Semantically Annotated Lesson Observation Data in Learning Analytics Datasets: a Reference Model

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Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has ...

Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied ran ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

This data package supports the publication 'Complexity of crack front geometry enhances toughness of brittle solids' by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski Nature physics (2024) - https://doi.org/10.1038/s41567-024-02435-x DOI: 1 ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

As computational thinking (CT) becomes increasingly acknowledged as an important skill in education, self-directed learning (SDL) emerges as a key strategy for developing this capability. The advent of generative AI (GenAI) conversational agents has disrup ...

The near-surface boundary layer above patchy snow cover in mountainous terrain is characterized by a highly complex interplay of various flows on multiple scales. In this study, we present data from a comprehensive field campaign that cover a period of 21 ...

Over the past few decades, the debates have shifted from whether to how Computer Science (CS) should be introduced into formal education. Given the diverse ways to introduce CS into formal education, and the struggles many countries have faced, considerabl ...

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 ...

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 ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP(2)EX; ORACLES; SOCRATES; MARCUS ...

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 ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Semantically Annotated Lesson Observation Data in Learning Analytics Datasets: a Reference Model

Chattez avec Graph Search

Dependable learning and control through Neural ODEs

Controlled learning of pointwise nonlinearities in neural-network-like architectures

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Federated One-Shot Learning with Data Privacy and Objective-Hiding

Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

Deterministic equivalent and error universality of deep random features learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Data and scripts for the RaFSIP scheme

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Robust machine learning for neuroscientific inference

Generative AI-Enabled Conversational Interaction to Support Self-Directed Learning Experiences in Transversal Computational Thinking

Turbulence in the Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow

Co-constructing an Effective, Sustainable and Scalable Primary School Computer Science Curricular Reform

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

DISCO: Distilling Counterfactuals with Large Language Models

Transfer learning application of self-supervised learning in ARPES

Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations

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

Text as a Richer Source of Supervision in Semantic Segmentation Tasks