Gravity field modeling with spherical harmonics is a fundamental task in physical geodesy. In conventional solutions, all spherical harmonic (SH) components below a prescribed maximum degree and order are typically retained, even though some componen...
With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to pro...
The self-attention mechanism has revolutionized sequence modeling but suffers from quadratic computational complexity with respect to sequence length, limiting its applicability to long sequences. We propose Sparse Projection Attention (SPA), a novel...
In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource...
Three-dimensional (3D) geological models are essential for geological analysis and mineral resource estimation. Although conventional on-site survey methods, such as boreholes, provide local engineering geological information for 3D geological modeli...
Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study in...
The paper is concerned with the issues of modeling dynamic systems with interval parameters. In previous works, the authors proposed an adaptive interpolation algorithm for solving interval problems; the essence of the algorithm is the dynamic constr...
In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imagi...
Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper comm...
Multi-objective derivative-free optimization has broad applications in engineering fields, with its main challenge being how to solve problems efficiently without gradient information. This article proposes a novel multi-objective derivative-free opt...
When the number of available training views is limited, the small quantity of images results in insufficient generation of Gaussian ellipsoids, leading to an empty Gaussian model. This constraint limits the generation of Gaussian ellipsoids within 3D...
Heterogeneous reactions are chemical reactions that occur at the interfaces of multiple phases, and often show a nonlinear dynamical behavior due to the effect of the time-variant surface area with complex reaction mechanisms. It is important to spec...
This paper integrates
Human motion prediction is critical for applications such as autonomous driving and healthcare. However, existing methods struggle to balance diversity (varied motion patterns), fidelity (realistic and accurate motion), and computational efficiency....
Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of c...
We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal...
Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is...
In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid b...
The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, di...
A sparse coded mask modeling technique is proposed to increase the transmission capacity of an optical wireless link based on Li-Fi. The learning model for the discrete multitone (DMT) signal waveform is implemented using the proposed technique, whic...
Deep neural networks increasingly convert street-level imagery into quantitative measures of urban perception, but the cost of transformer backbones limits repeated inference over city-scale image collections. This study proposes an optimized Sparse...
Land cover dynamics play a critical role in understanding environmental changes, but accurately modeling these dynamics remains a challenge due to the complex interactions between temporal and spatial factors. In this study, we propose a novel tempor...
High-resolution remote-sensing semantic segmentation requires models to simultaneously capture global scene semantics and preserve fine-grained local structures. Although satellite-pretrained vision foundation models provide strong transferable repre...
Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on
Joint sparse representation has been widely used for hyperspectral image classification in recent years, however, the equal weight assigned to each neighbouring pixel is less realistic, especially for the edge areas, and one fixed scale is not approp...
Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse gene...
This study presents a universal reconfigurable hardware accelerator for efficient processing of sparse decision trees, artificial neural networks and support vector machines. The main idea is to develop a hardware accelerator that will be able to dir...
Segmenting CAD models into developable surface patches is a fundamental problem in geometric modeling and manufacturing-oriented applications. Existing approaches often rely on discrete Gaussian curvature estimation or Gauss map analysis; however, th...
The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the im...
Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularizati...
This paper investigates the sparse
The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations...
Automatic detection of deceptive intent in negotiation dialogue remains difficult because deceptive utterances are rare, context-dependent, and pragmatically subtle. This study develops a deployment-oriented evaluation pipeline for negotiation analyt...
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussi...
Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was sug...
The widespread adoption of massively parallel processors over the past decade has fundamentally transformed the landscape of high-performance computing hardware. This revolution has recently driven the advancement of FPGAs, which are emerging as an a...
In data-sparse areas, due to the lack of hydrogeological data, numerical groundwater models have some uncertainties. In this paper, a nested model and a multi-index calibration method are used to improve the reliability of a numerical groundwater mod...
Thermomechanical coupling effects significantly influence the dynamic response of flexible multibody systems operating in thermal environments. Accurate simulation using conventional projection-based reduced-order models remains challenging due to st...
The network traffic prediction (NTP) model can help operators predict, adjust, and control network usage more accurately. Meanwhile, it also reduces network congestion and improves the quality of the user service experience. However, the characterist...
Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from suc...
This study details the development of a lightweight and high performance model, targeting real-time object detection. Several designed features were integrated into the proposed framework to accomplish a light weight, rapid execution, and optimal per...
In response to the challenges of personal privacy protection in the dialogue models of the information era, this study introduces an innovative privacy-preserving dialogue model framework. This framework seamlessly incorporates Fully Homomorphic Encr...
This paper describes an event-based water quality model for sparsely gauged catchments. The model was cultivated in a robust way to cope with practical issues, such as limited available data and error propagation. A simplified model structure and few...
High-fidelity reservoir simulation is too costly for multi-query tasks such as history matching and production optimisation. Existing reduced-order models (ROMs) mitigate this cost but generally lack uncertainty quantification, spatial sparsity, and...
Background/Objectives: Functional tissue units (FTUs), including tertiary lymphoid structures (TLSs), blood vessels, and glands, encode localized immune, vascular, and epithelial organization in histopathology. Accurate quantification of these struct...
This paper explores a stochastic model of noisy observations with a sparse true signal structure. Such models arise in a wide range of applications, including signal processing, anomaly detection, and performance monitoring in telecommunication netwo...
Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability....
Zero inflation is pervasive across text mining, event log, and sensor analytics, and it often degrades the predictive performance of analytical models. Classical approaches, most notably the zero-inflated Poisson (ZIP) and zero-inflated negative bino...
A new text matching model based on dynamic gated sparse attention feature distillation with a faithful semantic preservation strategy is proposed to address the fact that text matching models are susceptible to interference from weakly relevant infor...
An effective three-dimensional (3D) data representation is required to assess the spatial distribution of the photovoltaic potential over urban building roofs and facades using 3D city models. Voxels have long been used as a spatial data representati...
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