Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to b...
We propose a principal component analysis (PCA)-based approach to quantify (the node dissimilarity index, NDI) the extent of dissimilarity among nodes in a network with respect to values incurred for a suite of node-l...
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Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the ...
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Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities. While the theory of such models is well understood, their aggregatio...
A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of ...
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Metasurface-based holograms,or metaholograms,offer unique advantages including enhanced imaging quality,expanded field of view,compact system size,and broad operational ***-channel metaholograms,capable of switching b...
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Metasurface-based holograms,or metaholograms,offer unique advantages including enhanced imaging quality,expanded field of view,compact system size,and broad operational ***-channel metaholograms,capable of switching between multiple projected images based on the properties of illuminating light such as state of polarization and angle of incidence,have emerged as a promising solution for realizing switchable and dynamic holographic ***,existing designs typically grapple with challenges such as limited multiplexing channels and unwanted crosstalk,which severely constrain their practical ***,we present a new type of waveguidebased multi-channel metaholograms,which support six independent and fully crosstalk-free holographic display channels,simultaneously multiplexed by the spin and angle of guided incident light within the glass *** employ a k-space translation strategy that allows each of the six distinct target images to be selectively translated from evanescent-wave region to the center of propagation-wave region and projected into free space without crosstalk,when the metahologram is under illumination of a guided light with specific spin and azimuthal *** addition,by tailoring the encoded target images,we implement a three-channel polarization-independent metahologram and a two-channel full-color(RGB)***,the number of multiplexing channels can be further increased by expanding the k-space’s central-period region or combing the k-space translation strategy with other multiplexing techniques such as orbital angular momentum *** work provides a novel approach towards realization of high-performance and compact holographic optical elements with substantial information capacity,opening avenues for applications in AR/VR displays,image encryption,and information storage.
Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced bias...
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Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on identifying suicidal intentions in posts from the SuicideWatch subreddi...
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The widely used ReLU is favored for its hardware efficiency, as the implementation at inference is a one bit sign case, yet suffers from issues such as the "dying ReLU" problem, where during training, neuron...
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Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional in...
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Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.
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