A model-free data-interpretation approach for long-term monitoring of bridges

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Impact of quantum-chemical metrics on the machine learning prediction of electron density

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...

AMER INST PHYSICS2021

Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study

Shen Yang, Xianlin Wang

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that p ...

2021

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

Nikolaos Stergiopoulos, Stamatia Zoi Pagoulatou, Vasiliki Bikia, Georgios Rovas

In-vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thick ...

2021

H-mode scrape-off layer power width in the TCV tokamak

Basil Duval, Holger Reimerdes, Benoît Labit, Christian Gabriel Theiler, Umar Sheikh, Roberto Maurizio

Obtaining acceptable conditions at the divertor targets of a next-step fusion experiment based on the tokamak concept is expected to be particularly challenging because of the small predicted value of the plasma power exhaust channel width. An increased co ...

IOP Publishing Ltd2021

On the assessment of arterial compliance from carotid pressure waveform

Nikolaos Stergiopoulos, Patrick Segers, Stamatia Zoi Pagoulatou, Vasiliki Bikia, Georgios Rovas

In a progressively aging population, it is of utmost importance to develop reliable, noninvasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Various pathophysiological conditions are associated to changes ...

AMER PHYSIOLOGICAL SOC2021

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Viet Anh Nguyen, Daniel Kuhn, Bahar Taskesen

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

CAMBRIDGE UNIV PRESS2021

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

Katherine Colby Leonard, Robin Lewis Modini, Jenny Thomas, Fernando Perez Cruz, Julia Schmale, Christel Sybille Hassler, Irina Gorodetskaya, Sebastian Johannes Heinz-Josef Landwehr, Andrea Baccarini, Alireza Moallemi

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remai ...

2021

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