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Artificial Intelligence
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Article
Unsupervised domain adaptation via feature transfer learning based on elastic embedding
Supervised classification algorithms usually require a large quantity of well-labeled samples for training to achieve satisfied performance. Nevertheless, it is prohibitively difficult to create such datasets ...
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An evolutionary feature selection method based on probability-based initialized particle swarm optimization
Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing particle swarm optimization-based feature selection algorithm...
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Learning Feature Restoration Transformer for Robust Dehazing Visual Object Tracking
In recent years, deep-learning-based visual object tracking has obtained promising results. However, a drastic performance drop is observed when transferring a pre-trained model to changing weather conditions,...
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Open AccessResearch Progress on Bio-inspired Flap**-Wing Rotor Micro Aerial Vehicle Development
Flap**-wing rotor (FWR) is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and landing. This unique design combines active flap** motion and passive wing rotation around a vert...
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MBGNet:Multi-branch boundary generation network with temporal context aggregation for temporal action detection
Temporal action detection is an important and fundamental video understanding task that aims to locate the temporal regions where human actions or events may occur and to identify the classes of actions in unt...
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An optimization algorithm combining local exploitation and global exploration for computationally expensive problems
An adaptive ensemble of surrogates assisted optimization algorithm combining local exploitation and global exploration (CLEGE) for computationally expensive problems is presented in this work. At the first lev...
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PROUD: PaRetO-gUided diffusion model for multi-objective generation
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, t...
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UnseenSignalTFG: a signal-level expansion method for unseen acoustic data based on transfer learning
This study introduces a transfer learning-based approach for signal-level expansion of unseen acoustic signal data, aiming to address the scarcity of acoustic signal data in a specific domain. By establishing ...
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Multidta: drug-target binding affinity prediction via representation learning and graph convolutional neural networks
The prediction of Drug-Target Interactions (DTI) plays a pivotal role in drug repositioning research. While recent years have witnessed the proliferation of neural network-based methods for Drug-Target Affinit...
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Free editing of Shape and Texture with Deformable Net for 3D Caricature Generation
2D caricature editing has shown superior performance. However, 3D exaggerated caricature face (ECF) modeling with flexible shape and texture editing capabilities is far from achieving satisfactory high-quality...
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Efficient frictional contacts for soft body dynamics via ADMM
This paper addresses the longstanding challenge of soft body dynamics with frictional contact through a novel combination of Projective Dynamics for elasticity simulation and Alternating Direction Method of Mu...
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Predict industry merger waves utilizing supply network information
Predicting merger waves has been a classical yet challenging problem. In this paper, we propose approaches to predict industry merger waves relying on an integrated dataset including financial statements and s...
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Toward accurate and realistic garment texture transfer with attention to details
The categories and styles of garment are constantly diversifying. For designers, it is a pressing issue to evaluate how different fabrics will look on them in a timely manner for users. In this paper, we prese...
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A digital pen-based writing state recognition algorithm for student performance assessment
Technology-enhanced learning is an irresistible trend in intelligent education. However, most digital pen-based studies focus on handwriting character recognition, writing behavior research are extremely scarc...
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Open AccessHybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot
This paper investigates the motion control of the heavy-duty Bionic Caterpillar-like Robot (BCR) for the maintenance of the China Fusion Engineering Test Reactor (CFETR). Initially, a comprehensive nonlinear m...
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DAMAF: dual attention network with multi-level adaptive complementary fusion for medical image segmentation
Transformers have been widely applied in medical image segmentation due to their ability to establish excellent long-distance dependency through self-attention. However, relying solely on self-attention makes ...
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HAMIATCM: high-availability membership inference attack against text classification models under little knowledge
Membership inference attack opens up a newly emerging and rapidly growing research to steal user privacy from text classification models, a core problem of which is shadow model construction and members distri...
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Open AccessA Single Image High-Perception Super-Resolution Reconstruction Method Based on Multi-layer Feature Fusion Model with Adaptive Compression and Parameter Tuning
We propose a simple image high-perception super-resolution reconstruction method based on multi-layer feature fusion model with adaptive compression and parameter tuning. The aim is to further balance the high...
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A working memory model based on recurrent neural networks using reinforcement learning
Numerous electrophysiological experiments have reported that the prefrontal cortex (PFC) is involved in the process of working memory. PFC neurons continue firing to maintain stimulus information in the delay ...
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Action recognition method based on a novel keyframe extraction method and enhanced 3D convolutional neural network
At present, action recognition is a challenging task in the field of computer vision. Traditional action recognition methods cannot fully extract the spatiotemporal features of actions in video. To address the...
