Event-driven acquisition for content-enriched microscopy

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The ability of the brain to produce adaptive and dexterous movements relies on the seamless integration of sensory feedback into motor commands. Despite significant advances in motor neuroscience, critical gaps persist in understanding how proprioceptive a ...

Airborne bioparticles, including fungal spores, are of major concern for human and plant health, necessitating precise monitoring systems. While a European norm exists for manual volumetric monitoring, there's a growing interest in automated real-time meth ...

Fluorescence confocal laser-scanning microscopy (LSM) is one of the most popular tools for life science research. This popularity is expected to grow thanks to single-photon array detectors tailored for LSM. These detectors offer unique single-photon spati ...

Interferometric scattering (iSCAT) microscopy enables the label-free observation of biomolecules. Consequently, single-particle imaging and tracking with the iSCAT-based method known as mass photometry (MP) is a growing area of study. However, establishing ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Significance: Fluorescence guidance is used clinically by surgeons to visualize anatomical and/or physiological phenomena in the surgical field that are difficult or impossible to detect by the naked eye. Such phenomena include tissue perfusion or molecula ...

Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Accounting for these features is often critical in quantitatively interpreting microscopy images, especially those gathering information at scal ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Optimum groundwater management is important for addressing the global challenges of water scarcity, especially in arid regions. This study demonstrates the application of synthetic data combined with seismic methods to quantify water volumes in a controlle ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Event-driven acquisition for content-enriched microscopy

Chattez avec Graph Search

Reverse engineering primate sensorimotor control with machine learning

Advancing Automated Identification of Airborne Fungal Spores: Guidelines for Cultivation and Reference Dataset Creation

Compact and effective photon-resolved image scanning microscope

Surface passivation and functionalisation for mass photometry

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Subsurface fluorescence time-of-flight imaging using a large-format single-photon avalanche diode sensor for tumor depth assessment

Fluorescence microscopy: A statistics-optics perspective

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The multimodality cell segmentation challenge: toward universal solutions

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Safe Deep Neural Networks

Live-cell imaging powered by computation

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Sparse autoregressive neural networks for classical spin systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Uncertainty quantification in groundwater volume predictions from seismic data using neural networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks