Browsing by Author "Li, K."

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    iFUNDit: Visual Profiling of Fund Investment Styles
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Zhang, R.; Ku, B. K.; Wang, Y.; Yue, X.; Liu, S.; Li, K.; Qu, H.; Hauser, Helwig and Alliez, Pierre
    Mutual funds are becoming increasingly popular with the emergence of Internet finance. Clear profiling of a fund's investment style is crucial for fund managers to evaluate their investment strategies, and for investors to understand their investment. However, it is challenging to profile a fund's investment style as it requires a comprehensive analysis of complex multi‐dimensional temporal data. In addition, different fund managers and investors have different focuses when analysing a fund's investment style. To address the issue, we propose , an interactive visual analytic system for fund investment style analysis. The system decomposes a fund's critical features into performance attributes and investment style factors, and visualizes them in a set of coupled views: a fund and manager view, to delineate the distribution of funds' and managers' critical attributes on the market; a cluster view, to show the similarity of investment styles between different funds; and a detail view, to analyse the evolution of fund investment style. The system provides a holistic overview of fund data and facilitates a streamlined analysis of investment style at both the fund and the manager level. The effectiveness and usability of the system are demonstrated through domain expert interviews and case studies by using a real mutual fund dataset.
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    Shape Correspondence with Isometric and Non-Isometric Deformations
    (The Eurographics Association, 2019) Dyke, R. M.; Stride, C.; Lai, Y.-K.; Rosin, P. L.; Aubry, M.; Boyarski, A.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.; Fisher, M.; Groueix, T.; Guo, D.; Kim, V. G.; Kimmel, R.; Lähner, Z.; Li, K.; Litany, O.; Remez, T.; Rodolà, E.; Russell, B. C.; Sahillioglu, Y.; Slossberg, R.; Tam, G. K. L.; Vestner, M.; Wu, Z.; Yang, J.; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    The registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset of scanned 3D shapes undergoing various controlled deformations (articulating, bending, stretching and topologically changing), along with ground truth correspondences. With the aid of this tiered benchmark of increasingly challenging real scans, we explore this problem and investigate how robust current state-of- the-art methods perform in different challenging registration and correspondence scenarios. We discover that changes in topology is a challenging problem for some methods and that machine learning-based approaches prove to be more capable of handling non-isometric deformations on shapes that are moderately similar to the training set.