Visual mapping art : data visualization & crowdsourcing for an ostensive definition of art by Ong, Zi Feng

“…The core data will be gathered from existing art databases, such as the Library of Congress (LCC) (Class-N - Fine Arts) and the Universal Decimal Classification (UDC) (73/76 Various arts & crafts); and controlled vocabulary systems such as Art & Architecture Thesaurus (AAT). …”
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Deep learning for fabric defect detection by Nangia, Saniya

“…The base models, YOLOv8 and RT-DETR, are fine-tuned on a dataset of patterned fabric defects to find the optimal model configurations. …”
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Value investment with machine learning by Wang, Jiwei

“…Further optimization led to the creation of the Improved Fundamental and Technical Factors Model, which achieved an impressive annualized return of 50\% and a Sharpe ratio of 1.85. After fine-tuning key parameters, the final optimized model demonstrated exceptional performance, with an annualized return of 59.82\%, a Sharpe ratio of 2.13, and a win rate of 75\%. …”
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Ig.n.is. by Ong, Jian'an.

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Microstructural evolution during mechanical milling of Nd-Fe-B nanocomposites by Toh, Hon Kun.

“…The rate of chemical disorder as a function of milling intensity was studied by Extended X-ray Absorption Fine Structure (EXAFS) technique. XRD results revealed that higher milling intensity increased the rate of phase transformation and the rate of change in crystal sizes and strain but did not affect the steady state phase composition. …”
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Singapore premiere. by Ong, Shannon.

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Plateau by Png, Hui Min, Goh Timothy Wei Wen

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Parametric study of gravure printing process for R2R printed electronics by Pua, Suan Tai

“…In this study, however, various printing parameters were investigated to establish their effects on printed line width and film thickness, in an attempt to achieve fine line printing resolution over a large printing area. …”
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A supervised two-channel learning method for hidden Markov model and application on lip reading by Foo, Say Wei, Dong, Liang

“…This method is specially designed to train HMMs for fine recognition from similar observations. The prominent features of this method are 1.) the criterion function is based on the difference between training sequences, and 2.) a twochannel structure is adopted to maintain the validity of the HMM. …”
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Elastic modulus, hardness and creep performance of SnBi alloys using nanoindentation by Shen, Lu, Septiwerdani, Pradita, Chen, Zhong

“…At the intermediate stress region (200–370 MPa), dislocation climb is the dominant creep mechanism with stress exponents around 5–8. When fine lamellar structure is the dominant constituent of the microstructure, phase boundary sliding is identified as the rate-controlling mechanism in the low stress region (<200 MPa).…”
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DECA : recovering fields of physical quantities from incomplete sensory data by Vasilakos, Athanasios V., Xiang, Liu, Luo, Jun, Deng, Chenwei, Lin, Weisi

“…Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. …”
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Knowledge-based reactive planning and re-planning – a case-study approach by Djemai, Ramzi, Vassilev, Vassil, Ouazzane, Karim, Dey, Maitreyee

“…Planning for uncertainties arising from indoor evacuations can be complex as there’s a fine balance to strike between a too-detailed plan and one that’s too vague. …”
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Vision-language modelling for radiological imaging and reports in the low data regime by Windsor, R, Jamaludin, A, Kadir, T, Zisserman, A

“…Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with 10x the data. …”

A comparative analysis of missing data imputation techniques on sedimentation data by Loh, Wing Son, Ling, Lloyd, Chin, Ren Jie, Lai, Sai Hin, Loo, Kar Kuan, Seah, Choon Sen

“…A comparative analysis on the missing fine sediment data imputation performance was made based on four different techniques, namely the k-Nearest Neighbourhood (k-NN), Support Vector Regression (SVR), Multiple Regression (MR), and Artificial Neural Network (ANN), under the single imputation (SI) and multiple imputation (MI) regimes. …”
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Towards semantic, debiased and moment video retrieval by Satar, Burak

“…Our approach tackles this challenge for the first time on long, fine-grained, and untrimmed egocentric videos, while existing methodologies target short, coarse-grained, and trimmed third-person videos. …”
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Towards optimal scheduling of deep learning training jobs in GPU clusters by Gao, Wei

“…Second, it reuses the fine-tuning runtime of FMF workloads to reduce the significant context switch overhead. …”
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Laser Beacon Tracking for High-Accuracy Attitude Determination by Nguyen, Tam, Cahoy, Kerri

“…Advances in attitude determination have led to the development of several CubeSat-sized attitude sensors capable of achieving fine attitude knowledge,most of which utilize natural light sources as references, such as in the case of star trackers and sun sensors. …”
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Examples out of Thin Air: AI-Generated Dynamic Context to Assist Program Comprehension by Example by Mattis, Toni, Krebs, Eva, Rinard, Martin C., Hirschfeld, Robert

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4.602 Modern Art and Mass Culture, Spring 2004 by Jones, Caroline

“…It focuses on the way artists use the tension between fine art and mass culture to mobilize a critique of both. …”
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Surface modulation of hierarchical MoS2 nanosheets by Ni single atoms for enhanced electrocatalytic hydrogen evolution by Zhang, Huabin, Yu, Le, Chen, Tao, Zhou, Wei, Lou, David Xiong Wen

“…Here, a surface modulation strategy is demonstrated through the decoration of isolated Ni atoms onto the basal plane of hierarchical MoS2 nanosheets supported on multichannel carbon nanofibers for boosted hydrogen evolution activity. X-ray absorption fine structure investigation and density functional theory (DFT) calculation reveal that the MoS2 surface decorated with isolated Ni atoms displays highly strengthened H binding. …”
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