TRANSACTION MANAGEMENT IN MULTI-CORE MAIN-MEMORY DATABASE SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

Exploring the Learning Difficulty of Data Theory and Measure

As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. In addition, there is no formal definition of easy and hard samples even though they are crucial in many studies. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, a theoretical definition of learning difficulty is proposed on the basis of the bias-variance trade-off theory on generalization error. Theoretical definitions of easy and hard samples are established on the basis of the proposed definition. A practical measure of learning difficulty is given as well inspired by the formal definition. Second, the properties for learning difficulty-based weighting strategies are explored. Subsequently, several classical weighting methods in machine learning can be well explained on account of explored properties. Third, the proposed measure is evaluated to verify its reasonability and superiority in terms of several main difficulty factors. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.Comment: Ou Wu is the corresponding author of this wor

Electric Power Distribution System Reliability Evaluation Considering the Impact of Weather on Component Failure and Pre-Arranged Maintenance

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Insufficient ER-stress response causes selective mouse cerebellar granule cell degeneration resembling that seen in congenital disorders of glycosylation

BACKGROUND: Congenital disorders of glycosylation (CDGs) are inherited diseases caused by glycosylation defects. Incorrectly glycosylated proteins induce protein misfolding and endoplasmic reticulum (ER) stress. The most common form of CDG, PMM2-CDG, is caused by deficiency in the cytosolic enzyme phosphomannomutase 2 (PMM2). Patients with PMM2-CDG exhibit a significantly reduced number of cerebellar Purkinje cells and granule cells. The molecular mechanism underlying the specific cerebellar neurodegeneration in PMM2-CDG, however, remains elusive. RESULTS: Herein, we report that cerebellar granule cells (CGCs) are more sensitive to tunicamycin (TM)-induced inhibition of total N-glycan synthesis than cortical neurons (CNs). When glycan synthesis was inhibited to a comparable degree, CGCs exhibited more cell death than CNs. Furthermore, downregulation of PMM2 caused more CGCs to die than CNs. Importantly, we found that upon PMM2 downregulation or TM treatment, ER-stress response proteins were elevated less significantly in CGCs than in CNs, with the GRP78/BiP level showing the most significant difference. We further demonstrate that overexpression of GRP78/BiP rescues the death of CGCs resulting from either TM-treatment or PMM2 downregulation. CONCLUSIONS: Our results indicate that the selective susceptibility of cerebellar neurons to N-glycosylation defects is due to these neurons’ inefficient response to ER stress, providing important insight into the mechanisms of selective neurodegeneration observed in CDG patients

Lattice Boltzmann study on Kelvin-Helmholtz instability: the roles of velocity and density gradients

A two-dimensional lattice Boltzmann model with 19 discrete velocities for compressible Euler equations is proposed (D2V19-LBM). The fifth-order Weighted Essentially Non-Oscillatory (5th-WENO) finite difference scheme is employed to calculate the convection term of the lattice Boltzmann equation. The validity of the model is verified by comparing simulation results of the Sod shock tube with its corresponding analytical solutions. The velocity and density gradient effects on the Kelvin-Helmholtz instability (KHI) are investigated using the proposed model. Sharp density contours are obtained in our simulations. It is found that, the linear growth rate $\gamma$ for the KHI decreases with increasing the width of velocity transition layer ${D_{v}}$ but increases with increasing the width of density transition layer ${D_{\rho}}$. After the initial transient period and before the vortex has been well formed, the linear growth rates, $\gamma_v$ and $\gamma_{\rho}$, vary with ${D_{v}}$ and ${D_{\rho}}$ approximately in the following way, $\ln\gamma_{v}=a-bD_{v}$ and $\gamma_{\rho}=c+e\ln D_{\rho} ({D_{\rho}}<{D_{\rho}^{E}})$, where $a$, $b$, $c$ and $e$ are fitting parameters and ${D_{\rho}^{E}}$ is the effective interaction width of density transition layer. When ${D_{\rho}}>{D_{\rho}^{E}}$ the linear growth rate $\gamma_{\rho}$ does not vary significantly any more. One can use the hybrid effects of velocity and density transition layers to stabilize the KHI. Our numerical simulation results are in general agreement with the analytical results [L. F. Wang, \emph{et al.}, Phys. Plasma \textbf{17}, 042103 (2010)].Comment: Accepted for publication in PR

Modeling and Experimental Verification of an Electromagnetic and Piezoelectric Hybrid Energy Harvester

This paper describes mathematical models of an electromagnetic and piezoelectric hybrid energy harvesting system and provides an analysis of the relationship between the resonance frequency and the configuration parameters of the system. An electromagnetic and piezoelectric energy harvesting device was designed and the experimental results showed good agreement with the analytical results. The maximum load power of the hybrid energy harvesting system achieved 4.25 mW at a resonant frequency of 18 Hz when the acceleration was 0.7 g, which is an increase of 15% compared with the 3.62 mW achieved by a single electromagnetic technique

Externalizing traits: Shared causalities for COVID-19 and Alzheimer\u27s dementia using Mendelian randomization analysis

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer\u27s dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance–weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224–1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374–2.826), and AD (OR = 1.077, 95% CI = 1.037–1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases