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Atomic scale volume and grain boundary diffusion elucidated by in situ STEM

Johann Michler, Xavier Maeder, Amit Sharma, Laszlo Pethö

Diffusion is one of the most important phenomena studied in science ranging from physics to biology and, in abstract form, even in social sciences. In the field of materials science, diffusion in crystalline solids is of particular interest as it plays a p ...

Nature Portfolio2023

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Daniel Probst

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

2023

Methodology for selecting measurement points that optimize information gain for model updating

Ian Smith, Numa Joy Bertola

Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sens ...

2023

An Ontology-based Engineering system to support aircraft manufacturing system design

Jinzhi Lu, Xiaochen Zheng

During the conceptual design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated against performance indicators in a collaborative engineering process. Domain experts' knowledge and the motivations for decision-ma ...

ELSEVIER SCI LTD2023

Impromptu Conservation – Unveiling Huế's Mnemonic Sites: Heritagization, (de)Colonization, and Place-making

Phi Nguyen Yen

This dissertation interrogates postcolonial cities’ syncretic territories, using Huế’s mnemonic sites – historically and culturally significant locales that aid in remembrance – to explore displaced communities’ cohabitation tactics and decolonization effo ...

EPFL2023

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Volkan Cevher

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

2023

Avalanche risks management as intangible cultural heritage

Florence Graezer Bideau, Chiara Maria Bortolotto

This paper tackles Avalanche Risks Management (ARM), nominated by Switzerland and Austria and inscribed on the UNESCO Representative List of Intangible Cultural Heritage in 2018. On the one hand, this nomination conventionally highlights how traditional em ...

2023

Impact of Standard versus Tracked-based User Behavior into Energy Simulations of Open-Space Offices

Dolaana Khovalyg, Verena Marie Barthelmes, Arnab Chatterjee

This simulation-based study aimed to contribute to gaining knowledge on how much the deviation between occupant-related input by the national standard and by insitu measurements might impact predictions of heating/cooling needs in offices. Analysis was per ...

2023

Mobile eye tracking applied as a tool for customer experience research in a crowded train station

Andrea Regula Schneider

Train stations have increasingly become crowded, necessitating stringent requirements in the design of stations and commuter navigation through these stations. In this study, we explored the use of mobile eye tracking in combination with observation and a ...

2023

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