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Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star. The exoplanet signals can only be identified when…
Video and image streaming on edge devices requires low latency. To address this, Neural Networks (NNs) are widely used, and prior work mainly focuses on accelerating them with single hardware units such as Graphics Processing Units (GPUs),…
This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and…
The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating…
The optical, full-phase photometric variations of a short-period planet provide a unique view of the planet's atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small…
Vortex Fiber Nulling (VFN) is an interferometric method for suppressing starlight to detect and spectroscopically characterize exoplanets. It relies on a vortex phase mask and single-mode fiber to reject starlight while simultaneously…
We present a simple and powerful method for extracting transit signals associated with a known transiting planet from noisy light curves. Assuming the orbital period of the planet is known and the signal is periodic, we illustrate that…
Context: Transit surveys, both ground- and space- based, have already accumulated a large number of light curves that span several years. Aims: The search for transiting planets in these long time series is computationally intensive. We…
We propose a method to detect exoplanets based on their host star's intensity centroid after it passes thru a vortex filter. Based on our calculations with planets in face-on orbits, exoplanets with relative proximity to their host stars…
Instrumentation designed to characterize potentially habitable planets may combine adaptive optics and high-resolution spectroscopy techniques to achieve the highest possible sensitivity to spectral signs of life. Detecting the weak signal…
Since the discovery of RRATs, interest in single pulse radio searches has increased dramatically. Due to the large data volumes generated by these searches, especially in planned surveys for future radio telescopes, such searches have to be…
Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting…
Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with prediction accuracy. These difficulties stem from iterative…
Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on…
Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this problem. In this study, the authors propose a deep-learning…
High-dispersion coronagraphy (HDC) optimally combines high contrast imaging techniques such as adaptive optics/wavefront control plus coronagraphy to high spectral resolution spectroscopy. HDC is a critical pathway towards fully…
Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole…
We present a conceptual design for a space based Galactic Exoplanet Survey Telescope (GEST) which will use the gravitational microlensing technique to detect extra solar planets with masses as low as that of Mars at all separations >~ 1 AU.…
We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their…
In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. PDNs inductively learn an aggregated weight for each edge,…