Dimension reduction of the Gross-Pitaevskii equation for Bose-Einstein condensates

Master'sMASTER OF SCIENC

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods.Comment: The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023

Convergence rate of dimension reduction in Bose-Einstein condensates

In this paper, we study dimension reduction of the three-dimensional (3D) Gross-Pitaevskii equation (GPE) modelling Bose-Einstein condensation under different limiting interaction and trapping frequencies parameter regimes. Convergence rates for the dimension reduction of 3D ground state and dynamics of the GPE in the case of disk-shaped condensation and cigar-shaped condensation are reported based on our asymptotic and numerical results. In addition, the parameter regimes in which the 3D GPE cannot be reduced to lower dimensions are identified.Comment: 27pages; 9 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A fully digital physical unclonable function based temperature sensor for secure remote sensing

Turnkey solutions that combine energy-efficient remote sensing and secure communication of telemetry are desirable in data collection, risk control and situation appraisal with the large scale deployment of resource constrained Internet of Things devices. In this paper, a new low-cost physical unclonable function (PUF) based temperature sensor for secure remote temperature sensing is proposed. The design exploits the approximately linear positive temperature coefficient of CMOS inverter in super-threshold operation to calibrate the running frequency of ring oscillator (RO) in a reconfigurable RO PUF at different temperature. The RO frequency corresponding to the sensed temperature is fed into a randomizer seeded by the input challenge to select new RO pairs for comparison to generate a random, unique and physically unclonable digital tag, which is valid for a selected input challenge to a target device at a particular temperature. Using only standard logic cells and a very simple structure, the proposed temperature sensor can be easily implemented on FPGA and integrated into other digital systems. It protects the integrity of the sensed information by preventing falsified sensor data and masquerade sensing node. The FPGA implementation of our proposed design has demonstrated the feasibility of making a trust temperature telemetry system out of PUF.Ministry of Education (MOE)Accepted versionThis work was supported by the National Natural Science Foundation of China (Grant No. 61601168), the Singapore Ministry of Education MOE AcRF Tier I grant no. MOE 2018-T1-001-131 and the Fundamental Research Foundation of Shenzhen (Grant No. JCYJ20170302151209762)

Comparison of outcomes of self-expanding versus balloon-expandable valves for transcatheter aortic valve replacement: a meta-analysis of randomized and propensity-matched studies

Abstract Background The postoperative outcomes of transcatheter aortic valve replacement (TAVR) with the new generation of self-expanding valves (SEV) and balloon-expandable valves (BEV) remain uncertain. Methods We conducted a meta-analysis based on randomized controlled trials (RCTs) and propensity score-matched (PSM) studies to evaluate the performance of the new generation TAVR devices, with a focus on Edwards SAPIEN 3/Ultra BEV, Medtronic Evolut R/PRO SEV, and Boston ACURATE neo SEV. Our primary endpoints were mortality and complications at both 30 days and one year post-operation. Results A total of 4 RCTs and 14 PSM studies were included. Our findings showed no significant difference between SEV and BEV regarding 30-day and 1-year mortality rates. ACURATE SEV required less permanent pacemaker implantation (PPI) at 30-day as compared to SAPIEN BEV, while Evolut SEV required a higher rate of PPI than SAPIEN BEV. The incidence of stroke, major or life-threatening bleeding (MLTB), major vascular complications (MVC), coronary artery obstruction (CAO) and acute kidney injury (AKI) did not differ significantly between the two groups. SEV had a larger effective orifice area (EOA) and lower mean transvalvular gradients (MPG) compared to BEV. However, there was an increased risk of paravalvular leakage (PVL) associated with SEV. Conclusions In terms of 30-day mortality, stroke, bleeding, MVC, AKI, CAO, and one-year mortality, there was comparability between the two valve types following TAVR. SEV was associated with better hemodynamic outcomes, except for a higher incidence of PVL. Compared to SAPIEN BEV, ACURATE SEV had a lower risk of PPI at 30 days, while Evolut SEV was associated with a higher risk of PPI. These findings underscore the importance of personalized valve selection

A Novel Image Inpainting Method Used for Veneer Defects Based on Region Normalization

The quality of the veneer directly affects the quality and grade of a blockboard made of veneer. To improve the quality and utilization of a defective veneer, a novel deep generative model-based method is proposed, which can generate higher-quality inpainting results. A two-phase network is proposed to stabilize the network training process. Then, region normalization is introduced to solve the inconsistency problem between the mean and standard deviation, improve the convergence speed of the model, and prevent the model gradient from exploding. Finally, a hybrid dilated convolution module is proposed to reconstruct the missing areas of the panels, which alleviates the gridding problem by changing the dilation rate. Experiments on our dataset prove the effectiveness of the improved approach in image inpainting tasks. The results show that the PSNR of the improved method reaches 33.11 and the SSIM reaches 0.93, which are superior to other methods