Mapping drivers of tropical forest loss with satellite image time series and machine learning

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Traditional aerodynamic shape optimization (ASO) is costly and time-intensive, requiring extensive manual tuning for parameterization to define the design space and manipulate geometries. Conventional parameterization methods limit flexibility and demand s ...

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global oc ...

As air temperature and vapor pressure deficit (VPD) increase continuously, forests are losing more water through evapotranspiration, with large consequences for local and global hydrological cycles. In regions with high vegetation cover, soil warming can b ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

The worsening of drought events with rising air temperature alters tree water relations causing severe hydraulic impairments and widespread forest mortality. Mixing tree species with contrasting hydraulic traits could reduce forest vulnerability to extreme ...

The global construction industry contributes to 37% of carbon emissions associated to both building operations and construction. To help achieve the net-zero targets set by 2050, it is mandated to achieve a 50% reduction in carbon emissions by 2030. As we ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

The value of luxury goods, particularly investment-grade gemstones, is influenced by their origin and authenticity, often resulting in differences worth millions of dollars. Traditional methods for determining gemstone origin and detecting treatments invol ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Permanent CO storage in basalts through mineralisation offers a promising solution for reducing carbon emissions and mitigating climate change. This study focuses on the impact of potential mineralisation on the flow properties of the basaltic material. Fl ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Patterns in nature arise from processes interacting across a continuum of spatial scales, where new relationships emerge at each level of investigation. These patterns are nested features encompassing fine-scale local patterns, such as topography and geolo ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Chattez avec Graph Search

Automated Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Regional uniqueness of tree species composition and response to forest loss and climate change

Soil warming alters tree water use and canopy stomatal conductance in a mixed subtropical forest

Towards Trustworthy Deep Learning for Image Reconstruction

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

From roots to canopy: Unraveling the influence of species diversity on tree water relations under warmer and drier climates

An explorative method to support design decisions based on carbon constraints and daylight sufficiency needs

Predicting the long-term collective behaviour of fish pairs with deep learning

Understanding generalization and robustness in modern deep learning

Predicting protein interactions using geometric deep learning on protein surfaces

GEMTELLIGENCE: Accelerating gemstone classification with deep learning

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Impact of CO2-rich seawater injection on the flow properties of basalts

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Residual-based attention in physics-informed neural networks

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Machine learning-aided generative molecular design

Incorporating Projective Geometry into Deep Learning

At what spatial scale does natural selection operate? Using multiscale frameworks to calibrate the effects of environmental factors across landscape continuums

Robust machine learning for neuroscientific inference