DR-NTU (Data) (Nanyang Technological University)
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1955 research outputs found
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Post-pandemic Health Behavior
No full textThis dataset results from a 2024 national survey of health behavior in Indonesi
KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
No full textSensors are commonly deployed to perceive the environment. However, due to the high cost, sensors are usually sparsely deployed. Kriging is the tailored task to infer the unobserved nodes (without sensors) using the observed nodes (with sensors). The essence of kriging task is transferability. Recently, several inductive spatio-temporal kriging methods have been proposed based on graph neural networks, being trained based on a graph built on top of observed nodes via pretext tasks such as masking nodes out and reconstructing them. However, the graph in training is inevitably much sparser than the graph in inference that includes all the observed and unobserved nodes. The learned pattern cannot be well generalized for inference, denoted as graph gap. To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally. Nevertheless, the empty-shell virtual nodes without labels could have inferior features and lack supervision signals. To solve these issues, we pair each virtual node with its most similar observed node and fuse their features together; to enhance the supervision signal, we construct reliable pseudo labels for virtual nodes. As a result, the learned pattern of virtual nodes could be safely transferred to real unobserved nodes for reliable kriging. We name our new Kriging model with Increment Training Strategy as KITS. Extensive experiments demonstrate that KITS consistently outperforms existing methods by large margins, e.g., the improvement over MAE score could be as high as 18.33%
Replication Data for: Ecological drivers of nutrient runoff from natural and managed vegetation types
No full textTree inventory, tree wood densities, litterfall production, and litter trait data
Replication Data for: Coral skeletal isotopes (δ¹³C and δ¹¹B) as indicators of seawater light attenuation and pH chemistry in the Singapore Strait
No full textThis dataset includes all data and R scripts applied for statistical analyses investigating the relationships between seawater parameters and variations in coral isotopic compositions under the influence of Southwest monsoonal cycles. It also comprises calculations of seawater CO₂ chemistry estimations. Additionally, the dataset provides output images from these analyses, such as correlation plots and time series plots for visualizing seawater and coral isotopic parameters created using OriginPro software, relevant to the study
WildAvatar: Learning In-the-wild 3D Avatars from the Web
No full textExisting research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real world. On the other hand, the web abounds with off-the-shelf real-world human videos, but these videos vary in quality and require accurate annotations for avatar creation. To this end, we propose an automatic annotating pipeline with filtering protocols to curate these humans from the web. Our pipeline surpasses state-of-the-art methods on the EMDB benchmark, and the filtering protocols boost verification metrics on web videos. We then curate WildAvatar, a web-scale in-the-wild human avatar creation dataset extracted from YouTube, with 10,000+ different human subjects and scenes. WildAvatar is at least 10x richer than previous datasets for 3D human avatar creation and closer to the real world. To explore its potential, we demonstrate the quality and generalizability of avatar creation methods on WildAvatar. We will publicly release our code, data source links and annotations to push forward 3D human avatar creation and other related fields for real-world applications
Dataset for: A Systematic Review of Cognitive Flexibility in Bilinguals vs Monolinguals
No full textFrom very early on, papers like Bialystok, E. (1988) have explored and shown a link between bilingualism and cognitive control. Such theories encompass the core function of cognitive flexibility in reference to switching rules and the need to inhibit an ongoing task in order to be in control of the requirements of the next task (Liu et al., 2016). While existing research has explored the link between bilingualism and cognitive flexibility, it has largely failed to consider several contextual factors that may significantly influence the presentation of cognitive flexibility skills in individuals. Such factors could include the type of bilingualism (sequential or simultaneous), the age of language acquisition, native environment, cultural contexts such as multiculturalism or acculturation, and migrant generation. Failure to account for these variables in previous studies may not fully capture the diversity of cognitive outcomes among bilingual individuals. Moreover, inconsistencies in study design, sample characteristics, and the measures used to assess cognitive flexibility further complicate the interpretation of results. Through this systematic review, we aim to determine how existing literature accounts for individual differences in bilingualism, and to what extent it considers factors that might affect cognitive flexibility in adult bilinguals. Specifically, we will be looking into the following objectives: first, assessing quality indicators by examining methodological rigour (consistency in study design, sample characteristics, reliability of measures used to assess cognitive flexibility et cetera); second, to classify and evaluate studies based on different types of bilingualism (sequential versus simultaneous bilinguals), as well as age of language acquisition and contextual factors; and third, to explore how these classifications and individual differences influence cognitive flexibility outcomes. Through this review, we hope to gain a more precise understanding of cognitive profiles among bilinguals versus monolinguals in varied settings and provide clear directions for future research. Project initiated as part of a final year project at NTU 2024. V2 includes a correction to PRISMA Flowchart for formatting and citation
Replication Data for: Palm species traits determine soil nutrient effects on seedling performance
No full textThis dataset contains the field data and R code used to generate the figures, tables, and findings presented in the manuscript titled "Palm species traits determine soil nutrient effects on seedling performance.
Supplementary Material for PhD Thesis
No full textThis dataset is part of the Supplementary Material of Pradeep Kumar Gopalakrishnan's PhD thesis
MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention
No full textMultiview diffusion models have shown considerable success in image-to-3D generation for general objects. However, when applied to human data, existing methods have yet to deliver promising results, largely due to the challenges of scaling multiview attention to higher resolutions. In this paper, we explore human multiview diffusion models at the megapixel level and introduce a solution called mesh attention to enable training at 1024 resolution. Using a clothed human mesh as a central coarse geometric representation, the proposed mesh attention leverages rasterization and projection to establish direct cross-view coordinate correspondences. This approach significantly reduces the complexity of multiview attention while maintaining cross-view consistency. Building on this foundation, we devise a mesh attention block and combine it with keypoint conditioning to create our human-specific multiview diffusion model, MEAT. In addition, we present valuable insights into applying multiview human motion videos for diffusion training, addressing the longstanding issue of data scarcity. Extensive experiments show that MEAT effectively generates dense, consistent multiview human images at the megapixel level, outperforming existing multiview diffusion methods. Code and model will be publicly available
Anomaly detection for cryptocurrency
No full textFeatures used in unsupervised anomaly detection among influential wallet addresses of Bitcoin, Ethereum, Iotex and Tezo