Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets

Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a flat correlation structure between the outputs. However, such a formulation does not account for more elaborate relationships, for instance, if several replicates were observed for each output (which is a typical setting in biological experiments). This paper proposes an extension of MOGPs for hierarchical datasets (i.e. datasets for which the relationships between observations can be represented within a tree structure). Our model defines a tailored kernel function accounting for hierarchical structures in the data to capture different levels of correlations while leveraging the introduction of latent variables to express the underlying dependencies between outputs through a dedicated kernel. This latter feature is expected to significantly improve scalability as the number of tasks increases. An extensive experimental study involving both synthetic and real-world data from genomics and motion capture is proposed to support our claims.Comment: 29 page

Multi-output Gaussian Processes for Large-scale Multi-class Classification and Hierarchical Data

Multi-output Gaussian processes (MOGPs) can concurrently deal with multiple tasks by exploiting the correlation between different outputs. MOGPs have been mostly used for multi-output regression datasets, where the responses of each output are continuous values. However, MOGPs have inferior performance in some complex structured datasets. For example, MOGPs demand a large computational complexity in large-scale multi-class classification. The most common type of data in multi-class classification problems consists of image data, and MOGPs are not specifically designed to handle image datasets so MOGPs have poor performance on image data that has the nature of high dimensionality. Most applications of MOGPs are restricted to regression problems with a reduced number of outputs; and particularly, MOGPs present a limited performance on hierarchical datasets, i.e., datasets where the observations are connected to each other by means of parent-child relationships forming a tree structure. In this thesis, we address the aforementioned issues by proposing three new extensions of MOGPs separately. First, we develop a novel MOGP model to deal with large-scale multiclass classification by subsampling both training data sets and classes in each output. Second, we propose a novel model to deal with image input data sets by incorporating a convolutional kernel, which can effectively capture information from images, into our developed model above. Finally, we present a new hierarchical MOGP model with latent variables to handle hierarchical datasets, where we use a hierarchical kernel function to capture the correlation within hierarchical data structures and use latent variables to explore dependencies between outputs. The new models are applied in various synthetic and real datasets. The results of this thesis indicate that our proposed models can improve prediction performance in corresponding datasets

Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum

Engine knock limits the thermal efficiency improvement of spark-ignition (SI) engines. Thus, the extract research of the knock characteristics has a great significance for the development of gasoline engines. The research proposes a novel knock detection and diagnosis method in SI engines using the CEIITD (Complementary Ensemble Improved Intrinsic time-scale decomposition) and Bi-spectrum algorithm. The CEIITD algorithm is used to extract the knock characteristics. The results show that the CEIITD algorithm can effectively and clearly extract the knock shock characteristics (including light knock) through the vibration signals. A Bi-spectrum analysis can further distinguish between the light knock signal and normal combustion signal. The Bi-spectrum results also show that knock characteristic has a strong non-Gaussian property. At last, the Band pass filter and Improved ITD method were employed to identify the knock characteristics from these cylinder block vibration signals. The comparison result shows that the CEIITD method proposed in this paper is more suitable to detect the knock characteristic

Evaluation of Metformin on Cognitive Improvement in Patients With Non-dementia Vascular Cognitive Impairment and Abnormal Glucose Metabolism

Objective: Recent studies have suggested that metformin can penetrate the blood–brain barrier, protecting neurons via anti-inflammatory action and improvement of brain energy metabolism. In this study, we aim to investigate the effect of metformin on cognitive function in patients with abnormal glucose metabolism and non-dementia vascular cognitive impairment (NDVCI).Methods: One hundred patients with NDVCI and abnormal glucose metabolism were randomly allocated into two groups: metformin and donepezil (n = 50) or acarbose and donepezil (n = 50). The neuropsychological status, glucose metabolism, and common carotid arteries intima–media thickness (CCA-IMT) before and after a year of treatment, were measured and compared between the groups.Results: Ninety four patients completed all the assessment and follow-up. After a year of treatment, there was a decrease in Alzheimer’s disease Assessment Scale-Cognitive Subscale scores and the duration of the Trail Making Test in the metformin-donepezil group. Furthermore, these patients showed a significant increase in World Health Organization–University of California–Los Angeles Auditory Verbal Learning Test scores after treatment (all P < 0.05). However, there was no obvious improvement in cognitive function in the acarbose-donepezil group. We also observed a significant decrease in the level of fasting insulin and insulin resistance (IR) index in the metformin-donepezil group, with a lower CCA-IMT value than that in the acarbose-donepezil group after a year of treatment (P < 0.05).Conclusion: We conclude that metformin can improve cognitive function in patients with NDVCI and abnormal glucose metabolism, especially in terms of performance function. Improved cognitive function may be related to improvement of IR and the attenuated progression of IMT.Trial Registration:ChiCTR-IPR-17011855

