AIMC Topic: Computer Graphics

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Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer.

Journal of translational medicine
Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical...

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

IEEE transactions on visualization and computer graphics
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a...

Eliciting Model Steering Interactions From Users via Data and Visual Design Probes.

IEEE transactions on visualization and computer graphics
Visual and interactive machine learning systems (IML) are becoming ubiquitous as they empower individuals with varied machine learning expertise to analyze data. However, it remains complex to align interactions with visual marks to a user's intent f...

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...