Two models are introduced to study the problem of matching two correlated graphs when some of the nodes are corrupt. In the weak model, a random subset of nodes in one or both graphs can interact randomly with their n...
Two models are introduced to study the problem of matching two correlated graphs when some of the nodes are corrupt. In the weak model, a random subset of nodes in one or both graphs can interact randomly with their network. For this model, it is shown that no estimator can correctly recover a positive fraction of the corrupt nodes. Necessary conditions for any estimator to correctly identify and match all the uncorrupt nodes are derived, and it is shown that these conditions are also sufficient for the k-core estimator. In the strong model, an adversarially selected subset of nodes in one or both graphs can interact arbitrarily with their network. For this model, detection of corrupt nodes is impossible. Even so, we show that if only one of the networks is compromised, then under appropriate conditions, the maximum overlap estimator can correctly match a positive fraction of nodes albeit without explicitly identifying them.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which impacts social interaction, communication, and behavior. This study introduces a self-supervised learning approach for ASD prediction using restin...
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ISBN:
(数字)9798331528348
ISBN:
(纸本)9798331528355
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which impacts social interaction, communication, and behavior. This study introduces a self-supervised learning approach for ASD prediction using resting-state functional MRI (rs-fMRI) scans from the ABIDE I dataset, employing contrastive and non-contrastive models. The contrastive model enhances learning by maximizing agreement between augmented views of the same scan, while the non-contrastive model reconstructs input data through an autoencoder. Both models, pre-trained on ImageNet, were fine-tuned for ASD classification using Resnet18. Our results show that the contrastive model achieved 97.6% accuracy, with a precision of 95.7%, recall of 99.2%, and F1 score of 97.4%. The non-contrastive model reached 95.4% accuracy, with precision, recall, and F1 scores of 99.3%, 96.46%, and 97.82%. These findings highlight the performance of self-supervised learning in ASD diagnosis, showcasing robust pattern-capturing capabilities for neuroimaging data.
This paper presents a low cost Si IGBT circuit breaker for protection of the Wolfspeed 10 kV XHV-9 half bridge SiC MOSFET module. Based on the 10 kV SiC module short circuit (SC) protection requirement, both current l...
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The challenge of low-resolution video action recognition task lies in recovering and extracting feature representations that can effectively capture action characteristics with limited semantic information. In this pa...
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Coordinating communication and control is a key component in the stability and performance of networked multi-agent systems. While single user networked control systems have gained a lot of attention within this domai...
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RGB and thermal salient object detection (RGB-T SOD) aims to accurately locate and segment salient objects in aligned visible and thermal image pairs. However, existing methods often struggle to produce complete masks...
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HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often...
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Non-verbal prosodic patterns in speech have the power to communicate a speaker’s emotional state, health condition, gender and even personality traits, such as trustworthiness. While research has mainly focused on th...
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Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph...
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