Showing 821 - 840 results of 1,076 for search '((((pinaa OR (pingna OR spingna)) OR (spinae OR pinn)) OR fine) OR (spie OR pin))', query time: 0.20s Refine Results
  1. 821

    Drive-specific selection in multistable mechanical networks by Kedia, Hridesh, Pan, Deng, Slotine, Jean-Jacques, England, Jeremy L.

    Published 2024
    “…We found that there exists a range of forcing amplitudes for which the attractor states of driven disordered multistable mechanical networks are fine-tuned with respect to the pattern of external forcing to have low energy absorption from it. …”
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    Article
  2. 822

    Stability of internal gravity wave modes: from triad resonance to broadband instability by Akylas, T.R., Kakoutas, Christos

    Published 2024
    “…For short-scale perturbations such that 𝜇 ≪ 1 but 𝛼 = 𝜇/𝜖 ≫ 1, this triad resonance instability reduces to the familiar parametric subharmonic instability (PSI), where triads comprise fine-scale perturbations with half the basic-wave frequency. …”
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    Article
  3. 823

    Physics-informed deep learning for multi-species membrane separations by Rehman, Danyal, Lienhard, John H.

    Published 2024
    “…The neural methods are pre-trained on simulated data from continuum models and fine-tuned on independent experiments to learn multi-ionic rejection behaviour. …”
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    Article
  4. 824
  5. 825

    Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition by Zhang, Da, Wang, Qingyi, Song, Shaojie, Chen, Simiao, Li, Mingwei, Shen, Lu, Zheng, Siqi, Cai, Bofeng, Wang, Shenhao, Zheng, Haotian

    Published 2024
    “…In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. …”
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    Article
  6. 826

    Bridging the Health Divide: Achieving Equitable Healthcare Access in Kenya through Artificial Intelligence by Nyakiongora, Geoffrey Mosoti

    Published 2024
    “…A GPT model is developed and fine-tuned on a comprehensive dataset of Kenyan cultural information, healthcare data, and architectural knowledge. …”
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    Thesis
  7. 827
  8. 828

    Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization by Majumder, Navonil, Hung, Chia-Yu, Ghosal, Deepanway, Hsu, Wei-Ning, Mihalcea, Rada, Poria, Soujanya

    Published 2024
    “…The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.…”
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    Article
  9. 829

    Hardness of Approximate Diameter: Now for Undirected Graphs by Dalirrooyfard, Mina, Li, Ray, Vassilevska Williams, Virginia

    Published 2024
    “…A series of papers on fine-grained complexity have led to strong hardness results for diameter in directed graphs, culminating in a recent tradeoff curve independently discovered by [Li, STOC'21] and [Dalirrooyfard and Wein, STOC'21], showing that under the Strong Exponential Time Hypothesis (SETH), for any integer k?…”
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    Article
  10. 830

    Enabling Perspective-Aware Ai with Contextual Scene Graph Generation by Platnick, Daniel, Alirezaie, Marjan, Rahnama, Hossein

    Published 2025
    “…We evaluated PASGG-LM pipelines using state-of-the-art SGG models, including Motifs, Motifs-TDE, and RelTR, and showed that fine-tuning LLMs, particularly GPT-4o-mini and Llama-3.1-8B, improves performance in terms of R@K, mR@K, and mAP. …”
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    Article
  11. 831

    Decoding Codon Bias: The Role of tRNA Modifications in Tissue-Specific Translation by Ando, Daisuke, Rashad, Sherif, Begley, Thomas J., Endo, Hidenori, Aoki, Masashi, Dedon, Peter C., Niizuma, Kuniyasu

    Published 2025
    “…Our knowledge of the role of the tRNA epitranscriptome in fine-tuning translation via codon decoding at tissue or cell levels remains incomplete. …”
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    Article
  12. 832

    Characteristics of scanning curves of two soils by Tami, Denny, Rahardjo, Harianto, Leong, Eng Choon

    Published 2011
    “…The first slope model consisted of a fine sand layer overlying a gravelly sand layer, while the second slope model involved a silty sand layer overlying a gravelly sand layer. …”
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    Journal Article
  13. 833

    Non-motorised transport prioritisation model using spatial intelligence by Poyil, Rohith P., Lopez, Maria Cecilia Rojas, Wong, Yiik Diew

    Published 2020
    “…The concept of spatial intelligence was used to predict the NMT prioritisation scheme; spatial intelligence is the ability to visualise spatial features and apply spatial judgement for mobility problems involving navigation or to identify fine details or patterns. The NMT prioritisation model was developed based on NMT movements in relation to spatial features such as residential buildings, community centres, schools and hospitals, recreation and sports centres, economic zones, transport hubs, bus stops and green spaces. …”
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    Journal Article
  14. 834

    Computation of electromagnetic fields scattered from objects with uncertain shapes using multilevel Monte Carlo method by Litvinenko, Alexander, Yucel, Abdulkadir C., Bagci, Hakan, Oppelstrup, Jesper, Michielssen, Eric, Tempone, Raul

    Published 2020
    “…The CMLMC method optimally balances statistical errors due to sampling of the parametric space and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. …”
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    Journal Article
  15. 835

    Soil liquefaction assessment using soft computing approaches based on capacity energy concept by Chen, Zhixiong, Li, Hongrui, Goh, Anthony Teck Chee, Wu, Chongzhi, Zhang, Wengang

    Published 2021
    “…Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. …”
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    Journal Article
  16. 836

    Using AI for music source separation by Lee, Jasline Jie Yu

    Published 2021
    “…The objective is to analyse the impacts of different components present in both Spectrogram and Waveform based systems through fine-tuning, data handling and ablation testing. …”
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    Final Year Project (FYP)
  17. 837
  18. 838

    Language-guided visual retrieval by He, Su

    Published 2021
    “…For NLVL, we utilize the fine-grained semantic features of the sparse frames in the video. …”
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    Thesis-Master by Research
  19. 839

    Vehicle re-identification using machine learning by Tang, Lisha

    Published 2022
    “…PANet and PMNet construct a two-stage attention structure to perform a coarse-to-fine search among identities. Finally, we address this Re-ID issue as a multi-task problem and employ Homoscedastic Uncertainty Learning to automatically balance the loss weightings. …”
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    Thesis-Master by Research
  20. 840

    iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning by Jiang, Haoge, Esfahani, Mahdi Abolfazli, Wu, Keyu, Wan, Kong-wah, Heng, Kuan-kian, Wang, Han, Jiang, Xudong

    Published 2022
    “…In contrast to the conventional methods such as the DWA, our approach is found superior in the following ways: no need for prior knowledge of the environment and metric map, lower reliance on an accurate sensor, learning emergent behavior in dynamic scene that is intuitive, and more remarkably, able to transfer to the real robot without further fine-tuning. Our extensive studies show that in comparison to the original TD3, the proposed approach can obtain approximately 50% reduction in training to get same performance, 50% higher accumulated reward, and 30–50% increase in generalization performance when tested in unseen environments. …”
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    Journal Article