Publications

2023

• Abdel-Rehim, A., Orhobor, O., Hang, L., Ni, H., and King, R., (2023). Protein-Ligand Binding Affinity Prediction Exploiting Sequence Constituent Homology, accepted by Bioinformatics
• Li, Z., Peng, F.,  Xue, Y., Ni, H. and Jin L., (2023). Scene Table Structure Recognition with Segmentation and Key Point Collaboration, accepted by the 17th International Conference on Document Analysis and Recognition (ICDAR 2023).
• Huang, J., Peng, D., Li, H., Ni, H. and Jin L., (2023). SegCTC: Offline Handwritten Chinese Text Recognition via Better Fusion between Explicit and Implicit Segmentation, accepted by the 17th International Conference on Document Analysis and Recognition (ICDAR 2023).
• Huang, L.,  Chen, B., Liu, C., Peng, D., Zhou, W., Wu, Y., Li, H., Ni, H. and Jin L., (2023). EnsExam: A Dataset for Handwritten Text Erasure on Examination Papers, accepted by the 17th International Conference on Document Analysis and Recognition (ICDAR 2023).
• Lou, H., Li, S. and Ni, H., (2023). PCF-GAN: generating sequential data via the characteristic function of measures on the path space. arXiv preprint arXiv:2305.12511.
• Xie, X., Wu, Y., Ni, H. and He, C., (2023). NODE-ImgNet: a PDE-informed effective and robust model for image denoising. arXiv preprint arXiv:2305.11049.
• Li, S., Ni, H. and Zhu, Q., (2023). Small mass limit of expected signature for physical Brownian motion. arXiv preprint arXiv:2305.00343.
• Abdel-Rehim, A., Orhobor, O., Lou, H., Ni, H. and King, R.D., (2023). Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction. arXiv preprint arXiv:2301.07568.
• Gong, S., Hu, P., Meng, Q., Zhu, R., Chen, B., Ma, Z.M., Ni, H. and Liu, T.Y., (2023). Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations. AAAI.

2022

• Orhobor, O., Rehim, A., Lou, H., Ni, H., and King, R., (2022). A Simple Spatial Extension to the Extended Connectivity Interaction Features for Binding Affinity Prediction, Accepted by Royal Society Open Science.
• Oliva, P.V., Wu, Y., He, C., Ni, H. (2022).  Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN. Accepted by Journal of Computational Physics.
• Cheng, J., Zhang, X., Ni, H., Li, C., Xu, X., Wu, Z., Wang, L., Lin, W., Li, G., (2022). Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores. IEEE Transactions on Medical Imaging. doi:10.1109/TMI.2022.3147690
• Li, S., Ni, H. (2022). Expected signature of stopped Brownian motion on $d$-dimensional $C^{2, α}$-domains has finite radius of convergence everywhere: $2\leq d \leq 8$. Journal of Functional Analysis, https://doi.org/10.1016/j.jfa.2022.109447 

2021

• Liao, S., Lyons, T., Yang, W., Schlegel, K. and Ni, H. (2021). Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition, Proceedings in British Machine Vision Conference.
• Ni, H., Szpruch, L., Sabate-Vidales, M., Xiao, B., Wiese, M., Liao, S. (2021). Sig-Wassersterin GANs for Time Series Generation, Proceeding in 2nd ACM International Conference on AI in Finance.
• Schlegel, K. (2021). When is there a Representer Theorem? Reflexive Banach spaces. Advances in Computational Mathematics 47, 54.
• Li Y, Cheng J, Zhang X, Fang R, Liao L, Ding X, Ni H, Xu X, Wu Z, Hu D, Lin W. (2021). Learning Infant Brain Developmental Connectivity for Cognitive Score Prediction. International Workshop on Machine Learning in Medical Imaging 2021 Sep 27 (pp. 228-237). Springer, Cham.
• Chen, Y., Dong, J. and Ni, H. (2021) ε-Strong Simulation of Fractional Brownian Motion and Related Stochastic Differential Equations. Mathematics of Operations Research. 
• Ni, H., Dong, X., Jinsong, Z., Yu, G. (2021). An Introduction to Machine Learning in Quantitative Finance. World Scientific Publishing Co Pte Ltd.
• Ni, H., Dong, X., Zheng, J., Yu, G.Y. (2021). 机器学习在量化金融中的应用. China: Tsinghua University Press.
• Boedihardjo, H., Diehl, J., Mezzarobba, M., Ni, H. (2021). The expected signature of Brownian motion stopped on the boundary of a circle has finite radius of convergence. Bulletin of the London Mathematical Society, doi:10.1112/blms.12420

