Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

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Toward data-driven materials design: From atoms to pilot plants

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

EPFL2023

On Artificial Intelligence and Manipulation

Marcello Ienca

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about ne ...

SPRINGER2023

Validating functional redundancy with mixed generative adversarial networks

Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Trung-Dung Hoang

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

ELSEVIER2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

Enriching the Computational Toolbox for Organocatalysis

Simone Gallarati

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 ...

EPFL2023

Machine Learning Security in Industry: A Quantitative Survey

Kathrin Grosse

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Development of new volcano-shaped microelectrodes for single cell electrochemistry, electrophysiology and impedance studies

Nicolas Grégoire Maïno

Micro- nano-electrodes have demonstrated superior performances in measuring attenuated intracellular action potentials from electrogenic cell cultures compared to traditional multi-electrode arrays. Yet, the understanding of the critical electrode features ...

EPFL2023

Brain dynamics uncovered using a machine-learning algorithm

Mackenzie Mathis

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 ...

NATURE PORTFOLIO2023

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Isabella Di Lenardo, Lucas Arnaud André Rappo, Rémi Guillaume Petitpierre, Marion Kramer

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

Zenodo2023

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Development of new volcano-shaped microelectrodes for single cell electrochemistry, electrophysiology and impedance studies

Nicolas Grégoire Maïno

EPFL2023

Brain dynamics uncovered using a machine-learning algorithm

Mackenzie Mathis

NATURE PORTFOLIO2023

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Isabella Di Lenardo, Lucas Arnaud André Rappo, Rémi Guillaume Petitpierre, Marion Kramer

Zenodo2023