Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

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We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

SNAP-tag is a powerful tool for labeling proteins with synthetic fluorophores in bioimaging. However, its utility in live-cell applications can be constrained by its relatively slow labeling kinetics and the limited cell permeability of its substrates. Her ...

The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated b ...

Magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI), are non-invasive techniques used to quantify biochemical compounds in tissue, such as choline, creatine, glutamate, glutamine, γ-aminobutyric acid, N-acetylaspartate ...

Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

Investigating pathogenic microorganisms is essential due to their significant impact on global health. However, working with them presents significant challenges to researchers. Their virulence requires strict biosafety measures, often necessitating to stu ...

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs t ...

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: https://openaccess.thecvf.com/conte ...

In semiarid mountain ranges, rock glaciers constitute an important part of the periglacial environment. However, quantitative data on the state of rock glacier activity remain scarce. Here, we derive kinematic information on rock glaciers located in the Va ...

Functional magnetic resonance imaging (fMRI) relies upon the blood-oxygenation-level-dependent (BOLD) signal as a proxy for neuronal activity. Whole-brain coverage and millimetric resolution have propelled this modality as a workhorse for human neuroscienc ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, li ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

In this thesis, we discuss the problems of scattering and optical manipulation related to nanosystems of different complexities. The multipolar decomposition method is used to represent scattering processes in nanosystems as a series of elementary excitati ...

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in patients with Parkinson's disease, it has been argued to be indicative of a more severe and rapidly advanc ...

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

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Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

SNAP-tag2 for faster and brighter protein labeling

Automated Multicohort Mobility Assessment with an Instrumented L-test (iL-test)

A graphical pipeline platform for MRS data processing and analysis: MRSpecLAB

Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

Advancing high-resolution microscopes to enable time-lapse nano-characterization of pathogenic micro-organisms in-vitro

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Ground-based image deconvolution with Swin Transformer UNet

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

Rock Glacier Kinematics in the Valles Calchaquíes Region, Northwestern Argentina, From Multi-Temporal Aerial and Satellite Imagery (1968-2023)

Direct imaging of neuronal activity: a multivariate perspective on DIANA data in human subjects

Learning sparse features can lead to overfitting in neural networks

Brain networks subserving functional core processes of emotions identified with componential modeling

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Frequency and time domains interactions in nanophotonics

Persistent Homology with Improved Locality Information for more Effective Delineation

p Robotically-induced hallucination triggers subtle changes in brain network transitions