Analysing Online Platform Users’ Attitudes Toward Internet of Things

Internet of Things (IoT) is an increasingly important technology. Understanding the attitudes toward IoT may provide insights into the future development and management of IoT and the management of online platforms. In this paper, we examine online platform users’ attitudes toward IoT, by analysing Twitter data. We analyse the backgrounds of Twitter users associated with different attitudes, including the frequency of using Twitter and the geographical location of posts (i.e., called “tweets”). The research findings suggest that most tweets reflect positive attitudes toward IoT and concentrate on information technologies. Some users expressed concerns with security and privacy issues. Most Twitter users surveyed come from coastal areas of the USA

Design and Simulation Based on Pro/E for a Hydraulic Lift Platform in Scissors Type

AbstractScissors lift platform with a wide range, the main platform, lift mechanism and the bottom are composed of three parts. Lifting from low to high lifting, the scissors posts, and the hydraulic cylinder layout multiple, mobile way has traction, self-propelled, booster, etc. Scissors lift mechanism of scissors post number and cylinder layout by lifting height. This paper is about a design based 3D software Pro/E with 8m high scissors lift platform, which gives a entire platform dimension with 1800 × 900mm2. A rated load of features so that the whole platform can be set up by two pairs of scissors refers to like products. The platform is designed to be folded away doors, to save more space for convenient storage. Lift platform uses a hydraulic driver, which runs smoothly, stably, and accuracy factors relative to high

Polarimetry noise in fiber-based optical coherence tomography instrumentation

High noise levels in fiber-based polarization-sensitive optical coherence tomography (PS-OCT) have broadly limited its clinical utility. In this study we investigate contribution of polarization mode dispersion (PMD) to the polarimetry noise. We develop numerical models of the PS-OCT system including PMD and validate these models with empirical data. Using these models, we provide a framework for predicting noise levels, for processing signals to reduce noise, and for designing an optimized system

EasyCellType: Marker-Based Cell-Type Annotation by Automatically Querying Multiple Databases

MOTIVATION: Cell label annotation is a challenging step in the analysis of single-cell RNA sequencing (scRNA-seq) data, especially for tissue types that are less commonly studied. The accumulation of scRNA-seq studies and biological knowledge leads to several well-maintained cell marker databases. Manually examining the cell marker lists against these databases can be difficult due to the large amount of available information. Additionally, simply overlapping the two lists without considering gene ranking might lead to unreliable results. Thus, an automated method with careful statistical testing is needed to facilitate the usage of these databases. RESULTS: We develop a user-friendly computational tool, EasyCellType, which automatically checks an input marker list obtained by differential expression analysis against the databases and provides annotation recommendations in graphical outcomes. The package provides two statistical tests, gene set enrichment analysis and a modified version of Fisher\u27s exact test, as well as customized database and tissue type choices. We also provide an interactive shiny application to annotate cells in a user-friendly graphical user interface. The simulation study and real-data applications demonstrate favorable results by the proposed method. Availability and implementation https://biostatistics.mdanderson.org/shinyapps/EasyCellType/; https://bioconductor.org/packages/devel/bioc/html/EasyCellType.html

Quantifying Epistemic Uncertainty in Deep Learning

Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, especially the epistemic component, in deep learning into procedural variability (from the training procedure) and data variability (from the training data), which is the first such attempt in the literature to our best knowledge. We then propose two approaches to estimate these uncertainties, one based on influence function and one on batching. We demonstrate how our approaches overcome the computational difficulties in applying classical statistical methods. Experimental evaluations on multiple problem settings corroborate our theory and illustrate how our framework and estimation can provide direct guidance on modeling and data collection effort to improve deep learning performance

Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks

Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but also from the training procedure that often injects substantial noises and biases. These hinder the attainment of statistical guarantees and, moreover, impose computational challenges on UQ due to the need for repeated network retraining. Building upon the recent neural tangent kernel theory, we create statistically guaranteed schemes to principally \emph{quantify}, and \emph{remove}, the procedural uncertainty of over-parameterized neural networks with very low computation effort. In particular, our approach, based on what we call a procedural-noise-correcting (PNC) predictor, removes the procedural uncertainty by using only \emph{one} auxiliary network that is trained on a suitably labeled data set, instead of many retrained networks employed in deep ensembles. Moreover, by combining our PNC predictor with suitable light-computation resampling methods, we build several approaches to construct asymptotically exact-coverage confidence intervals using as low as four trained networks without additional overheads