Property:References
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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. +
1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016.
2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011.
3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011. +
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 +
Wang, Ziyu, et al. "PowerGAN: a machine learning approach for power side‐channel attack on compute‐in‐memory accelerators." Advanced Intelligent Systems 5.12 (2023): 2300313. +
https://www.atosmedical.com +
http://chargefinder.com/ +
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. +
Q
Yousif, Hayder A., et al. "Assessment of muscles fatigue based on surface EMG signals using machine learning and statistical approaches: a review." IOP conference series: materials science and engineering. Vol. 705. No. 1. IOP Publishing, 2019.
Karlik, Bekir. "Machine learning algorithms for characterization of EMG signals." International Journal of Information and Electronics Engineering 4.3 (2014): 189.
Rampichini, S., Vieira, T. M., Castiglioni, P., & Merati, G. (2020). Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review. Entropy, 22(5), 529.
Carroll, T. J., Taylor, J. L., & Gandevia, S. C. (2017). Recovery of central and peripheral neuromuscular fatigue after exercise. Journal of Applied Physiology, 122(5), 1068-1076.
Yousefi, J., & Hamilton-Wright, A. (2014). Characterizing EMG data using machine-learning tools. Computers in biology and medicine, 51, 1-13. +
1. Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301.
2. Laxminarayana, N., Mishra, N., Tiwari, P., Garg, S., Behera, B. K., & Farouk, A. (2022). Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems. IEEE Transactions on Artificial Intelligence.
3. Tiwari, P., & Melucci, M. (2018, October). Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management.
4. Zhang, Y., Liu, Y., Li, Q., Tiwari, P., Wang, B., Li, Y., ... & Song, D. (2021). CFN: a complex-valued fuzzy network for sarcasm detection in conversations. IEEE Transactions on Fuzzy Systems, 29(12), 3696-3710.
5. Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like influence diagrams for decision-making. Neural Networks, 132, 190-210. +
R
http://www.raspberrypi.org/
http://lnxpps.de/rpie/
http://islab.hh.se/mediawiki/index.php/ReDi2Service
http://www.youtube.com/watch?v=KJ5hMkWPEGY +
Chia Bejarano, N.; Ambrosini, E.; Pedrocchi, A.; Ferrigno, G.; Monticone, M.; Ferrante, S., "A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors," in Neural Systems and Rehabilitation Engineering, IEEE Transactions on , vol.23, no.3, pp.413-422, May 2015
J. Lee and E. Park, “Quasi real-time gait event detection using shank-attached gyroscopes,” Med. & Bio. Eng. & Comp., vol. 49, no. 6, pp. 707–712, 2011. +
1) “Dynamic Semantic Compression for CNN Inference in Multi-Access Edge Computing: A Graph Reinforcement Learning-Based Autoencoder”
Nan Li, Alexandros Iosifidis, Qi Zhang — IEEE Transactions on Wireless Communications, Vol. 24, Issue 3, Mar 2025.
2) “CStream: Parallel Data Stream Compression on Multicore Edge Devices” Xianzhi Zeng, Shuhao Zhang — published in 2023 (preprint / arXiv)
3) “To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices”
Liang Li, Dian Shi, Ronghui Hou, Hui Li, Miao Pan, Zhu Han — FLIP-type / IEEE / arXiv etc., 2020. +
1. Li, Weiwen, et al. "A reconfigurable second-order OAM patch antenna with simple structure." IEEE Antennas and Wireless Propagation Letters 19.9 (2020): 1531-1535.
2. Kang, Le, et al. "A mode-reconfigurable orbital angular momentum antenna with simplified feeding scheme." IEEE Transactions on Antennas and Propagation 67.7 (2019): 4866-4871.
3. Kang, Le, et al. "An OAM-mode reconfigurable array antenna with polarization agility." IEEE Access 8 (2020): 40445-40452.
4. Wu, Jie, et al. "A broadband electronically mode-reconfigurable orbital angular momentum metasurface antenna." IEEE Antennas and Wireless Propagation Letters 18.7 (2019): 1482-1486. +
IS A GOOD REPRESENTATION SUFFICIENT FOR SAMPLE EFFICIENT REINFORCEMENT LEARNING?, Simon S. Du, Sham M. Kakade, 2020
Learning State Representations for Query Optimization with Deep Reinforcement Learning, Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi, 2018
State Representation Learning for Control: An Overview, Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, and David Filliat, 2018 +
Bengio, Yoshua, Aaron Courville, and Pascal Vincent. "Representation learning: A review and new perspectives." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828.
Jaeger, Herbert. "Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the" echo state network" approach. GMD-Forschungszentrum Informationstechnik, 2002.
Jaeger, Herbert, et al. "Optimization and applications of echo state networks with leaky-integrator neurons." Neural networks 20.3 (2007): 335-352.
Lukoševičius, Mantas. "A practical guide to applying echo state networks." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 659-686.
Wang, Lin, Zhigang Wang, and Shan Liu. "An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm." Expert Systems with Applications 43 (2016): 237-249.
Li, Decai, Min Han, and Jun Wang. "Chaotic time series prediction based on a novel robust echo state network." IEEE Transactions on Neural Networks and Learning Systems 23.5 (2012): 787-799.
Krause13, André Frank, et al. "Evolutionary Optimization of Echo State Networks: multiple motor pattern learning." (2010).
Marco Rigamonti et al., "Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016.
Chen, Huanhuan, Peter Tiňo, and Xin Yao. "Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space." Computers & Chemical Engineering 67 (2014): 33-42.
Quevedo, Joseba, et al. "Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network." Engineering Applications of Artificial Intelligence 30 (2014): 18-29.
Fan, Yuantao, et al. "Predicting Air Compressor Failures with Echo State Networks." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016.
Bengio, Yoshua, Aaron Courville, and Pascal Vincent. "Representation learning: A review and new perspectives." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828.
Jaiswal, Ashish, et al. "A survey on contrastive self-supervised learning." Technologies 9.1 (2020): 2.
Liu, Xiao, et al. "Self-supervised learning: Generative or contrastive." IEEE Transactions on Knowledge and Data Engineering (2021).
Wan, Chuan, et al. "Representation Learning for Fault Diagnosis with Contrastive Predictive Coding." 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). IEEE, 2021.
Jiang, Guoqian, et al. "Stacked multilevel-denoising autoencoders: A new representation learning approach for wind turbine gearbox fault diagnosis." IEEE Transactions on Instrumentation and Measurement 66.9 (2017): 2391-2402.
Xiao, Dengyu, et al. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization." Journal of Intelligent Manufacturing 32.2 (2021): 377-391.
Li, Guoqiang, et al. "Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data." Applied Acoustics 191 (2022): 108663.
Wang, Tian, et al. "Data-driven prognostic method based on self-supervised learning approaches for fault detection." Journal of Intelligent Manufacturing 31.7 (2020): 1611-1619.
Quevedo, Joseba, et al. "Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network." Engineering Applications of Artificial Intelligence 30 (2014): 18-29.
Fan, Yuantao, et al. "Predicting Air Compressor Failures with Echo State Networks." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. +
Statistical Relational Learning
Knowledge Representation +
NIPS 2016 Tutorial on GANs
https://arxiv.org/pdf/1701.00160.pdf
Effective data generation for imbalanced learning using Conditional Generative Adversarial Networks
https://www.researchgate.net/publication/319672232_Effective_data_generation_for_imbalanced_learning_using_Conditional_Generative_Adversarial_Networks
BAGAN: Data Augmentation with Balancing GAN
https://arxiv.org/abs/1803.09655
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://arxiv.org/pdf/1606.03657.pdf +
-robot artwork
Michael Raschke, Katja Mombaur, Alexander Schubert. An optimisation-based robot platform for the generation of action paintings. Int. J. Arts and Technology, Vol. 4, No. 2, 2011 181
-emotion recognition from eeg
Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, Jeng-Ren Duann, and Jyh-Horng Chen. EEG-Based Emotion Recognition in Music Listening.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 7, JULY 2010 +
