Related papers: Self-Supervised ConvLSTM for Fermi Large Area Tele…

In this work, we aim for temporally consistent semantic segmentation throughout frames in a video. Many semantic segmentation algorithms process images individually which leads to an inconsistent scene interpretation due to illumination…

The Large Area Telescope on the Fermi gamma-ray Space Telescope provides unprecedented sensitivity for all-sky monitoring of gamma-ray activity. It has detected a few Galactic sources, including 2 gamma-ray binaries and a microquasar. In…

The modern power grid is facing increasing complexities, primarily stemming from the integration of renewable energy sources and evolving consumption patterns. This paper introduces an innovative methodology that harnesses Convolutional…

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude $\gamma$-ray sources below their nominal threshold. To do so, we rely on a recent determination of the differential source-count distribution of…

The analysis of physiological processes over time are often given by spectrometric or gene expression profiles over time with only few time points but a large number of measured variables. The analysis of such temporal sequences is…

Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising results in pre-training large models, which -- when finetuned for…

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of…

The Fermi Gamma Ray Burst Monitor (GBM) is an all sky gamma-ray monitor well known in the gamma-ray burst community. Although GBM excels in detecting the hard, bright extragalactic GRBs, its sensitivity above 8 keV and all-sky view make it…

With a decade of gamma-ray data from the Fermi-LAT telescope, we can now hope to answer how well we know the local Universe at gamma-ray frequencies. On the other hand, with gamma-ray data alone it is not possible to directly access the…

We propose a hybrid meta-learning framework for forecasting and anomaly detection in nonlinear dynamical systems characterized by nonstationary and stochastic behavior. The approach integrates a physics-inspired simulator that captures…

The LAT instrument on the Fermi Gamma-Ray Space Telescope is performing an all-sky survey from 20 MeV to 300 GeV with unprecedented statistics and angular resolution. This is providing a wealth of new information on the non-thermal emission…

In this paper, we present a novel hybrid deep learning model, named ConvLSTMTransNet, designed for time series prediction, with a specific application to internet traffic telemetry. This model integrates the strengths of Convolutional…

We investigate whether a novel method of quantum machine learning (QML) can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we…

HERMES (High Energy Rapid Modular Ensemble of Satellites) pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy…

Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional…

Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad point spread function of modern space-based instruments. In…

Predicting future states of dynamic agents is a fundamental task in autonomous driving. An expressive representation for this purpose is Occupancy Flow Fields, which provide a scalable and unified format for modeling motion, spatial extent,…

We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved confounding latent variable. Previous work has shown that as…

We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsupervised, however, can be readily extended to supervised or…

The supporting instrument on board the Fermi Gamma-ray Space Telescope, the Gamma-ray Burst Monitor (GBM) is a wide-field gamma-ray monitor composed of 14 individual scintillation detectors, with a field of view which encompasses the entire…