This article tells the story of an interdisciplinary hackathon emphasizing exploration, collaboration, and creative engagement with globalization-related cultural datasets. Participants worked in teams to produce research posters, which were then presented at the opening of the international conference Image Deluge & Globalization in Geneva. The article discusses three projects: one on 19th-century Spanish chapbooks using topic modeling and network analysis; another on leopard motifs in print imagery; and a third on olfactory heritage. The hackathon highlighted both the potential and the limitations of working with digital cultural data, offering insights into interdisciplinary methods. Résumé: Cet article raconte un hackathon interdisciplinaire favorisant l’exploration, la collaboration et la créativité face à des données culturelles liées à la mondialisation. Les participants et participantes ont réalisé des posters scientifiques, présentés à Genève lors de l’ouverture de la conférence internationale Image Deluge & Globalization. L’article raconte trois projets : une analyse textuelle d’imprimés espagnols de colportage du XIXe siècle ; une étude du motif léopard dans les médias illustrés ; et une exploration du patrimoine olfactif. Le hackathon a permis de mettre en lumière à la fois les potentialités et les contraintes des données culturelles numériques, tout en offrant une réflexion sur les méthodes interdisciplinaires.

Enteroviruses (EVs) are ubiquitous contaminants of surface waters, where they can remain infectious for long periods of time. Most methods used for EV monitoring are unable to distinguish between infectious and non-infectious parti cles, or between EV types. Because different types exhibit both distinct environmen tal persistence and health implications, there is a need for type-resolved infectivity measurements. Here, we developed integrated cell culture-nanopore sequencing (ICC-NanoporeSeq), a method combining short-term cell culture amplification with nanopore sequencing of the VP1 gene. The ICC approach was adapted from a previously described ICC-reverse transcriptase quantitative PCR (ICC-RTqPCR) protocol, while the NanoporeSeq workflow was derived from a clinical EV typing protocol and optimized for environmentally circulating EV types. Using samples containing known concentra tions of 10 EV types, the NanoporeSeq method accurately and reproducibly recovered the original proportions of all EV types after correction of biases. When applied to wastewater extracts, the method detected 45 EV types across five species. Furthermore, type-specific calibration curves generated with ICC-NanoporeSeq enabled quantification of the infectious concentrations of six EV types of the Enterovirus B species, allowing a simultaneous and type-resolved assessment of infectivity in mixed samples. Overall, ICC-NanoporeSeq provides a scalable approach for the parallel analysis of multiple EV types. Compared with the predecessor ICC-RTqPCR method, it eliminates the need for multiple type-specific PCR primers, and can therefore be readily expanded to include additional EV types. IMPORTANCE Current methods used to detect enteroviruses (EVs) in environmental samples generally measure viral genome copies without determining whether viruses remain infectious, limiting their use in public health risk assessment or water quality monitoring. At the same time, available infectivity assays are often labor-intensive and cannot distinguish between different EV types. Here, we developed integrated cell culture-nanopore sequencing (ICC-NanoporeSeq), a method combining cell culture and nanopore sequencing to simultaneously quantify the infectious concentrations of multiple EV types in samples containing mixed EV populations. The method pro vides an efficient and scalable approach for studying EVs in complex environmental matrices. ICC-NanoporeSeq has potential applications in wastewater-based epidemiol ogy, environmental surveillance, and disinfection studies, where understanding the persistence of different EV types simultaneously is crucial.

Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple models. This paradigm enables each model to remain local and proprietary while a central server queries them and aggregates predictions. Yet, the robustness of federated inference has been largely neglected, leaving them vulnerable to even simple attacks. To address this critical gap, we formalize the problem of robust federated inference and provide the first robustness analysis of this class of methods. Our analysis of averaging-based aggregators shows that the error of the aggregator is small either when the dissimilarity between honest responses is small or the margin between the two most probable classes is large. Moving beyond linear averaging, we show that the problem of robust federated inference with non-linear aggregators can be cast as an adversarial machine learning problem. We then introduce an advanced technique using the DeepSet aggregation model, proposing a novel composition of adversarial training and test-time robust aggregation to robustify non-linear aggregators. Our composition yields significant improvements, surpassing existing robust aggregation methods by 4.7 − 22.2% in accuracy points across diverse benchmarks.

Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL makes it vulnerable to backdoor attacks, where a model is taught to behave normally on standard inputs while executing hidden, malicious actions when encountering data with specific triggers. Backdoor attacks in DL remain understudied and existing defenses often overlook DL constraints. We introduce ARGUS, a novel backdoor detection framework native to DL that requires neither a central coordinator nor prior knowledge of the trigger. In ARGUS, honest nodes locally analyze received model updates to identify potential backdoor triggers. Nodes then collectively share their triggers with their neighbors and use a structural similarity metric to separate true backdoors from false alarms induced by data heterogeneity. A key insight is that false positive triggers exhibit inconsistencies across participants while true positive ones show consistent patterns. Model updates that fail this collaborative test are rejected, and persistently malicious senders are eventually evicted. We provide the first theoretical convergence guarantees for a DL-specific backdoor detection mechanism, showing that filtering out suspicious model updates with high probability preserves a convergence rate comparable to standard DL. We implement and evaluate ARGUS on three standard datasets and against three state-of-the-art baselines. Across settings, ARGUS reduces attack success rates by up to 90 points compared to no defense, while preserving model utility within 5 percentage points of an omniscient oracle. Furthermore, the effectiveness of ARGUS compared to baselines improves as data heterogeneity increases.

This thesis explores the role of carbon in climate-neutral energy systems. Climate mitigation is often framed as the decarbonization of the energy system, but in reality, in a climate-neutral system, carbon would still be present and circulate through remaining fossil fuels, biomass, CO2, synthetic fuels, chemicals, and negative emission technologies. The interactions between these components lead to complex mechanisms, calling for a systemic vision of carbon flows in the energy system.

To address this challenge, this work develops the Energy-Carbon Cycle Framework, which formalises the coupling between carbon sources and sinks under a neutrality constraint. It shows that residual fossil use is a possible system choice only with sufficient availability of carbon sinks. This concept is then translated into practice within the EnergyScope TD energy system model through a method that tracks the carbon flows between the different energy system technologies.

The proposed method is applied to the French energy system under climate neutrality constraints. This future climate-neutral energy system is compared with the national French strategy and other reference prospective documents. The results propose a clear merit order of lignocellulosic biomass use, in which sacrificing biomass to store carbon to reach climate neutrality is prioritised.

Building on this foundation, the thesis explores the specific case of the aviation sector. It is shown that e-bio-fuels are the most optimal pathway to produce sustainable aviation fuels (SAF), although this optimality is subject to uncertainty, which can favour the e-fuel pathway. The production cost of SAF, the total system cost, and the energy required for their production all depend strongly on the selectivity of the Fischer-Tropsch process towards jet fuel.

This work highlights that domestic production of SAF can reduce the need for flexible gas-based electricity production. These results are evaluated for different uncertainties surrounding the energy system, but the impact of interconnection and the recent downward trend in renewable energy CAPEX significantly reduce the initial values, bringing them to almost no effect.

Energy and carbon exchange between countries forces the expansion of the scope of this work to a European energy system, via the EnergyScope Multi-Cells model. To reduce the computational load of such an expansion, spatial and temporal aggregation methods are proposed and validated, leading to a reduction of the model's solve time by approximately 98 % without major change in energy system behaviour.

This new version of the model allows for uncertainty analysis of the European energy system. The robust decision-making method is used to identify carbon management strategies that are robust to uncertainties across multiple metrics, including total cost and life cycle impact assessment. This method particularly underlines that achieving European climate neutrality requires 339â 1018 MtCO2/yr of geological sequestration.

Overall, this work demonstrates that carbon remains a central component of climate-neutral energy systems. Rather than being eliminated, carbon resources must be understood from a systemic perspective, balancing two main roles: fulfilling the demand for carbonaceous sustainable fuels and enabling the system to reach climate neutrality by offsetting carbon sources with carbon sinks.

Understanding turbulence in magnetically confined fusion plasmas is a major element in the reliable design and operation of fusion power plants. Turbulence produces interacting fluctuations ranging from the Larmor radius to the size of the machine, which are notoriously difficult to model. In particular, despite recent advances, the interaction of microturbulence, i.e. turbulence on the scale of the Larmor radius, with macroturbulence, on the scale of the machine size, is not well understood. In order to describe the microturbulence, usually a gyrokinetic ordering is imposed, where the amplitude of the turbulent fluctuations is assumed to be small and separated from the background ($\delta$-f approach). On the other hand, in order to describe the macroturbulence, a drift-kinetic ordering is usually imposed, where the turbulent fluctuations are assumed to have a large spatial extent and large amplitude, with no separation between equilibrium and fluctuating quantities (full-f approach). In this thesis, we combine both orderings and self-consistently derive a comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model. The standard two-fluid drift-reduced Braginskii model and the $\delta$-f gyrokinetic model are retrieved in the appropriate limits. Then, we use an orthogonal Hermite-Laguerre basis expansion to efficiently include collisions and obtain a numerically efficient model.

