Fabrizio Palumbo

• Aaby, Pernille; Biermann, Daniel; Yazidi, Anis; Borges Moreno e Mello, Gustavo & Palumbo, Fabrizio (2023). Exploring Multilingual Word Embedding Alignments in BERT Models: A Case Study of English and Norwegian. I Bramer, Max & Stahl, Frederic (Red.), Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023. Springer. ISSN 978-3-031-47993-9. s. 47–58. doi: 10.1007/978-3-031-47994-6_4.
• Fridman, Marina; Krøvel, Roy & Palumbo, Fabrizio (2023). How (not to) Run an AI Project in Investigative Journalism. Journalism Practice. ISSN 1751-2786. doi: 10.1080/17512786.2023.2253797.
• Krøvel, Roy; Palumbo, Fabrizio & Orgeret, Kristin (2023). Introduction: Understanding Roots and Betweenness Defining Safety of Journalists as a Sub-Field of Research. Reading between the Lines. Journalism Studies. ISSN 1461-670X. 24(7), s. 825–837. doi: 10.1080/1461670X.2023.2206494.
• Opalic, Sven Myrdahl; Palumbo, Fabrizio; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2023). COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control. Journal of Energy Storage. ISSN 2352-152X. 72(B). doi: 10.1016/j.est.2023.108202. Vis sammendrag

  The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system. The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

• Palumbo, Fabrizio; Buji, Adam; Yazidi, Anis & Haugerud, Hårek (2022). Genetic Algorithms For Tightening Security. I Ukjent, Ukjent (Red.), Proceedings of the 14th IFIP Wireless and Mobile Networking Conference. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-3-903176-52-2. doi: 10.23919/WMNC56391.2022.9954297.
• Lien, Henrik; Biermann, Daniel; Palumbo, Fabrizio & Goodwin, Morten (2022). An Exploration of Semi-supervised Text Classification. I Iliadis, Lazaros; Jayne, Chrisina; Tefas, Anastasios & Pimenidis, Elias (Red.), Engineering Applications of Neural Networks - 23rd International Conference, EAAAI/EANN 2022. Springer Nature. ISSN 978-3-031-08222-1. s. 477–488. doi: 10.1007/978-3-031-08223-8_39. Fulltekst i vitenarkiv Vis sammendrag

  Good performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these limitations, researchers have developed Semi-Supervised learning (SSL) algorithms exploiting the use of unlabeled data, which are generally easy and free to access. With SSL, unlabeled and labeled data are combined to outperform Supervised-Learning algorithms. However, setting up SSL neural networks for text classification is cumbersome and frequently based on a trial and error process. We show that the hyperparameter configuration significantly impacts SSL performance, and the learning rate is the most influential parameter. Additionally, increasing model size also improves SSL performance, particularly when less pre-processing data are available. Interestingly, as opposed to feed-forward models, recurrent models generally reach a performance threshold as pre-processing data size increases. This article expands the knowledge on hyperparameters and model size in relation to SSL application in text classification. This work supports the use of SSL work in future NLP projects by optimizing model design and potentially lowering training time, particularly if time-restricted.

• Palumbo, Fabrizio; Serneels, Bram & Yaksi, Emre (2021). Optimized protocol for conditioned place avoidance learning in juvenile zebrafish. STAR Protocols. ISSN 2666-1667. 2(2). doi: 10.1016/j.xpro.2021.100465. Fulltekst i vitenarkiv
• Fore, Stephanie Liesbeth; Acuna Hinrichsen, Francisca Daniela; Mutlu, Aytac Kadir; Bartoszek-Kandler, Ewelina Magdalena; Serneels, Bram & Faturos, Nicholas Guy [Vis alle 14 forfattere av denne artikkelen] (2020). Functional properties of habenular neurons are determined by developmental stage and sequential neurogenesis. Science Advances. ISSN 2375-2548. doi: 10.1126/sciadv.aaz3173. Fulltekst i vitenarkiv
• Palumbo, Fabrizio; Serneels, Bram; Pelgrims, Robbrecht & Yaksi, Emre (2020). The Zebrafish dorsal habenula is required for updating learned behaviors. Cell reports. ISSN 2211-1247. 32. doi: 10.1016/j.celrep.2020.108054. Fulltekst i vitenarkiv
• Kermen, Florence; Darnet, Lea; Wiest, Christoph; Palumbo, Fabrizio; Bechert, Jack & Uslu, Ozge [Vis alle 7 forfattere av denne artikkelen] (2020). Stimulus-specific behavioral responses of zebrafish to a large range of odors exhibit individual variability. BMC Biology. ISSN 1741-7007. 18(1). doi: 10.1186/s12915-020-00801-8. Fulltekst i vitenarkiv
• Foré, Stephanie; Palumbo, Fabrizio; Pelgrims, Robbrecht & Yaksi, Emre (2018). Information processing in the vertebrate habenula. Seminars in Cell and Developmental Biology. ISSN 1084-9521. 78, s. 130–139. doi: 10.1016/j.semcdb.2017.08.019.

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• Kvalnes, Pål Arne; Palumbo, Fabrizio & Krøvel, Roy (2023). Kva skal journalistar med kunstig intelligens? [Internett]. oslomet.no.
• Palumbo, Fabrizio; Næss, Arne Fredrik; Thommessen, Louise Scharff & Kasnes, Ellen Benedikte (2023). Hjemme best? 90 år gamle Lilly er prisgitt hjelpen hun får hjemme. Med skjult kamera har NRK dokumentert at hun ikke får hjelpen hun trenger. [Internett]. https://www.nrk.no.
• Palumbo, Fabrizio (2022). Artificial Investigative Journalism, How much of it can actually be artificial?
• Lal, Pradeep; Fiskum, Vegard; Kermen, Florence; Ostenrath, Anna Maria; Palumbo, Fabrizio & Jurisch-Yaksi, Nathalie [Vis alle 9 forfattere av denne artikkelen] (2022). The role of GnRH3 neurons in modulating olfactory computations and odor induced feeding behaviors.
• Palumbo, Fabrizio; Pelgrims, Robbrecht & Yaksi, Emre (2018). Studying conditioned place avoidance in juvenile zebrafish.
• Lal, Pradeep; Weth-Malsch, Daniela; Fiskum, Vegard; Kermen, Florence; Palumbo, Fabrizio & Pelgrims, Robbrecht [Vis alle 10 forfattere av denne artikkelen] (2018). Gonadotropin-releasing hormone (GnRH) neuromodulation of olfactory computations in zebrafish brain.
• Lal, Pradeep; Weth-Malsch, Daniela; Fiskum, Vegard; Palumbo, Fabrizio; Kermen, Florence & Pelgrims, Robbrecht [Vis alle 10 forfattere av denne artikkelen] (2018). The function of gonadotropin-releasing hormone (GnRH) neurons in olfactory computations.
• Lal, Pradeep; Kermen, Florence; Palumbo, Fabrizio; Pelgrims, Robbrecht & Yaksi, Emre (2017). Gondotropin-releasing hormone (GnRH) neuromodulation of olfactory computaions in zebrafish brain.

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