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

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CO2 conversion plays a vital role in addressing climate change by cutting greenhouse gas emissions and promoting a circular carbon economy. Rather than releasing carbon dioxide into the atmosphere, carbon dioxide recycling technologies allows transformatio ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

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 ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

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 ...

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

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 ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

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 ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

Secondary aluminium production relies on natural gas to transform both primary and recycled aluminium into semi-fabricated products, leading to significant atmospheric emissions, energy losses, and resource consumption. Despite the potential for waste heat ...

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 ...

Fuel cells are considered one of the most efficient and environmental-friendly technologies available for converting chemical energy into electricity and heat, and considered a key technology to close the hydrogen cycle. In particular, combined heat and po ...

Industrial chemistry heavily relies on traditional separation methods which are both energy-demanding and environmentally detrimental. This thesis addresses critical separation challenges, specifically carbon capture applications and the separation of ethy ...

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

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Low-Power Tunable Micro-Plasma Devices for Efficient and Scalable CO2 Conversion

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Learning to Assemble with Alternative Plans

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

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

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

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

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Safe Deep Neural Networks

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Incorporating Projective Geometry into Deep Learning

Probing the latent hierarchical structure of data via diffusion models

Glenohumeral joint force prediction with deep learning

A systemic study for decarbonizing secondary aluminium production via waste heat recovery, carbon management and renewable energy integration

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

Role and Opportunities of Gas bearing supported Turbomachinery in small-scale SOFC-CHP systems

Harnessing Porous Materials for Efficient Gas Capture: A Computational Exploration of Structure, Function, and Workflow Automation