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1. Model-assisted deep learning of rare extreme events from partial observations

   https://doi.org/10.1063/5.0077646  

   Asch, Anna; J. Brady, Ethan; Gallardo, Hugo; Hood, John; Chu, Bryan; Farazmand, Mohammad (April 2022, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data are obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead, we only use a small subset of observable quantities, which can be measured in practice. We investigate the feasibility of this model-assisted framework on three different dynamical systems (Rössler attractor, FitzHugh–Nagumo model, and a turbulent fluid flow) and three different deep neural network architectures (feedforward, long short-term memory, and reservoir computing). In each case, we study the prediction accuracy, robustness to noise, reproducibility under repeated training, and sensitivity to the type of input data. In particular, we find long short-term memory networks to be most robust to noise and to yield relatively accurate predictions, while requiring minimal fine-tuning of the hyperparameters. 

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2. Machine learning for suicide risk prediction in children and adolescents with electronic health records

   https://doi.org/10.1038/s41398-020-01100-0  

   Su, Chang; Aseltine, Robert; Doshi, Riddhi; Chen, Kun; Rogers, Steven C.; Wang, Fei (December 2020, Translational Psychiatry)

   null (Ed.)

   Abstract Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents. 

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3. Tracking Fast and Slow Changes in Synaptic Weights from Simultaneously Observed Pre- and Postsynaptic Spiking

   https://doi.org/10.1162/neco_a_01426  

   Wei, Ganchao; Stevenson, Ian H. (July 2021, Neural Computation)

   Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common generalized linear model to infer both short- and long-term changes in the coupling between a pre- and postsynaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights (1) for both depressing and facilitating synapses, (2) with a variety of long-term changes (including realistic changes, such as due to STDP), (3) with a range of pre and postsynaptic firing rates, and (4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission. 

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4. Recurrent Recommendation with Local Coherence

   https://doi.org/10.1145/3289600.3291024  

   Wang, Jianling; Caverlee, James (January 2019, Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining)

   We propose a new time-dependent predictive model of user-item ratings centered around local coherence -- that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences. 

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5. Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-ion Battery

   https://doi.org/10.1115/1.4063985  

   Shi, Junchuan; Wei, Yupeng; Wu, Dazhong (November 2023, Journal of Computing and Information Science in Engineering)

   Abstract Monitoring the health condition as well as predicting the performance of Lithium-ion batteries are crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately. 

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