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DR-NTU (Data) is for research data deposit. For research paper deposits, please use DR-NTU.

Mission: DR-NTU (Data) curates, stores, preserves, makes available and enables the download of digital data generated by the NTU research community. The repository develops and provides guidance for managing, sharing, and reusing research data to promote responsible data sharing in support of open science and research integrity.

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What can be deposited? Final, non-sensitive research data from projects carried out at NTU. The uploaded content must not infringe upon the copyrights or other intellectual property rights, and must be void of all identifiable information.

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441 to 450 of 3,261 Results
Sep 25, 2024 - S-Lab for Advanced Intelligence
Xu, Baixin; Hu, Jiangbei; Hou, Fei; Lin, Kwan-Yee; Wu, Wayne; Qian, Chen; He, Ying, 2024, "Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering", https://doi.org/10.21979/N9/0C9BU9, DR-NTU (Data), V1
The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains...
Sep 25, 2024 - S-Lab for Advanced Intelligence
Xu, Qianxiong; Lin, Guosheng; Loy, Chen Change; Long, Cheng; Li, Ziyue; Zhao, Rui, 2024, "Eliminating Feature Ambiguity for Few-Shot Segmentation", https://doi.org/10.21979/N9/CIOE8Y, DR-NTU (Data), V1
Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively activate query foreground (FG) features that correspond to the same-class support FG features. Howev...
Sep 25, 2024 - S-Lab for Advanced Intelligence
Feng, Ruicheng; Li, Chongyi; Loy, Chen Change, 2024, "Kalman-Inspired Feature Propagation for Video Face Super-Resolution", https://doi.org/10.21979/N9/FMVNYY, DR-NTU (Data), V1
Despite the promising progress of face image super-resolution, video face super-resolution remains relatively under-explored. Existing approaches either adapt general video super-resolution networks to face datasets or apply established face image super-resolution models independ...
Sep 25, 2024 - Sreelakshmi CHERUVALLI
Regina, Viduthalai Rasheedkhan; Cheruvalli, Sreelakshmi; Kwok, Weihao; Chopra, Tarun; Rice, Scott, 2024, "Related data for: Decoding scalp health and microbiome dysbiosis", https://doi.org/10.21979/N9/A1SDY5, DR-NTU (Data), V1
Microbial colonization in hair follicles was investigated by direct imaging using scanning electron microscope.
Sreelakshmi CHERUVALLI(Nanyang Technological University)
Sep 25, 2024School of Biological Sciences (SBS)
Sep 23, 2024 - CHEONG Siew Ann
Cheong, Siew Ann, 2024, "Quasi-Differentiation and Its Applications to Noisy Time Series Data from Complex Systems", https://doi.org/10.21979/N9/4Y4GFI, DR-NTU (Data), V1
Data and scripts used for the manuscript with the same title
Sep 23, 2024 - MOE AcRF Tier 2 grant MOE-000442-00
Kang, Zheng Tien, 2024, "Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes", https://doi.org/10.21979/N9/7YNZAQ, DR-NTU (Data), V2
This repository include the processed ultrametric distance matrices data, MATLAB scripts and data holder files (in .mat format) used to generate the results and figures in the PLOS paper with the above title.
Sep 21, 2024 - TURLAPATI Sri Harsha
Turlapati, Sri Harsha, 2024, "Tracing curves in the plane: geometric-invariant learning from human demonstrations", https://doi.org/10.21979/N9/QLAMWF, DR-NTU (Data), V2
The empirical laws governing human-curvilinear movements have been studied using various relationships, including minimum jerk, the 2/3 power law, and the piecewise power law. These laws quantify the speed-curvature relationships of human movements during curve tracing using crit...
Sep 20, 2024 - S-Lab for Advanced Intelligence
Hu, Tao; Hong, Fangzhou; Liu, Ziwei, 2024, "StructLDM: Structured Latent Diffusion for 3D Human Generation", https://doi.org/10.21979/N9/BXUEXV, DR-NTU (Data), V1
Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compact 1D latent space, ignoring the articulated structure and semantics of human body topology. In this...
Candra Adi WIGUNA(Nanyang Technological University)
Sep 19, 2024School of Electrical and Electronic Engineering (EEE)
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