On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

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Neural quantum state (NQS) ans & auml;tze have shown promise in variational Monte Carlo algorithms by their theoretical capability of representing any quantum state. However, the reason behind the practical improvement in their performance with an increase ...

Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-growing R ...

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focuse ...

Directly regressing the non-rigid shape and camera pose from the individual 2D frame is ill-suited to the Non-Rigid Structure-from-Motion (NRSfM) problem. This frame-by-frame 3D reconstruction pipeline overlooks the inherent spatial-temporal nature of NRSf ...

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions ...

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 ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

Investigating the intangible nature of a cultural domain can take multiple forms, addressing for example the aesthetic, epistemic and social dimensions of its phenomenology. The context of Southern Chinese martial arts is of particular significance as it c ...

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this qu ...

When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for parametrized, time-dependent problems in engineering computations, it is often t ...

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switch ...

Urban data strongly favored by urban digitization and web 2.0 constitute the raw material whose sources often diverge. However, as far as we know, little or no work has been done to explore the sources and targeted applications for urban planning indicator ...

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...

In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many ma ...

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on ...

This paper reports on experimental testing that was carried out on a prototype adaptive structure designed to counteract the effect of loading through controlled large shape changes. The prototype is 6.6 m truss equipped with 12 linear actuators which has ...

This paper proposes a novel class of neural-network inspired statistical data-driven models, especially derived for the purpose of design optimization of medium frequency transformers. These models allow for an efficient (3-5 orders of magnitude faster com ...

Recent technological advances in light-sheet microscopy make it possible to perform whole-brain functional imaging at the cellular level with the use of Ca2+ indicators. The outstanding spatial extent and resolution of this type of data open unique opportu ...

On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

Chattez avec Graph Search

Efficiency of neural quantum states in light of the quantum geometric tensor

Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

Deep Non-Rigid Structure-From-Motion: A Sequence-to-Sequence Translation Perspective

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification

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

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

The Facets of Intangible Heritage in Southern Chinese Martial Arts: Applying a Knowledge-Driven Cultural Contact Detection Approach

Relative stability toward diffeomorphisms indicates performance in deep nets

Multi-fidelity surrogate modeling using long short-term memory networks

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Urban Data: Sources and Targeted Applications for Urban Planning Indicators Modelling

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Self-learning surrogate models in superstructure optimization

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect

Design and control of a prototype structure that adapts to loading through large shape changes

FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data