Merging Inhomogeneous Proximity Sensor Systems for Social Network Analysis

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Privacy-Preserving Machine Learning on Graphs

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Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

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Heading for the Abyss: Control Strategies for Exploiting Swinging of a Descending Tethered Aerial Robot

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The use of aerial vehicles for exploration and data collection has the potential to significantly aid environmental monitoring in environments which are dangerous and hard to navigate. However, within these environments navigation can often be restricted b ...

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Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations

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In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP(2)EX; ORACLES; SOCRATES; MARCUS ...

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Transfer learning application of self-supervised learning in ARPES

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There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

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A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representati ...

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The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

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Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

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This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

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Resistances to change in urban travel behaviors

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The social discourse surrounding the climate emergency progressively infuses the society, transforming into both micro- and macro-social injunctions to change. Yet, society - grounded in a territorial, social, and cultural contingency - appears to resist t ...

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Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Alexandre Massoud Alahi, Dolaana Khovalyg, Mohamed Ossama Ahmed Abdelfattah, Mohamad Rida

2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

EPFL2023

Resistances to change in urban travel behaviors

Marc-Edouard Baptiste Grégoire Schultheiss

EPFL2023