. The first part of this thesis focuses on the evolution of quantum physics teaching practices and the use of digital technology in education. It then presents three projects designed in collaboration with experts in education and physics. The first, called HOBIT, consists of a device that combines tangible interaction and augmented reality to simulate an optical bench where learners manipulate physical reproductions of optical elements to perform wave optics experiments. The second, SHIRE, is an extension of the HOBIT experience on digital software that can be run on learners' computers. Despite the loss of tangible interactions, this approach broadens the range of educational scenarios in which the simulation can take place and opens up new opportunities. This project also includes a study of the use of the software by physics teachers. Finally, HQBIT, presented in the last part of this thesis, integrates a simulation of quantum physics as well as new educational enhancements. The goal is to create a hybrid digital tool conducive to training a new generation of students in the concepts and experimentation of quantum optics. A user study with students completes this project and provides an initial evaluation of this tool.
    To conclude this thesis, I explore the prospects offered by HQBIT in terms of collaboration, assessment of learning outcomes, and the evolution towards quantum technologies.

    Salle Ada Lovelace (Inria)
  • 09:30
    12:00

    The agricultural sector is undergoing a triple crisis, not only social but also economical and ecological, which requires a change in the production model. For political institutions, the solution lies in a twin transition, using information and communication technologies (ICT) as a lever to make the sector more sustainable. However, there are many uncertainties regarding the consequences of such a strategy. In particular, from an environmental point of view, few studies assess the effects of large-scale deployment of digital agriculture technologies. Yet the harmful effects of ICT on the environment are well known, and an increase in digital equipment and infrastructure for agriculture could be a threat to the sector’s sustainability. This thesis introduces methods for assessing the environmental impacts of large-scale deployment of digital agricultural equipment and its dependence on infrastructure. The general framework adopts a prospective scenario-based approach and parametric and consequential modelling. A first contribution concerns the estimation of the impacts of digital technologies deployed on farms in a given agricultural context. This context is defined as a type of production and a distribution of farms of varying sizes. This method is applied to two case studies in mainland France: the identification and monitoring of oestrus in dairy cattle, and robots for mechanical weeding and automated sowing in large-scale cereal crops. The results reveal heterogeneous impacts, depending on the number of pieces of equipment deployed, their mass and their technological complexity. Scenarios involving the most advanced technologies and using a larger mass of equipment tend to have the highest impacts. A second contribution extends the case study dedicated to weeding robots and focuses on the impacts associated with their use of the mobile network. This method simulates the deployment of robots in a given area, based on a set of agricultural parcels and existing mobile network infrastructure, with each site supporting a maximum data volume. The scenarios considered vary according to the design of the robots, and the volumes of data sent by the robots differ greatly from one design to another. Based on a traffic simulation of data and its impact on the mobile network, we estimate the additional impact on existing infrastructure in terms of embodied carbon footprint of required network equipment, and that associated with their energy consumption. We also estimate the proportion of agricultural land that can be managed by the deployed robots. The results show that for intensive use of the network, the capacity of the current network greatly limits the number of robots that can be deployed, even when the capacity of existing sites is maximized. Furthermore, for the most intensive scenarios, the additional power consumption of the network is of the same order of magnitude as that of the robots themselves. Thus, for these scenarios, only a small fraction of the parcels can be managed without adding sites, and increasing this fraction would imply a significant increase in the overall footprint of the mobile network in question. This manuscript provides an initial overview of the consequential carbon footprint associated with the deployment of digital equipment on farms and their use of the mobile network.

    Amphi LaBRI
  • 11:00
    12:00

    Hussein Kazemi (LaBRI)

    Title: Trajectory visibility at first sight

    Abstract:

    Let P be a simple polygon with n vertices, and let two moving entities q(t) and r(t) travel at constant (possibly distinct) speeds vq and vr along line-segment trajectories τq and τr inside P. We study the exact first-visibility time t∗= min t≥0: q(t) r(t) ⊆P, the earliest moment at which the segment joining q(t) and r(t) lies entirely within P.

    Prior work by Eades et al. focused on this question in the setting of a simple polygon. They gave a one-shot decision algorithm running in O(n) time. For a stationary entity and a moving one, they suggested a structure that, after O(n log n) pre-processing, answers the decision query in O(log n) time, requiring O(n) space. In addition, for moving entities, after preprocessing time of O(n log⁵ n), they construct a data structure with O(n^{3/4} log³ n) query time and O(n log⁵ n) space. Variants for polygonal domains with holes or when entities cross the boundary of P lie beyond our scope.

    In this work, we go beyond the decision to compute t* exactly under three models for a simple polygon P. When both trajectories are known in advance, we preprocess P in O(n) time and space and thereafter answer each query in O(log n) time. If one trajectory τr is fixed while τq is given as query, we build a structure in O(n log n) time and space that computes t∗in O(log² n) time per query. In a setting where the trajectories are not known in advance, we develop a randomized structure with O(n^{1+ε}) expected pre-processing time and O(n) space, achieving an O(√n polylog(n)) expected query time for any fixed ε > 0.

    https://algodist.labri.fr/index.php/Main/GT

    English
    LaBRI 178