AIMC Topic: Computer Graphics

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Graph Artificial Intelligence in Medicine.

Annual review of biomedical data science
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical dataset...

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement.

IEEE transactions on visualization and computer graphics
For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is la...

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey.

IEEE transactions on visualization and computer graphics
The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational compl...

Personalized Language Model Selection Through Gamified Elicitation of Contrastive Concept Preferences.

IEEE transactions on visualization and computer graphics
Language models are widely used for different Natural Language Processing tasks while suffering from a lack of personalization. Personalization can be achieved by, e.g., fine-tuning the model on training data that is created by the user (e.g., social...

Sensory Attenuation With a Virtual Robotic Arm Controlled Using Facial Movements.

IEEE transactions on visualization and computer graphics
When humans generate stimuli voluntarily, they perceive the stimuli more weakly than those produced by others, which is called sensory attenuation (SA). SA has been investigated in various body parts, but it is unclear whether an extended body induce...

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, t...

TacPrint: Visualizing the Biomechanical Fingerprint in Table Tennis.

IEEE transactions on visualization and computer graphics
Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players' habitual movement patterns and characteristics, allowing them to iden...

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Journal of translational medicine
BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction be...

Learning dynamic graph representations through timespan view contrasts.

Neural networks : the official journal of the International Neural Network Society
The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological properties within static graphs to create self-supervised signals, neglect...

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

IEEE journal of biomedical and health informatics
Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively...