This study examines the persistent, though often overlooked, practice of material reuse in francophone Switzerland, exploring advertisements from 1851 to 1968 that illustrate a continuous and practical approach to reclaiming construction materials. These ads reveal reuse as an adaptable and community-centered practice, enduring even as industrialization reshaped construction priorities. By analyzing 21 ads and categorizing them into six core themes-including community resilience, public authority involvement, and heritage preservation-this paper underscores reuse as a culturally embedded and economically driven norm. By drawing comparisons with other European contexts, the study highlights how historical reuse principles might inform contemporary circular economy frameworks, especially concerning community-based material networks and ethical considerations in heritage conservation.

Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expensive active sensing. However, existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments, depth ranges, and lighting conditions, due to the scarcity of real-world data. We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation within an iterative optimization-based stereo matching architecture. We introduce a dedicated two-stage training strategy to utilize the relative monocular depth features for our omnidirectional stereo matching before scale-invariant fine-tuning. DFI-OmniStereo achieves state-of-the-art results on the real-world Helvipad dataset, reducing disparity MAE by approximately 16% compared to the previous best omnidirectional stereo method.

Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K frames from video sequences across diverse environments, including crowded indoor and outdoor scenes with diverse lighting conditions. Collected using two 360° cameras in a top-bottom setup and a LiDAR sensor, the dataset includes accurate depth and disparity labels by projecting 3D point clouds onto equirectangular images. Additionally, we provide an augmented training set with a significantly increased label density by using depth completion. We benchmark leading stereo depth estimation models for both standard and omnidirectional images. Results show that while recent stereo methods perform decently, a significant challenge persists in accurately estimating depth in omnidirectional imaging. To address this, we introduce necessary adaptations to stereo models, achieving improved performance.

Switzerland's net-zero strategy is evolving from a predominantly efficiency-and technology-driven framework into a more nuanced suite of scenarios. Yet, its core assumptions-reliance on coordinated policy, linear trajectories, and technological optimism-remain largely unchallenged. Here we propose broadening the scenario space to include alternative pathways grounded in human needs, wellbeing, and participatory governance, as well as a range of technology futures. Drawing on a comparative analysis of main Swiss scenarios (EP2050+, Long-term Climate Strategy, DeCIRRA), the main IPCC and IEA scenarios relevant to country-level policy, and a new set of divergent narrative scenarios, we identify key blind spots, including limited attention to sufficiency, equity, social fragmentation, and deep uncertainty. Building upon the existing analytical basis and extending it by integrating plural perspectives, of societal transformation, political diversity, and deliberative democratic governance, we aim to enrich the current foundations of Swiss climate policy, and enable a more resilient response to future disruptions on the path to net zero.

This article examines how the English and French patent regimes contributed to the formation of technological knowledge in the eighteenth and nineteenth centuries. From the 1730s in England and the 1790s in France, patent requests were required to include a textual and often visual specification of the invention claimed by the applicant. We study the evolution of the specification genre, the chain of actors involved in the production of such documents, and the circulation of these texts and drawings in the public sphere of technology. To do so, we draw on a case study of six pairs of patents in the lace and tulle industry. By focusing on patents taken out in both countries for the same invention, we analyze how local cultures of invention and legal regimes shaped how technology was described. Tracing the history of Technology understood as the science of industrial arts requires, we argue, a deep dive into the archives of practice.

The rise of high-throughput first-principles calculations of materials since the turn of the millennium has gifted the fields of physics, quantum chemistry, and materials science with a mountainous and ever-growing pile of data ready to be mined for hidden gems of scientific insight and discovery. For the last decade and a half, researchers have been hard at work designing machine learning (ML) methods to both sift through these data and leverage them to further accelerate the determination of quantum-mechanically accurate properties. For some tasks, like predicting the potential energy between atoms used to integrate Newton's equations of motion, efficient and accurate ML methods are already well-established as indispensable tools for understanding the macroscopic properties of materials at a level of accuracy that was previously prohibitively expensive. A key factor in the rapid success of these approaches has been the wealth of detailed knowledge of the physical laws governing the interactions of electrons and atoms which serve to constrain and guide the development of ML in the field. However, the majority of these approaches gloss over the key players that actually make molecules and materials what they are: the electrons. In this thesis, we aim to investigate the construction and use of ML methods which prioritize the electronic structure of materials, beginning by exploiting the fundamentally quantum nature of electronic matter and the associated tools of electronic structure theory to formulate representations of materials. Many complex material properties, like their interactions with light and transport of electricity, cannot be readily explained by the geometry of their constituent atoms and are therefore difficult to predict from the standard atomic descriptions used for ML. We show that by using electronic-structure based descriptions and simple ML models, the quality of data-driven predictions targeting first-principles calculations of these properties can be improved. There are also certain electronic behaviors which are not well-described by standard first-principles simulation approaches. In particular, standard approximate density-functional theory (DFT) models fail to reproduce the experimentally measured properties of some materials containing transition-metal elements due to a poor treatment of localized d and f electron orbitals. Corrective approaches, like DFT + Hubbard, can in many cases restore the expected behavior, but they require expensive first-principles calculations to determine the parameters controlling the strength of the corrections. In the second part of the thesis, we show that by using electronic-structure based equivariant neural network models, these Hubbard parameters can be accurately predicted orders of magnitude more quickly than with physical calculations. We also present ongoing efforts to extend this approach towards a universal model which could significantly facilitate the use of first-principles DFT + Hubbard more broadly. Finally, in the last part of the thesis, we describe preliminary work towards the prediction of electronic Hamiltonians, fundamental physical quantities describing the behavior of electrons, represented on Wannier function bases. This approach takes advantage of the locality of the Wannier functions to learn, in principle, their well-converged and compact Hamiltonians, providing indirect access to many relevant complex electronic properties.

