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Found 9 papers, 3 papers with code
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images.
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation
CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
Feature-based Transformer with Incomplete Multimodal Brain Images for Diagnosis of Neurodegenerative Diseases
Benefiting from complementary information, multimodal brain imaging analysis has distinct advantages over single-modal methods for the diagnosis of neurodegenerative diseases such as Alzheimer’s disease.
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
In this work, we aim to offer a better understanding of SSM for item recommendation.
Non-reciprocal energy transfer through the Casimir effect
The Casimir effect can dominate the interaction between microstructures at small separations and has been utilized to realize nonlinear oscillation, quantum trapping, phonon transfer, and dissipation dilution.
Quantum Physics Mesoscale and Nanoscale Physics Optics
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario.
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions
To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatial-temporal deformations of micro-expression sequence.
