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

“…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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Supermind Ideator: How Scaffolding Human-AI Collaboration Can Increase Creativity by Heyman, Jennifer, Rick, Steven, Giacomelli, Gianni, Wen, Haoran, Laubacher, Robert, Taubenslag, Nancy, Knicker, Max, Jeddi, Younes, Ragupathy, Pranav, Curhan, Jared, Malone, Thomas

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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

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

“…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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Speed-Modulated Ironing: High-Resolution Shade and Texture Gradients in Single-Material 3D Printing by Ozdemir, Mehmet, AlAlawi, Marwa, Dogan, Mustafa Doga, Martinez Castro, Jose, Mueller, Stefanie, Doubrovski, Zjenja

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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

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

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

“…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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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

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

“…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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WAHRSIS : a low-cost high-resolution whole sky imager with near-infrared capabilities by Dev, Soumyabrata, Savoy, Florian M., Lee, Yee Hui, Winkler, Stefan

“…Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of cloud formations, which can be useful in many applications. …”
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Non-motorised transport prioritisation model using spatial intelligence by Poyil, Rohith P., Lopez, Maria Cecilia Rojas, Wong, Yiik Diew

“…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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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

“…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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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

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

“…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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Sproutly : plantable packaging by Dhanusha Surendran

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Language-guided visual retrieval by He, Su

“…For NLVL, we utilize the fine-grained semantic features of the sparse frames in the video. …”
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Vehicle re-identification using machine learning by Tang, Lisha

“…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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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

“…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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PromptChart: prompting InstructGPT for zero & few-shot chart question answering and summarization by Do, Xuan Long

“…Existing state-of-the-art models rely on fine-tuning strategies and struggle with tasks that involve logical and arithmetic reasoning. …”
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