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1,311 to 1,320 of 5,121 Results

main_BLCE_NMSE_vs_SNR.py Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach Python Source Code - 4.3 KB - MD5: a184500a39984227155c6cb6ffd05b04
main_unifiedConfig.py Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach Python Source Code - 2.1 KB - MD5: c448b9bdf456f66a4bf3084aaee006a4
utils.py Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach Python Source Code - 6.2 KB - MD5: 30fd1e036ec33b3e5b9426127206404f
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...
Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction Feb 3, 2025 - Coronavirus Neutralizing Antibodies Rashid, Shamima; Wan, Zhang; Kwoh, Chee Keong; Lin, Zhuoyi; Ng, Shaun Yue Hao; Yin, Rui; Senthilnath, J., 2025, "Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction", https://doi.org/10.21979/N9/IZZKTZ, DR-NTU (Data), V1, UNF:6:XqxFQ/x98XTQs7+Ns8GGJw== [fileUNF] This dataset contains 3 versions of epitope-paratope data and their neutralizing data for the SARS-CoV 2 virus. We pre-processed and annotated antibody-antigen binding data from the Observed Antibody Space (OAS) database to obtain these paratopes and epitopes.
Citation Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction Plain Text - 281 B - MD5: b6f4c46fb08d07a94449762ae037d9e1 Citation Information
CoV-AbDab_281221_Shaun.xlsx Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction MS Excel Spreadsheet - 8.9 MB - MD5: 2713a4b215b8c6a8429e44dc0d6f6342 Raw Data
CoV-AbDab_310.tab Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction Tabular Data - 23.8 KB - 6 Variables, 310 Observations - UNF:6:1S8xNlxC/A5bjyz5wcJXPQ== the data with PDB identifiers in columns

main_BLCE_NMSE_vs_SNR.py

Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach

Python Source Code - 4.3 KB -

main_unifiedConfig.py

Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach

Python Source Code - 2.1 KB -

utils.py

Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach

Python Source Code - 6.2 KB -

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...

Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

Feb 3, 2025 - Coronavirus Neutralizing Antibodies

Rashid, Shamima; Wan, Zhang; Kwoh, Chee Keong; Lin, Zhuoyi; Ng, Shaun Yue Hao; Yin, Rui; Senthilnath, J., 2025, "Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction", https://doi.org/10.21979/N9/IZZKTZ, DR-NTU (Data), V1, UNF:6:XqxFQ/x98XTQs7+Ns8GGJw== [fileUNF]

This dataset contains 3 versions of epitope-paratope data and their neutralizing data for the SARS-CoV 2 virus. We pre-processed and annotated antibody-antigen binding data from the Observed Antibody Space (OAS) database to obtain these paratopes and epitopes.

Citation

Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

Plain Text - 281 B -

Citation Information

CoV-AbDab_281221_Shaun.xlsx

Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

MS Excel Spreadsheet - 8.9 MB -

Raw Data

CoV-AbDab_310.tab

Feb 3, 2025 - Related Data for: PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

Tabular Data - 23.8 KB - 6 Variables, 310 Observations -

the data with PDB identifiers in columns

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