Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning

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Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-c ...

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual arti ...

The transition towards clean renewable energy sources, where wind and solar are prone to variation, requires adequate energy storage. Power-to-methane (PtM) systems can be part of the solution. Specifically, solid-oxide-electrolyser (SOE) based PtM systems ...

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Som ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

In this work, a tool to design district heating networks (DHN) is presented and applied to the city of Lausanne as a case study. The evaluation of the buildings’ heat/cooling demand is performed using a Geographic Information System (GIS) database, built f ...

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

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 ...

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 ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

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 ...

Water can be vitrified if it is cooled at high rates, which makes it possible to outrun crystallization in so-called no man’s land, a range of deeply supercooled temperatures where water crystallizes rapidly. Here, we study the reverse process in pure wate ...

The pre-thermal quench (pre-TQ) dynamics of a pure deuterium ( D 2 ) shattered pellet injection (SPI) into a 3 MA / 7 MJ JET H-mode plasma is studied via 3D non-linear MHD modelling with the JOREK code. The interpretative modelling captures the overall evo ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning

Chattez avec Graph Search

Performance of energy piles foundation in hot-dominated climate: A case study in Dubai

Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression

Design and assessment of an evaporating water-cooled methanation reactor for a SOE-based power-to-methane system

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Robust NAS under adversarial training: benchmark, theory, and beyond

Decomposition method to evaluate district heating/cooling network potential at urban scale

Towards practical reinforcement learning for tokamak magnetic control

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

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

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Topics in statistical physics of high-dimensional machine learning

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Is the powersave governor really saving power?

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Flash Melting Amorphous Ice

Interpretative 3D MHD modelling of deuterium SPI into a JET H-mode plasma

Unveiling the complexity of learning and decision-making

Partial discharge localization in power transformer tanks using machine learning methods