Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

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We make use of the unprecedented depth, spatial resolution, and field of view of the Euclid Early Release Observations (EROs) of the Perseus galaxy cluster to detect and characterise the dwarf galaxy population in this massive system. Using a dedicated ann ...

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 ...

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on ...

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

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Time-delay cosmography can be used to infer the Hubble parameter H-0 by measuring the relative time delays between multiple images of gravitationally lensed quasars. A few of such systems have already been used to measure H-0: Their time delays were determ ...

The largest source of systematic errors in the time-delay cosmography method likely arises from the lens model mass distribution, where an inaccurate choice of model could in principle bias the value of H-0. A Bayesian hierarchical framework has been propo ...

Strongly lensed quadruply imaged quasars (quads) arc extraordinary objects. They arc very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distrib ...

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

Chattez avec Graph Search

Euclid: Early Release Observations – Dwarf galaxies in the Perseus galaxy cluster

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

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Multilingual Vision-Language Pre-training for the Remote Sensing Domain

SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

AIFORE: Smart Fuzzing Based on Automatic Input Format Reverse Engineering

Time-delay estimation in unresolved lensed quasars

TOCOSMO VIII. A key test of systematics in the hierarchical method of time-delay cosmography

Finding quadruply imaged quasars with machine learning - I. Methods

Quantization for Distributed Processing and Learning of Structured Data