From basic sciences and engineering to epileptology: A translational approach

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One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

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 ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

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 ...

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 ...

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 ...

This dataset supports the publication 'Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid' by Lebo Molefe and John M. Kolinski, Physical Review E, (2023). The data are surface profiles of textured surf ...

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 ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

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 ...

A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representati ...

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherica ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

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 ...

Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsour ...

Nuclear magnetic resonance (NMR) methods are powerful tools employed in many fields, including physics, chemistry, material science, biology, and medicine. The use of NMR methodologies in an even wider range of applications is often hindered by the relativ ...

Accurate segmentation of pulmonary airways and vessels is crucial for the diagnosis and treatment of pulmonary diseases. However, current deep learning approaches suffer from disconnectivity issues that hinder their clinical usefulness. To address this cha ...

From basic sciences and engineering to epileptology: A translational approach

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Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

On Artificial Intelligence and Manipulation

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

Optimizing Data Processing for Nanodiamond Based Relaxometry

Dataset for "Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid"

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

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

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

incentive Mechanism Design for Responsible Data Governance: A Large-Scale Field Experiment

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

Transformer Models for Vision

Validating functional redundancy with mixed generative adversarial networks

DISCO: Distilling Counterfactuals with Large Language Models

Single Chip Dynamic Nuclear Polarization Microsystems

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset