Comparing mortgage rates that borrowers obtain to rates that lenders could offer for the same loan, we find that many homeowners significantly overpay for their mortgage, with overpayment varying across borrower types and with market interest rates. Survey data reveal that borrowers' mortgage knowledge and shopping behavior strongly correlate with the rates they secure. We also document substantial variation in how expensive and profitable lenders are, without any evidence that expensive loans are associated with a better borrower experience. Despite many lenders operating in the U.S. mortgage market, limited borrower sophistication may provide lenders with market power.

This paper proposes a novel data-driven approach for feedback linearization of nonlinear control-affine systems by leveraging the Koopman operator framework. We establish theoretical connections between feedback linearization on the original state manifold and the higher-dimensional Koopman observable manifold using concepts from system immersion. For systems with exact Koopman bilinear representations, we provide closed-form solutions to the feedback linearization problem without solving partial differential equations. When exact bilinear representations are not available, we develop an approximate method based on singular value decomposition that converges to the exact solution as the observables are enriched. The simulation results and numerical examples demonstrate the effectiveness of the approach.

An amplitude analysis of the Ξ _c⁺ → pK⁻π⁺ decay together with a measurement of the Ξ _c⁺ polarization vector in semileptonic beauty-hadron decays is presented. The analysis is performed using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb ^{− 1} . An amplitude model is developed and the resonance fractions as well as two- and three-body decay parameters are reported. A sizeable Ξ _c⁺ polarization is found. A large sensitivity of the Ξ _c⁺ → pK⁻π⁺ decay to the polarization is seen, making the amplitude model suitable for Ξ _c⁺ polarization measurements in other systems.

Precise and flexible control of gene expression is essential for dissecting gene function in complex biological systems. Although recent developments in genetic engineering and CRISPR/Cas9 technology have expanded tools for gene activation, suppression and editing, their application in physiologically relevant models remains challenging, time consuming, and expensive. Here, we present a modular, doxycycline-inducible vector system that integrates gene overexpression, shRNA-mediated knockdown, and CRISPR/Cas9-mediated regulation within a single, lentivirus-compatible system. The modular design allows rapid exchange of selection markers, epitope tags and reporters via Gateway cloning, providing broad adaptability across experimental settings. In addition to standard fluorescent and luminescent reporters, the system includes advanced sensors, such as FUCCI cell cycle reporters, to enable monitoring of cellular processes. By combining fluorescence barcoding with combinatorial genetic perturbations, the platform supports multiplexed analysis of gene function and genetic interactions through phenotypic characterization by multiplex fluorescence imaging or flow cytometry. We demonstrate its utility in vivo with breast cancer intraductal xenografts to reveal that ER+ breast cancer cells (MCF7) rely on androgen (AR), estrogen (ER) and progesterone receptors (PR) for in vivo growth. This versatile gene perturbation system provides tight temporal control, streamlined implementation, and high-content phenotyping capacity facilitating efficient in vitro and in vivo studies while reducing the use of animals in in vivo validation experiments. It thus expands the experimental repertoire for dynamic, multigene interrogation in complex systems.

Any building designed for human occupancy needs to be visually comfortable. Glare from daylight is one of the main causes of visual discomfort. Glare perception is evaluated by empirical glare models either by photometric measurements or by lighting simulations. This study explores an alternate solution that implements deep learning methods to develop glare prediction models from video recordings of human faces exposed to different levels of sunlight indoors. We trained and evaluated 12 widely used Convolutional Neural Network (CNN) architectures over a data set of 78 facial videos of 21 human participants experiencing glare in a daylit office-like setup. Results indicate that the best-performing CNN achieves an accuracy of (1) 87% in predicting glare on the repeated participants in unseen lighting conditions of different intensity and (2) 67% on new participants’ faces with previously seen lighting conditions. We propose future research directions to improve predictions from such models.

The electron-positron stage of the proposed Future Circular Collider (FCC-ee) aims to achieve unprecedented precision at large centre-of-mass energies, serving as a high-luminosity Higgs, top, and electroweak factory that goes beyond what was enabled by the 2012 Higgs-boson discovery at the Large Hadron Collider (LHC) at CERN. As a lepton collider, FCC-ee is strongly constrained by synchrotron radiation power across its four operating modes: the energy loss per turn must be replenished by the radio-frequency system while respecting the nominal 50 MW per-beam limit.

This thesis explores the use of nested magnets, which introduce a dipole component into the quadrupoles and sextupoles of the arc lattice to reduce the per-turn energy loss from synchrotron radiation. At the FCC-ee scale, this is a novel design choice that poses non-trivial optics challenges. We develop and compare several lattice families to address these issues, ultimately converging on a Common Layout Configuration that shares a unified design across the operational energies. The resulting optics deliver a robust reduction in synchrotron radiation losses, which can be traded for consumption power savings or, alternatively, converted into luminosity gains; these gains are confirmed for both of the currently considered optics, despite their very different interaction region designs and chromatic correction schemes. In addition to the production optics, we introduce a simplified ballistic optics, which turned out fundamental for the commissioning phase and for studies of errors under relaxed optics constraints. Finally, we assess the impact of systematic quadrupolar errors in the arc dipoles.

This thesis investigates the relationship between transport and urbanization in the cross-border metropolitan construction of the Great Geneva, exploring how planning practices can respond to the imperatives of the socio-ecological transition.

This research is the result of an encounter between an urban model, the Transit-Oriented Development (TOD), and a territory, the cross-border metropolis of Greater Geneva, each serving as a lens through which to analyse the other critically.

Linking mass transit and compact growth, TOD has become an influential global model for reducing automobile dependency, guiding sustainable growth, and fostering metropolitan cohesion. Yet its application, rooted in normative assumptions, remains insufficiently questioned in relation to asymmetric territorial contexts and socio-spatial equity.

Cross-border territories are critical 'in-between' spaces, overlapping national, legal, cultural, political, and spatial systems. Greater Geneva, one of Europe's most functionally and institutionally integrated metropolitan regions, yet marked by asymmetries, disparities, and environmental pressures, presents a unique terrain for questioning the assumptions, rationalities, and potential of the TOD model.

Organized into three parts that address the past, present, and future of transport and urbanization coordination, the research employs a hybrid methodology that combines historical inquiry, statistical and cartographic analysis, qualitative interviews, photographic explorations, and research on-and by-design. The first part traces the longue durée rurbanization process of metropolitan construction revealing the mismatch between the urbanization processes and the envisioned transit system. It further examines how transport networks, legal framework, and planning have contributed to producing and structuring enduring territorial hierarchies. The second part identifies three structural paradoxes shaping cross-border TOD implementation: the specialization paradox, the growth paradox, and the radio-centric paradox, each revealing the economic, political, and social limitations of the current model. Collectively, these tensions reveal that TOD in Greater Geneva operates more as a vehicle of metropolization than as an instrument of territorial cohesion, perpetuating uneven development. Beyond critique, the research explores alternative imaginaries emerging from spatial design and prospective planning, highlighting the potential and limits for an inclusive and sustainable metropolitan vision.

This research concludes that the socio-ecological transition demands a paradigmatic shift, from technical infrastructure-led coordination toward a relational territorial project. (Re)territorializing transport and urbanization calls for reconciling connectivity and proximity in context-sensitive ways, moving beyond binary readings toward multi-scalar configurations where strong and weak networks coexist. Hybrid coordination envisions transport not as a hierarchical or purely functional system, aimed at optimizing speed or concentrating growth, but as an organizational framework seeking to cultivate the conditions for inhabiting the entire territory as a distributive agent of social, ecological, and spatial equity.

Ultimately, this thesis contributes to critical TOD scholarship and to broader debates on how territories in transition can move beyond functionalist planning paradigms toward a renewed project of territorial cohesion.

