Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers f...
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WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body...
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Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom,it has advantages that have led to the use of obstruction-free implementations for softwar...
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Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom,it has advantages that have led to the use of obstruction-free implementations for software transactional memory(STM)and in anonymous and fault-tolerant distributed ***,existing work can only verify obstruction-freedom of specific data structures(e.g.,STM and list-based algorithms).In this paper,to fill this gap,we propose a program logic that can formally verify obstruction-freedom of practical implementations,as well as verify linearizability,a safety property,at the same *** also propose informal principles to extend a logic for verifying linearizability to verifying *** this approach,the existing proof for linearizability can be reused directly to construct the proof for both linearizability and ***,we have successfully applied our logic to verifying a practical obstruction-free double-ended queue implementation in the first classic paper that has proposed the definition of obstruction-freedom.
The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has be...
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The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited di...
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The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited diversity of image style,distortion of detailed texture,unbalanced color tone,and lengthy training *** address these issues,we propose an asymmetric pre-training and fine-tuning(APF)-GAN model.
Thermal stable intermetallic particles are important for the heat resistance of magnesium(Mg)*** this work,many lath-like particles formed in α-Mg grains of a Mg-8Gd-3Sm-0.7Al casting alloy when heat-treated at 873 *...
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Thermal stable intermetallic particles are important for the heat resistance of magnesium(Mg)*** this work,many lath-like particles formed in α-Mg grains of a Mg-8Gd-3Sm-0.7Al casting alloy when heat-treated at 873 ***-resolution high-angle annular dark field scanning transmission electron microscopy(HAADF-STEM) characterizations indicate that most of them are Mg-containing Al_(2)(Gd,Sm),with the atomic ratio of Mg:Al:(Gd,Sm) being ~1:1:1;a small part of them with relatively wider thickness are long-period stacking ordered(LPSO) phases simultaneously containing both 14H and 18R *** followed common orientation relationships with Mg matrix as those reported in previous *** addition,many Mg laths were observed in the primary blocky Al_(2)(Gd,Sm) phase at grain boundaries,where the atomic ratio of Al:(Gd,Sm) in the Al_(2)(Gd,Sm) matrix was 2:***,density functional theory(DFT) calculations illustrated the detail structure of the re-constructed Mg/Al_(2)RE interface and simultaneously deduced the underlying reason for the re-dissolution of the newly formed Mg-containing Al_(2)(Gd,Sm) plates in α-Mg matrix.
Accurate segmentation of brain tumors is crucial for their early diagnosis. Multi-modal MRI images can provide complementary information, which is essential for improving segmentation accuracy. This paper presents a b...
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The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivi...
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Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas ***,the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to...
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Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas ***,the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic *** rock physical modeling and reservoir parameter analyses conducted in this study,a more suitable indicator for estimating gas enrichment,termed the gas content indicator,has been *** indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight ***,a new seismic amplitude variation versus offset(AVO)equation is derived to directly extract reservoir properties,such as the gas content indicator and porosity,from prestack seismic *** accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz *** ensure reliable estimations of reservoir properties from partial angle-stacked seismic data,the proposed AVO equation is reformulated within the elastic impedance inversion *** estimated gas content indicator and porosity exhibit favorable agreement with logging data,suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas ***,the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.
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