@article{filipovic2026askap,
title = {ASKAP EMU detection of an Odd Radio Circle (ORC) candidate: J094412--751016 (Anglerfish)},
author = {Miroslav D. Filipovi{\'c} and Zachary Smeaton and Aaron Bradley
and Roland Kothes and Evan J. Crawford and Adeel
Ahmad and Takuya Akahori and Luke Barnes and
Cristobal Bordiu and Shi Dai and Stefan William
Duchesne and Yjan Gordon and Nikhel Gupta and Andrew
Hopkins and Bärbel Silvia Koribalski and Sanja
Lazarević and Denis Leahy and Kieran Luken and Peter
Macgregor and Anilkumar Mailvaganam and Saad Mehmood
and Ray Norris and Nastasia Novaretti and Laurence
Park and Simone Riggi and Christopher Riseley and
Gavin Rowell and Manami Sasaki and Stanislav Shabala
and Sam Taziaux and Nicholas Tothil andl Dejan
Urošević and Velibor Velović and Tessa Vernstrom and
Jennifer West and and Tayyaba Zafar},
journal = {Publications of the Astronomical Society of Australia},
volume = {43},
pages = {e043},
year = {2026},
keywords = {laurence},
doi = {10.1017/pasa.2026.10186},
publisher = {Cambridge University Press},
abstract = {We report diffuse extended radio-continuum emission spatially coinciding with the IR source, WISEA J094409.17−751012.8,
and a semi-variable star, V687 Carinae. We use 944 MHz radio data from the large-scale Evolutionary Map of the Universe (EMU) survey
to analyse this diffuse emission (EMU J094412−751016), which we nickname ‘Anglerfish’. We investigate if the spatially correlated
infrared (IR) source, WISEA J094409.17−751012.8, is physically related to Anglerfish. The IR colours of WISEA J094409.17−751012.8
are indicative of an elliptical galaxy, raising the possibility that Anglerfish may belong to the newly discovered class of extragalactic
radio sources known as Odd Radio Circles (ORCs) with WISEA J094409.17−751012.8 as the host galaxy. We also investigate the possibility
that Anglerfish is physically related to the star, V687 Carinae, and whether it may be a remnant from a previous epoch of stellar mass-loss.
We determine that a physical association between the radio emission and the star is unlikely due to the star’s weak stellar winds compared
to the theoretical expansion velocity of the ‘shell’. It is possible that Anglerfish may be a Galactic high-latitude supernova remnant; however,
we find that the observed size and luminosity are not consistent with this scenario. We also investigate the ORC scenario, which we deem the
most likely scenario based on the Anglerfish’s observed properties such as size, brightness, lack of other frequency detections, and possible
host galaxy identification. We therefore propose Anglerfish as an ORC candidate, but note that additional radio and optical observations are
vital to further constrain the properties and confirm this classification.}
}
@inproceedings{patel2025tag,
author = {Patel, Vishal A.
and Guo, Yi
and Park, Laurence
and Obst, Oliver},
editor = {Quan, Thanh Tho
and Sombattheera, Chattrakul
and Pham, Hoang-Anh
and Tran, Ngoc Thinh},
title = {TAG: Temporal Attention Graph for Heterogeneous Traffic Trajectory Prediction},
booktitle = {Multi-disciplinary Trends in Artificial Intelligence},
year = {2026},
publisher = {Springer Nature Singapore},
keywords = {laurence},
address = {Singapore},
pages = {226--238},
abstract = {Heterogeneous traffic patterns are commonly observed in pedestrian rich public spaces and unregulated vehicular environments. This poses significant challenges for trajectory prediction due to their complex, dynamic inter-agent relationships. These environments feature diverse agent types whose motions continuously influence one another, creating evolving, non-Euclidean interaction structures that traditional models struggle to capture. To tackle this problem, we propose a novel framework that learns the time-varying importance of all agents in a scene, enabling the model to focus on contextually relevant interactions across time. Our approach incorporates an enhanced spatiotemporal attention mechanism, which avoids simplistic proximity-based or frame-wise weighting. Instead, it adaptively attenuates agent features based on their temporal influence. Influence is learned through a custom attention architecture integrated with Graph Convolutional Networks (GCNs) and Temporal Convolutional Neural Networks (TCNNs). This design helps to extract subtle motion patterns across heterogeneous agents and improves prediction quality. We validate our framework using the ApolloScape dataset, known for its multi-agent and dynamic environment, as well as the ETH and UCY pedestrian datasets. Results show that our method achieves state-of-the-art performance, particularly excelling in heterogeneous environments. The model's adaptive attention and dynamic interaction encoding contribute to more accurate and generalisable trajectory forecasts.},
isbn = {978-981-95-4963-4}
}
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