This thesis focuses on the analysis and reproduction of integral experiments for validating stainless-steel nuclear data, through two complementary programs conducted in the CROCUS zero-power reactor at EPFL. These programs are the semi-integral PETALE program, which includes criticality and transmission experiments with heavy reflectors, and the hybrid pile-oscillation program BLOOM. Both belong to HARVEST X, an EPFL coordinated initiative to reproduce, extend, and cross-validate experiments for stainless-steel and its Fe-Ni-Cr components. The main application concerns heavy reflectors in light-water reactors, with broader relevance to reactor pressure vessel fluence, Generation IV designs (especially fast reactors), and fusion systems. With PETALE, the aim is to deliver benchmark-quality results, including comprehensive covariance data and reduced risks of elemental compensation in alloys using pure elemental reflectors. A further goal is to provide feedback on modern nuclear data libraries, notably JEFF-3.3 and the newly released JEFF-4.0, and to prepare for data assimilation. To achieve these objectives, a new dosimetry analysis framework was developed, enabling the quantification of correlations among its 480 activation dosimetry measurements. This approach supports robust estimation of C/E ratios and their renormalization, including full covariance propagation, without ad hoc assumptions. High-resolution modelling with JEFF-3.3 and JEFF-4.0 confirms improved iron data in the fast neutron range. Trends observed in C/Es with JEFF 3.3 - front-to-back reflector differences of 5.7 % at ~2 MeV and 6.5 % at ~3.6 MeV - are reduced to below 1% with JEFF-4.0. Overall, stainless-steel performance improves as well, although the new chromium and nickel evaluations perform less favorably. These results highlight the need for careful treatment of alloys with reduced iron concentrations compared to typical light water reactor grades. Preliminary assimilation attempts with JEFF-3.3 covariance data suggest a 4-8% increase of the 56Fe inelastic cross section would improve agreement with fast-range observations. The BLOOM program was designed and carried out to complement PETALE, focusing on pile-oscillation experiments. Conducted in 2024, it involved 45 samples oscillated using a dedicated experimental channel, the SAFFRON array, and current mode-operated fission chambers. The measured reactivity worth have uncertainties as low as 0.015 pcm, the theoretical pile-noise limit. Local flux perturbations of ~10% (up to 30%) were measured with relative uncertainties of 0.1-1% (down to 0.05%). Simulations of 30 oscillations using Serpent2 and the black-body exact perturbation method yield complementary results to PETALE's transmission data. Chromium sample results improve with JEFF-4.0 (C/E are +1%, +3.5% previously), whereas nickel results slightly degrade (+1% bias vs. <0.5%). The next step of HARVEST-X involves the ongoing benchmarking of PETALE within ICSBEP. Additional experimental follow-ups are planned, notably the accelerator-based GRAPE campaign. Further oscillations and analysis are also in preparation for BLOOM, including validation using local signals, and higher reactor power oscillations to improve precision. Complete data assimilation studies for dedicated applications are still to be performed, in collaboration with the Paul Scherrer Institute.

Geospatial information is embedded in both natural systems and human society, making it inherently multimodal and heterogeneous. It describes objects and events associated with geospatial locations on Earth, with various forms such as remote sensing imagery, ground-level imagery, and textual descriptions.

With the rise of geospatial artificial intelligence (AI), multimodal learning has become a key to integrating these diverse sources. However, the inherent heterogeneity across modalities poses significant challenges, constraining both the development and generalizability of geospatial AI systems. Considering the sparsity of aligned data across modalities, external knowledge offers a potential solution. By providing modality-agnostic semantics and priors and guiding the multimodal learning process, it plays a critical role in bridging modality gaps.

Despite efforts to incorporate external knowledge into multimodal systems, the current understanding of how external knowledge integration influences multimodal complementarity, alignment, and representation remains limited, particularly within geospatial contexts. To address these challenges, this dissertation investigates knowledge-aware multimodal systems that integrate diverse types of knowledge, including commonsense and geospatial knowledge, across multiple modalities with a focus on geospatial vision-language and geo-localization tasks.

