In this paper, we define and characterize the embedding of edges and higher-order entities in directed graphs (digraphs) and relate these embeddings to those of nodes. Our edge-centric approach consists of the following: (a) Embedding line digraphs (...
In the domain of video recognition, video transformers have demonstrated remarkable performance, albeit at significant computational cost. This paper introduces TSNet, an innovative approach for dynamically selecting informative tokens from given vid...
A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This appr...
In the pursuit of real-time object detection with constrained computational resources, the optimization of neural network architectures is paramount. We introduce novel sparsity induction methods within the YOLOv4-Tiny framework to significantly impr...
Federated learning (FL) demonstrates significant potential in Industrial Internet of Things (IIoT) settings, as it allows multiple institutions to jointly construct a shared learning model by exchanging model parameters or gradient updates without th...
Persistent Homology (PH) is a method of Topological Data Analysis that characterizes the topological structure of a space. Unfortunately, the computation of PH for high-dimensional and big data is not possible due to the exponential growth of the con...
Conventional machine learning (ML) and deep learning approaches require sharing customers’ sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a signifi...
The performance of multiagent systems depends heavily on information flow. As agents are populated more densely, some information flow can be redundant. Thus, there can be a tradeoff between communication overhead and control performance. To address...
A digital image compression framework based on nonlinear partial differential equations (PDEs) is proposed in this research article. First, a feature keypoint-based sparsification algorithm is proposed for the image coding stage. The interest keypoin...
One of the development directions of new-generation mobile communications is using multiple-input multiple-output (MIMO) channels with a large number of antennas. This requires the development and utilization of new approaches to signal detection in...
In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing th...
Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low...
Homomorphic encryption (HE) secure aggregation requires coordinate-aligned updates, whereas personalized federated learning (PFL) lets each client share a different parameter subset. Top-k sparsification widens this gap: per-client supports must be u...
Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To...
This paper illustrates the development of a recursive QR technique for the analysis of transient events, such as disruptions or scenario evolution, in fusion devices with three-dimensional conducting structures using an integral eddy current formulat...
Attempts to discover knowledge through data are gradually becoming diversified to understand complex aspects of social phenomena. Graph data analysis, which models and analyzes complex data as graphs, draws much attention as it combines the latest ma...
To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector r...
Many inference problems relate to a dynamical system, as represented by dx/dt = f (x), where x ∈ ℝn is the state vector and f is the (in general nonlinear) system function or model. Since the time of Newton, researchers have pondered the problem of s...
With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the...
Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new f...
Consider the dynamical system
To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal spa...
In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Proj...
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...
Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability. Although deep learning models achieve superior...
Equipment fault prediction in maglev train systems poses substantial challenges: fault events are inherently rare, class distributions are severely imbalanced, and individual equipment units are subject to complex spatial and functional couplings tha...
Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architect...
Transfer learning enables pretrained deep networks to adapt to new tasks but typically preserves their original computational structure, leaving much of the inference cost unchanged. Structured pruning can reduce this complexity; however, introducing...
In this study, we investigate the effect of weight thresholding (WT) on the robustness of real-world complex networks. Here, we assess the robustness of networks after WT against various node attack strategies. We perform WT by removing a fixed fract...
Background: Prostate cancer (PCa) is one of the most prevalent malignancies in men, and active surveillance (AS) is the recommended management strategy for low- and favourable intermediate-risk disease. Predicting which patients will progress during...
Graph-based multi-view clustering has gained significant attention in recent years due to its superior ability to reveal clustering structures. However, existing methods often incur high computational costs when capturing local information and overlo...
FasterAI is a PyTorch-based library, aiming to facilitate the use of deep neural network compression techniques, such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick imple...
Random Fourier mapping (RFM) in kernel adaptive filters (KAFs) provides an efficient method to curb the linear growth of the dictionary by projecting the original input data into a finite-dimensional space. The commonly used measure in RFM-based KAFs...
Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming i...
During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcemen...
Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates th...
Stock prediction requires the joint modeling of temporal dynamics and cross-stock dependence. Existing graph-based and hypergraph-based forecasting methods often process spatial relation modeling and temporal evolution in separate stages, which may w...
To address the limitations of conventional mobile robot path planning results in terms of geometric continuity, kinematic executability, and adaptability to dynamic environments, this study proposes a reference trajectory generation method oriented t...
Federated learning (FL) enables privacy-preserving collaborative model training across edge devices without exposing raw user data, but it is vulnerable to privacy leakage through shared model updates, making differential privacy (DP) essential. Exis...
This paper presents a unified framework for high-precision dynamic target tracking that combines phase-map-based visual servoing with Model Predictive Control (MPC). Phase maps obtained from fringe projection provide dense, subpixel geometric feedbac...
Active voltage control (AVC) in active distribution networks coordinates the reactive power outputs of distributed inverters to maintain bus voltages within secure limits. Although multi-agent reinforcement learning (MARL) shows promise for AVC, curr...
Simultaneous Localization and Mapping (SLAM) is a fundamental methodology that underpins autonomous navigation in robotic systems. Conventional approaches perform well in static environments but rely on the assumption of environmental rigidity, which...
Visual Internet-of-Things (IoT) cameras and institution-controlled edge gateways increasingly collect artwork images in museums, galleries, and heritage sites. Centralizing these images can expose collection contents, exhibition layouts, and contextu...
Accurate multi-horizon solar power forecasting is vital for modern power distribution networks, yet standard deep learning models face limitations due to weather stochasticity and prohibitive computational requirements on resource-constrained edge de...
The sparsity of images in a certain transform domain or dictionary has been exploited in many image processing applications. Both classic transforms and sparsifying transforms reconstruct images by a linear combination of a small basis of the transfo...
Prompt tuning visual-language models (VLMs) for specialized tasks often involves leveraging task-specific textual tokens, which can tailor the pre-existing, broad capabilities of a VLM to more narrowly focused applications. This approach, exemplified...
With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whit...
Infrared small target detection (IRSTD) is a fundamental task in remote sensing-based surveillance and early warning systems. However, extremely small target size, low signal-to-noise ratio, and complex background clutter make reliable detection high...
To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PA...
With the in-depth development of digital twin technology in modern agriculture, smart pig farm construction is evolving from basic environmental modeling toward refined, bio-behavior-driven approaches. This study addresses the non-standard body confi...
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