Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

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Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today’s world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increas ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

We introduce an infrared metasurface sensor based on plasmonic surface-enhanced infrared absorption spectroscopy combined with immunoassay, which detects alpha-synuclein, an early structural biomarker protein for neurodegenerative diseases with clinical sp ...

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction prod ...

Various design settings for in-context learning (ICL), such as the choice and order of the in-context examples, can bias the model's predictions. While many studies discuss these design choices, there have been few systematic investigations into categorizi ...

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how t ...

Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs ...

Recent traffic flow prediction methods are lacking abilities to determine predictive features. Thus, they will propagate the error in the next timestamps. In this paper, first, we assess the role of spatial and temporal features on the traffic speed predic ...

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic perfor ...

Education is an art close to theater. A teacher is taking a role; he works his speeches and his gestures and he plays with the attention of his audience. But it is harder: more than entertaining, a teacher must shape the skills, the knowledge and the motiv ...

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important ...

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large ne ...

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

Chattez avec Graph Search

How to minimize the annotation effort in aerial wildlife surveys

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Probing the effects of broken symmetries in machine learning

Deep learning augmented nanoplasmonic infrared sensor for structural protein biomarker-based detection of neurodegenerative diseases

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Robust Training and Verification of Deep Neural Networks

Automated image-based generation of finite element models for masonry buildings

A recipe for cracking the quantum scaling limit with machine learned electron densities

What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

Mitigating Label Biases for In-context Learning

The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

STATNet: Spatial-temporal attention in the traffic prediction

Context-Aware Image Super-Resolution Using Deep Neural Networks

Multikernel Regression with Sparsity Constraint

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Mutual Understanding in Educational Human-Robot Collaborations

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Local SGD Converges Fast and Communicates Little