Dysregulation of respiratory center drive (P0.1) and muscle strength in patients with early stage idiopathic Parkinson's disease

Objective: The goal of this study is to evaluate pulmonary function and respiratory center drive in patients with early-stage idiopathic Parkinson's disease (IPD) to facilitate early diagnosis of Parkinson's Disease (PD). Methods: 43 IPD patients (Hoehn and Yahr scale of 1) and 41 matched healthy individuals (e.g., age, sex, height, weight, BMI) were enrolled in this study. Motor status was evaluated using the Movement Disorders Society-Unified PD Rating Scale (MDS-UPDRS). Pulmonary function and respiratory center drive were measured using pulmonary function tests (PFT). All IPD patients were also subjected to a series of neuropsychological tests, including Non-Motor Symptoms Questionnaire (NMSQ), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Beck Depression Inventory (BDI) and Mini Mental State Examination (MMSE). Results: IPD patients and healthy individuals have similar forced vital capacity (FVC), forced expiratory volume in 1s (FEV1), forced expiratory volume in 1s/forced vital capacity (FEV1/FVC), peak expiratory flow (PEF), and carbon monoxide diffusion capacity (DLCOcSB). Reduced respiratory muscle strength, maximal inspiratory pressure (PImax) and maximal expiratory pressure (PEmax) was seen in IPD patients (p = 0.000 and p = 0.002, respectively). Importantly, the airway occlusion pressure after 0.1 s (P0.1) and respiratory center output were notably higher in IPD patients (p = 0.000) with a remarkable separation of measured values compared to healthy controls. Conclusion: Our findings suggest that abnormal pulmonary function is present in early stage IPD patients as evidenced by significant changes in PImax, PEmax, and P0.1. Most importantly, P0.1 may have the potential to assist with the identification of IPD in the early stage

Plateau tectonic karst development characteristics and underground conduits identification in the northern part of Kangding

Restricted by the natural environment and technical methods, the study on the evolution characteristics of karst development and the karst groundwater cycle process in the Qinghai-Xizang Plateau is relatively weak, which restricts the economic development, construction of livelihood facilities and the prevention and control of geological disasters.This paper systematically analyzes the karst development characteristics of the carbonate rock distribution area in the northern part of Kangding in Sichuan Province through the methods of geological survey, chemistry-isotope characteristics analysis of karst groundwater, surface water and precipitation water, spring water flow dynamics and water balance calculations.And the karst runoff zone was identified.The results show that the carbonate rocks in the northern part of Kangding are distributed in the alpine valley area.The distribution of karst strata, the degree of karst development, karst water supply and runoff are mainly controlled by structures.The degree of karst development on the contact zone of soluble and non-soluble rocks and the vicinity of active faults is relatively strong.The karst water flows as pipeline runoff, which is mainly discharged in the form of karst springs.The spring flow is about 1.5×104 m3/d and the flow dynamics are relatively stable.Through the analysis of hydrogeological conditions, the karst water runoff zone of the Tonghua Formation and the karst water runoff zone of the Yala River fault have been identified.Hydrochemistry-isotopic data, karst spring flow dynamics and water balance calculation results show that Yala River water is the main source of replenishment for large karst springs.Karst groundwater mainly flows downstream along the karst water runoff zone of the Yala River fault and is discharged in a concentrated manner