Property:References

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Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses +	For general understanding: https://www.youtube.com/watch?v=8PQO4P8pR8o&t=839s Papers: 1. The strong gravitational lens finding challenge - Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119 2. Testing convolutional neural networks for finding strong gravitational lenses in KiDS - Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2019a, MNRAS, 482, 807 3. Finding strong gravitational lenses through self-attention - Study based on the Bologna Lens Challenge - Thuruthipilly, H., Adam Zadrozny, Agnieszka Pollo, and Marek Biesiada. A&A, 664:A4 4. The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples - Pearson, J., Li, N., & Dye, S. 2019, MNRAS, 488, 991 5. Deep convolutional neural networks as strong gravitational lens detectors - Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 6. Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks - K. Rojas, E. Savary, B. Clément, M. Maus, F. Courbin, C. Lemon, J. H. H. Chan, G. Vernardos, R. Joseph, R. Cañameras, A. Galan, DOI: 0.1051/0004-6361/202142119
Merging Clothoids with B-Splines +	http://en.wikipedia.org/wiki/B-spline http://en.wikipedia.org/wiki/Clothoid
Mining For Meanings In Robot Maps +	Pronobis, Andrzej, and Rajesh PN Rao. "Learning Deep Generative Spatial Models for Mobile Robots." arXiv preprint arXiv:1610.02627 (2016). Khalil, Wisama, and Etienne Dombre. Modeling, identification and control of robots. Butterworth-Heinemann, 2004. Shahbandi, Saeed Gholami, Björn Åstrand, and Roland Philippsen. "Semi-supervised semantic labeling of adaptive cell decomposition maps in well-structured environments." Mobile Robots (ECMR), 2015 European Conference on. IEEE, 2015.
Model Heterogeneity in Federated Learning +	Advances and Open Problems in Federated Learning: https://hal.inria.fr/hal-02406503/document FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf
Model Volvo Truck Lifetime Repair History +	http://www.sciencedirect.com/science/article/pii/S000437029800023X http://www.pomdp.org/index.shtml http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.7714 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1619 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.8737
Model behaviour of agents in a warehouse setting +	SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Lidström, Kristoffer; Situation-Aware vehicles – supporting the next generation of cooperative traffic system, PhD thesis, Örebro university, 2012. Lundström, Jens; Järpe, Eric; Verikas, Antanas; Detecting and exploring deviating behaviour of smart home residents, Expert systems with applications., 55, s. 429-440, 2016 Lidström, Kristoffer; Larsson, Tony; Act normal: using uncertainty about driver intentions as a warning criterion, 16th World Congress on Intelligent Transportation Systems (ITS WC), 21-25 September, 2009, Stockholm, Sweden Lidström, Kristoffer; Model-based Estimation of Driver Intentions Using Particle Filtering, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008
Model-Based Testing of Zero-Copy Protocols +	1. Jan Tretmans. Model-based testing and some steps towards test-based modelling. In Marco Bernardo and Valérie Issarny, editors, Formal Methods for Eternal Networked Software Systems, LNCS 6659, pages 297–326. Springer, 2011. 2. Wojciech Mostowski, Thomas Arts, and John Hughes. Modelling of Autosar Libraries for Large Scale Testing. Proceedings, 2nd Workshop on Models for Formal Analysis of Real Systems (MARS), EPTCS 244, 2017. 3. ICEORYX home page: https://iceoryx.io/ 4. ALEX.AI home page: https://www.apex.ai/
Modeling patient trajectories using different representation learning techniques +	Attention is all you need: https://arxiv.org/abs/1706.03762 Bert: Pre-training of deep bidirectional transformers for language understanding: https://arxiv.org/abs/1810.04805 BEHRT: transformer for electronic health records: https://www.nature.com/articles/s41598-020-62922-y MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning: https://arxiv.org/abs/2107.09288 Heterogeneous Similarity Graph Neural Network on Electronic Health Records: https://arxiv.org/abs/2101.06800 Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer: https://arxiv.org/abs/1906.04716 Variationally Regularized Graph-based Representation Learning for Electronic Health Records: https://arxiv.org/pdf/1912.03761.pdf
Multi-robot trajectory adaptation using a state-time elastic band approach +	tbd
Multiband RF Rectifier for Self-Powered IoT Devices +	1. Muhammad, Surajo, et al. "Harvesting Systems for RF Energy: Trends, Challenges, Techniques, and Tradeoffs." Electronics 11.6 (2022): 959. 2. Rotenberg, Samuel A., et al. "Efficient rectifier for wireless power transmission systems." IEEE Transactions on Microwave Theory and Techniques 68.5 (2020): 1921-1932. 3. Vu, Hong Son, et al. "Multiband ambient RF energy harvesting for autonomous IoT devices." IEEE Microwave and Wireless Components Letters 30.12 (2020): 1189-1192. 4. Eid, A., et al. "Flexible w-band rectifiers for 5g-powered IoT autonomous modules." 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019.
