11 to 20 of 8,572 Results
Adobe PDF - 132.6 KB -
MD5: ce142bb6f074b5ba86c080fa1ea6ae92
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Apr 28, 2026 - S-Lab for Advanced Intelligence
Xu, Yuanmu; Hou, Guanli; Hu, Jiangbei; Ren, Tenglong; Wang, Xiaokun; Zhang, Yalan; Ban, Xiaojuan; Qian, Chen; Hou, Fei; He, Ying, 2025, "PGA-NeuS: Physics and Geometry-Augmented Neural Implicit Surfaces for Rigid Bodies", https://doi.org/10.21979/N9/LTXKFL, DR-NTU (Data), V2
This paper tackles the challenges of physics-based simulation of rigid bodies in neural rendering, focusing on 3D model representation and collision handling. A synthetic and real-world dataset is also included in the paper. |
Compressed Archive - 462.9 MB -
MD5: bf5921041d638ae5ea5bbc8d236344b6
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Mar 31, 2026 - IMPRINT
Sng, Kelly Hwee Leng, 2025, "Stimulus Set: Auditory and Visual Primes", https://doi.org/10.21979/N9/CCS5CO, DR-NTU (Data), V2, UNF:6:Bu8oTA3HERpwEcSEvzDnaQ== [fileUNF]
Visual primes in this study were selected based on aspects of daily lives that are more relatable to the average Singaporean, and revolve around themes such as neighbourhood icons, food, and clothing. Another search strategy revolved around cultural festivals, celebrations or pra... |
Mar 30, 2026 - ZHANG Wei
Zhang, Wei, 2025, "Behavioural data for: Adult-to-infant unidirectional neural coupling mediates selective social learning in infants from the UK and Singapore", https://doi.org/10.21979/N9/4EBTKT, DR-NTU (Data), V3, UNF:6:5J47rXrfNe5NnMtZ24KS0A== [fileUNF]
Summary: Contains anonymous behavioural data of infants' attention, CDI, and learning performance for the study. Demographical information are skipped to protect the participants' privacy. Related code repository: https://github.com/Baby-Linc-Singapore/BABBLE_CODE/ Detailed file... |
Tabular Data - 7.8 KB - 2 Variables, 118 Observations - UNF:6:zffG8GdtjYayuehhMFIymQ==
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Jan 16, 2026 - Junqi ZHAO
Li, Miaoyu; Chao, Qin; Li, Boyang, 2025, "Replication Data for: Two Causally Related Needles in a Video Haystack", https://doi.org/10.21979/N9/WCSXMT, DR-NTU (Data), V2
Causal2Needles is a benchmark dataset and evaluation toolkit designed to assess the capabilities of both proprietary and open-source multimodal large language models in long-video understanding. It features a large number of "2-needle" questions, where the model must locate and r... |
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MD5: a014a77831f333b24b641b19d5803bed
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MD5: a3fda65c27ef3373c2f0ddba1e20662e
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