In the realm of discrete-time modeling for gene regulatory networks, significant focus has been placed on addressing the time lags inherent in the process of DNA transcription to RNA and the subsequent translation of ...
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This article focuses on the problem of fixed-time adaptive fuzzy control for a class of nontriangular nonlinear systems with unknown control directions under the event-triggered framework. To tackle the algebraic loop...
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With the enhancement of Large Language Models (LLMs) performance, the demand for reliable methods to distinguish AI-generated text from human-written content is steadily growing. This study presents a novel detection ...
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Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications w...
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Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications with Noise(DBSCAN)and hierarchical clustering that can easily fall into the local optimal ***,convex clustering is vulnerable to the occurrence of outlier features,as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate *** accurately identify outlier features,this paper decomposes data into a clustering structure component and a normalized component that captures outlier *** from existing convex clustering evaluating features with the exact measurement,the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and *** solve the proposed model,we design an efficient algorithm and prove the global convergence of the *** on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.
We introduce a novel BiMoeFormer based on the Transformer architecture for 3D human motion prediction. Previous approaches primarily focus on the relationships between body joints in human poses, while neglecting thei...
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Dimensionality reduction (DR) is a frequently used method to handle data with many dimensions. This article presents a novel approach to nonlinear dimensionality reduction, referred to as supervised data-dependent ker...
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Amorphous alloys without crystalline defects(dislocation,crystal boundary)are ideal hydrophobic coating materials due to their low surface *** work used a synergistic method of detonation spraying and surface modifica...
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Amorphous alloys without crystalline defects(dislocation,crystal boundary)are ideal hydrophobic coating materials due to their low surface *** work used a synergistic method of detonation spraying and surface modification to obtain the superhydrophobic Febased amorphous coatings with high hardness and dense structure on the Q 235 *** results showed that the water contact angles(WCA)of the superhydrophobic coating was 160°±3.6°,and water droplets could bounce off the superhydrophobic coating surface,illustrating the excellent self-cleaning performance of ***,the corrosion current density(i_(corr))of the superhydrophobic coating further decreased by 2 orders of magnitude down to8.008×10^(-8)A·cm^(-2)compared to the as-deposited coating with 5.473×10^(-6)A·cm^(-2);the corrosion potential(E_(corr))of the superhydrophobic coating shifted by 34 mV to the positive side compared with that of the as-deposited coating(-310 mV).Likewise,the impedance modulus|Z|values of the superhydrophobic coating increased by nearly2 orders of magnitude up to 1×10^(5.6)compared to the asdeposited coating with 1×10^(3.8).Even through lasting immersion in NaCl for 10 days,|Z|values of the superhydrophobic coating were still much higher than those of the as-deposited *** superhydrophobic Fe-based amorphous coatings could respond to their applications under extreme conditions due to their excellent hydrophobicity and self-cleaning properties,illustrating their promising future in aerospace,automotive,and machinery industries.
The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection m...
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The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was significant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classifi*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).
Federated Learning (FL) has gained prominence for collaborative training across multiple devices without data sharing. However, traditional FL overlooks two crucial aspects: collaborative fairness and privacy protecti...
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Given the high annotation costs and ethical considerations associated with medical images, leveraging a limited number of annotated samples for Few-Shot Medical Image Segmentation (FSMIS) has become increasingly preva...
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