Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.

Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

Latent Growth Mixture Modeling to Estimate Differential PTSD Trajectories and Associated Risk Factors in Psychiatric Staff Following Workplace Violence

Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

Evaluation of CMIP5 Climate Models in Simulating 1979–2005 Oceanic Latent Heat Flux over the Pacific

The climatological mean state, seasonal variation and long-term upward trend of 1979-2005 latent heat flux （LHF） in historical runs of 14 coupled general circulation models from CMIP5 （Coupled Model Intercomparison Project Phase 5） are evaluated against OAFlux （Objectively Analyzed air-sea Fluxes） data. Inter-model diversity of these models in simulating the annual mean climatological LHF is discussed. Results show that the models can capture the climatological LHF fairly well, but the amplitudes are generally overestimated. Model-simulated seasonal variations of LHF match well with observations with overestimated amplitudes. The possible origins of these biases are wind speed biases in the CMIP5 models. Inter-model diversity analysis shows that the overall stronger or weaker LHF over the tropical and subtropical Pacific region, and the meridional variability of LHF, are the two most notable diversities of the CMIP5 models. Regression analysis indicates that the inter-model diversity may come from the diversity of simulated SST and near-surface atmospheric specific humidity. Comparing the observed long-term upward trend, the trends of LHF and wind speed are largely underestimated, while trends of SST and air specific humidity are grossly overestimated, which may be the origins of the model biases in reproducing the trend of LHF.

Latent Variable Modeling Approach for Assessing Social Impacts of Mine Closure

Mining stimulates environmental and economic impacts on the neighboring community right from the inception to the closure of its operations. The society in the neighborhood of mining gradually adopts a characteristic life-style that is highly influenced by the mining. In order to sustain the societal development beyond the mine closure, it is necessary to plan post mining activities in the area. Thus, it is essential to predict the impacts of mine closure well before the closure. Many societal and family attributes are affected by mine closure. Impact on these attributes is reflected on the overall quality of life of the neighboring community. There are no adequate indicators and/or methodology available to measure social impacts of mine closure on a neighboring community. This paper made an attempt to develop such methodology to predict the degree of adverse effects of mine closure on the quality of life of neighboring communities using the Structural Equation Modeling (SEM) and the Latent Variables Interaction Model (LVM).

New approaches to cognitive work analysis through latent variable modeling in mining operations

This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations.

A STUDY ON THE IMPACTS OF LATENT HEAT PARAMETERIZATION SCHEME ON PREDICTION SKILL OF ENSO WITH A SIMPLE OCEAN-ATMOSPHERE COUPLED MODEL

This study revises Weare's latent heat parameterization scheme and conducts an associated theoretic analysis.The revised Weare's scheme is found to present potentially better results than Zebiak's scheme.The Zebiak-Cane coupled ocean-atmosphere model,initialized by the National Centers for Environmental Prediction and the National Center for Atmospheric Research(NCEP/NCAR) reanalysis of wind stress anomaly at 925 hPa,is referred to as the ZCW coupled model.The atmosphere models of the ZCW coupled model that use Zebiak's scheme and the revised Weare's scheme are referred to as the ZCW0 and ZCWN atmosphere models,respectively.The coupled ocean-atmosphere models that use Zebiak's scheme and the revised Weare's scheme are referred to as the ZCW0and ZCWN coupled models,respectively.The simulations between the ZCW0 and ZCWN atmosphere models and between the ZCW0 and ZCWN coupled models are analyzed.The results include:(1) The evolution of heat,meridional wind and divergence anomalies simulated by similar ZCW0 and ZCWN atmosphere models,although the magnitudes of the former are larger than those of the latter;(2) The prediction skill of the Nio3 index from 1982 to 1999 by the ZCWN coupled model shows improvement compared with those by the ZCW0 coupled model;(3) The analysis of El Nio events in 1982/1983,1986/1987,and 1997/1998 and La Nia events in 1984/1985,1988/1989,and 1998/2000 suggests that the ZCWN coupled model is better than the ZCW0 coupled model in predicting warm event evolution and cold event generation.The results also show the disadvantage of the ZCWN coupled model for predicting El Nio.

A Comparison of Statistics for Assessing Model Invariance in Latent Class Analysis

Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.

