Learning decision rules for energy efficient building control

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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

Background and Objectives: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowledge ex ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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 ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their u ...

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

Computer simulations are often used as support material for science education, as they can engage students through inquiry-based learning, promote their active interaction in the experimentation phase, and help them visualize abstract concepts. For instanc ...

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further l ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

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 ...

Learning decision rules for energy efficient building control

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Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Active learning and margin strategies for arrhythmia classification in implantable devices

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Reliable data-driven decision-making through optimal transport

Scalable constrained optimization

In-Sensor Passive Speech Classification with Phononic Metamaterials

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO2 Sensor and Automated Data Segmentation

Digital Twins of Stone Masonry Buildings for Damage Assessment

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Machine learning-aided generative molecular design