Enhancing downstream ML performance with unconditional diffusion models for return predictions by Agarwala, Pratham

“…Previous methods, reliant on simple transformations or generative adversarial networks (GANs) with inherent training instability, fall short in addressing these challenges. …”
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Exploiting dynamic bifurcation in elastic ribbons for mode skipping and selection by Huang, Weicheng, Yu, Tian, Vella, Dominic, Hsia, K. Jimmy, Liu, Mingchao

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Develop AI based image restoration algorithm for deep tissue imaging in photoacoustic system by Hao, Zeliang

“…The initial model, GAN-Blur, is trained to induce blurring in sharp images through an unpaired dataset containing both sharp and blurred images, guiding the second model (GAN-Deblur) in learning the proper deblurring of such images. …”
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Eccentric compression performance of jointed precast concrete-encased concrete-filled steel tube columns with dry connections by Zhu, Gaoming, Tan, Kang Hai

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Revolutionizing ELISA: a one-step blocking approach using lipid bilayer coatings by An, Kyongman, Tae, Hyunhyuk, Shin, Jihoon, Bae, Jieun, Han, Sunguck, Choi, Gichan, Kim, Tae-Hoon, Cho, Nam-Joon

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Pumice cone eruptions at Aluto volcano, Ethiopia by Clarke, Ben, Calder, Eliza S., Fontijn, Karen, Desalegn, Firawalin, Tierz, Pablo, Yirgu, Gezahegn

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Interface dynamics in electroosmotic flow systems with non-Newtonian fluid frontiers by Chen, Di-Lin, Liu, Shu-Yan, Luo, Kang, Yi, Hong-Liang, Yang, Chun

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Net section fracture of aluminium alloy angle-to-plate connections: testing, modelling and design by Jiang, Ke, Li, Shuai, Guo, Tong, Liang, Yating, Zhao, Ou

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Multilevel mechanisms driving intraplate volcanism in Central Mongolia revealed by adjoint waveform tomography of receiver function and ambient noise data by Xu, Mijian, Wang, Kai, Chen, Jing, He, Jing, Liu, Qinya, Liu, Yiduo, Huang, Zhouchuan, Tong, Ping

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Corrosion and stress corrosion cracking resistances of the 17-4 precipitation hardened martensitic stainless steel additively manufactured using binder jet printing by Xiong, Yida, Radhakrishnan, Jayaraj, Huang, Sheng, Chua, Yusheng, Shi, Wei, Ramamurty, Upadrasta

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Theoretical study of spermatozoa sorting by dielectrophoresis or magnetophoresis with supervised learning by Koh, James Boon Yong

“…Supervised learning is proposed to reduce the computational costs by making predictions after a subset of the data is computed and used for training. By fitting the model to a tenth of the sample size required for statistical convergence, predicted results are precise and accurate to a handful of percentage points. …”
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Phase Fourier Reconstruction for Anomaly Detection on Metal Surface Using Salient Irregularity by Hung, Tzu-Yi, Vaikundam, Sriram, Natarajan, Vidhya, Chia, Liang-Tien

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Landslide susceptibility assessment in Limbe (SW Cameroon) : a field calibrated seed cell and information value method by del Marmol, M.-A., Trefois, P., Jacobs, P., Suh, C. E., Che, Vivian Bih., Kervyn, Matthieu., Fontijn, Karen., Ernst, Gerald G. J.

“…Landslide data is randomly divided into a training (75%) and validation set (25%) and seed cells are generated by creating 25 m buffer zones around the head scarp of each scar. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Sun, Jingjing, Guan, Xin, Sun, Xiaojun, Cao, Xiaojing, Tan, Yepei, Liao, Jiarong

“…Using bootstrap sampling, the RFR model generates multiple training subsets from the original data and randomly selects subsets of variables to construct regression trees. …”
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HELMET: a hybrid machine learning framework for real-time prediction of edema trajectory in large middle cerebral artery stroke by Phillips, E, O'Donoghue, O, Zhang, Y, Tsimpos, P, Mallinger, LA, Chatzidakis, S, Pohlmann, J, Du, Y, Kim, I, Song, J, Brush, B, Smirnakis, S, Ong, CJ, Orfanoudaki, A

“…HELMET combines transformer-based large language models with supervised ensemble learning, demonstrating the value of merging human expertise and multimodal health records in developing clinical risk scores. Both models were trained on a retrospective cohort of 15,696 observations from 623 patients hospitalized with large middle cerebral artery ischemic stroke and were externally validated using 3,713 observations from 60 patients at a separate hospital system. …”

Combined electron imaging and diffraction methods for understanding disordered rocksalt cathodes by Hedley, E

“…It is proposed that by changing the training dataset, the POE-NN method is highly adaptable to a range of experimental conditions.…”

Accelerating convolutional neural networks through enhanced designs by Gennari do Nascimento, M

“…Our third paper, HyperBFP, focuses on expanding DSConv to the case of performing training and backpropagation in low precision. Quantizing gradients is more challenging than quantizing weights and activations in general, since it requires higher precision to work. …”

Self-supervised machine learning to characterise step counts from wrist-worn accelerometers in the UK Biobank by Small, SR, Chan, S, Walmsley, R, von Fritsch, L, Acquah, A, Mertes, G, Feakins, BG, Creagh, A, Strange, A, Matthews, CE, Clifton, DA, Price, AJ, Khalid, S, Bennett, D, Doherty, A

“…</p <br> <p><strong>Methods:</strong> We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. 39 individuals will free-living ground-truth annotated step counts were used for model development. …”

Barriers and facilitators to the initiation of injectable therapies for type 2 diabetes mellitus: a mixed methods study by de Lusignan, S, McGovern, A, Hinton, W, Whyte, M, Munro, N, Williams, ED, Marcu, A, Williams, J, Ferreira, F, Mount, J, Tripathy, M, Konstantara, E, Field, BCT, Feher, M

“…Primary care HCPs initiating injectables require additional training to provide practical demonstrations, patient education and how to identify and address concerns. …”

Inflammation and colorectal cancer: elucidation of the cellular and molecular mechanisms by which interleukin 22 contributes to the development and progression of colorectal cancer by McCuaig, S

“…However, using independent training and validation cohorts in tumour transcriptomic datasets totalling 1820 patients, we identified that in patients with high tumoural expression of either or both subunits of the heterodimeric IL-22 receptor (<em>IL22RA1</em>, <em>IL10RB</em>), KRAS mutation markedly worsens prognosis. …”