Short-Term Shoreline Evolution Mapping by using UAV by Siddig, Mohammad lkhmal, Kaamin, Masiri, Daucla, Mohd Effendi, Mokhtar, Mardiha, Sabri, Muhamad Syamil, Ariffinm, Effi Helmy

“…The "Pix4D Capture" application has been programmed with the flight planning route, and the drone takes pictures along the entire grid line provided. The data on shoreline evolutions were processed using the Pix4d Mapper and Global Mapper tools. …”
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Towards Spatially Varying Gloss Reproduction for 3D Printing by Piovarci, Michal, Foshey, Michael, Babaei, Vahid, Rusinkiewicz, Szymon, Matusik, Wojciech, Didyk, Piotr

“…Based on the new hardware setup, we present the complete pipeline for controlling the gloss of a given 2.5 D object, from printer calibration, through material selection, to the manufacturing of models with spatially-varying reflectance. …”
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Measuring the hole-state anisotropy in MgB2 by electron energy-loss spectroscopy by Klie, Robert F., Su, Haibin, Zhu, Yimei, Davenport, James W., Idrobo, Juan-Carlos, Browning, Nigel D., Nellist, Peter D.

“…In particular, we have studied two different crystal orientations, [110] and [001], with respect to the incident electron beam direction, and found significant changes in the near-edge fine structure of the B K-edge. Density-functional theory suggests that the pre-peak of the B K-edge core loss is composed of a mixture of pxy- and pz-hole states and we will show that these contributions can be distinguished only with an experimental energy resolution better than 0.5 eV. …”
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0-1 Knapsack in Nearly Quadratic Time by Jin, Ce

“…On the upper bound side, currently the fastest algorithm runs in 𝑂e(𝑛 + 𝑤 12/5 max ) time (Chen, Lian, Mao, and Zhang 2023), improving the earlier 𝑂(𝑛 + 𝑤 3 max)-time algorithm by Polak, Rohwedder, and Węgrzycki (2021). …”
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Population genomics and transcriptomics of Plasmodium falciparum in Cambodia and Vietnam uncover key components of the artemisinin resistance genetic background by Nayak, S, Peto, TJ, Kucharski, M, Tripura, R, Callery, JJ, Quang Huy, DT, Gendrot, M, Lek, D, Nghia, HDT, van der Pluijm, RW, Dong, N, Long, LT, Vongpromek, R, Rekol, H, Hoang Chau, N, Miotto, O, Mukaka, M, Dhorda, M, von Seidlein, L, Imwong, M, Roca, X, Day, NPJ, White, NJ, Dondorp, AM

“…The emergence of Plasmodium falciparum parasites resistant to artemisinins compromises the efficacy of Artemisinin Combination Therapies (ACTs), the global first-line malaria treatment. Artemisinin resistance is a complex genetic trait in which nonsynonymous SNPs in PfK13 cooperate with other genetic variations. …”

In vitro bioactivity and biocompatibility assessment of PCL/PLA–scaffolded mesoporous silicate bioactive glass: role of boron activation by Md. Zain, Siti Khalijah, Sazali, Ezza Syuhada, Ghoshal, Sib Krishna, Hisam, Rosdiyana

“…Four samples of the form xB2O3 – (80–x) SiO2 – 15CaO – 5P2O5 with x is 0, 5, 10, and 15 mol% were prepared using a unified sol-gel/evaporation induced self-assembly (SG-EISA) process. …”

Tuning the shear thickening of suspensions through surface roughness and physico-chemical interactions by Bourrianne, Philippe, Niggel, Vincent, Polly, Gatien, Divoux, Thibaut, McKinley, Gareth H.

“…Finally, we demonstrate how mixtures of particles with opposing surface chemistry make it possible to finely tune the shear-thickening response of the suspension at a fixed volume fraction, paving the way for a fine control of the shear-thickening transition in engineering applications.…”
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Stability of internal gravity wave modes: from triad resonance to broadband instability by Akylas, T.R., Kakoutas, Christos

“…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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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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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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