Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

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Data-Driven Kinematic Modeling of Physical Origami Robots

Jamie Paik, Mustafa Mete, Alexander Michael Schüssler

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

2024

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures

Ye Wei

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineer ...

2024

Data-driven frequency-based feedforward control design for a robotic arm joint

Alireza Karimi, Philippe Louis Schuchert

Next-generation motion control systems require fast and precise control, but advanced control strategies often rely on complex and expensive models. This paper proposes a data-driven approach to tune controllers for a joint used in a robotic arm. This is a ...

IEEE2024

Discovering meaningful units from text sequences

Melika Behjati

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

EPFL2024

Euclid preparation LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods

Frédéric Courbin, Georges Meylan

Euclid will collect an enormous amount of data during the mission’s lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for comput ...

2024

BlueCelluLab: Biologically Detailed Neural Network Experimentation API

Henry Markram, Eilif Benjamin Muller, Werner Alfons Hilda Van Geit, Lida Kanari, Mustafa Anil Tuncel, Darshan Mandge, Aurélien Tristan Jaquier

The NEURON simulator, established in 1984 and continuously developed since, stands as the preeminent tool for neuron simulation within computational neuroscience. Its widespread adoption and compatibility with computational clusters and supercomputers unde ...

The Open Journal2024

A Karhunen–Loève theorem for random flows in Hilbert spaces

Victor Panaretos, Leonardo Vincenzo Santoro, Kartik Waghmare

We develop a generalisation of Mercer’s theorem to operator-valued kernels in infinite dimensional Hilbert spaces. We then apply our result to prove a Karhunen-Loève theorem, valid for mean-square continuous random functions valued in a separable Hilbert s ...

2024