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

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, afflict tens of millions of patients worldwide. They are characterized by protein aggregation and progressive neuronal loss, leading to cognitive and motor impairments, and ult ...

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 ...

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 ...

Measurements of galaxy clustering are affected by redshift-space distortions (RSDs). Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We d ...

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 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 ...

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 ...

The increasing demand for precision, reproducibility, and scalability in scientific research has driven the development of advanced robotic systems for laboratory automation. This thesis presents the design, implementation, and validation of a mobile robot ...

Context. ALMA observations show that dusty, distant, massive ( M similar to & 1011 M fi) galaxies usually have a remarkable star-formation activity, contributing of the order of 25% of the cosmic star-formation rate density at z similar to 3-5, and up to 3 ...

We present the properties of two candidate massive (M ⋆ ∼ 1011 M ⊙) and dusty (A v > 2.5 mag) galaxies at z = 5-7 in the first 0.28 deg2 of the COSMOS-Web survey. One object is spectroscopically confirmed at z spec = 5.051, while the other has a robust z p ...

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

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 ...

The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To ov ...

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 ...

We present an investigation into the first 500 Myr of galaxy evolution from the Cosmic Evolution Early Release Science (CEERS) survey. CEERS, one of 13 JWST ERS programs, targets galaxy formation from z similar to 0.5 to >10 using several imaging and spect ...

We develop a data-driven framework to identify the interconnections between firms using an information-theoretic measure. This measure generalizes Granger causality and is capable of detecting nonlinear relationships within a network. Moreover, we develop ...

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chai ...

We derived stellar ages and metallicities [Z/H] for similar to 70 passive early-type galaxies (ETGs) selected from VANDELS survey over the redshift range 1.0 < z < 1.4 and stellar mass range 10 < log(M-*/M-?) < 11.6. We find significant systematics in thei ...

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

Chattez avec Graph Search

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

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

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

Euclid preparation LXIX. The impact of relativistic redshift-space distortions on two-point clustering statistics from the Euclid wide spectroscopic survey

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

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

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

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Materials science experiments by robotic automation

Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees

Not-so-little Red Dots: Two Massive and Dusty Starbursts at z ∼ 5-7 Pushing the Limits of Star Formation Discovered by JWST in the COSMOS-Web Survey

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

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"

Bootstrapping Autonomous Driving Radars with Self-Supervised Learning

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

CEERS Key Paper. I. An Early Look into the First 500 Myr of Galaxy Formation with JWST

Analysis of Large Market Data Using Neural Networks: A Causal Approach

Facilitated machine learning for image-based fruit quality assessment

The star formation history and the nature of the mass-metallicity relation of passive galaxies at 1.0 < z < 1.4 from VANDELS