Quasi-Monte Carlo Approximations for Exponentiated Quadratic Kernel in Latent Force Models

In this project, we consider obtaining Fourier features via more efficient sampling schemes to approximate the kernel in LFMs. A latent force model (LFM) is a Gaussian process whose covariance functions follow an Exponentiated Quadratic (EQ) form, and the solutions for the cross-covariance are expensive due to the computational complexity. To reduce the complexity of mathematical expressions, random Fourier features (RFF) are applied to approximate the EQ kernel. Usually, the random Fourier features are implemented with Monte Carlo sampling, but this project proposes replacing the Monte-Carlo method with the Quasi-Monte Carlo (QMC) method. The first-order and second-order models’ experiment results demonstrate the decrease in NLPD and NMSE, which revealed that the models with QMC approximation have better performance.

Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.

基于加速无约束张量隐因子分解模型的Web服务Qo S估计

针对基于张量非负隐因子分解模型的Web服务QoS估计方法过于依赖非负初始随机数据以及特意设计的非负训练方法,导致模型的兼容性和扩展性不高的问题,提出了加速无约束张量隐因子分解模型。其主要思想包括三部分:将非负性约束从决策参数转移到输出的隐因子,并通过单元素映射函数连接它们;运用结合动量方法的随机梯度下降算法,有效提高模型的收敛速度与估计精度;给出加速无约束张量隐因子分解模型的详细算法和结果分析。在实际工业应用中的2个动态QoS数据集上的实证研究表明,与最先进的QoS估计模型相比,所提模型具有较高的计算效率和估计精度。

基于潜类别增长模型分析老年帕金森病患者用药依从性轨迹及影响因素

目的:利用潜类别增长模型(latent class growth model,LCGM)分析老年帕金森病患者用药依从性轨迹,并验证其影响因素。方法:对124例原发性老年帕金森病患者进行12个月随访调查,调查工具包括一般资料调查表和Morisky用药依从性量表。通过潜类别增长模型识别患者用药依从性轨迹,采用有序多分类Logistic回归分析用药依从性轨迹的影响因素。结果:老年帕金森病患者用药依从性分为“高-持续型”“中-下降型”和“低-下降型”3种类型,且该3种类型文化程度、工作状态、用药种类、智力状态比较,差异有统计学意义(P<0.05)。有序多分类Logistic回归显示,工作状态、用药种类、智力状态是患者用药依从性轨迹的影响因素(P<0.05)。结论:老年帕金森患者用药依从性分为3种轨迹,工作状态、用药种类和智力状态是用药依从性轨迹类别的影响因素。

基于全局与序列混合变分Transformer的多样化图像描述生成方法

多样化图像描述生成已成为图像描述领域研究热点.然而,现有方法忽视了全局和序列隐向量之间的依赖关系,严重限制了图像描述性能的提升.针对该问题,本文提出了基于混合变分Transformer的多样化图像描述生成框架.具体地,首先构建全局与序列混合条件变分自编码模型,解决全局与序列隐向量之间依赖关系表示的问题.其次,通过最大化条件似然推导混合模型的变分证据下界,解决多样化图像描述目标函数设计问题.最后,无缝融合Transformer和混合变分自编码模型,通过联合优化提升多样化图像描述的泛化性能.在MSCOCO数据集上实验结果表明,与当前最优基准方法相比,在随机生成20和100个描述语句时,多样性指标m-BLEU(mutual overlap-BiLingual Evaluation Understudy)分别提升了4.2%和4.7%,同时准确性指标CIDEr(Consensus-based Image Description Evaluation)分别提升了4.4%和15.2%.

基于隐结构模型分析食管鳞癌证候分类及特征

目的探索食管癌证候分类及特征,为临床辨治提供参考。方法采集全国10个省份的食管癌患者四诊信息,构建数据库。运用Lantern 5.0软件构建隐结构模型,通过综合聚类提取食管癌常见证候。结果共纳入2027例食管癌患者,160个症状构建隐结构模型,得到34个隐变量,综合聚类得到6种常见证候。结论食管癌的主次病因可以概括为外邪直中是引发食管癌的主因,高龄和情志抑郁是促因。食管癌常见肝胃不和证、脾肾亏虚证、气虚阳微证、痰气互阻证、血瘀痰滞证、阴虚内热证。

基于词嵌入的科研主题排序研究

为准确把握科研领域内文献主题的发展变化,常利用隐式语义特征提取科研主题分布。但由于主题挖掘技术本身的限制,并非所有主题都具有同等重要性或意义。有些主题可能包含太多背景词,信息空泛,或者主题词之间缺乏连贯性,导致主题缺乏实际意义。针对上述问题,在已有研究基础上,基于词嵌入,提出一种新的多维度评估主题质量算法;针对科研文档的特点,利用语料库的统计特征对无意义主题距离评估方法进行优化,并最终将二者融合到一个统一的主题排序框架中。实验结果表明,本文提出的方法可以有效提高主题排序整体效果,能够识别出非重要和质量差的主题,主题排序的整体效果优于现有方法。

