Bayesian collective learning emerges from heuristic social learning by Krafft, P.M., Shmueli, Erez, Griffiths, Thomas L., Tenenbaum, Joshua B., Pentland, Alex

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Inference for autoregressive and moving average models with extreme value distribution via simulation study by Samuel, Bako Sunday

“…To achieve our objectives, a stationary autoregressive and moving average models with Gumbel distributed innovation is proposed and we characterise the short-term dependence among maxima, arising from light-tailed Gumbel distribution over a range of sample sizes with varying degrees of dependence. …”
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Optimal portfolio and trading strategy using machine learning by Ouazzane, Karim, Tang, Kai Hung Po Yung, Ghanem, Mohamed Chahine

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Multiscale approach to nematic liquid crystals via statistical field theory by Lu, Bing-Sui

“…We propose an approach to a multiscale problem in the theory of thermotropic uniaxial nematics based on the method of statistical field theory. This approach enables us to relate the coefficients A, B, C, L1, and L2 of the Landau-de Gennes free energy for the isotropic-nematic phase transition to the parameters of a molecular model of uniaxial nematics, which we take to be a lattice gas model of nematogenic molecules interacting via a short-ranged potential. …”
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The Role of Emerging Technologies of Students' Academic Achievement at Politeknik Seberang Perai (PSP) by Omar, Nurul Ihsaniah, Ibrahim, Mohammad Noor

“…Education is crucial for global progress, constantly evolving with emerging technologies that impact teaching and learning. …”
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Modeling and forecasting customer demands by See, Tian Pau.

“…In this paper, I have made an attempt to seek what statistical models and forecasting techniques which are appropriate to support decision making in the operational level of supply chain while dealing with customer demands on short life cycle products to avoid undesired production condition. …”
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Uncertainty quantification framework for combined statistical spatial downscaling and temporal disaggregation for climate change impact studies on hydrology by Rajendran Queen Suraajini

“…The Statistical Downscaling Model (SDM) is the bridging model which is used to downscale the output from the General Circulation Model (GCM) for increasing the spatial resolution of future climate scenarios. …”
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Malaysian teachers’ perceptions towards Emergency Remote Teaching (ERT): an analysis based on school level and subjects taught by Loo, Daron Benjamin

“…The data was analysed using descriptive statistics. The main fi ndings include teachers of all levels and subjects having a clear understanding of students’ current learning situation, yet there were some uncertainty regarding the planning and implementation of online teaching and learning, especially among primary-level teachers and teachers of science subjects. …”
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A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. …”
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Bayesian neural network language modeling for speech recognition by Xue, Boyang, Hu, Shoukang, Xu, Junhao, Geng, Mengzhe, Liu, Xunying, Meng, Helen

“…State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. …”
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Topological data analysis for fake news detection by Deng, Ran

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Methods and systems for ontology learning, exploitation, and analysis by Xing, Jiang

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Differentially private deep learning for time series data by Dwitami, Inggriany

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Measuring dynamical uncertainty with Revealed Dynamics Markov Models by Bramson, Aaron, Baland, Adrien, Iriki, Atsushi

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Toward a generic federated learning platform optimized for computer vision applications by Zhuang, Weiming

“…Built on EasyFL, we optimize algorithms for the following three computer vision applications: person re-identification (ReID), unsupervised person ReID, and self-supervised learning. We propose federated person ReID (FedReID), a new paradigm of person ReID training, and investigate its performance under statistical heterogeneity via benchmark analysis. …”
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Environmental risk factors for self-harm during imprisonment: A pilot prospective cohort study by Stephenson, T, Harris, I, Armstrong, C, Fazel, S, Short, R, Blackwood, N

Environmental risk factors for self-harm during imprisonment: a prospective cohort study by Stephenson, T, Harris, I, Armstrong, C, Fazel, S, Short, R, Blackwood, N

Modelling and characterization of membrane fouling in osmotically-driven membrane processes (ODMPs) by Lai, Li

“…From qualitive analysis to quantitative analysis, the location of peak raised by colloidal deposition is used to differentiate the deposition on surface of active layer and inside of support layer after being analyzed by arrival time shift and confirmed by statistical testing. Moreover, the further short-time Fourier transformation (STFT) was applied to transfer the original time domain data into time-frequency domain, which reveal a noticeable magnitude reduction on high frequency components when Rayleigh scattering was triggered by particle inside of membrane pores. …”
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Forecasting of hydropower production using Box-Jenkins model at Tasik Kenyir, Terengganu by Nornabila, Abu, Nurul Nadrah Aqilah, Tukimat, Noratikah, Abu

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Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model by Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan

“…Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. …”
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