@conference {2025,
	title = {Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction},
	year = {2025},
	abstract = {Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining{\textquoteright}s evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms LSTM-based and TSLANet baselines, achieving 83.7\% accuracy and 83.1\% macro F1-score. The model demonstrates strong economic relevance, achieving 93.6\% precision in detecting unprofitable periods and 98.5\% precision for profitable ones, while avoiding misclassification of profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations. The model is available at https://github.com/AMAAI-Lab/MineROI-Net},
	url = {https://arxiv.org/abs/2512.05402},
	author = {S. Wickramasinghe and B. Das and D. Herremans}
}
@article {2022,
	title = {Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia},
	journal = {Egusphere preprint},
	year = {2022},
	abstract = {Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment{\textendash}Southeast Asia (SEACLID/CORDEX{\textendash}SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of {\textquoteright}black-box{\textquoteright} methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established. },
	doi = {https://doi.org/10.5194/egusphere-2022-234},
	url = {https://egusphere.copernicus.org/preprints/2022/egusphere-2022-234/},
	author = {O. D. Levers and D. Herremans and A. Dipankar and L. Blessing}
}
@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being},
	journal = {Music \& Science},
	year = {2021},
	abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquotedblleft}Music, Computing, and Health{\textquotedblright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.},
	doi = {https://doi.org/10.1177/205920432199770},
	url = {https://journals.sagepub.com/doi/full/10.1177/2059204321997709},
	author = {K. Agres and Schaefer, Rebecca and Volk, Anja and Van Hooren, Susan and Holzapfel, Andr{\'e} and Dalla Bella, Simone and M{\"u}ller, Meinard and de Witte, Martina and D. Herremans and Ramirez Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@article {122,
	title = {Machine Learning Research that Matters for Music  Creation: A Case Study},
	journal = {Journal of New Music Research},
	volume = {48},
	year = {2019},
	pages = { 36-55},
	abstract = {Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.},
	doi = {10.1080/09298215.2018.1515233},
	author = {B. Sturm and O. Ben-Tal and U. Monaghan and N. Collins and D. Herremans and E. Chew and G. Hadjeres and E. Deruty and F. Pachet}
}
