Hyeonjeong Shin’s research while affiliated with Korea Advanced Institute of Science and Technology and other places

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Publications (4)


Figure A-2: Reprojection schema: Green lines outline MSG pixels, magenta lines outline the pixels of the destination grid (where OPERA data is reprojected), The colored pixels are the OPERA pixels.
Figure A-3: Longitude-latitude probability maps (%) for low rain rates measured by the OPERA network between 2019 and 2021 for the months of January (a) and July (b). Values are shown for square areas of the same size of the outputs to be predicted. Grey shading indicates areas outside the OPERA coverage.
Characteristics of the SEVIRI instrument on board of the Meteosat Second Genera- tion (MSG) satellites from EUMETSAT.
Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
  • Conference Paper
  • Full-text available

January 2023

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306 Reads

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5 Citations

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Federico Serva

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Republic Of Korea

Weather4cast again advanced modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hires rain radar movies from multi-band satellite sensors, requiring data fusion, multi-channel video frame prediction, and super-resolution. Accurate predictions of rain events are becoming ever more critical, with climate change increasing the frequency of unexpected rainfall. The resulting models will have a particular impact where costly weather radar is not available. We here present highlights and insights emerging from the thirty teams participating from over a dozen countries. To extract relevant patterns, models were challenged by spatio-temporal shifts. Geometric data augmentation and test-time ensemble models with a suitable smoother loss helped this transfer learning. Even though, in ablation, static information like geographical location and elevation was not linked to performance, the general success of models incorporating physics in this competition suggests that approaches combining machine learning with application domain knowledge seem a promising avenue for future research. Weather4cast will continue to explore the powerful benchmark reference data set introduced here, advancing competition tasks to quantitative predictions, and exploring the effects of metric choice on model performance and qualitative prediction properties. *

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Figure 2: An overview of our modified U-Net architecture. Each blue box corresponds to the residual convolutional unit and each green block denotes the residual transposed convolutional unit. During the propagation, the region-conditioned context is added to the last output of the encoder while the shortcut from the encoder unit to the corresponding decoder unit is transformed with orthogonal 3D 1×1×1 convolutional opertors as well as FiLM layer. The arrow denotes the propagation of a multi-channel feature map.
The leaderboard score (i.e., IoU) of our solution for stage2 compared to the baseline score submitted by the organizer.
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition

December 2022

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68 Reads

The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contexts covering wider regions are given. In this paper, we show that a general baseline 3D U-Net can be significantly improved with region-conditioned layers as well as orthogonality regularizations on 1x1x1 convolutional layers. Additionally, we facilitate the generalization with a bag of training strategies: mixup data augmentation, self-distillation, and feature-wise linear modulation (FiLM). Presented modifications outperform the baseline algorithms (3D U-Net) by up to 19.54% with less than 1% additional parameters, which won the 4th place in the core test leaderboard.



Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs

October 2021

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30 Reads

A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying constituent (near) bi-cliques (i.e., complete bipartite graphs). Even when we restrict our attention to maximal ones, there can be exponentially many near bi-cliques, and thus finding all of them is practically impossible for large graphs. Then, two questions naturally arise: (Q1) What is a "good" set of near bi-cliques? That is, given a set of near bi-cliques in the input dynamic graph, how should we evaluate its quality? (Q2) Given a large dynamic graph, how can we rapidly identify a high-quality set of near bi-cliques in it? Regarding Q1, we measure how concisely, precisely, and exhaustively a given set of near bi-cliques describes the input dynamic graph. We combine these three perspectives systematically on the Minimum Description Length principle. Regarding Q2, we propose CutNPeel, a fast search algorithm for a high-quality set of near bi-cliques. By adaptively re-partitioning the input graph, CutNPeel reduces the search space and at the same time improves the search quality. Our experiments using six real-world dynamic graphs demonstrate that CutNPeel is (a) High-quality: providing near bi-cliques of up to 51.2% better quality than its state-of-the-art competitors, (b) Fast: up to 68.8x faster than the next-best competitor, and (c) Scalable: scaling to graphs with 134 million edges. We also show successful applications of CutNPeel to graph compression and pattern discovery.

Citations (2)


... Spatio-temporal Modeling To achieve computationally efficient video modeling, as proposed in , our satellite prediction model also adopts an encoder, translator, and decoder structure. However, as highlighted in (Lam et al. 2023) and (Gruca et al. 2023), large context is crucial in weather and satellite image prediction tasks. To incorporate large context while maintaining an efficient architecture, we integrate the large-kernel attention block from (Guo et al. 2023). ...

Reference:

Data-driven Precipitation Nowcasting Using Satellite Imagery
Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts