Since the turn of the century,innovations in science and technology have been in full swing across the globe.A new wave of scientific inventions and industrial transformations are constantly reshaping the economic str...
Since the turn of the century,innovations in science and technology have been in full swing across the globe.A new wave of scientific inventions and industrial transformations are constantly reshaping the economic structure of the *** the forefront of this trend is the digitalization of *** combined with a new form of productivity such as artificial intelligence (AI),this wave of scientific and technological revolution is driving digital transformations and adoptions of smart devices throughout society.
Due to the consideration of cost of maintaining a well production, the abandoned wells in Gulf of Mexico are getting more and more, so there are more needs to repurpose these abandoned wells as storage of carbons, was...
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The maximum likelihood threshold (MLT) of a graph ☆G☆ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. We recent...
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The maximum likelihood threshold (MLT) of a graph ☆G☆ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. We recently proved a new characterization of the MLT in terms of rigidity-theoretic properties of ☆G☆. This characterization was then used to give new combinatorial lower bounds on the MLT of any graph. We continue this line of research by exploiting combinatorial rigidity results to compute the MLT precisely for several families of graphs. These include graphs with at most nine vertices, graphs with at most 24 edges, every graph sufficiently close to a complete graph and graphs with bounded degrees.
Modern dynamical systems are generally non-linear, which make traditional linear controllers infeasible in some cases. To cope with the need for controlling such modern systems, researchers have developed robust nonli...
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Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** ...
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Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret *** image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding *** this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet *** wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding *** the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret *** lowresolution secret images are combined using densemodules to obtain a high-quality secret *** experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various *** proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical *** conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing *** ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various *** research and experimentation can explore the algorith
The approach of TOPSIS was employed to consider the personnel selection problem. The value of the selection criteria is presented by fuzzy numbers. Finally, ranking of alternatives was performed for selection the best...
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Eigenfunctions and eigenvalues of physical systems and engineering structures can reveal many of the system’s fundamental features and, therefore, become a basis for the study of inverse problems. In this series of p...
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The emergence of order in nature manifests in different phenomena, with synchronization being one of the most representative examples. Understanding the role played by the interactions between the constituting parts o...
The emergence of order in nature manifests in different phenomena, with synchronization being one of the most representative examples. Understanding the role played by the interactions between the constituting parts of a complex system in synchronization has become a pivotal research question bridging network science and dynamical systems. Particular attention has been paid to the emergence of chimera states, where subsets of synchronized oscillations coexist with asynchronous ones. Such coexistence of coherence and incoherence is a perfect example where order and disorder can persist in a long-lasting regime. Although considerable progress has been made in recent years to understand such coherent and (coexisting) incoherent states, how they manifest in real-world networks remains to be addressed. Based on a pattern formation mechanism, in this paper, we shed light on the role that non-normality, a ubiquitous structural property of real networks, has in the emergence of several diverse dynamical phenomena, e.g., amplitude chimeras or oscillon patterns. Specifically, we demonstrate that the prevalence of source or leader nodes in networks leads to the manifestation of phase chimera states. Throughout the paper, we emphasize that non-normality poses ongoing challenges to global synchronization and is instrumental in the emergence of chimera states.
Stroke is one of the leading causes of disability and death worldwide, underscoring the need for early and precise diagnosis to enhance patient outcomes. Artificial intelligence (AI) has emerged as a promising tool fo...
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ISBN:
(数字)9798350391213
ISBN:
(纸本)9798350391220
Stroke is one of the leading causes of disability and death worldwide, underscoring the need for early and precise diagnosis to enhance patient outcomes. Artificial intelligence (AI) has emerged as a promising tool for stroke diagnosis, offering the potential to significantly improve diagnostic accuracy and efficiency. This paper reviews recent AI-based methods for stroke diagnosis, focusing on available datasets with various modalities and the AI techniques employed. We analyze the limitations of existing approaches, including data and model biases, lack of generalizability, and transparency issues. We then propose future directions for AI in stroke diagnosis, aiming to address these identified gaps, including the development of more robust and adaptable models, and improved data integration strategies. Finally, we emphasize the importance of ethical considerations and regulatory frameworks to ensure the responsible implementation of AI in this critical field. A GitHub repository is created to include recent codes and platforms that are available for stroke diagnosis.
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