Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

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Objective. Non-invasive brain stimulation (NIBS) offers therapeutic benefits for various brain disorders. Personalization may enhance these benefits by optimizing stimulation parameters for individual subjects. Approach. We present a computational pipeline ...

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. I ...

The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and intr ...

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Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

Non-regular or irregular statistical problems are those that do not satisfy a set of standard regularity conditions that allow useful theoretical properties of inferential procedures to be proven. Non-regular problems are prevalent; a classical example is ...

The process by which the cerebral cortex generates movements to achieve different tasks remains poorly understood. Here, we leveraged the rich repertoire of well-controlled primate locomotor behaviors to study how task-specific movements are encoded across ...

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quanti ...

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizi ...

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Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust ...

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

These are pytorch model weights for the angiopy-segmentation deep learning model, descriped in a paper published in 2024 in the International Journal of Cardiology: AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery se ...

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

Short-term residential load forecasting (STRLF) holds great significance for the stable and economic operation of distributed power systems. Different households in the same region may exhibit similar consumption patterns owing to the analogous environment ...

The rapid evolution of Deep Learning (DL) has brought about significant transformations across scientific domains, marked by the development of increasingly intricate models demanding powerful GPU platforms. However, edge applications like wearables and mo ...

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Chattez avec Graph Search

Precision non-invasive brain stimulation: an in silico pipeline for personalized control of brain dynamics

Graph Neural Networks With Adaptive Structures

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Adaptive Interface-Pinns (Adai-Pinns) for Transient Diffusion: Applications to Forward and Inverse Problems in Heterogeneous Media

Intelligent and Self-Driving Microscopy of Protein Aggregation in Neurodegenerative Diseases

Online Prediction of Core Body Temperature from Sweat Wearable Printed Sensors Using Recurrent Neural Network

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Non-regular Inference: Universal Inference and Discrete Profiling

Regional specialization of movement encoding across the primate sensorimotor cortex

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Hierarchical syntax model of music predicts theta power during music listening

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Temporal Fluctuations of Brain Structure-Function Coupling as a Potential Marker of Drowsiness During Resting State

Neural Distributed Controllers with Port-Hamiltonian Structures

Bridging Discrete Choice Modeling and Neural Networks: Unifying Statistical Rigor and Predictive Power

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

PyTorch 2 Model for the prediction of single-artery segmentations for "AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation"

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset