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• Piano, Leonardo; Battistin, Claudia; Abeele, Jeriek Paul Van den & Pompianu, Livio (2026). REMEDy: a dataset for rationale extraction and span-based moderation of dialogue prompts. Neural Computing & Applications. ISSN 0941-0643. 38(16). doi: 10.1007/s00521-026-12381-9. Full text in Research Archive
• Ulan, Maria; Husom, Erik Johannes & Abeele, Jeriek Paul Van den (2026). Talk is Cheap, Energy is Not: Towards a Green, Context-Aware Metrics Framework for Automatic Speech Recognition. In Dutra, Inês; Pechenizkiy, Mykola; Cortez, Paulo; Pashami, Sepideh; Jorge, Alípio M.; Soares, Carlos; Abreu, Pedro H. & Gama, João Alves (Ed.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings, Part IX. Springer. ISSN 9783032061188. p. 36–54. doi: 10.1007/978-3-032-06118-8_3. Full text in Research Archive Show summary

  Automatic Speech Recognition (ASR) systems are increasingly deployed across diverse computing environments, from cloud servers to edge devices. While accuracy has traditionally been the primary evaluation metric, the inference efficiency of these systems, including energy consumption, memory usage, and hardware utilisation, significantly impacts their practical usability. This paper introduces a novel benchmarking framework that assesses ASR models during inference from both performance and sustainability perspectives. We introduce a multi-metric evaluation approach quantifying Word Error Rate (WER), Real-Time Factor (RTF), Energy Per Audio Second (EPAS), inference latency, GPU Memory Efficiency (GME), and Hardware Utilisation Rate (HUR). Our framework includes configurable weighting schemes tailored for various deployment scenarios: balanced general-purpose evaluation, resource-constrained environments, high-throughput batch inference, and real-time processing. To demonstrate the utility of the framework, we benchmark several state-of-the-art ASR architectures (Whisper, Wav2Vec2, HuBERT, WavLM, UniSpeech, and SpeechT5) in both FP16 and FP32 precision on NVIDIA Jetson AGX Orin hardware. The proposed methodology supports researchers and practitioners in making informed model selection decisions based on context-specific inference requirements. By illuminating performance–consumption trade-offs, the metrics framework can help to reduce computational costs and the carbon footprint of ASR systems, while maintaining acceptable accuracy.

• Ananiev, Viktor; Balázs, Csaba; Beniwal, Ankit; Braseth, Lasse Lorentz; Buckley, Andy & Butterworth, Jonathan [Show all 21 contributors for this article] (2023). Collider constraints on electroweakinos in the presence of a light gravitino. European Physical Journal C. ISSN 1434-6044. 83(6). doi: 10.1140/epjc/s10052-023-11574-z. Full text in Research Archive Show summary

  Using the GAMBIT global fitting framework, we constrain the MSSM with an eV-scale gravitino as the lightest supersymmetric particle, and the six electroweakinos (neutralinos and charginos) as the only other light new states. We combine 15 ATLAS and 12 CMS searches at 13 TeV, along with a large collection of ATLAS and CMS measurements of Standard Model signatures. This model, which we refer to as the G~-EWMSSM, exhibits quite varied collider phenomenology due to its many permitted electroweakino production processes and decay modes. Characteristic G~-EWMSSM signal events have two or more Standard Model bosons and missing energy due to the escaping gravitinos. While much of the G~-EWMSSM parameter space is excluded, we find several viable parameter regions that predict phenomenologically rich scenarios with multiple neutralinos and charginos within the kinematic reach of the LHC during Run 3, or the High Luminosity LHC. In particular, we identify scenarios with Higgsino-dominated electroweakinos as light as 140 GeV that are consistent with our combined set of collider searches and measurements. The full set of G~-EWMSSM parameter samples and GAMBIT input files generated for this work is available via Zenodo.

• Abdussalam, Shehu S.; Agocs, Fruzsina J.; Allanach, Benjamin C.; Athron, Peter; Balázs, Csaba & Bagnaschi, Emanuele [Show all 75 contributors for this article] (2022). Simple and statistically sound recommendations for analysing physical theories. Reports on progress in physics (Print). ISSN 0034-4885. 85(5). doi: 10.1088/1361-6633/ac60ac. Full text in Research Archive
• Buckley, Andy; Kvellestad, Anders; Raklev, Are; Scott, Pat; Sparre, Jon Vegard & Abeele, Jeriek Paul Van den [Show all 7 contributors for this article] (2020). Xsec: the cross-section evaluation code. European Physical Journal C. ISSN 1434-6044. 80(12). doi: 10.1140/epjc/s10052-020-08635-y. Full text in Research Archive Show summary

  The evaluation of higher-order cross-sections is an important component in the search for new physics, both at hadron colliders and elsewhere. For most new physics processes of interest, total cross-sections are known at next-to-leading order (NLO) in the strong coupling αs, and often beyond, via either higher-order terms at fixed powers of αs, or multi-emission resummation. However, the computation time for such higher-order cross-sections is prohibitively expensive, and precludes efficient evaluation in parameter-space scans beyond two dimensions. Here we describe the software tool xsec, which allows for fast evaluation of cross-sections based on the use of machine-learning regression, using distributed Gaussian processes trained on a pre-generated sample of parameter points. This first version of the code provides all NLO Minimal Supersymmetric Standard Model strong-production cross-sections at the LHC, for individual flavour final states, evaluated in a fraction of a second. Moreover, it calculates regression errors, as well as estimates of errors from higher-order contributions, from uncertainties in the parton distribution functions, and from the value of αs. While we focus on a specific phenomenological model of supersymmetry, the method readily generalises to any process where it is possible to generate a sufficient training sample.

• Abeele, Jeriek Paul Van den (2020). Illuminating elite patches of chemical space. Chemical Science. ISSN 2041-6520. 11(42), p. 11485–11491. doi: 10.1039/d0sc03544k. Full text in Research Archive Show summary

  In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [J. H. Jensen, Chem. Sci., 2019, 10, 3567–3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [J. B. Mouret and J. Clune, Proceedings of the Artificial Life Conference, 2012, pp. 593–594], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

• Angelis, Alessandro De; Tatischeff, Vincent; Grenier, Isabelle A.; McEnery, Julie E.; Mallamaci, Manuela & Tavani, Marco [Show all 51 contributors for this article] (2018). Science with e-ASTROGAM: A space mission for MeV–GeV gamma-ray astrophysics. Journal of High Energy Astrophysics. ISSN 2214-4048. 19, p. 1–106. doi: 10.1016/j.jheap.2018.07.001. Full text in Research Archive

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• Abeele, Jeriek Paul Van den (2026). AI for a safer and smarter tomorrow. doi: https:/www.sure-ai.no/events/sure-ai-annual-conference-2026/. Full text in Research Archive

View all works in NVA