Related papers: ANTARES: A Prototype Transient Broker System

Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation…

ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by…

In this paper we present the SOXS Scheduler, a web-based application aimed at optimising remote observations at the NTT-ESO in the context of scientific topics of both the SOXS Consortium and regular ESO proposals.This paper will give…

Automotive radar sensors play a key role in the current development of advanced driver assistance systems (ADAS). Their ability to detect objects even under adverse weather conditions makes them indispensable for environment-sensing tasks…

Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any previous survey before. With this deluge of photometric data,…

In low-altitude surveillance and early warning systems, detecting weak moving targets remains a significant challenge due to low signal energy, small spatial extent, and complex background clutter. Existing methods struggle with extracting…

The ANTARES collaboration aims to build a deep underwater Cherenkov neutrino telescope in the Mediterranean Sea at 2500 m depth, about 40 km off-shore of La Seyne sur Mer, near Toulon. The collaboration was formed in 1996 and the experiment…

The Zwicky Transient Facility (ZTF) survey generates real-time alerts for optical transients, variables, and moving objects discovered in its wide-field survey. We describe the ZTF alert stream distribution and processing (filtering)…

Ambient-awareness in conjunction with pervasive computing is a significant challenge for system designers. It follows the necessity of gathering raw, massive and heterogeneous environmental data \newrrr{which we} obtained, while middleware…

The ANTARES deep-sea neutrino telescope will be located at a depth of 2400 m in the Mediterranean Sea. Deployment of the detector will commence this Autumn and is expected to be completed by the end of 2004. With a surface area of the order…

Automotive radar sensors play a key role in the current development of autonomous driving. Their ability to detect objects even under adverse conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. The…

In this paper, we present HOLMES, a system that implements a new approach to the detection of Advanced and Persistent Threats (APTs). HOLMES is inspired by several case studies of real-world APTs that highlight some common goals of APT…

The ANTARES Collaboration is building an underwater neutrino telescope in the Mediterranean sea. The telescope is designed to search for high energy (E $>1$ TeV) galactic and extra-galactic neutrino sources, but could also be sensitive to…

A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the…

The ANTARES detector is a Cherenkov underwater neutrino telescope operating in the Mediterranean Sea. Its construction was completed in 2008. Even though optimised for the search of cosmic neutrinos, this telescope is also sensitive to…

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts.…

Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated…

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly…

This paper is a contribution towards interpretability of the deep learning models in different applications of time-series. We propose a temporal attention layer that is capable of selecting the relevant information to perform various…

Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architectures exploit either temporal or inter-channel dependencies,…