Simulations are carried out, first, for a higher-order drift-kinetic model in a linear plasma device showing clear limitations of this model. Simulations using the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model are then carried out in the same geometry. A hybrid spectral and finite difference numerical scheme is implemented and the simulations show that the limitations seen in the extended drift-kinetic model are removed. Microturbulence is not present at the large ion-ion collision frequency, however, when collisions are suppressed, the gyrokinetic fields are seen to affect the turbulence.

Stepping up in complexity, we carry out simulations of the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model in a tokamak geometry. We use two different levels of resistivity corresponding to increasing the input power. At low power, the simulations share several similarities with the simulations in the linear plasma device. At high power, the formation of a transport barrier at the plasma edge through an increase in the radial electric field is observed while the gyrokinetic fields are dominating the turbulence in the core of the device through universal modes.

Finally, we focus on the boundary conditions of the model. In the vicinity of solid walls, a Debye sheath forms where the quasi-neutrality assumption is violated. Therefore, any global simulation must impose boundary conditions at the sheath entrance. We consistently derive sheath boundary conditions in the case of perpendicular incidence of the magnetic field with the wall. Simulations with these boundary conditions are carried out in a linear plasma device geometry. The sheath boundary conditions are shown to have an impact on the particle balance and equilibrium state of the plasma.

Low-density lipoproteins (LDL) are naturally occurring, monodisperse nanoparticles composed of proteins and lipids with an average diameter in the range of 20 nm. Their biological functions such as cholesterol trafficking and their roles in various diseases have been extensively studied. However, their properties as nanoparticles are seldom discussed. In this thesis, we investigate the formation of a protein corona on LDL, establish methods for its characterization, and explore the use of LDL-protein complex in cancer detection, biomarker discovery and disease monitoring. First, we show that LDL isolated from human serum or plasma carry a protein corona. Polyethylene glycol (PEG)-based precipitation followed by size-exclusion chromatography (SEC) preserves exchangeable proteins associated with LDL, while density-gradient ultracentrifugation largely removes the soft corona and yields LDL whose protein composition is dominated by ApoB-100. The two isolation strategies produce particles with distinct hydrodynamic size, homogeneity, and protein composition. Furthermore, incubation studies with purified proteins show negligible interaction of LDL with human serum albumin and apotransferrin, while immunoglobulin G exhibits only weak association, indicating that corona formation is selective rather than a consequence of indiscriminate adsorption of abundant plasma proteins. Building on this discovery, we focus on the study of LDL together with their protein corona, that we call protein-rich lipoproteins (ProLips), specifically on their potential in cancer detection. The tool we use is a proteomic comparison between samples from cancer patients and sample from healthy donors. Compared with whole-serum proteomics, ProLips proteomics consistently reveals substantially more differentially expressed proteins and provides clearer separation between healthy individuals and patients with melanoma, colon carcinoma, and liver cancer. The reduced contribution of highly abundant plasma proteins improves the detection of disease-associated proteins. Finally, six proteins as a melanoma-associated biomarker set are identified and validated. At last, we evaluate whether ProLips proteomics can monitor disease progression and treatment response. In a melanoma rat model, ProLips protein profiles distinguish different stages of tumor development. We show that in colorectal cancer patients sampled before, during, and after surgery, temporal changes in ProLips composition reflect surgery-associated remodeling, while persistent deviations after surgery are associated with clinical relapse. In liver cancer patients receiving immunotherapy, ProLips proteomics captures individualized treatment-associated changes, and both TMT- and DIA-based analyses reveal consistent biological trends despite differences in individual proteins identified.

