Building Successful Long Child-Robot Interactions in a Learning Context

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The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these ...

This paper lays out a case study of Software Construction, a new course in the undergraduate computer and communication sciences curriculum at EPFL. We hope to share some generalizable lessons we learned from running its first edition in Fall 2023. The cou ...

We investigate the learning of a convex patch-based synthesis model for the reconstruction of images. In essence, we propose to learn a dictionary via bilevel optimization for denoising. Using implicit differentiation, we find a closed-form formula of the ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

The contribution of school curricula to public understanding and acceptance of evolution is still mostly unknown, due to the scarcity of studies that compare the learning goals present in different curricula. To overcome this lack of data we analysed 19 sc ...

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

Building Successful Long Child-Robot Interactions in a Learning Context

Chattez avec Graph Search

Prediction rigidities for data-driven chemistry

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Lessons learned from the first edition of a large second-year course in software construction

Learning a Convex Patch-Based Synthesis Model via Deep Equilibrium

Robust time series denoising with learnable wavelet packet transform

Data-driven models in fusion exhaust: AI methods and perspectives

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Surface segregation in high-entropy alloys from alchemical machine learning

Evolution in European and Israeli school curricula - a comparative analysis

Live-cell imaging powered by computation

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes