Related papers: HIGAN: Cosmic Neutral Hydrogen with Generative Adv…
This review summarizes recent studies of the cold neutral hydrogen gas associated with galaxies probed via the HI 21-cm absorption line. HI 21-cm absorption against background radio-loud quasars is a powerful tool to study the neutral gas…
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D…
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality,…
Despite having been discovered more than three decades ago, High Temperature Superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a…
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only…
Over the past decade, Lyman-alpha and metal line absorption observations have established the ubiquity of a gas-rich circumgalactic medium (CGM) around star-forming galaxies at z~0.2 potentially tracing half of the missing baryonic mass…
Generating synthetic data has become a popular alternative solution to deal with the difficulties in accessing and sharing field measurement data in power systems. However, to make the generation results controllable, existing methods (e.g.…
The paper proposes a method to effectively fuse multi-exposure inputs and generate high-quality high dynamic range (HDR) images with unpaired datasets. Deep learning-based HDR image generation methods rely heavily on paired datasets. The…
Atomic hydrogen (HI) is an ideal tracer of gas flows in and around galaxies, and it is uniquely observable in the nearby Universe. Here we make use of wide-field (~1 square degree), spatially resolved (down to 22"), high-sensitivity…
Neutral hydrogen traces gravitational potentials. In the nearby universe, 21cm emission-line surveys show that the bulk of the HI resides in well-formed, optically-luminous galaxies. At high redshift, 21cm line absorption against background…
Conditional Generative Adversarial Networks (cGANs) have enabled controllable image synthesis for many vision and graphics applications. However, recent cGANs are 1-2 orders of magnitude more compute-intensive than modern recognition CNNs.…
Extracting non-Gaussian information from the next generation weak lensing surveys will require fast and accurate full-sky simulations. This is difficult to achieve in practice with existing simulation methods: ray-traced $N$-body…
Predictive Virtual Machine (VM) auto-scaling is a promising technique to optimize cloud applications operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads…
The 21cm line of neutral hydrogen is a powerful probe of the high-redshift universe (Cosmic Dawn and the Epoch of Reionization), with an unprecedented potential to inform us about key processes of early galaxy formation, the first stars and…
Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to…
The neutral hydrogen properties of 1,822 galaxies are being studied with the Green Bank 100m and the Parkes 64m telescopes as part of the 'Cosmic Flows' program. Observed parameters include systemic velocities, profile line widths, and…
Hyperedge prediction is a fundamental task to predict future high-order relations based on the observed network structure. Existing hyperedge prediction methods, however, suffer from the data sparsity problem. To alleviate this problem,…
Image inpainting is a restoration method that reconstructs missing image parts. However, a carefully selected mask of known pixels that yield a high quality inpainting can also act as a sparse image representation. This challenging spatial…
Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has two main drawbacks: (i) Wasserstein-1 (or Earth-Mover)…
We present a detection of 21-cm emission from large-scale structure (LSS) between redshift 0.78 and 1.43 made with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Radio observations acquired over 102 nights are used to construct…