Bootstrap in High Dimension with Low Computation

The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for modern high-dimensional problems, it could suffer from huge computational costs due to the need to repeatedly generate resamples and refit models. We study the use of bootstraps in high-dimensional environments with a small number of resamples. In particular, we show that by using sample-resample independence from a recent "cheap" bootstrap perspective, running a number of resamples as small as one could attain valid coverage even when the dimension grows closely with the sample size, thus supporting the implementability of the bootstrap for large-scale problems. We validate our theoretical results and compare the performance of our approach with other benchmarks via a range of experiments

Impact on Travelers Hedonic and Utilitarian Shopping Behavior by Adoption of Mobile Application: Results from a Quasi-experiment

The continuing development of mobile technology has led to an explosion of mobile applications, which have exposed a broader consumer base to mobile consumption. It is currently unclear how mobile apps using will affect travelers’ shopping behavior, particularly from the perspective of the hedonic and utilitarian shopping behavior of travelers. Using a special quasi-experiment launching by an airline, we collected the datasets of more than 10000 travelers and to investigate the impact of mobile app on the travelers’ shopping behavior. The results suggested that mobile apps adoption improved travelers’ hedonic shopping behavior (e.g., ancillary services purchasing), while the utilitarian shopping conduct (e.g. booking tickets in advance) decreased. It was also found that the mobile app adoption increased hedonic shopping in males but decreased hedonic and utilitarian shopping in frequent flyers and members. This investigation can help with the management of travelers’ purchasing habits and provide guidance for industrial decision makers

Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method.

We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability of PM7 model parameter values consistent with the uncertainty in the training data and (b) uncertainty propagation from the training data to the model predictions. Experimental heats of formation of a homologous series of linear alkanes are used as the property of interest. The training data are chemically accurate, i.e., they have very low uncertainty by the standards of computational chemistry. The analysis does not find evidence of PM7 consistency with the entire data set considered as no single set of parameter values is found that captures the experimental uncertainties of all training data. A set of parameter values for PM7 was able to capture the training data within ±1 kcal/mol, but not to the smaller level of uncertainty in the reported data. Nevertheless, PM7 was found to be consistent for subsets of the training data. In such cases, uncertainty propagation from the chemically accurate training data to the predicted values preserves error within bounds of chemical accuracy if predictions are made for the molecules of comparable size. Otherwise, the error grows linearly with the relative size of the molecules

Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning

A New Measurement Method of Relative Volume Wear Ratio Based on Discharge Debris Composition Analysis in Micro-EDM

In microelectrical discharge machining (micro-EDM) milling process, due to the unavoidability of electrode wear, selection of electrode with high electrical erosion resistance and accurate electrode compensation is entitled to be conducted to ensure high precision and high quality. The RVWR is used as criterion for electrode wear characteristics and is fundamental to achieve accurate electrode compensation; however, it is hardly measured accurately with conventional methods. In this paper, firstly, the error of RVWR measured by conventional measurement method is analyzed. Thereafter, for accurately measuring RVWR, a new measurement method is proposed based on electrical debris composition analysis. The RVWR of widely used tungsten, molybdenum, and copper electrode in machining different materials is measured, respectively, and the optimum electrode is selected based on the measuring results. Finally, microgrooves on different materials are machined with tungsten electrode, and the experiment results show that the microstructures have good bottom surface profiles, which indicates that the proposed method is effective to precisely measure the RVWR and guarantee accurate electrode compensation in micro-EDM process

Microsphere femtosecond laser sub-50 nm structuring in far field via non-linear absorption

Creation of arbitrary features with high resolution is critically important in the fabrication of nano-optoelectronic devices. Here, sub-50 nm surface structuring is achieved directly on Sb2S3 thin films via microsphere femtosecond laser irradiation in far field. By varying laser fluence and scanning speed, nano-feature sizes can be flexibly tuned. Such small patterns are attributed to the co-effect of microsphere focusing, two-photons absorption, top threshold effect, and high-repetition-rate femtosecond laser-induced incubation effect. The minimum feature size can be reduced down to ~30 nm (λ/26) by manipulating film thickness. The fitting analysis between the ablation width and depth predicts that the feature size can be down to ~15 nm at the film thickness of ~10 nm. A nano-grating is fabricated, which demonstrates desirable beam diffraction performance. This nano-scale resolution would be highly attractive for next-generation laser nano-lithography in far field and in ambient air

Transcriptome analysis reveals the molecular basis of the response to acute hypoxic stress in blood clam Scapharca broughtonii

Hypoxia tolerance and adaptive regulation are important for aquatic animals, especially for species with poor mobility, such as most bivalves. Previous studies have confirmed that the blood clam Scapharca broughtonii has strong hypoxia resistance. However, the molecular mechanism supporting its hypoxic tolerance is still largely limited. To further screen the genes and their potential regulation of hypoxia tolerance, the transcriptome changes of S. broughtonii after acute hypoxic stress were explored by RNA sequencing. In this study, the average value of Q30 is 92.89%, indicating that the quality of sequencing is relatively high. The Unigenes obtained were annotated using four databases, namely Interpo, KEGG, Swisspro and TrEMBL. The annotation rates in these four databases were 71.82%, 75.95%, 92.98%, and 79.26%, respectively. And also, there were 649 DEGs in group B (dissolved oxygen (DO) of 2.5 mg/L) compared with group D (DO of 7.5 mg/L), among which 252 were up-regulated, and 397 were down-regulated. There were 965 DEGs in group A (DO of 0.5 mg/L), 2.5 mg/L, and 7.5 mg/L, compared with group B, among which 530 were up-regulated, and 435 were down-regulated. Meanwhile, there were 2,040 DEGs in group A compared with group D, among which 901 were up-regulated, and 1,139 were down-regulated. The main metabolic-related pathways of KEGG enriched in this study included Insulin secretion, Insulin signaling pathway, MAPK signal transduction pathway, and PPAR signaling pathway. These pathways may be critical metabolic pathways to solve energy demand and rebuild energy balance in S. broughtonii under hypoxic conditions. This study preliminarily clarified the response of S. broughtonii to hypoxia stress on the molecular levels, providing a reference for the following study on the response laws of related genes and pathways under environmental stress of S. broughtonii