A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction ($≈ \mathcal{O}(10^{−6}))$ of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.     

Dendrites,deep learning,and sequences in the hippocampus

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label “neural.” These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory ‐ apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.     

基于社交媒体中文本信息的早期抑郁症检测

诊断抑郁症的传统方法是通过面对面的评估和交谈.但是,许多患有抑郁症的患者不愿意在早期阶段就医,从而使病情恶化.为了在早期判断抑郁症患者的情况,提出一种利用社交媒体文本信息的时间序列特征和多示例学习的检测模型,考虑到抑郁症状不会立即出现,所以时序样本的使用显得非常重要,因此使用无监督LSTM提取时间序列特征,训练分类器实...     

ARIMA与LSTM模型在医院出院人次预测中的比较研究

目的通过ARIMA乘积季节模型和LSTM神经网络模型拟合某三甲专科医院的月出院人次并进行预测,比较两种模型的预测效果。方法运用某三甲专科医院2013—2018年度的月出院人次,分别构建ARIMA乘积季节模型和LSTM神经网络模型,然后利用所得的模型对2019年度的月出院人次进行预测并与实际数据进行比较。采用平均绝对百分误差(MAPE)对模型的预测效果进行评价。结果ARIMA乘积季节模型和LSTM神经网络模型的预测数据与2019年度1~12月份实际出院人次的MAPE值分别为7.90%和14.26%。结论ARIMA乘积季节模型的预测效果要好于LSTM神经网络模型,ARIMA模型预测结果表明2019年度某三甲专科医院的月出院人次呈增长趋势,与实际数据的吻合度较好。     

基于CLSTM的步态分类方法

步态分类在人体运动能量消耗评估等应用中具有重要意义,提高分类精度和降低对统计特征的依赖是步态分类的研究热点。采用传统的步态分类方法提取的步态特征用于细分化步态时不能得到较好的效果。考虑到步态的连续性和不同轴之间信号的相关性,本文提出了基于CLSTM的步态分类方法：采用卷积神经网络（CNN）操作,通过计算多轴步态数据提取步态特征;基于长短期记忆（LSTM）构建步态时间序列模型,学习步态特征图时间维度上的长期依赖性。基于USC-HAD数据集的实验结果表明,用此方法提取了步态序列特征,很好地利用了步态时间序列特点,提升了11种步态的分类精度。     

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis

BackgroundThe COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.ObjectiveThe objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.MethodsTo test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.ResultsOur results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.ConclusionsAlthough a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.     

ARIMA乘积季节模型与LSTM神经网络模型对我国麻疹发病数预测效果的比较

目的 探讨长短期记忆神经网络(long short term memory, LSTM)模型和差分整合移动平均自回归(autoregressive integrated moving average model, ARIMA)乘积季节模型在全国麻疹发病趋势预测中的应用,为麻疹的早期防控提供科学依据。方法 选取2005年1月至2016年2月全国麻疹月发病数分别构建LSTM模型和ARIMA乘积季节模型,同时运用得到的模型对2016年3月至2018年12月发病数进行预测,运用两种评价指标平均绝对百分比误差(mean absolute percentage error, MAPE)和均方根误差(root mean square error, RMSE)检验模型的外推预测精度。最后应用模型预测2019年1月至2019年5月的全国麻疹月发病数。结果 LSTM模型和最优模型ARIMA(0,1,1)(0,1,1)₁₂外推预测的均方根误差(RMSE)分别为0.25和1.54,平均绝对百分比误差(MAPE)分别为3.6%和18.7%,提示LSTM神经网络的外推预测精度优于ARIMA模...     

基于人工脉冲神经网络的门诊静脉采血智能预约平台设计

基于人工脉冲神经网络建立新型门诊静脉采血智能预约平台。在数据源整合处理模块中,基于脉冲神经网络设计局部递归的人工脉冲神经网络,实现多类型预约数据源的整合处理。在序列特征挖掘模块中,利用多层双向LSTM网络建立PDCA模型,提取门诊静脉采血预约数据的形态特征和语义特征,通过多层双向LSTM网络融合序列特征实体信息。在预约模块中,门诊静脉采血智能预约主要通过自助设备与互联网来完成,通过JavaScrip编写自助设备与互联网页面的门诊静脉采血预约程序。平台数据库由多种数据表构成,具体包括医生信息表、预约时间点分配表等。平台性能测试结果显示,设计平台的数据库每秒查询率更高,最高可达到54239次,信息抽取准确率最高为98.60%。     

基于ARIMA和LSTM神经网络的乌鲁木齐市乙型肝炎发病预测研究

目的 了解乌鲁木齐市2012—2021年乙肝发病趋势,建立合适的发病预测模型,探讨ARIMA模型和LSTM神经网络在乙肝发病预测中的应用。方法 根据2012—2021年乙肝月报告病例数据,建立ARIMA模型和LSTM神经网络模型,对乌鲁木齐市乙肝发病数进行拟合及预测,通过比较RMSE的大小评价模型效果。结果 LSTM神经网络模型拟合和预测的RMSE分别为50.13、42.70,ARIMA(0,1,1)(0,0,2)12模型拟合和预测的RMSE分别为67.62、66.85。前者的拟合及预测效果显著优于后者。结论 乌鲁木齐市10年来乙肝发病呈逐年下降趋势,且存在一定季节性变化。LSTM神经网络模型可较好地拟合和预测乌鲁木齐市乙肝的发病数及趋势,且模型效果优于ARIMA(0,1,1)(0,0,2)12,能在一定程度上提高预测精确度。     

Application of deep learning to improve sleep scoring of wrist actigraphy

BackgroundEstimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.Research questions(1) Does the actigraphy mode of operation -Proportional Integrating Measure (PIM) or Zero Crossing Mode (ZCM), responsive respectively to intensity and frequency of movements- impact sleep scoring; and (2) Can a better performing sleep scoring IA be developed by a deep learning approach combining PIM/ZCM data.Study design and MethodsZCM and PIM plus electroencephalographic (EEG) data of 40 healthy adults (17 female, mean age: 26.7 years) were obtained from a single in-home nighttime sleep study. Effect of mode of operation was first evaluated by applying several classic deep learning models to PIM only, ZCM only, and combined ZCM/PIM data. After, a novel deep learning model was developed incorporating combined ZCM/PIM data, and its performance was compared with existing Cole-Kripke, rescored Cole-Kripke, Sadeh, and UCSD IAs.ResultsRelative to the EEG reference, ZCM/PIM combined mode produced higher agreement of scoring sleep/wake epochs than only ZCM or PIM modes. The proposed novel deep learning model showed 87.7% accuracy (0.2–1% higher than the other IAs), 94.1% sensitivity (0.7–4.3% lower than the other IAs), 64.0% specificity (9.9–21.5% higher than the other IAs), and 59.9% Kappa agreement (∼6.9–11.6% higher than other IAs) in detecting sleep epochs. The proposed deep learning model did not differ significantly from the reference EEG in estimating sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE). Amount of bias and minimum detectable change in estimating SOL, WASO, TST and SE by the deep learning model was smaller than other four IAs.InterpretationThe proposed novel deep learning algorithm simultaneously incorporating ZCM/PIM mode data performs significantly better in assessing sleep than existing conventional IAs.