• Wu, Y., Ni, H., Lyons, T. J., & Hudson, R. L. (2021). Signature features with the visibility transformation. 2020 25th International Conference on Pattern Recognition (ICPR). doi:10.1109/icpr48806.2021.9412642

2020

• Kormilitzin, A., Vaci, N., Liu, Q., Ni, H., Nenadic, G., Nevado-Holgado, A. (2020). An efficient representation of chronological events in medical texts.
• Ni, H., Zhang, X., Chen, J., Li, C., Xu, X., Wu, Z., ...Li, G. (2020). Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features.
• Zhang, X., Ding, X., Wu, Z., Xia, J., Ni, H., Xu, X., ...Li, G. (2020). Siamese Verification Framework for Autism Identification during Infancy Using Cortical Path Signature Features.
• Yang, G., Chen, J., Gao, Z., Li, S., Ni, H., Angelini, E., ...Wage, R. (2020). Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Future Generation Computer Systems, 107 215-228. doi:10.1016/j.future.2020.02.005
• Ni, H., Szpruch, L., Wiese, M., Liao, S., Xiao, B. (2020). Conditional Sig-Wasserstein GANs for Time Series Generation. arXiv preprint, arXiv:2006.05421
• Schlegel, K. (2020). Approximate Representer Theorems in Non-reflexive Banach Spaces. Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:827-844

2019

• Wang, B., Liakata, M., Ni, H., Lyons, T., Nevado-Holgado, A.J., Saunders, K. (2019). A Path Signature Approach for Speech Emotion Recognition.
• Liao, S., Lyons, T., Yang, W., Ni, H. (2019). Learning stochastic differential equations using RNN with log signature features. arXiv preprint, arXiv:1908.08286
• Schlegel, K. (2019). When is there a representer theorem? Nondifferentiable regularisers and Banach spaces. Journal of Global Optimization 74

2018

• Wilson-Nunn, D., Lyons, T., Papavasiliou, A., Ni, H. (2018). A Path Signature Approach to online Arabic Handwriting Recognition.
• Chang, J., Lyons, T., Ni, H. (2018). Corrigendum to “Super-multiplicativity and a lower bound for the decay of the signature of a path of finite length” [C. R. Acad. Sci. Paris, Ser. I 356 (7) (2018) 720–724]. Comptes Rendus Mathématique, 356 (10), 987. doi:10.1016/j.crma.2018.09.009
• Yang, G., Chen, J., Gao, Z., Zhang, H., Ni, H., Angelini, E., ...Firmin, D. (2018). Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images.
• Chen, J., Yang, G., Gao, Z., Ni, H., Angelini, E., Mohiaddin, R., ...Zhang, H. (2018). Multiview two-task recursive attention model for left atrium and atrial scars segmentation.
• Ni, H., Lyons, T., Chang, J. (2018). Super-multiplicativity and a lower bound for the decay of the signature of a path of finite length. Comptes Rendus Mathématique, doi:10.1016/j.crma.2018.05.010

2017

• Xie, Z., Sun, Z., Jin, L., Ni, H., Lyons, T. (2017). Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence,
• Ni, H., Weixin, Y., Lianwen, J., Terry, L. (2017). Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network.
• Ni, H., Xu, W., Chang, J., Duffield, N. (2017). Signature inversion for monotone paths. Electronic Communications in Probability, doi:10.1214/17-ECP70
• Yang, W., Lyons, T., Ni, H., Schmid, C., Jin, L. (2017). Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition. arXiv preprint, arXiv:1707.03993

2015

• Ni, H., Xu, W. (2015). Concentration and exact convergence rates for expected Brownian signatures. ELECTRONIC COMMUNICATIONS IN PROBABILITY, 20 doi:10.1214/ECP.v20-3636
• Lyons, T., Ni, H. (2015). EXPECTED SIGNATURE OF BROWNIAN MOTION UP TO THE FIRST EXIT TIME FROM A BOUNDED DOMAIN. ANNALS OF PROBABILITY, 43 (5), 2729-2762. doi:10.1214/14-AOP949
• Ni, H. (2015). A multi-dimensional stream and its signature representation. arXiv preprint, arXiv:1509.03346
• Levin, D., Lyons, T., Ni, H. (2015). Learning from the past, predicting the statistics for the future, learning an evolving system. arXiv preprint, arXiv:1309.0260

2014

• Lyons, T., Ni, H., Oberhauser, H. (2014). A Feature Set for Streams and a Demonstration on High-Frequency Financial Tick Data.
• Boedihardjo, H., Ni, H., Qian, Z. (2014). Uniqueness of signature for simple curves. JOURNAL OF FUNCTIONAL ANALYSIS, 267 (6), 1778-1806. doi:10.1016/j.jfa.2014.06.006