The emerging technology of wearable robots is gradually transforming our daily life. Up to 2026, many commericallized exoskeleton products have flourished, including those from companies ReWalk, HAL, ULS Robotics, Kenqing Technologies, ready on market to improve wearer's mobility and endurance. Nearly all the market-available exoskeletons are motor-driven, based on bulky external components, rigid transmission elements, or complex control architectures, which decreases wearing willingness. Developing soft wearable robots can well address some of the challenges of hard exoskeletons, improving wearing comfort, unobtrusiveness, and social ac-ceptance. However, to realize soft wearable robots critically depends on soft, thin, compact, and lightweight actuators to deliver sufficient torque (10 ~ 100 Nm) and displacement (5 ~ 30 cm) while maintaining energy-efficiency and reliability. This thesis investigates soft actuators in textile format (textile actuators) to enable unobtrusive actuation on body. This thesis explores the design, modeling, fabrication, experimental characterization, and application scenarios of fabric actuators based on shape memory alloys (SMAs) and electrostatic clutches (ESClutches). This thesis has three contributions to soft wearable robotics. Firstly, by encapsulating ESClutches and commercially available SMA springs into woven fabrics, we developed an energy-efficient fabric actuator (weighing 55 grams) that can lift 40 N mass and consumes milliwatts power for statically holding the mass. Secondly, using mechanics-informed principles, we developed X-Crossing architecture to enable SMA fabrics of 4.5 grams lifting 1 kg mass with 50% contraction, outperforming published SMA fabrics, including knitted and knotted SMA fabric actuators. Thirdly, we developed variable-stiffness mechanics model for X-Crossing SMA fabric actuator. This model predicts the actuator performances and add understandings to the actuator characteristics, including the peak in its performance curves and fundamental limits on actuator contraction. In this thesis, we also demonstrate the applications of developed fabric actuators. The clutchable fabric actuator mounted on the upper limb of a mannequin lifted 2.6 kilograms of mass, reducing 70 % energy consumption by using ESClutches compared to that without using ESClutches. Using this actuator, we demonstrate a waistband with controllable tightness as exoskeleton anchoring for force transmission to body, and using the ESClutches reduces 80% of energy consumption in defined tasks. We also demonstrate the X-Crossing architectured SMA fabric actuator can be well controlled to bend a mannequin elbow to target angles, and this fabric actuator can be fabricated as wrist sleeve to generate compression up to 13 kPa, and then maintain a residual compression pressure of 5 kPa with zero energy cost.

Previous studies in mouse and human melanoma indicated that the TGF-β superfamily member Activin-A (encoded by INHBA) is a major driver of metastatic progression and primary resistance to immune checkpoint blockade (ICB). The goal of this thesis was to test if activin-induced tumor immune evasion involves the type I activin receptor ALK4, and to identify relevant direct target cells. Systemic Alk4 depletion in the tumor microenvironment (TME) of YUMM3.3 mouse melanoma grafts using Rosa26-CreERT2 shows that this receptor is required to mediate CD8 T cell exclusion and tumor growth by paracrine Activin-A signaling. Blocking monocyte/macrophage recruitment to the tumor in Ccr2 knockout mice, or eliminating them by pharmacological depletion, is shown to not impair Activin-A oncogenic function. By contrast, Alk4 deletion in DCs using Zbtb46-Cre or the conventional DC1-specific Xcr1-Cre identifies cDC1 cells as essential mediators. Blocking cDC1 maturation in Batf3-/- host mice further corroborated this conclusion. Furthermore, analysis of human TCGA-SKCM and single cell transcriptomes reveals that a tumor cell-intrinsic Activin-A signaling signature score correlates with poor overall survival and with an immune-excluded phenotype characterized by impaired DC cross-presentation and profound CD8+ T cell exhaustion also in human melanoma. Mechanistically, Alk4 signaling in cDC1 cells is shown to promote ER stress, paralyzing the antigen-processing machinery. The cDC1 malfunction correlated with entrapment of CD8+ T cells in a CD44 CD62L double-negative state in tumor-draining lymph nodes, indicating incomplete T cell priming. In line with this interpretation, deletion of Alk4 specifically in cDC1s restored antigen presentation, T cell polyfunctionality, and tumor immune surveillance.

Contrasting the novel oncogenic function in cDC1 cells, ALK4 signaling in melanoma cells themselves can be tumor-suppressive. This was first shown in B16-F1 mouse melanoma expressing constitutively active mutant Alk4 (caAlk4) as a doxycycline-inducible transgene. Here, I show that caALK4 similarly inhibits melanoma growth in a Tet-OFF setting and in YUMM3.3 melanoma, and that it involves stimulation of anti-tumor immunity by tumor cell-intrinsic Smad2/3 signaling. Finally, yet another novel protective function of Activin-A signaling is shown to rely on ALK4 signaling in endothelial cells, which limited tumor infiltration by Tregs and neutrophils. Altogether, these findings highlight Activin-A/ALK4 signaling as a critical bottleneck for melanoma immunogenicity. Therapeutic targeting of ALK4-induced ER stress specifically in cDC1 cells, ideally without blocking tumor-suppressive ALK4 functions in other cell types, may provide a potent new approach to overcome immune checkpoint therapy resistance.