Related papers: SNIascore: Deep Learning Classification of Low-Res…

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus…

We present 13 Type Ia supernovae (SNe Ia) observed in the restframe near-infrared (NIR) from 0.02 < z < 0.09 with the WIYN High-resolution Infrared Camera (WHIRC) on the WIYN 3.5-m telescope. With only 1-3 points per light curve and a prior…

Upcoming Fast Radio Burst (FRB) surveys will search $\sim$10\,$^3$ beams on sky with very high duty cycle, generating large numbers of single-pulse candidates. The abundance of false positives presents an intractable problem if candidates…

We study the feasibility of detecting weak lensing spatial correlations between Supernova (SN) Type Ia magnitudes with present (Dark Energy Survey, DES) and future (Large Synoptic Survey Telescope, LSST) surveys. We investigate the angular…

We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer…

Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic segmentation in…

Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies using images is non-trivial, resulting in a subset of SNe with…

Accurate standardisation of Type Ia supernovae (SNIa) is instrumental to the usage of SNIa as distance indicators. We analyse a homogeneous sample of 22 low-z SNIa, observed by the Carnegie Supernova Project (CSP) in the optical and near…

We describe a research program to improve the understanding of Type Ia Supernovae (SNe Ia) by modeling and observing near infrared (NIR) spectra of these events. The NIR between 0.9 microns and 2.5 microns is optimal for examining certain…

Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used…

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based…

Full-waveform inversion (FWI) is a widely used technique in seismic processing to produce high resolution Earth models that fully explain the recorded seismic data. FWI is a local optimisation problem which aims to minimise in a…

Underwater image enhancement (UIE) is fundamental for marine applications, including autonomous vision-based navigation. Deep learning methods using convolutional neural networks (CNN) and vision transformers advanced UIE performance.…

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical…

Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation. However, training such networks is difficult due to the non-differentiable…

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the…

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project…

Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power. New hardware platforms using lower precision arithmetic…

The 5-year project Supernova Legacy Survey (SNLS) delivers $\sim 100$ Type-Ia supernovae (SNe Ia) per year, in the redshift range $0.3 < z < 1.0$, with well-sampled $g'r'i'z'$ lightcurves. The SNLS Collaboration uses the 1 deg$^2$ Megacam…

We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive…