Statistical validation and calibration of computer models

This thesis deals with modeling, validation and calibration problems in experiments of computer models. Computer models are mathematic representations of real systems developed for understanding and investigating the systems. Before a computer model is used, it often needs to be validated by comparing the computer outputs with physical observations and calibrated by adjusting internal model parameters in order to improve the agreement between the computer outputs and physical observations. As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges and stimulates the development of novel statistical modeling methods. One challenge is to deal with computer models with random inputs (random effects). This kind of computer models is very common in engineering applications. For example, in a thermal experiment in the Sandia National Lab (Dowding et al. 2008), the volumetric heat capacity and thermal conductivity are random input variables. If input variables are randomly sampled from particular distributions with unknown parameters, the existing methods in the literature are not directly applicable. The reason is that integration over the random variable distribution is needed for the joint likelihood and the integration cannot always be expressed in a closed form. In this research, we propose a new approach which combines the nonlinear mixed effects model and the Gaussian process model (Kriging model). Different model formulations are also studied to have an better understanding of validation and calibration activities by using the thermal problem. Another challenge comes from computer models with functional outputs. While many methods have been developed for modeling computer experiments with single response, the literature on modeling computer experiments with functional response is sketchy. Dimension reduction techniques can be used to overcome the complexity problem of function response; however, they generally involve two steps. Models are first fit at each individual setting of the input to reduce the dimensionality of the functional data. Then the estimated parameters of the models are treated as new responses, which are further modeled for prediction. Alternatively, pointwise models are first constructed at each time point and then functional curves are fit to the parameter estimates obtained from the fitted models. In this research, we first propose a functional regression model to relate functional responses to both design and time variables in one single step. Secondly, we propose a functional kriging model which uses variable selection methods by imposing a penalty function. we show that the proposed model performs better than dimension reduction based approaches and the kriging model without regularization. In addition, non-asymptotic theoretical bounds on the estimation error are presented.Ph.D.Committee Chair: Tsui, Kwok-Leung; Committee Member: Goldsman, David; Committee Member: Hung, Ying; Committee Member: Shi, Jianjun; Committee Member: Vengazhiyil, Rosha

Strategy and Incentive in Contest and Tournament

Ph.DDOCTOR OF PHILOSOPH

TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding

ICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the effectiveness and efficiency of manual coding, many methods have been proposed to automatically predict ICD codes from clinical notes. However, most previous works ignore the decisive information contained in structured medical data in EHRs, which is hard to be captured from the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal Attention Network (TreeMAN) to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features via the attention mechanism. Tree-based features are constructed according to decision trees learned from structured multimodal medical data, which capture the decisive information about ICD coding. We can apply the same multi-label classifier from previous text models to the multimodal representations to predict ICD codes. Experiments on two MIMIC datasets show that our method outperforms prior state-of-the-art ICD coding approaches. The code is available at https://github.com/liu-zichen/TreeMAN

Expressive paragraph text-to-speech synthesis with multi-step variational autoencoder

Neural networks have been able to generate high-quality single-sentence speech with substantial expressiveness. However, it remains a challenge concerning paragraph-level speech synthesis due to the need for coherent acoustic features while delivering fluctuating speech styles. Meanwhile, training these models directly on over-length speech leads to a deterioration in the quality of synthesis speech. To address these problems, we propose a high-quality and expressive paragraph speech synthesis system with a multi-step variational autoencoder. Specifically, we employ multi-step latent variables to capture speech information at different grammatical levels before utilizing these features in parallel to generate speech waveform. We also propose a three-step training method to improve the decoupling ability. Our model was trained on a single-speaker French audiobook corpus released at Blizzard Challenge 2023. Experimental results underscore the significant superiority of our system over baseline models.Comment: 5 pages, 1 figure, 2 table

Fit evaluation of virtual garment try-on by learning from digital pressure data

Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies

Translational medical bioengineering research of traumatic brain injury among Chinese and American pedestrians caused by vehicle collision based on human body finite element modeling

Based on the average human body size in China and the THUMS AM50 finite element model of the human body, the Kriging interpolation algorithm was used to model the Chinese 50th percentile human body, and the biological fidelity of the model was verified. We built three different types of passenger vehicle models, namely, sedan, sports utility vehicle (SUV), and multi-purpose vehicle (MPV), and used mechanical response analysis and finite element simulation to compare and analyze the dynamic differences and head injury differences between the Chinese 50th percentile human body and the THUMS AM50 model during passenger vehicle collisions. The results showed that there are obvious differences between the Chinese mannequin and THUMS in terms of collision time, collision position, invasion speed, and angle. When a sedan collided with the mannequins, the skull damage to the Chinese human body model was more severe, and when a sedan or SUV collided, the brain damage to the Chinese human body was more severe. The abovementioned results suggest that the existing C-NCAP pedestrian protection testing regulations may not provide the best protection for Chinese human bodies, and that the regulations need to be improved by combining collision damage mechanisms and the physical characteristics of Chinese pedestrians. This thorough investigation is positioned to shed light on the fundamental biomechanics and injury mechanisms at play. Furthermore, the amalgamation of clinically rooted translational and engineering research in the realm of traumatic brain injury has the potential to establish a solid foundation for discerning preventive methodologies. Ultimately, this endeavor holds the potential to introduce effective strategies aimed at preventing and safeguarding against traumatic brain injuries

Decreased phase information transfer from the mPFC to the BLA: During exploratory behavior in CUMS rats

IntroductionDepression is a mental disorder characterized by aberrant exploratory behavior. Environmental factors, such as chronic stress, are commonly used to induce depression-like behavior in rodent models. The medial prefrontal cortex (mPFC) and the basolateral amygdala (BLA) are crucial sites in subjects with chronic stress-induced depression. The transmission of amplitude information from the mPFC to the BLA was abated during exploratory behavior in depressive rats; however, the nature of the phase interaction between these two sites remains unknown.MethodsWe used chronic unpredictable mild stress (CUMS) to model depression in rats and acquired local field potentials (LFPs) via multiple electrodes implanted in the mPFC and the BLA while rats (both the control and CUMS groups, respectively) were allowed to explore freely in an open field. The weighted phase lag index (WPLI) within the mPFC and the BLA and phase transfer entropy (PTE) from the mPFC to BLA were computed for two groups of rats (control and CUMS rats) to quantify the phase information transmission.ResultsRats subjected to CUMS showed a decrease in exploratory behavior. The WPLI within the mPFC and the BLA showed strikingly higher phase synchrony at theta frequencies (4–12 Hz) than other frequency bands during exploratory behavior in both the control and CUMS groups. The results of theta PTE from the mPFC to BLA showed that PTE was significantly decreased in the CUMS group compared with the control group.DiscussionsThese findings demonstrated that attenuated phase information transfer might restrain exploratory behavior in CUMS rats

Assessing energy saving potentials of office buildings based on adaptive thermal comfort using a tracking-based method

Occupants’ thermal comfort and their adaptation behaviors are essential aspects of building design and energy operation. There is a growing need to better understand the impact of seasonal variation on occupants’ dynamic thermal comfort which provides evidence for building energy flexible design and management. The aim of this study is to investigate the interaction between occupants’ thermal sensation and adaptive behavior in office buildings. Such understanding can provide detailed adaptation rules of human behavior in dynamic office buildings and quantify energy demands. In this study, a framework of a tracking method is proposed, which combines data collection (continuous monitoring of environmental parameters and daily questionnaire surveys), time boundary division and data analysis. Using the tracking method, field surveys were carried out in three mixed-mode office buildings in Chongqing, China. The time-series data was analyzed based on the indoor operative temperature under free-running conditions and five seasonal periods are classified i.e. Latter Spring (LS), Early Cooling period (EC), Middle Cooling period (MC), Latter Cooling period (LC) and Early Autumn (EA). Results show that for the same outdoor temperatures in different seasons, occupants’ clothing insulation varied, indicating that the occupants were more sensitive to environmental changes in EA than in LS, as well as in EC than in LC. The study that flexible energy operation based on the thermal comfort demand can achieve energy savings compared with fixed temperature

Inhibition of P-Glycoprotein by HIV Protease Inhibitors Increases Intracellular Accumulation of Berberine in Murine and Human Macrophages

Background HIV protease inhibitor (PI)-induced inflammatory response in macrophages is a major risk factor for cardiovascular diseases. We have previously reported that berberine (BBR), a traditional herbal medicine, prevents HIV PI-induced inflammatory response through inhibiting endoplasmic reticulum (ER) stress in macrophages. We also found that HIV PIs significantly increased the intracellular concentrations of BBR in macrophages. However, the underlying mechanisms of HIV PI-induced BBR accumulation are unknown. This study examined the role of P-glycoprotein (P-gp) in HIV PI-mediated accumulation of BBR in macrophages. Methodology and Principal Findings Cultured mouse RAW264.7 macrophages, human THP-1-derived macrophages, Wild type MDCK (MDCK/WT) and human P-gp transfected (MDCK/P-gp) cells were used in this study. The intracellular concentration of BBR was determined by HPLC. The activity of P-gp was assessed by measuring digoxin and rhodamine 123 (Rh123) efflux. The interaction between P-gp and BBR or HIV PIs was predicated by Glide docking using Schrodinger program. The results indicate that P-gp contributed to the efflux of BBR in macrophages. HIV PIs significantly increased BBR concentrations in macrophages; however, BBR did not alter cellular HIV PI concentrations. Although HIV PIs did not affect P-gp expression, P-gp transport activities were significantly inhibited in HIV PI-treated macrophages. Furthermore, the molecular docking study suggests that both HIV PIs and BBR fit the binding pocket of P-gp, and HIV PIs may compete with BBR to bind P-gp. Conclusion and Significance HIV PIs increase the concentration of BBR by modulating the transport activity of P-gp in macrophages. Understanding the cellular mechanisms of potential drug-drug interactions is critical prior to applying successful combinational therapy in the clinic

Virome and metagenomic analysis reveal the distinct distribution of microbiota in human fetal gut during gestation

Studies have shown that fetal immune cell activation may result from potential exposure to microbes, although the presence of microbes in fetus has been a controversial topic. Here, we combined metagenomic and virome techniques to investigate the presence of bacteria and viruses in fetal tissues (small intestine, cecum, and rectum). We found that the fetal gut is not a sterile environment and has a low abundance but metabolically rich microbiome. Specifically, Proteobacteria and Actinobacteria were the dominant bacteria phyla of fetal gut. In total, 700 species viruses were detected, and Human betaherpesvirus 5 was the most abundant eukaryotic viruses. Especially, we first identified Methanobrevibacter smithii in fetal gut. Through the comparison with adults’ gut microbiota we found that Firmicutes and Bacteroidetes gradually became the main force of gut microbiota during the process of growth and development. Interestingly, 6 antibiotic resistance genes were shared by the fetus and adults. Our results indicate the presence of microbes in the fetal gut and demonstrate the diversity of bacteria, archaea and viruses, which provide support for the studies related to early fetal immunity. This study further explores the specific composition of viruses in the fetal gut and the similarities between fetal and adults’ gut microbiota, which is valuable for understanding human fetal immunity development during gestation