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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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321 to 330 of 3,296 Results
Bitcoin degree distribution and influential players
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Bitcoin degree distribution and influential players", https://doi.org/10.21979/N9/88TWWS, DR-NTU (Data), V2, UNF:6:pGkBnt0442bPx5NzBs4Qcw== [fileUNF]
Data for in-degree and out-degree distribution of Bitcoin transactions and most influential sender and recipient.
Tezos degree distribution and influential players
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Tezos degree distribution and influential players", https://doi.org/10.21979/N9/F52IHE, DR-NTU (Data), V2, UNF:6:YUaeioGoSAveKkmPE3ADZA== [fileUNF]
In-degree and out-degree distribution and influential players for Tezos
Verification of exponential law in cryptocurrency
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Verification of exponential law in cryptocurrency", https://doi.org/10.21979/N9/EQJBFQ, DR-NTU (Data), V1, UNF:6:zWxAiTkB/8qnPIO/4qTOBA== [fileUNF]
Contains number of nodes, number of edges and average out-degree for Bitcoin, Ethereum, Iotex and Tezos month by month.
Anomaly detection for cryptocurrency
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Anomaly detection for cryptocurrency", https://doi.org/10.21979/N9/MRLOYJ, DR-NTU (Data), V1
Features used in unsupervised anomaly detection among influential wallet addresses of Bitcoin, Ethereum, Iotex and Tezos
First year trends and stats of cryptocurrency
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "First year trends and stats of cryptocurrency", https://doi.org/10.21979/N9/CJJWDP, DR-NTU (Data), V1, UNF:6:xQb+v3BpMCuAAvh2du5rJQ== [fileUNF]
Contains month-by-month and year-by-year statistics on number and volume of transactions, number of new wallet addresses as well as day-by-day statistics on number and volume of transactions for Bitcoin, Ethereum, Iotex and Tezos.
Ethereum degree distribution and influential players
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Ethereum degree distribution and influential players", https://doi.org/10.21979/N9/HG9V1I, DR-NTU (Data), V1, UNF:6:TSpLFe60X3KUge/mp7/qMw== [fileUNF]
In-degree and out-degree distribution and most influential wallet addresses for Ethereum
Lotex degree distribution and influential players
Feb 7, 2025 - Kelvin FOO Moo Chen
Foo, Kelvin Moo Chen, 2025, "Lotex degree distribution and influential players", https://doi.org/10.21979/N9/UBSBU6, DR-NTU (Data), V1, UNF:6:gOMU679pPqY5Xm1UeUiKOQ== [fileUNF]
In-degree and out-degree distribution, influential players for Iotex.
GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation
Feb 5, 2025 - S-Lab for Advanced Intelligence
Lan, Yushi; Zhou, Shangchen; Lyu, Zhaoyang; Hong, Fangzhou; Yang, Shuai; Dai, Bo; Pan, Xingang; Loy, Chen Change, 2025, "GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation", https://doi.org/10.21979/N9/ZQ85KI, DR-NTU (Data), V1
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quali...
Trajectory attention for fine-grained video motion control
Feb 4, 2025 - S-Lab for Advanced Intelligence
Xiao, Zeqi; Ouyang, Wenqi; Zhou, Yifan; Yang, Shuai; Yang, Lei; Si, Jianlou; Pan, Xingang, 2025, "Trajectory attention for fine-grained video motion control", https://doi.org/10.21979/N9/II0EM4, DR-NTU (Data), V1
Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention...
Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration
Feb 4, 2025 - S-Lab for Advanced Intelligence
Liao, Kang; Yue, Zongsheng; Wang, Zhouxia; Loy, Chen Change, 2025, "Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration", https://doi.org/10.21979/N9/DMB2QK, DR-NTU (Data), V1
Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data...
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