Large-scale detection of marine debris in coastal areas with Sentinel-2

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Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

Petroleum-based microplastic particles (MPs) are carriers of antimicrobial resistance genes (ARGs) in aquatic environments, influencing the selection and spread of antimicrobial resistance. This research characterized MP and natural organic particle (NOP) ...

Microplastics, especially aged microplastics can become vectors of metals from environment to organisms with potential negative effects on food chain. However, a few studies focused on the bioavailability of adsorbed metals and most studies related to aged ...

Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the e ...

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 ...

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 ...

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 ...

Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspectio ...

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 ...

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 ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

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 ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Plastic litter is a major environmental hazard that endangers human, animal, and plant health on the planet. A substantial portion of plastic pollutants is washed from rivers and beaches into the oceans and aggregates at the surface as marine debris before ...

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Enhancing the surface charge density and reducing the output impedance via innovative techniques are essential to accelerate the commercialization of triboelectric nanogenerators (TENGs). However, methods for increasing output performance have numerous lim ...

Large-scale detection of marine debris in coastal areas with Sentinel-2

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Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

Parity in bacterial communities and resistomes: Microplastic and natural organic particles in the Tyrrhenian Sea

Adsorption of Copper by Naturally and Artificially Aged Polystyrene Microplastics and Subsequent Release in Simulated Gastrointestinal Fluid

Inhalation of Microplastics—A Toxicological Complexity

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

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

Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

Incorporating Projective Geometry into Deep Learning

Understanding generalization and robustness in modern deep learning

Reliable data-driven decision-making through optimal transport

Towards Trustworthy Deep Learning for Image Reconstruction

Aggregating Spatial and Photometric Context for Photometric Stereo

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

High-performance triboelectric nanogenerator via photon-generated carriers for green low-carbon system