Nine recommendations for the governance of AI systems

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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, ...

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

Parliament dynamics might seem erratic at times. Predicting future voting patterns could support policy design based on the simulation of voting scenarios. The availability of open data on legislative activities and machine learning tools might enable such ...

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

The channel impulse response (CIR) of the wireless propagation channel is influenced by the surrounding environment and thus can be used to retrieve environmental information such as location, the presence of objects, and the speed of objects. In this work ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

This work presents a novel hybrid (physics- and data -driven) model for short-term (intra-day and day-ahead, 3h -24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points close ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynam ...

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniqu ...

This study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncov ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

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 ...

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

Nine recommendations for the governance of AI systems

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

Enriching the Computational Toolbox for Organocatalysis

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Predicting party switching through machine learning and open data

Brain dynamics uncovered using a machine-learning algorithm

Learning-based Hand Gesture Classification using Channel Impulse Response with UWB

Electronic Excited States from Physically Constrained Machine Learning

Data-driven approaches for non-invasive cuffless blood pressure monitoring

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Acoustic emission signature of martensitic transformation in laser powder bed fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction

Robustness of Local Predictions in Atomistic Machine Learning Models

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

Machine Learning Security in Industry: A Quantitative Survey