Numerical Approaches To A Thermoelastic Kirchhoff-Love Plate System

In this work, theory background of the sobolev spaces and finite element spaces are reviewed first. Then the details of how the thermoelastic Kirchhoff-Love(KL) plates numerically established are presented. Later we approaches to the thermoelastic KL system numerically with mixed element method, H^1−Galerkin method and interior penalty discontinuous galerkin method(IP-DG). What is more, the SIP-DG also applied to solve this KL system numerically. The well-posedness, existence, uniqueness and convergence properties are theoretical analyzed. The gain of the convergence rate is also O(h^k), that is 1 less than the observed convergence rate. When discussing the H1-Galerkin method, the main advantages over traditional mixed element method, is LBB condition naturally inherent. It is proved that the existence and uniqueness of solutions for such discrete scheme. Furthermore, the semi discrete and fully discrete error estimates details are proposed to show the theoretical convergence rate is O(h^k), which is also 1 lesser than the observed convergence rate O(h^k). And optimal convergence rate can be obtained only for some variables

Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression

In the U.S., corn is the most produced crop and has been an essential part of the American diet. To meet the demand for supply chain management and regional food security, accurate and timely large-scale corn yield prediction is attracting more attention in precision agriculture. Recently, remote sensing technology and machine learning methods have been widely explored for crop yield prediction. Currently, most county-level yield prediction models use county-level mean variables for prediction, ignoring much detailed information. Moreover, inconsistent spatial resolution between crop area and satellite sensors results in mixed pixels, which may decrease the prediction accuracy. Only a few works have addressed the mixed pixels problem in large-scale crop yield prediction. To address the information loss and mixed pixels problem, we developed a variational autoencoder (VAE) based multiple instance regression (MIR) model for large-scaled corn yield prediction. We use all unlabeled data to train a VAE and the well-trained VAE for anomaly detection. As a preprocess method, anomaly detection can help MIR find a better representation of every bag than traditional MIR methods, thus better performing in large-scale corn yield prediction. Our experiments showed that variational autoencoder based multiple instance regression (VAEMIR) outperformed all baseline methods in large-scale corn yield prediction. Though a suitable meta parameter is required, VAEMIR shows excellent potential in feature learning and extraction for large-scale corn yield prediction

Particle Thompson sampling

Thompson sampling is an effective Bayesian heuristic for solving stochastic bandit problems. But it is hard to implement in practice due to the intractability of maintaining a continuous posterior distribution. Particle Thompson sampling (PTS) is an approximation of Thompson sampling based on the simple idea of replacing the continuous distribution by a discrete distribution supported at a set of particles. It is very flexible and easy to implement. This dissertation aims to analyze, improve and apply PTS. Firstly, we provide a thorough analysis of PTS for the two-arm Bernoulli bandit problem and a preliminary analysis of PTS for general stochastic bandit problems. Our main findings are that, fit particles survive, unfit particles decay, and most particles eventually decay. Secondly, we propose regenerative particles Thompson sampling (RPTS), an attempt to improve PTS based on the heuristic: delete the decaying unfit particles and regenerate new particles in the vicinity of fit surviving particles. Empirical evidence shows that RPTS outperforms PTS for a set of representative bandit problems. Finally, we apply PTS and RPTS to network slicing, a 5G communication network problem, to demonstrate the flexibility and efficacy of the algorithms

Per-flow cardinality estimation based on virtual LogLog sketching

Flow cardinality estimation is the problem of estimating the number of distinct elements in a data flow, often with a stringent memory constraint. It has wide applications in network traffic measurement and in database systems. The virtual LogLog algorithm proposed recently by Xiao, Chen, Chen and Ling estimates the cardinalities of a large number of flows with a compact memory. The purpose of this thesis is to explore two new perspectives on the estimation process of this algorithm. Firstly, we propose and investigate a family of estimators that generalizes the original vHLL estimator and evaluate the performance of the vHLL estimator compared to other estimators in this family. Secondly, we propose an alternative solution to the estimation problem by deriving a maximum-likelihood estimator. Empirical evidence from both perspectives suggests the near-optimality of the vHLL estimator for per-flow estimation, analogous to the near-optimality of the HLL estimator for single-flow estimation

Establishment of gender- and age-specific reference intervals for serum liver function tests among the elderly population in northeast China: a retrospective study

Reference intervals (RIs) for younger population may not apply to the elderly population. The aim of this study was to establish gender- and age-specific RIs for serum liver function tests among the elderly population and to compare with younger population RIs currently used in China and other countries. This was a retrospective study, and subjects (≥ 18 year-old) were recruited from the laboratory information system (LIS) at the First Hospital of Jilin University between April 2020 and April 2021. The following parameters were collected: aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), total bilirubin (TBIL), and direct bilirubin (DBIL). The Tukey method was used to eliminate outliers. Reference intervals were established by the nonparametric method. A total of 23,597 healthy individuals were enrolled in the study. From all parameters AST, ALT, TP and ALB required no gender partition, while ALT, GGT, TP, ALB and DBIL required different partitions for age. Activities and concentrations of ALT, ALB, and TP showed a downward trend in the elderly aged 60-89. In contrast, DBIL showed a gradual upward trend. The RIs for liver function tests among healthy elderly population were different from those among young population in China. There were apparent gender and age differences in the RIs of liver function for elderly and significant differences compared with national standards and RIs in other countries. Therefore, it is necessary to establish gender- and age-specific RIs for serum liver function tests among the elderly population

Non-adiabatic Dynamics in a Continuous Circularly Polarized Laser Field with Floquet Phase-space Surface Hopping

Non-adiabatic chemical reactions involving continuous circularly polarized light (cw CPL) have not attracted as much attention as dynamics in unpolarized/linearly polarized light. However, including circularly (in contrast to linearly) polarized light allows one to effectively introduce a complex-valued time-dependent Hamiltonian, which offers a new path for control or exploration through the introduction of Berry forces. Here, we investigate several inexpensive semiclassical approaches for modeling such nonadiabatic dynamics in the presence of a time-dependent complex-valued Hamiltonian, beginning with a straightforward instantaneous adiabatic fewest-switches surface hopping (IA-FSSH) approach (where the electronic states depend on position and time), continuing to a standard Floquet fewest switches surface hopping (F-FSSH) approach (where the electronic states depend on position and frequency), and ending with an exotic Floquet phase-space surface hopping (F-PSSH) approach (where the electronic states depend on position, frequency, and momentum). Using a set of model systems with time-dependent complex-valued Hamiltonians, we show that the Floquet phase-space adiabats are the optimal choice of basis as far as accounting for Berry phase effects and delivering accuracy. Thus, the F-PSSH algorithm sets the stage for modeling nonadiabatic dynamics under strong externally pumped circular polarization in the future.Comment: 40 pages, 4 figure