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Name of the new projectEstimating agricultural development indicators over large areas from satellite images – an approach using convolutional neural networks and transfer learning +	Xie, M., N. Jean, M. Burke, D. Lobell & S. Ermon (2015) Transfer learning from deep features for remote sensing and poverty mapping. arXiv preprint arXiv:1510.00098. Jean, N., M. Burke, et al (2016) Combining satellite imagery and machine learning to predict poverty. Science, 353, 790-794.
Network-assisted positioning in confined spaces using 802.11 Access Layer information +	IEEE 802.11az Indoor Positioning with mmWave (https://doi.org/10.48550/arXiv.2303.05996)
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Object Movement Prediction for Autonomous Cars +	https://motchallenge.net https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection https://arxiv.org/pdf/1909.07707.pdf
On control of robots in remote workspaces using lasers +	In the video clip, http://mobile-robotics.com/ragvald.php , a rate gyro is used to stabilized the heading of a mini UGV prototype.
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Pallet Detection and Mapping +	Belongie, Serge, Jitendra Malik, and Jan Puzicha. "Shape matching and object recognition using shape contexts." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.4 (2002): 509-522. Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001. Lowe, David G. "Local feature view clustering for 3D object recognition." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001. Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision 60.2 (2004): 91-110. Bay, Herbert, et al. ”Speeded-up robust features (SURF).” Computer vision and image understanding 110.3 (2008): 346-359. Pinto, Nicolas, David D. Cox, and James J. DiCarlo. "Why is real-world visual object recognition hard?." PLoS computational biology 4.1 (2008): e27.
Peer Group Discovery in District Heating Substations and Heat Pumps for Self-Monitoring +	Y. Kim and S. Sohn, "Stock fraud detection using peer group analysis", Expert Systems with Applications, vol. 39, no. 10, pp. 8986-8992, 2012. D. Weston, D. Hand, N. Adams, C. Whitrow and P. Juszczak, "Plastic card fraud detection using peer group analysis", Advances in Data Analysis and Classification, vol. 2, no. 1, pp. 45-62, 2008. D. Weston, N. Adams, Y. Kim and D. Hand, "Fault Mining Using Peer Group Analysis", Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 453-461, 2012.
Piglets Detection and Counting using Deep Neural Networks +	Yolo: https://pjreddie.com/darknet/yolo/
Pose & distance of toy-vehicles in front by spirals +	https://www.youtube.com/watch?v=nixxFXZ-X3s
Positioning of the user at the HINT +	wireless communication, digital communication
Predicting Energy Consumption for Heavy-Duty Vehicles (in collaboration with Volvo) +	Koprinska, I., Wu, D., & Wang, Z. (2018, July). Convolutional neural networks for energy time series forecasting. In 2018 international joint conference on neural networks (IJCNN) (pp. 1-8). IEEE. Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., ... & Zhang, Q. (2020). Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems, 33, 17766-17778. Geng, X., He, X., Xu, L., & Yu, J. (2022). Graph correlated attention recurrent neural network for multivariate time series forecasting. Information Sciences, 606, 126-142. Nan, S., Tu, R., Li, T., Sun, J., & Chen, H. (2022). From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus. Energy, 261, 125188. De Cauwer, C., Verbeke, W., Coosemans, T., Faid, S., & Van Mierlo, J. (2017). A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies, 10(5), 608. Arastehfar, S., Matinkia, M., & Jabbarpour, M. R. (2022). Short-term residential load forecasting using graph convolutional recurrent neural networks. Engineering Applications of Artificial Intelligence, 116, 105358. Shchetinin, E. Y. (2018). Cluster-based energy consumption forecasting in smart grids. In Distributed Computer and Communication Networks: 21st International Conference, DCCN 2018, Moscow, Russia, September 17–21, 2018, Proceedings 21 (pp. 445-456). Springer International Publishing. Le, T., Vo, M. T., Kieu, T., Hwang, E., Rho, S., & Baik, S. W. (2020). Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors, 20(9), 2668.
Predicting Energy Consumption for Heavy-Duty Vehicles via Time Series Embeddings (in collaboration with Volvo) +	Nalmpantis, C., & Vrakas, D. (2019, May). Signal2vec: Time series embedding representation. In International conference on engineering applications of neural networks (pp. 80-90). Cham: Springer International Publishing. Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25. Lee, S., Park, T., & Lee, K. (2023). Learning to embed time series patches independently. arXiv preprint arXiv:2312.16427. https://github.com/seunghan96/pits Luo, D., & Wang, X. (2024). Moderntcn: A modern pure convolution structure for general time series analysis. In The Twelfth International Conference on Learning Representations. https://github.com/luodhhh/ModernTCN?tab=readme-ov-file Fraikin, A., Bennetot, A., & Allassonnière, S. (2023). T-Rep: Representation Learning for Time Series using Time-Embeddings. arXiv preprint arXiv:2310.04486. Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2022). Transformers in time series: A survey. arXiv preprint arXiv:2202.07125. Ahmed, S., Nielsen, I. E., Tripathi, A., Siddiqui, S., Ramachandran, R. P., & Rasool, G. (2023). Transformers in time-series analysis: A tutorial. Circuits, Systems, and Signal Processing, 42(12), 7433-7466.
Privacy-Preserved Generator for Generating Synthetic EHR data +	Papers: https://ieeexplore.ieee.org/abstract/document/8975823 https://proceedings.neurips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf https://arxiv.org/pdf/2009.09283.pdf https://dl.acm.org/doi/abs/10.1145/2810103.2813687 Data: https://lcp.mit.edu/mimic
Project with Atos +	https://www.atosmedical.com
Project with chargefinder.com +	http://chargefinder.com/
Protein Language Models for drug discovery +	Chen, J., Gu, Z., Xu, Y., Deng, M., Lai, L. and Pei, J., 2023. QuoteTarget: A sequence‐based transformer protein language model to identify potentially druggable protein targets. Protein Science, 32(2), p.e4555. Chen, L., Fan, Z., Chang, J., Yang, R., Hou, H., Guo, H., Zhang, Y., Yang, T., Zhou, C., Sui, Q. and Chen, Z., 2023. Sequence-based drug design as a concept in computational drug design. Nature Communications, 14(1), p.4217. Chen, D., Liu, J. and Wei, G.W., 2024. Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions. Nature Machine Intelligence, 6(7), pp.799-810. Jiang, J., Chen, L., Ke, L., Dou, B., Zhang, C., Feng, H., Zhu, Y., Qiu, H., Zhang, B. and Wei, G., 2024. A review of transformers in drug discovery and beyond. Journal of Pharmaceutical Analysis, p.101081.

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