An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach

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The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stan ...

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

Synthetically grown high-quality single crystal diamond is an excellent base material for quantum photonics. Planar single crystal diamond substrates several square millimeters in size are nowadays commercially available. They can be precisely structured u ...

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interfe ...

Wind energy is the most mature renewable energy technology; however, its exploitation in cities is often met with skepticism. Thanks to their ability to operate at low wind speed regardless of direction, vertical axis wind turbines (VAWTs) offer an attract ...

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device select ...

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

Proteins are macromolecular machines central to virtually every fundamental biological function. Three-dimensional protein structures, determined by their amino acid composition, are directly linked to their function. Research into structure-function relat ...

Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

Sign language is vital for communication within the deaf and hard-of-hearing community. Avatar-based methods and deep learning techniques like Generative Adversarial Networks have shown promise in generating sign language video content. One of the challeng ...

The Energy Return On Investment (EROI) is a recognised indicator for assessing the relevance of an energy project in terms of net energy delivered to society. For woody biomass divergences remain on the right methodology to assess the EROI leading to large ...

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

This paper presents a solution to address wear and tear of Run-of-River (RoR) Hydropower Plants (HPPs) providing enhanced Frequency Containment Reserve (FCR). In this respect, the study proposes the integration of a Battery Energy Storage System (BESS) wit ...

Dynamic human energy expenditure (EE) consists of energy costs for resting metabolism, food digestion, physical activity, and thermoregulation. Currently, multiple models predict EE mainly concerning physical activity, thus, discarding other factors contri ...

An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach

Chattez avec Graph Search

Automated Ensemble Multimodal Machine Learning for Healthcare

Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Micro- and nanofabrication techniques for single crystal diamond photonics

Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

Meteorological Assessment of Vertical Axis Wind Turbine Energy Microgeneration Potentials Across Two Swiss Cities Located in Complex Terrain

Non-asymptotic performance of social machine learning under limited data

Backpropagation-free Training of Analog AI Accelerators

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

Multi-Channel Mosra: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and A Teacher Model

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Optimal Inference in Contextual Stochastic Block Models

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Multi-motif scaffolding for de novo pathogen antigen mimetics by deep generative learning

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

Estimating the energy return on investment of forestry biomass: Impacts of feedstock, production techniques and post-processing

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students

Enhanced Frequency Containment Reserve provision from battery hybridized hydropower plants: Theory and experimental validation

Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models