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Found 23 papers, 3 papers with code
Knowledge Editing for Large Language Model with Knowledge Neuronal Ensemble
A knowledge neuronal ensemble represents a group of neurons encoding specific knowledge, thus mitigating the issue of frequent parameter modification caused by coupling in parameter localization.
k-HyperEdge Medoids for Clustering Ensemble
In this paper, the clustering ensemble is formulated as a k-HyperEdge Medoids discovery problem and a clustering ensemble method based on k-HyperEdge Medoids that considers the characteristics of the above two types of clustering ensemble methods is proposed.
Profit Maximization In Arbitrage Loops
Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop.
An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges
In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in the form of both arbitrage loops (e. g. the arbitrage path starts from token A and back to token A again in the end, A, B,..., A) and non-loops (e. g. the arbitrage path starts from token A and stops at a different token N, A, B,..., N).
MDeRainNet: An Efficient Neural Network for Rain Streak Removal from Macro-pixel Images
Furthermore, to improve the generalization performance of our network on real-world rainy scenes, we propose a novel semi-supervised learning framework for our MDeRainNet, which utilizes multi-level KL loss to bridge the domain gap between features of synthetic and real-world rain streaks and introduces colored-residue image guided contrastive regularization to reconstruct rain-free images.
Unified End-to-End V2X Cooperative Autonomous Driving
V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology.
Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics
This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability.
An Error-Matching Exclusion Method for Accelerating Visual SLAM
In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system.
A Feature Matching Method Based on Multi-Level Refinement Strategy
Feature matching is a fundamental and crucial process in visual SLAM, and precision has always been a challenging issue in feature matching.
A Robust Error-Resistant View Selection Method for 3D Reconstruction
To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method.
Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An Advanced Data Engineering Workflow and Comprehensive Datasets for Proof-of-Stake Incentives
Ethereum 2. 0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention.
Region Feature Descriptor Adapted to High Affine Transformations
To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification.
A Highlight Removal Method for Capsule Endoscopy Images
The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge.
Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet
Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in three-dimensional space is a challenging but practical problem.
Distributed Robust Learning-Based Backstepping Control Aided with Neurodynamics for Consensus Formation Tracking of Underwater Vessels
This paper addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises.
Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter
This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots.
EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units
Here, we propose the EEG opto-processor based on diffractive photonic computing units (DPUs) to effectively process the extracranial and intracranial EEG signals and perform epileptic seizure detection.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle Aided with Bioinspired Neural Dynamics
In comparative studies, the proposed combined hybrid control strategy has ensured control signals smoothness, which is critical in real world applications, especially for an unmanned underwater vehicle that needs to operate in complex underwater environments.
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process
However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem.
Consensus Formation Tracking for Multiple AUV Systems Using Distributed Bioinspired Sliding Mode Control
Consensus formation tracking of multiple autonomous underwater vehicles (AUVs) subject to nonlinear and uncertain dynamics is a challenging problem in robotics.
All-optical graph representation learning using integrated diffractive photonic computing units
Photonic neural networks perform brain-inspired computations using photons instead of electrons that can achieve substantially improved computing performance.
Deformable Object Tracking with Gated Fusion
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.
