Wearable electromyographic device and system and methods for motion decoding of body parts

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Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process an ...

We investigate reduced order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a reduc ...

Hyperbolic conservation laws play a critical role in various fields, including aerodynamics, physics, and oceanography. However, traditional physics-informed neural networks (PINNs), despite their remarkable capabilities in solving partial differential equ ...

Reservoir Computing (RC) has become popular in recent years thanks to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive power ...

Precisely localizing anomalies in medical images remains a significant challenge due to their heterogeneous nature across modalities and organs. While initial efforts excelled in identifying prominent anomalies, detecting minute target lesions posed signif ...

Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented regio ...

Human movements emerge from complex interactions between the musculoskeletal system, the neural system and the external environment. Their control is realised through a hierarchical neural system composed of the brain, the spinal cord (SC), and remaining n ...

Here we provide the weights of the neural network policies used for the analysis presented in our article. The archives whose names start with a number (01 - 32) correspond to the 32 curriculum steps to train the Baoding Balls policy which ranked first at ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that fa ...

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks ...

Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Th ...

The Neurorobotics Platform of the Human Brain Project hosts many different large-scale models that can easily be connected with each other. Here, we linked a deep neural network for saliency computation to a spiking cortical model for visual segmentation ( ...

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders wou ...

Recent advances in traffic flow theory at the network level, namely the Macroscopic Fundamental Diagram (MFD), reveals the existence of well-defined laws of congestion dynamics at aggregated levels. The same knowledge for multimodal networks however is lim ...

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the ...

Wearable electromyographic device and system and methods for motion decoding of body parts

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X-ray Micro-CT based characterization of rock cuttings with deep learning

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

A coordinate transformation-based physics-informed neural networks for hyperbolic conservation laws

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

Robust semantic learning for precise medical image segmentation

IM-Context: In-Context Learning for Imbalanced Regression Tasks

Investigation of the role of spinal circuits in human motor control, from healthy to rehabilitation conditions

Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights

The inductive bias of deep learning: Connecting weights and functions

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Using the Neurorobotics platform to explain global processing in visual crowding

Deep Learning for Logic Optimization Algorithms

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

A Dynamic Network Approach for Multimodal Urban Mobility : Modeling, Pricing and Control

Autonomous reinforcement learning with experience replay