For geospatial vision-language applications, this dissertation investigates the complementarity of external knowledge to multimodal learning. To improve cross-modal alignment, KTIR incorporates external commonsense knowledge to bridge semantic gaps between remote sensing imagery and textual descriptions by expanding the semantic scope of text. In addition, ConVQG integrates external knowledge into multimodal fusion by proposing a contrastive visual question generation pipeline that generates knowledge-enriched, image-grounded questions.

In the context of geo-localization, this dissertation develops generalizable representations for both retrieval-based and navigation-based approaches by incorporating external knowledge in the learning process. For retrieval-based geo-localization, ConGeo proposes a model-agnostic pipeline that integrates the location prior in the contrastive learning objective to achieve robust cross-modal alignment between ground and aerial views. Building on this alignment, GeoExplorer further addresses the generalizability of representation in active, navigation-based geo-localization and proposes a curiosity-driven reinforcement learning pipeline to emphasize comprehensive environment modeling and exploration for geo-localization.

Taken together, the dissertation highlights the multifaceted complementarity of external knowledge in geospatial multimodal information and introduces knowledge-aware systems that leverage such knowledge to improve cross-modal alignment and enhance the generalizability of geospatial representations. With these contributions, the dissertation aims to foster knowledge-enriched, robust, and practically applicable geospatial AI systems that leverage the distinctiveness of geospatial information and accommodate real-world complexity.

The growing demand for real-time machine learning has accelerated the adoption of edge computing, which processes data locally to reduce latency, enhance privacy, and improve energy efficiency. Ultra-low-power edge applications (TinyAI) face stringent constraints in performance, power, and area, often operating within milliwatt budgets and on millimeter-scale system-on-chips (SoCs). Prototyping such systems requires hardware/software co-design platforms capable of real-time execution and accurate performance and energy measurements.

To overcome these challenges, I developed the FPGA EMUlation (FEMU), an open-source and configurable RISC-V framework for prototyping and evaluating TinyAI applications on SoC-based FPGAs. This approach combines a heterogeneous system implemented in reconfigurable logic with a software environment running on an application-class processor under a standard operating system. Virtualized I/O enables flexible communication, signal processing, and verification. Custom accelerators can be added as software models or register transfer level (RTL) modules, while silicon-derived models provide performance and energy estimations.

At the core of FEMU lies a heterogeneous system that combines a flexible host to run control and communication tasks with specialized accelerators tailored to specific application domains. However, each accelerator presents unique requirements in terms of ports, bandwidth, performance, area, and power trade-offs. As a result, a highly configurable host is essential to adapt both hardware and software to the diverse needs of these accelerators.

To address these limitations, I designed the eXtendible Heterogeneous Energy Efficient Platform (X-HEEP), an open-source and configurable RISC-V platform that integrates seamlessly within FEMU to support the connection of TinyAI accelerators. X-HEEP provides configurable central processing units (CPUs), memory, interconnects, and peripherals, while its eXtendible Accelerator InterFace (XAIF) enables the integration of accelerators with a wide range of requirements. The platform supports FPGA prototyping, ASIC implementation, and mixed SystemC/RTL simulation, streamlining design space exploration and optimization.

While most accelerators target domain-specific tasks, more versatile solutions are required for addressing a broader range of applications with varying demands. In this context, graphics processing units (GPUs) have proven particularly effective in exploiting data parallelism across diverse workloads. When integrated with a host like X-HEEP, GPUs form accelerated processing units (APUs), creating unified systems capable of efficiently handling both control-oriented and compute-intensive tasks.

To explore this direction, I developed the embedded GPU (e-GPU), an open-source and configurable RISC-V platform that integrates within X-HEEP to investigate the feasibility and trade-offs of leveraging GPUs in the TinyAI domain. Its extensive configurability enables area and power optimization to meet the requirements of these applications. Furthermore, a custom Tiny Open Computing Language (Tiny-OpenCL) implementation provides a lightweight programming framework for these resource-constrained devices.

Collectively, FEMU, X-HEEP, and e-GPU form a unified, flexible, and efficient ecosystem for TinyAI design, addressing the challenges of configurability, extensibility, and programmability through dedicated hardware/software co-design.