After how many drinks does someone experience acute consequences-determining thresholds for binge drinking based on two event-level studies: Optimal thresholds for binge drinking

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Data and code associated with the paper 'Mode-Specific Coupling of Nanoparticle-on-Mirror Cavities with Cylindrical Vector Beams'

Christophe Marcel Georges Galland, Sachin Suresh Verlekar, Valeria Vento, Philippe Andreas Rölli

Data and code associated with the following paper: V. Vento et al, Nano Lett. 2023 A thorough explanation of the experiment performed is available there. The name of each sub-folder and file in Maps_data_code.zip indicates the correspondin ...

Zenodo2023

ALMOST SURE SCATTERING OF THE ENERGY-CRITICAL NLS IN d > 6

Katie Sabrina Catherine Rosie Marsden

We study the energy-critical nonlinear Schrodinger equation with randomised initial data in dimensions d > 6. We prove that the Cauchy problem is almost surely globally well-posed with scattering for randomised supercritical initial data in H-s(Rd) wheneve ...

Amer Inst Mathematical Sciences-Aims2023

Data and Workflow to: Three-dimensional buoyant hydraulic fractures: finite volume release (Möri and Lecampion, (2023))

Brice Tanguy Alphonse Lecampion, Andreas Möri

This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fractures: finite volume release" by Möri and Lecampion, (2023). ...

Zenodo2023

Optimizing Data Processing for Nanodiamond Based Relaxometry

Mayeul Sylvain Chipaux

The nitrogen-vacancy (NV) center in diamond is a powerful and versatile quantum sensor for diverse quantities. In particular, relaxometry (or T1), can be used to detect magnetic noise at the nanoscale. For experiments with single NV centers the analysis of ...

WILEY2023

Validating functional redundancy with mixed generative adversarial networks

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

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

Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home

Nicolas Henchoz, Delphine Ribes Lemay, Emily Clare Groves, Andrea Regula Schneider, Margherita Motta

Self-tracking technologies open new doors to previously unimaginable scenarios. The diagnosis of diseases years in advance, or supporting the health of astronauts on missions to Mars are just some of many example applications. During the COVID-19 pandemic, ...

2023

Shared metadata for data-centric materials science

Giovanni Pizzi, Gian-Marco Rignanese

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

Nature Portfolio2023

The Impact of Data Persistence Bias on Social Media Studies

Tugrulcan Elmas

Social media studies often collect data retrospectively to analyze public opinion. Social media data may decay over time and such decay may prevent the collection of the complete dataset. As a result, the collected dataset may differ from the complete data ...

Assoc Computing Machinery2023

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Jürg Alexander Schiffmann, Cyril Picard

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023