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With a new probabilistic technique for sampling interstellar object (ISO) orbits with high efficiency, we assess the observability of ISOs under a realistic cadence for the upcoming Vera Rubin Observatory's Legacy Survey of Space and Time…

Earth Observation (EO) has traditionally involved the transmission of a large volume of raw data to map the Earth surface. This results in congestion to the satellite network and delays in the availability of the results, invalidating the…

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to…

Humans are good at recomposing novel objects, i.e. they can identify commonalities between unknown objects from general structure to finer detail, an ability difficult to replicate by machines. We propose a framework, ISCO, to recompose an…

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in…

Large area surveys will dominate the forthcoming decades of astronomy and their success requires characterizing thousands of discoveries through additional observations at higher spatial or spectral resolution, and at complementary cadences…

We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully convolutional…

Context is important for accurate visual recognition. In this work we propose an object detection algorithm that not only considers object visual appearance, but also makes use of two kinds of context including scene contextual information…

Due to ionosphere absorption and the interference by natural and artificial radio emissions, ground observation of the sky at the decameter or longer is very difficult. This unexplored part of electromagnetic spectrum has the potential of…

The geosynchronous (GSO) debris environment is continually evolving. Regular monitoring of the region is consequently of great importance, though the trade-off between coverage and sensitivity makes this challenging for the population of…

Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) are promising candidates for…

We propose DaigNet, a new approach to object detection with which we can detect an object bounding box using diagonal constraints on adjacency matrix of a graph convolutional network (GCN). We propose two diagonalization algorithms based on…

We present a new algorithm to search for distant clusters of galaxies on catalogues deriving from imaging data, as those of the ESO Imaging Survey. Our algorithm is a matched filter one, similar to that adopted by Postman et al. (1996),…

Direct-sampling observations of interstellar neutral (ISN) species and their secondary populations give information about the physical state of the local interstellar medium and processes occuring in the outer heliosheath. Such observations…

I describe a European collaborative project to survey \sim 20 square degrees of the sky at 15\micron and 90\micron with ISO. This is the largest open time project being undertaken by ISO. The depth and areal coverage were designed to…

Space debris and inactive satellites pose a threat to the safety and integrity of operational spacecraft and motivate the need for space situational awareness techniques. These uncooperative targets create a challenging tracking and…

Stellar occultations have been used to search for Kuiper Belt and Oort Cloud objects. We propose a search for interstellar objects based on the characteristic durations ($\sim 0.1 \mathrm{s}$) of their stellar occultation signals and high…

Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds components such as boundary modules, attention mechanisms, and…

Observed Young Stellar Objects (YSOs) are used to study star formation and characterize star forming regions. For this purpose, YSO candidate catalogs are compiled from various surveys, especially in the infrared (IR), and simple selection…

Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are…