干燥物系解析理论研究进展

干燥是含湿物料与有限介质两个独立物系,在限定的工艺条件下,自发进行能量传递和转换的过程。它隶属热力学范畴,而又不同于一般的热力过程,在物系边界存在诸多错综复杂的随机因素交互作用,使得基于热力学熵参数无法对实际过程的能效进行实时的定量评价。基于传递定律建立扩散模型,得不到传递系数严格意义的数学解,存在微积分结果偏离实际较远的情况;基于反应工程原理建立干燥动力学模型,存在指前因子,活化能,机理函数等待定的物理量,实际应用存在很大的局限性。如何从理论上完整地解析出实际过程,得到其分析解是热力学应用技术基础科学研究领域自古以来的重大理论难题。近十几年,笔者从非均相系热力学基础和干燥㶲分析入手,以干燥㶲传递和转换时的自由能消耗为统一尺度,以水分活度为一切干燥物系的共同属性,揭示了干燥物系固有特征函数及其理论解,丰富了热力学应用技术基础理论。该文从干燥物系解析理论发展的历史现状,阐释揭示物料干燥理论过程、评价工艺装备能效的解析理论与方法并指明其应用与发展的技术途径,为揭示物系传递机理、评价工艺装备系统能效、实现干燥过程自适应控制和制订科学的工艺能效评价标准提供参考。

生成式人工智能在面料外观仿真上的研究

数字经济对纺织服装产品的数字孪生仿真有更高的要求,服装数字孪生的品质关键在于纺织面料数字化的质量与效率。本文提出了一种基于机器学习的织物仿真方法。以潜在扩散模型为基础,采用LoRA的微调模型方法,以标签化的织物外观图片集为训练集,训练一个织物外观仿真的模型。与数字服装领域通过扫描面料获得其外观图片的方法相比,该方法速度快、效果好。与成熟的商用图片生成程序生成的图片相比,该模型生成的图片更具有针对性,仿真效果更加逼真。该模型生成的织物外观图片丰富多样,能够根据不同的文本提示词生成不同的织物外观图片,提高了织物外观的设计效率,降低了产品的研发成本,为服装行业的数字化发展和企业的智能制造提供了新的思路和参考。

基于真实世界数据探讨中医药治疗慢性支气管炎用药规律

目的归纳总结中医药治疗慢性支气管炎(CB)的用药规律,为临床用药提供参考。方法基于健康信息系统(HIS)电子病历数据提取2016年1月1日-2021年12月31日于河南中医药大学第一附属医院呼吸科住院的CB患者病历资料。经筛选后,将处方中药录入Excel 2019建立数据库。基于Lantern5.0软件对频率>6%的中药进行隐结构模型学习,得到隐变量及显变量,对模型进行诠释。利用SPSS Modeler 18.0软件将频率>6%的中药用Apriori算法建立模点,得到药物之间的关联规则,分析中医药治疗CB的用药规律。结果共纳入病例3410例,涉及中药423味,累计用药82766次,其中频率>6%的中药109味,累计频次为69845,频次前5位中药依次为川贝母、茯苓、白术、紫菀、陈皮,以化痰止咳平喘药、补虚药、清热药、解表药、活血化瘀药为主。药性偏温、寒、平,药味以苦、甘、辛为主,主归肺经、脾经、肝经、胃经。隐结构模型分析得到隐变量49个、隐类149个,结合专业知识推断得到10个综合聚类模型,21个核心方剂,如桑白皮汤、血府逐瘀汤、小青龙汤、二陈汤、沙参麦冬汤、六味地黄丸、银翘散、止嗽散、玉屏风散、血府逐瘀汤合导痰汤等,推断CB证候有痰热郁肺证、气滞血瘀证、寒饮射肺证、痰湿蕴肺证、肺气阴两虚证、肾阴虚证、风热犯肺证、风寒袭肺证、肺脾气虚证、痰瘀互结证。关联规则分析共得到强关联规则41条,其中二项强关联规则5条,三项强关联规则36条,置信度较高的为防风+黄芪→白术、防风+党参→白术、党参+陈皮→白术等,提升度较高的为柴胡+桑白皮→黄芩、紫苏子+射干→川贝母、苦杏仁+清半夏→陈皮等。结论中医药治疗CB以化痰止咳平喘为主,且常用活血化瘀法以助化痰,并注重补肺固表、健脾益气等法的应用。