DR-NTU (Data) (Nanyang Technological University)
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1955 research outputs found
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Replication Data for: On-chip topological leaky-wave antenna for full-space terahertz wireless connectivity
No full textThis dataset contains all the data used in the paper, titled 'On-chip topological leaky-wave antenna for full-space terahertz wireless connectivity'
Replication Data for: Super-resolution optical trapping of multiple cold atoms
No full textRaw data and analysis program for the generation of figures of https://doi.org/10.1103/nj9v-gvz
Replication Data for: Antiscreening and Nonequilibrium Layer Electric Phases in Graphene Multilayers
No full textData files for figures in "Antiscreening and Nonequilibrium Layer Electric Phases in Graphene Multilayers
Preregistration for 'How do Tertiary Students Adjust Their Humour Behaviours Across Ingroup/Outgroup membership and Relational Closeness?'
No full textHumour is a strategic tool for social bonding (Crawford, 2002). In an intercultural context, humour may help in overcoming social barriers, depending on the dynamics of the relationships and cultural backgrounds of the involved individuals. (Nevo, Nevo & Yin, 2001; Holmes & Marra, 2002). Laughter is understood to be a social activity, with behavioural contagion linked to agreement, affiliation, and acknowlement of communication (Provine, 1993, Smoski,2003). Social Identity Theory has established that individuals alter their communication based on their interactions with an ingroup vs an outgroup, including their use of humour (Tajfel & Turner, 1979; Hay, 2001; Boxer & Cortés-Conde, 1997).
Martin (2003) created the Humour Styles Questionnaire (HSQ) that categorised humour styles into four types: affiliative, self-enhancing, aggressive, and self-defeating. These styles are largely consistent personality traits that determine how people use humour to cope, relate, or distance themselves from others. Despite being a widely used tool for measuring trait-based humour styles, the Humour Styles Questionnaire (HSQ) does not account for the way humour behaviours change depending on the social situation, such as when interacting with members of the ingroup versus those of the outgroup or with friends versus strangers.
In the current study, we aim to investigate how humour behaviours (positive vs negative humour directed towards self, the interlocutor, and an unrelated third-party) adapt depending on group membership (ingroup vs outgroup) and relational closeness (friend vs stranger), using a novel tool, a questionnaire about directed use of humour, with international students selected as the target 'ingroup'.
Project initiated in the 2025-6 academic year. V2 corrects access permissions for the documents
SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (CVPR 2024)
No full textDynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper, we propose a new 4D motion modeling paradigm, SurMo, that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template, which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows
Related Data for: Widespread retreat of coastal habitat is likely at warming levels above 1.5 °C
No full textThis dataset is a comprehensive open-access database of the paper titled" Widespread retreat of coastal habitat is likely at warming levels above 1.5 °C"
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
No full textAutoregressive 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 approaches to 3D object generation and understanding remains largely unexplored. This paper introduces Scale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale 3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for efficient autoregressive generation and detailed understanding. By predicting the next scale in a multi-scale latent representation instead of the next single token, SAR3D reduces generation time significantly, achieving fast 3D object generation in just 0.82 seconds on an A6000 GPU.
Additionally, given the tokens enriched with hierarchical 3D-aware information, we finetune a pretrained LLM on them, enabling multimodal comprehension of 3D content.
Our experiments show that SAR3D surpasses current 3D generation methods in both speed and quality and allows LLMs to interpret and caption 3D models comprehensively
Related Data for: Peatland degradation: examining impacts on soil, plant diversity, forest structure, and biomass
No full textThis dataset contains the field data and R code used to generate the figures, tables, and findings presented in the manuscript titled "Peatland degradation: examining impacts on soil, plant diversity, forest structure, and biomass.