Related papers: A Generative Model of Galactic Dust Emission Using…
Ambient air pollution measurements from regulatory monitoring networks are routinely used to support epidemiologic studies and environmental policy decision making. However, regulatory monitors are spatially sparse and preferentially…
Approximating complex probability densities is a core problem in modern statistics. In this paper, we introduce the concept of Variational Inference (VI), a popular method in machine learning that uses optimization techniques to estimate…
Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability…
Multi-wavelength spectroscopy can be used to constrain the dust and gas properties in debris disks. Circumstellar dust absorbs and scatters incident stellar light. The scattered light is sometimes resolved spatially at visual and…
I review my work with Bruce Draine on dust emissivity at microwave frequencies (3 cm - 3 mm). This emissivity explains the recently detected "anomalous" component of the galactic foreground emission. Both small (a<0.001 micron) and large…
This paper proposes a short review on our knowledge on the dust emission in the diffuse sky from far-IR to the mm wavelength. The current understanding is based mainly on the analysis of the COBE data combined with several templates of gas…
Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding…
Variational Autoencoders (VAEs) have gained significant popularity among researchers as a powerful tool for understanding unknown distributions based on limited samples. This popularity stems partly from their impressive performance and…
Variational Autoencoders (VAEs) have been a pioneering force in the realm of deep generative models. Amongst its legions of progenies, Wasserstein Autoencoders (WAEs) stand out in particular due to the dual offering of heightened generative…
Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has…
We combine a semi-analytic model of galaxy formation with simple analytic recipes describing the absorption and re-emission of starlight by dust in the interstellar medium of galaxies. We use the resulting models to predict galaxy counts…
Spectral distortions of the cosmic microwave background (CMB) are sensitive to energy injection by exotic physics in the early universe. The proposed Primordial Inflation Explorer (PIXIE) mission has the raw sensitivity to provide…
The characterization of the Galactic foregrounds has been shown to be the main obstacle in the challenging quest to detect primordial B-modes in the polarized microwave sky. We make use of the Planck-HFI 2015 data release at high…
Dust is ubiquitous in the Universe and its influence on the observed Electromagnetic (EM) radiation needs to be correctly addressed. In recent years it became clear that scattering of EM radiation from interstellar dust grains could change…
Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively. Recently, Deep Clustering has gained popularity due toits flexibility in fitting the specific…
Dust has long been identified as a barrier to measuring inherent galaxy properties. However, the link between dust and attenuation is not straightforward and depends on both the amount of dust and its distribution. Herschel imaging of…
Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, PC constitutes one of the main bottlenecks of the system's…
The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-supervised classification. Combining the power of these two…
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making.…
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and…