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

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State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

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 ...

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 ...

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 ...

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 ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

Chat with Graph Search

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Robustness of Local Predictions in Atomistic Machine Learning Models

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

On Artificial Intelligence and Manipulation

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

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

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