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

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Optical manipulation and detection of biological particulates are crucial procedures in biophotonics. Optical sectioning (OS) opens the avenue to three-dimensional (3D) microscopy, but nonoptical approaches, including sample adhesion and mechanical scannin ...

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 ...

Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Desp ...

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 ...

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs ...

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 ...

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 Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges fo ...

Since their discovery in 2012, carbazolyl (iso)phthalonitrile derivatives (Cz(I)PN) have found significant applications as photocatalysts in organic chemistry. Herein, we introduce two efficient methods for incorporating carbazolyl cyanobenzenes into vario ...

Centrioles are evolutionarily conserved microtubule-based organelles essential for cell division, ciliogenesis, and cell polarity. While centrioles are critical for many cellular processes, their selective elimination is crucial during specific development ...

Membrane potential fluctuations have previously been detected using second harmonic (SH) water imaging on neuronal cells and model lipid bilayer membranes. We report that such fluctuations are also visible when membrane potential-sensitive fluorophores are ...

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 ...

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 ...

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated obje ...

Coherent perfect absorption (CPA), or anti-lasing, has been so far inherently restricted to continuous wave scenarios, drastically restricting its applications to standard linear steady-state systems. However, future technologies based on enhanced light-ma ...

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 ...

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 ...

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

Chattez avec Graph Search

Optical tweeze-sectioning microscopy for 3D imaging and manipulation of suspended cells

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

Spatial and stoichiometric in situ analysis of biomolecular oligomerization at single-protein resolution

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

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

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Graph Neural Networks With Adaptive Structures

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

Deep learning approach for identification of Hii regions during reionization in 21-cm observations – III. image recovery

Peptide‐Carbazolyl Cyanobenzene Conjugates: Enabling Biomolecule Functionalization via Photoredox and Energy Transfer Catalysis

Dissecting the mechanisms of centriole stability in Caenorhabditis elegans: Focus on SAS-1

Membrane Potential Fluctuations and Water Asymmetry on Plasma Cell and Model Lipid Membranes: Origins, Implications and Properties

Non-regular Inference: Universal Inference and Discrete Profiling

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

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

T-matrix representation of optical scattering response: Suggestion for a data format

Topological hysteretic winding for temporal anti-lasing

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"

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