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Over the past few decades, the hydrology community has witnessed notable advancements in streamflow prediction, particularly with the introduction of cutting-edge machine-learning algorithms. Recurrent neural networks, especially Long…

In the era of sky surveys like Palomar Transient Factory (PTF), Zwicky Transient Facility (ZTF) and the upcoming Vera Rubin Observatory (VRO) and ILMT, a plethora of image data will be available. ZTF scans the sky with a field of view of 48…

The increasing data volume of high-energy space monitors necessitates real-time, automated transient classification for multi-messenger follow-up. Conventional methods rely on empirical features like hardness ratios and reliable…

The up-coming Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) opens a new opportunity to rapidly survey the southern Sky at optical wavelenghts (\ie ugrizy bands). In this study, we aim to test the possibility of using LSST…

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract…

Making accurate motion prediction of surrounding agents such as pedestrians and vehicles is a critical task when robots are trying to perform autonomous navigation tasks. Recent research on multi-modal trajectory prediction, including…

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of their underlying physical processes. However, upcoming deep photometric surveys, including the…

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam…

We present the first data release (DR1) of the Spectroscopic Classification of Astronomical Transients (SCAT) survey, covering the first $\approx 5$ years of observations (March 2018 - January 2023). DR1 includes 1810 spectra of 1330…

Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes. Shifts in such processes, e.g. caused by external events or internal…

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST…

This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems.…

Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected…

The Legacy Survey of Space and Time (LSST), being conducted by the Vera C. Rubin Observatory, is a wide-field multi-band survey that will revolutionize our understanding of extragalactic sources through its unprecedented combination of area…

We introduce the State Stream Transformer (SST), a novel LLM architecture that reveals emergent reasoning behaviours and capabilities latent in pretrained weights through addressing a fundamental limitation in traditional transformer…

Lane detection is a crucial perception task for all levels of automated vehicles (AVs) and Advanced Driver Assistance Systems, particularly in mixed-traffic environments where AVs must interact with human-driven vehicles (HDVs) and…

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an…

Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework. Although GNNs…

Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token-oriented methods, such as pruning and skipping, have shown great promise in reducing inference…

The Large Synoptic Survey Telescope (LSST) is a proposed 8.4-meter telescope that will be located in the Andes mountains in Chile. Every 17 seconds, a 6.4 GB image is transferred to Illinois for immediate processing. That transfer needs to…