With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significa...
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With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.
An axial wrinkle defect was observed in the inner wall of the sinking zone of a thick-wall steel tube processed by cold radial *** can evolve into *** present study focuses on the evolution of wrinkles and the effects...
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An axial wrinkle defect was observed in the inner wall of the sinking zone of a thick-wall steel tube processed by cold radial *** can evolve into *** present study focuses on the evolution of wrinkles and the effects of process parameters on them using a three-dimensional radial forging process finite element model,radial forging experiments,and surface morphology *** results indicated that the vertical section angle of the hammer die and the size of the tube blank significantly affect the evolution of *** height-to-width ratioλwas introduced to describe the morphology of *** had an approximately linear relationship with the radius reduction in the sinking *** was a linear correlation between the growth slope ofλand the axial to circumferential strain ratio|εr/εθ|,which can predict theλunder few process *** the 30SiMn2MoVA steel,at the junction of the forging and sinking zones,the threshold ofλof the wrinkle that can evolve into a fissure is approximately 1.123.
Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection bet...
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Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.
Many practical time series forecasting (TSF) tasks are plagued by data limitations. To alleviate this challenge, we design a data-level augmentation framework. It involves a time series generation (TSG) module and a s...
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Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...
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Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser *** to the lack of supplemental airflow above the chamber,the conventional build chamber wi...
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Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser *** to the lack of supplemental airflow above the chamber,the conventional build chamber with a single gas inlet exhibits a pronounced tendency for gas to flow upward near the *** phenomenon results in the formation of a large vortex within the build *** vortex leads to the chaotic motion trajectory of the spatter in the build *** design defects of the existing build chamber based on dual gas inlets are shown in this *** established a coupled computational fluid dynamics-discrete phase model(CFD-DPM)model to optimize the build chamber by adjusting the position and structure of the second gas *** homogeneity of the flow is increased with a distance of 379 mm between the two inlets and a wider-reaching second *** Coanda effect is also crucial in the spatter-removal *** Coanda effect is reduced by modifying the right sidewall of the build chamber and increasing the pressure difference between the inlet and ***,we found that the spatter-removal rate rose from 8.9%to 76.1%between the conventional build chamber with a single gas inlet and the optimized build chamber with two gas inlets.
Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured inform...
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Voronoi diagrams on triangulated surfaces based on the geodesic metric play a key role in many applications of computer *** methods of constructing such Voronoi diagrams generally depended on having an exact geodesic ...
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Voronoi diagrams on triangulated surfaces based on the geodesic metric play a key role in many applications of computer *** methods of constructing such Voronoi diagrams generally depended on having an exact geodesic ***,exact geodesic computation is time-consuming and has high memory usage,limiting wider application of geodesic Voronoi diagrams(GVDs).In order to overcome this issue,instead of using exact methods,we reformulate a graph method based on Steiner point insertion,as an effective way to obtain geodesic ***,since a bisector comprises hyperbolic and line segments,we utilize Apollonius diagrams to encode complicated structures,enabling Voronoi diagrams to encode a medial-axis surface for a dense set of boundary *** on these strategies,we present an approximation algorithm for efficient Voronoi diagram construction on triangulated *** also suggest a measure for evaluating similarity of our results to the exact *** our GVD results are constructed using approximate geodesic distances,we can get GVD results similar to exact results by inserting Steiner points on triangle *** results on many 3D models indicate the improved speed and memory requirements compared to previous leading methods.
Image-based indoor localization using smartphones has gained wide popularity in daily life. Existing solutions usually utilize visual landmarks and extract fingerprint features for localization, so the fingerprint den...
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Image-based indoor localization using smartphones has gained wide popularity in daily life. Existing solutions usually utilize visual landmarks and extract fingerprint features for localization, so the fingerprint density directly affects the localization accuracy. However, the collection of dense fingerprints with high resolution is extremely labor-intensive during site surveys and faces heavy computation/storage overhead during matching fingerprint features. Meanwhile, the efficient extraction of features restricts users to shoot with specific poses, otherwise the localization accuracy will be seriously decreased. To tackle these challenges, we propose an Automated Real-time Generative Image Localization System, named ARGILS. Our core idea is to replace the dense sampling with the cross-sparse sampling and generate fingerprint features for missing locations, and finally obtain locations quickly and efficiently through feature orthogonal decomposition. Specifically, cross-sparse sampling refers to fingerprint sampling to achieve full scene coverage and facilitate the generation of missing fingerprint features. We design a distance-constrained generative adversarial network to generate features for missing locations to extend the database, ensuring high localization accuracy with sparse sampling. Additionally, we develop an orthogonal feature extraction method that decomposes image features into horizontal and vertical directions in 2D space. Then two-stage retrieval is used in both directions to obtain the precise location. To address the impact of obstacles, we introduce a scanning localization that improves localization robustness through keyframe filtering and clustering. We have implemented ARGILS and performed extensive real-world evaluations. Experiment results show that when reducing 75% site survey effort, the average location error of ARGILS is around 2.5m in a shopping mall, 48% lower than state-of-the-art methods. ARGILS can also efficiently speed up the
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