Bing Su
5 Papers
Bing Su is an academic researcher. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 2, co-authored 4 publications.
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Papers
Environment variables affect CPUE and spatial distribution of fishing grounds on the light falling gear fishery in the northwest Indian Ocean at different time scales
Haibin Han,Chao Yang,Heng Zhang,Zhou Fang,Bohui Jiang,Bing Su,Jianghua Sui,Yunzhi Yan,Delong Xiang +8 more
TL;DR: Based on the fishing log data of light falling gear in the northwest Indian Ocean from 2016 to 2020, this article analyzed the impact of different time scales on the catch rate and fishing ground center of gravity of light-falling gear fishing grounds.
Functional orientation and spatial siting of subway stations based on land potential and mixed land use
Shuxin Jin,Di Wang,Bing Su +2 more
TL;DR: In this paper , the authors evaluated the land potential alongside a specific subway line, and conducted interaction analysis for between passenger volumes and station-surrounding land mixing degrees, and sited the subway stations according to the distance constrains and compared the planning outputs from their methods and the plans proposed by the government section.
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Assessment and management recommendations for the status of Japanese sardine Sardinops melanostictus population in the Northwest Pacific
Chao Yang,Haibin Han,Hengyi Zhang,Yongchuang Shi,Bing Su,Peiwen Jiang,Delong Xiang,Yuyan Sun,Yang Li +8 more
TL;DR: In this article , the authors used the historical yearly catch data of Japanese sardine from 1990 to 2021 and biological data (manily for body length) in April-November 2020-2022 to assess its maximum sustainable yield (MSY) and stock resource status using Catch-MSY and LBB models, respectively.
8
Decaying Contrast for Fine-Grained Video Representation Learning
Heng-Wei Zhang,Bing Su +1 more
- 04 Jun 2023
TL;DR: Zhang et al. as mentioned in this paper propose a decaying strategy to grasp the gradual evolution along the temporal dimension for fine-grained spatio-temporal representation learning, which consists of two contrastive losses.
Exploring Temporal Concurrency for Video-Language Representation Learning
Heng Zhang,Daqing Liu,Zezhong Lv,Bing Su,Dacheng Tao +4 more
- 01 Oct 2023
TL;DR: This paper proposes to learn video-language representations by modeling video-language pairs as Temporal Concurrent Processes (TCP) via a process-wised distance metric learning framework and introduces a regularization term that enforces the embeddings of each modality approximating a stochastic process to guarantee the inherent dynamics.