In network data analysis, one of the important problems is determining if two collections of networks are drawn from the same distribution. This problem can be modeled in the framework of two-sample hypothesis testing. Several graph-based two-sample...
This paper critically examines the weighted sample average approximation (wSAA) framework, a widely used approach in prescriptive analytics for managing uncertain optimization problems featuring non-linear objectives. Our research pinpoints a key def...
We propose a method of enlarging the training dataset for a single-sample-per-person (SSPP) face recognition problem. The appearance of the human face varies greatly, owing to various intrinsic and extrinsic factors. In order to build a face recognit...
Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint...
Techniques used in forensic analysis have to find an appropriate balance between fairness to the defendant and efficiency and safety for forensic personnel. Safety issues are particularly acute in analyzing large seizures of bags likely containing he...
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments...
The classification performance of an inference model trained in a supervised manner depends substantially on the size and quality of the labeled training data. The characteristics of the underlying data distribution significantly impact the generaliz...
Let Λ⊆Rd be a discrete uniformly separated subset. In the unweighted case and p=2, Landau obtained the necessary conditions for sampling and interpolation of functions in Paley–Wiener space in terms of the upper and lower uniform de...
The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based class...
Heart disease is a serious threat to human health. Accurate prediction is very important for disease prevention and treatment. The purpose of this study is to establish a more suitable prediction model of heart disease. Based on LightGBM, we have dee...
Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and tempo...
23 May 2022
Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distributio...
Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negat...
Determination of time-weighted average (TWA) concentrations of volatile organic compounds (VOCs) in air using solid-phase microextraction (SPME) is advantageous over other sampling techniques, but is often characterized by insufficient accuracies, pa...
The clustering ensemble method has attracted much attention because it can improve the stability and robustness of single clustering methods. Among them, similarity-matrix-based methods or graph-based methods have had a wide range of applications in...
Information regarding newly added construction land can be extracted from high-resolution remote sensing images. The retrieval accuracy of land cover changes across the country has improved, and the illegal use of land is actively monitored. To addre...
Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we...
The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simulta...
The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification o...
After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current vi...
Globally, aquaculture contributes to sustainable food and nutritional stability. However, stress conditions constitute a major threat affecting farmed-fish welfare and fish farms’ performances. In this regard, the present study was aimed at mea...
This paper introduces ‘SkeletonCLIP++’, an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, ‘Weighted Frame Integration&rsqu...
Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the...
Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing met...
The bootstrap method is often used for variance estimation in sample surveys with a stratified multistage sampling design. It is typically implemented by producing a set of bootstrap weights that is made available to users and that accounts for the c...
11 July 2024
(1) Background: As Kaplan–Meier (KM) analysis is limited to single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for either progression-free survival (PFS) or overall survival (OS). This discards e...
The conditioning theory of the ML-weighted least squares and ML-weighted pseudoinverse problems is explored in this article. We begin by introducing three types of condition numbers for the ML-weighted pseudoinverse problem: normwise, mixed, and comp...
Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (ta...
Mainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimi...
The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS,...
An efficient method such as ranked set sampling is used for estimating the population parameters when the actual observation measurement is expensive and complicated. In this paper, we consider the problem of estimating the two-parameter xgamma (TPXG...
Short-term load forecasting is a prerequisite and basis for power system planning and operation and has received extensive attention from researchers. To address the problem of concept drift caused by changes in the distribution patterns of electrici...
Federated learning is a distributed learning framework designed to protect user privacy, widely applied across various domains. However, existing federated learning algorithms face challenges, including slow convergence, significant loss fluctuations...
A DataMatrix (DM) code is an automatic identification barcode based on a combination of coding and image processing. Traditional DM code sampling methods are mostly based on simple segmentation and sampling of a DM code. However, the obtained DM code...
This article completes our studies on the formal construction of asymptotic approximations for statistics based on a random number of observations. Second order Chebyshev–Edgeworth expansions of asymptotically normally or chi-squared distribute...
(1) Background: This study aimed to evaluate whether the implementation of a modified blood-sampling protocol, which focused on need-based laboratory testing and minimized venous sampling by replacing it with point-of-care testing (POCT) via capillar...
Acoustic emission (AE) monitoring is an effective tool to quantify the dynamic damage that may cause heavy casualties and huge property losses in rock engineering. Instead of traditional failure evaluation methods, in this paper, the coal failure mec...
Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical...
The automatic semantic segmentation of point cloud data is important for applications in the fields of machine vision, virtual reality, and smart cities. The processing capability of the point cloud segmentation method with PointNet++ as the baseline...
The global μ-synchronization problem for nonlinear multi-weighted complex dynamical networks with uncertain parameter perturbation and mixed time-varying delays is investigated in this paper. Unlike other existing works, all delays, including samp...
Robust statistical analysis is essential for advancing evidence-based nutrition research, particularly when investigating the complex relationships between dietary exposure and health outcomes. Multiple regression is a widely used analytical techniqu...
Understanding the extent and timing of forest disturbances and their impacts is critical to formulating effective management and policy responses. Broad-scale inventory programs provide key estimates of forest parameters that indicate the extent and...
Oats are susceptible to contamination by Fusarium mycotoxins, including deoxynivalenol (DON), zearalenone (ZEN), and T-2/HT-2 toxins, posing food safety risks. This study analyses the variation in levels of 14 mycotoxins in 200 individual oat kernels...
In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov–Nagumo mean, generalizes the classical mean and appears in many disciplines,...
The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region. This...
Pain is a reliable indicator of health issues; it affects patients’ quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monit...
The Minimum Vertex Weighted Coloring (MinVWC) problem is an important generalization of the classic Minimum Vertex Coloring (MinVC) problem which is NP-hard. Given a simple undirected graph G=(V,E), the MinVC problem is to find a coloring s.t. any pa...
Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal d...
To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics need to be characterized, but its atomistic details are hard to obtain both experimentally and computationally. Here, we review...
The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentati...
of 256