As it reveals the increasing complexity of the contemporary world, the socio-ecological transition demands a renewal of our gaze. By observing to imagine and drawing to narrate, architects and urbanists shape space through its representations and narratives, accompanying societies in their quest to find their place in the world. In this regard, cartography is an essential project tool for spatial designers, allowing them to sketch out possibilities. However, maps have long promoted a conception of inhabitability closely linked to technical control of the environment, embodying a parametric and anthropocentric gaze designed for a single subject: the human being. Today, socio-ecological challenges highlight the presence of other subjects who, also involved in the transformation of cities and territories, prove our relationships of coinhabitations. This paradigm shift therefore calls for a re-invention of our urban and territorial imaginaries, a re-writing of our narratives of inhabiting, and a re-thinking of our cartographic practices and gazes, in order to finally read the geo-eco-sociological realities of the Earth. By proposing a new narrative based on coexistence, the research supports the urgent need to re-present the conditions of our being-in-the-world in order to understand the dual nature - social and ecological - of the contemporary crisis.

Through what processes of re-presentation can the narrative of inhabitability evolve into a narrative of co-inhabitability?

To address this question, the research proposes a series of cartographic narratives to help interpret and conceptualize territories in transition, according to their potential conditions of coinhabitability. These conceptual and methodological explorations - guided by the research hypothesis that reconsiders the concept of territory as Territory-Subject - reveal the dynamic and evolving nature of territories both in space and time. The Atlas thus emerges as an obvious methodological choice. By combining approaches and diversifying themes, it brings together heterogeneous materials in dialogue. As a practical tool, the Atlas enables us to sketch out a new territorial narrative of coinhabitability, supported by the production of hybrid cartographies. The narrative and cartographic explorations re-present two territories, Geneva and Paris, which constitute both practical terrains and supports for analysis. Integrated into the research, these exercises contribute to the development of the new gaze required by the socio-ecological transition. They also offer a critical lever for de-constructing and re-interpreting existing cartographic practices, thus paving the way for an Atlas of the new gaze.

The global plastic waste crisis remains a fundamental challenge for mankind to address, as vast amounts of plastic continue to accumulate in the environment, threatening ecosystems and human health. Biobased and biodegradable plastics, particularly materials based on aliphatic polyesters, offer a promising alternative to conventional non-degradable, petroleum-based polyolefins, but typically suffer from inferior thermomechanical performance. To comply with existing industrial processing techniques, such as blow molding or film drawing, these sustainable materials must be modified to exhibit sufficiently high melt strength and melt extensibility. In this context, polymers bearing end groups capable of supramolecular aggregation are particularly promising. Recent studies on high-molar-mass oligopeptide-modified polymers have shown that these materials exhibit rubber-like behavior and strain hardening in the polymer melt when blended them with additives that boost supramolecular motif concentration. This enables melt drawing to high draw ratios and the fabrication of highly oriented films. The present thesis investigates the dependence of this behavior on the deformation rate, the supramolecular motif and its concentration, and the molecular-scale network dynamics by using 1,3,5-benzenetricarboxamide (BTA) as a supramolecular motif in view of its reliable self-assembly into nanofibrils, comparatively high transition temperatures, and industrial relevance. For a representative aliphatic polyester, we demonstrate that BTA-based polymer end groups efficiently co-assemble with an additive into nanofibrils that serve multiple functions. They are highly efficient nucleating agents for the crystallization of the polyester matrix, and form supramolecular networks that give rise to a high-melt-strength rubbery regime extending to temperatures of up to 149 °C. The melt behavior under large tensile deformations, such as during film melt drawing, is governed by the competition between polymer chain stretching and relaxation processes, which are modulated by the supramolecular network dynamics. Using nuclear magnetic resonance spectroscopy, we establish a site-specific, non-destructive method to determine molecular scale dissociation rate constants for the nanofibrils in the bulk melt. Applying this technique, we show that BTA-based networks exhibit faster molecular-scale dissociation kinetics than oligopeptide-based materials. Consequently, despite their higher dissociation temperatures, melts containing BTA-based networks fail via viscous flow at strain rates and temperatures where oligopeptide-based blends remain extensible and display pronounced strain hardening. However, blending polyesters modified with BTA-based end groups with 1 wt% of a chiral BTA analogue yields aggregates with significantly slower exchange dynamics, resulting in strain hardening and dramatic improvements in melt extensibility (up to 3200%). Films produced by melt-drawing of such blends are optically transparent and exhibit a highly oriented "shish-kebab" morphology. This work demonstrates that tuning the dynamics of supramolecular networks is a key design principle for enabling large-strain processability in high-molar-mass supramolecular polymer materials. Our strategy is applicable to other polymer types and may facilitate the fabrication of oriented materials from sustainable polymers for many important applications, including barrier films, foams, and food packaging.

Clonal hematopoiesis of indeterminate potential (CHIP) and myelodysplastic syndromes (MDS) are disorders associated with clonal expansion within the hematopoietic system that can result in cytopenias, increased risk of cardiovascular and autoimmune disease and progression to acute myeloid leukemia (AML). Multiple recent investigations into hematopoietic malignancies such as multiple myeloma (MM) and AML leveraging single-cell transcriptomics to investigate the tissue at cellular resolution have highlighted the importance of the bone marrow microenvironment in creating favorable conditions for mutant cells and promoting disease progression and relapse. However a detailed understanding of how various bone marrow niches are perturbed in CHIP and MDS is missing. An ancillary, but important technical question related to single-cell profiling of cancer tissues is accurate discrimination of mutant cells from their non-malignant counterparts. This is not always straightforward, particularly in the context of blood cancers, because of a lack of definitive features between the two on the level of RNA expression or cell surface markers. The aim of this thesis is to provide a detailed characterization of how the bone marrow niche is affected in CHIP and MDS and how these alterations might contribute to pathogenesis of these conditions. We first describe SpliceUp, a novel computational method for identifying cells with mutations in splicing factor genes, which is a common feature of various blood cancers, including MDS, in single-cell transcriptomic data. Next we provide a detailed bone marrow niche characterization of healthy controls, CHIP and MDS patients using a combination of bulk and single-cell transcriptomics, proteomics, cell sorting and imaging techniques. We highlight several important features associated with inflammatory changes in the bone marrow in CHIP and MDS, such as alteration in stromal and T cell composition and emergence of inflammatory MSCs (iMSCs) and IFN-response T cells restricted to the disease conditions. Our findings indicate a set of common inflammatory changes in the niche partially overlapping with existing findings in AML and MM, as well as certain unique features like residual expression of HSPC-support genes in iMSC specific to our MDS samples. This work provides a general characterization of the bone marrow microenvironment in CHIP and MDS and puts it in the context of existing knowledge about other hematological malignancies. It highlights the importance of the bone marrow niche in these disorders and provides a reference for future studies targeting the individual components of this niche.

Non-regular or irregular statistical problems are those that do not satisfy a set of standard regularity conditions that allow useful theoretical properties of inferential procedures to be proven. Non-regular problems are prevalent; a classical example is the Gaussian mixture model, while more recently, the advent of machine learning has introduced models that are highly non-regular as well as black-box. In this thesis, we make some contributions to non-regular statistical inference. Our framework is likelihood-based; we give an overview of likelihood theory in Chapter 1.

In Chapter 2, we study universal inference, a method proposed by~\citet{Wasserman16880} that can construct finite-sample level $\alpha$ tests with minimal regularity conditions. We identify three sources of the resulting loss of power in the normal case, as a trade-off to this great generality. We show that universal inference becomes catastrophically conservative as the number of nuisance parameters grows, and propose a correction factor to mitigate this conservativeness while maintaining finite-sample level $\alpha$ error control. We demonstrate the viability of the correction factor on the non-regular problem of testing for the number of components in a two-component Gaussian mixture model. We also study the $K$-fold variant of universal inference and caution against using certain splits that lead to degenerate statistics.

In Chapter 3, we apply universal inference to construct model confidence sets with finite-sample coverage guarantees, which we dub universal model confidence sets (UMCS). We study the asymptotic properties of UMCS and establish its ability to include true and correct models and exclude wrong ones. We examine the application of the quasi-reverse information projection (qRIPR) to mitigate the conservativeness of UMCS, and study some cases where the application of qRIPR maintains the e-value property of the universal inference statistics, a property central to its error-controlling feature. We test the performance of UMCS to pick out signal covariates on a high-dimensional gene example.

In Chapter 4 we study discrete profiling, an extension of profile likelihood through the introduction of discrete nuisance parameters that index different functional forms modelling uncertainty. We extend the phenomenon of an observed bias in mis-specified normal linear models to a general one using asymptotic theory, and examine the ability of the discrete profiling algorithm to asymptotically detect mis-specified and slightly mis-specified models. We derive an expanded form for the discrete profile likelihood statistic and study its asymptotic properties under different cases of mis-specification. We corroborate our theory on the task of finding the best Student's $t$ density to model a normal density.

We conclude in Chapter 5 and discuss directions for future work.