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1,291 to 1,300 of 5,197 Results

Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution Apr 14, 2025 - AcRF Tier 2 Influenza Ivan, Fransiskus Xaverius; Deshpande, Akhila; Lim, Chun Wei; Kwoh, Chee Keong, 2019, "Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution", https://doi.org/10.21979/N9/PDYCUD, DR-NTU (Data), V2 Various computational and statistical approaches have been proposed to uncover the mutational patterns of rapidly evolving influenza viral genes. A problem that draws much attention is to identify pairs of sites that potentially co-mutate to contribute to the overall fitness of t...
codes.tar.gz Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution Gzip Archive - 48.3 MB - MD5: bb209381404d10226a7e4ee0f284be41 Code Codes for executing the analysis pipeline.
Documentation - Codes (Tool) for Uncovering Mutational Patterns of Influenza Virus Evolution.pdf Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution Adobe PDF - 653.3 KB - MD5: 2238b7ff53a88f26a9c388838525bba7 Documentation Documentation of the codes (tool), which describe the input and output of the codes, environment for running the codes, and the steps for running the codes or analysis pipeline.
indata.tar.gz Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution Gzip Archive - 2.7 MB - MD5: 908471814acc1085328004725841c5fe Data Examples of metadata and sequence data (contain records obtained from the NCBI Influenza Virus Resource) for analysis pipeline.
Methods.pdf Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution Adobe PDF - 1.0 MB - MD5: daabe8f3f6ec2c81ab4da386c92505e6 Documentation Description of methods for the analysis pipeline.
MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers Apr 9, 2025 - S-Lab for Advanced Intelligence Chen, Yiwen; He, Tong; Di, Huang; Ye, Weicai; Chen, Sijin; Tang, Jiaxiang; Chen, Xin; Cai, Zhongang; Yang, Lei; Yu, Gang; Lin, Guosheng; Zhang, Chi, 2025, "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers", https://doi.org/10.21979/N9/BJPHCP, DR-NTU (Data), V1 Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industr...
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE Apr 9, 2025 - S-Lab for Advanced Intelligence Chen, Yongwei; Lan, Yushi; Zhou, Shangchen; Wang, Tengfei; Pan, Xingang, 2025, "SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE", https://doi.org/10.21979/N9/OK5SSQ, DR-NTU (Data), V1 Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI). Despite these advances, applying autoregressive...
Text4Seg: Reimagining Image Segmentation as Text Generation Apr 9, 2025 - S-Lab for Advanced Intelligence Lan, Mengcheng; Chen, Chaofeng; Zhou, Yue; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2025, "Text4Seg: Reimagining Image Segmentation as Text Generation", https://doi.org/10.21979/N9/FF4YJY, DR-NTU (Data), V1 Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge. In this paper, we introduce Text4Seg, a novel text-as-mask paradigm that c...
SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images Mar 19, 2025 - S-Lab for Advanced Intelligence Jin, Daisheng; Hu, Jiangbei; Xu, Baixin; Dai, Yuxin; Qian, Chen; He, Ying, 2025, "SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images", https://doi.org/10.21979/N9/3DCDXV, DR-NTU (Data), V1 In this study, we introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. To synthesize 3D facial models more realistically, w...
Disco4D: Disentangled 4D Human Generation and Animation from a Single Image Mar 19, 2025 - S-Lab for Advanced Intelligence Pang, Hui En; Liu, Shuai; Cai, Zhongang; Lei, Yang; Zhang, Tianwei; Liu, Ziwei, 2025, "Disco4D: Disentangled 4D Human Generation and Animation from a Single Image", https://doi.org/10.21979/N9/VX5WAH, DR-NTU (Data), V1 We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancin...

Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Apr 14, 2025 - AcRF Tier 2 Influenza

Ivan, Fransiskus Xaverius; Deshpande, Akhila; Lim, Chun Wei; Kwoh, Chee Keong, 2019, "Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution", https://doi.org/10.21979/N9/PDYCUD, DR-NTU (Data), V2

Various computational and statistical approaches have been proposed to uncover the mutational patterns of rapidly evolving influenza viral genes. A problem that draws much attention is to identify pairs of sites that potentially co-mutate to contribute to the overall fitness of t...

codes.tar.gz

Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Gzip Archive - 48.3 MB -

Code

Codes for executing the analysis pipeline.

Documentation - Codes (Tool) for Uncovering Mutational Patterns of Influenza Virus Evolution.pdf

Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Adobe PDF - 653.3 KB -

Documentation

Documentation of the codes (tool), which describe the input and output of the codes, environment for running the codes, and the steps for running the codes or analysis pipeline.

indata.tar.gz

Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Gzip Archive - 2.7 MB -

Data

Examples of metadata and sequence data (contain records obtained from the NCBI Influenza Virus Resource) for analysis pipeline.

Methods.pdf

Apr 14, 2025 - Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Adobe PDF - 1.0 MB -

Documentation

Description of methods for the analysis pipeline.

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

Apr 9, 2025 - S-Lab for Advanced Intelligence

Chen, Yiwen; He, Tong; Di, Huang; Ye, Weicai; Chen, Sijin; Tang, Jiaxiang; Chen, Xin; Cai, Zhongang; Yang, Lei; Yu, Gang; Lin, Guosheng; Zhang, Chi, 2025, "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers", https://doi.org/10.21979/N9/BJPHCP, DR-NTU (Data), V1

Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industr...

SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE

Apr 9, 2025 - S-Lab for Advanced Intelligence

Chen, Yongwei; Lan, Yushi; Zhou, Shangchen; Wang, Tengfei; Pan, Xingang, 2025, "SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE", https://doi.org/10.21979/N9/OK5SSQ, DR-NTU (Data), V1

Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI). Despite these advances, applying autoregressive...

Text4Seg: Reimagining Image Segmentation as Text Generation

Apr 9, 2025 - S-Lab for Advanced Intelligence

Lan, Mengcheng; Chen, Chaofeng; Zhou, Yue; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2025, "Text4Seg: Reimagining Image Segmentation as Text Generation", https://doi.org/10.21979/N9/FF4YJY, DR-NTU (Data), V1

Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge. In this paper, we introduce Text4Seg, a novel text-as-mask paradigm that c...

SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images

Mar 19, 2025 - S-Lab for Advanced Intelligence

Jin, Daisheng; Hu, Jiangbei; Xu, Baixin; Dai, Yuxin; Qian, Chen; He, Ying, 2025, "SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images", https://doi.org/10.21979/N9/3DCDXV, DR-NTU (Data), V1

In this study, we introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. To synthesize 3D facial models more realistically, w...

Disco4D: Disentangled 4D Human Generation and Animation from a Single Image

Mar 19, 2025 - S-Lab for Advanced Intelligence

Pang, Hui En; Liu, Shuai; Cai, Zhongang; Lei, Yang; Zhang, Tianwei; Liu, Ziwei, 2025, "Disco4D: Disentangled 4D Human Generation and Animation from a Single Image", https://doi.org/10.21979/N9/VX5WAH, DR-NTU (Data), V1

We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancin...

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