From Experimental Neuroscience to Neuroengineering: Modulating Sleep Architecture and Deep Sleep through Motor Plasticity and Vestibular Stimulation

Learning Under Constraints Inference, Selection and Validation with Maximum Entropy Models

Maximum entropy models are probabilistic models that reproduce a prescribed set of empirical constraints while leaving everything else maximally random. They provide a general framework for con- structing reference models, rooted in information theory, and are particularly useful for studying complex systems. This thesis investi- gates the use of maximum entropy models in statistical inference, and in particular for model selection, hypothesis testing, and statistical validation. The first...

Coastal Sanctuaries in Magna Graecia: Testing the Concept of Epithalassic Sanctuaries

This dissertation investigates the so-called “epithalassic sanctuaries” of Magna Graecia in order to assess whether the term identifies a coherent category of cult places or merely constitutes a modern interpretative construct. Although the expression has become increasingly common in archaeological scholarship to describe sanctuaries located in close proximity to the sea, its analytical validity has never been systematically evaluated. The research therefore examines whether coastal sanctuar...

On the Renormalization of Random Network Models

This thesis studies the renormalization of random network models, with a focus on the topological and algebraic struc- tures that emerge when networks are coarse-grained or par- tially observed. It is divided into two parts. The first anal- yses the Multi-Scale Model (MSM), a random graph model that arises as a fixed point of a node-aggregation renormal- ization procedure. In the infinite-mean fitness regime, Chap- ter 2 characterizes the spectrum of the adjacency matrix: the leading eigen...

Temporal-Difference Methods for Learning State Estimators and Smoothers

This thesis addresses the problem of learning recursive state estimators and smoothers from sequences of measured system outputs, for linear and nonlinear discrete-time stochastic systems with additive noise. The process and measurement models are assumed known, while the corresponding ground-truth state trajectories are not available. In the linear setting, dynamic programming is used to decompose the discounted optimal estimation problem into a sequence of subproblems indexed by time, leadi...

Multinational firms in (Global) Cities: Mimicry, Knowledge Networks, and Economic Growth

A Unified Geometric Deep Learning Framework for Financial Time Series: Integrating Temporal, Graph, and Signature-Based Representations

Financial time series exhibit complex statistical properties, in- cluding nonlinear dependencies, non-stationarity, and evolv- ing relationships among assets. Traditional econometric mod- els and purely temporal machine learning approaches often struggle to capture these characteristics as they focus mainly on sequential dynamics while overlooking the underlying ge- ometric structure of financial data. This thesis investigates the role of geometric representations in financial time series ...

Mind, Media, and Networks

The increasing complexity of psychological phenomena in digital environments calls for innovative theoretical and methodological approaches. While psychology offers a rich body of established theories to study and interpret human behavior, it is not clear how they apply to contemporary digital contexts. In this thesis, I combine established frameworks with emerging approaches to examine how traditional concepts apply to the new, digital world. Across three empirical studies, I explore key mec...

How visual experience shapes sleep physiology and phenomenology in blind and sighted individuals