AIMC Topic: Computer Graphics
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A GPU-accelerated fuzzy method for real-time CT volume filtering.
PloS one
Jan 2, 2025
During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be...
DropNaE: Alleviating irregularity for large-scale graph representation learning.
Neural networks : the official journal of the International Neural Network Society
Dec 6, 2024
Large-scale graphs are prevalent in various real-world scenarios and can be effectively processed using Graph Neural Networks (GNNs) on GPUs to derive meaningful representations. However, the inherent irregularity found in real-world graphs poses cha...
PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial ob...
SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal Prediction.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for in...
How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical perception literature has shown that human chart takeaways are sensitive...
AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical ...
How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?
IEEE transactions on visualization and computer graphics
Nov 25, 2024
In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisio...
HuBar: A Visual Analytics Tool to Explore Human Behavior Based on fNIRS in AR Guidance Systems.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reaso...
Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts.
IEEE transactions on visualization and computer graphics
Nov 25, 2024
Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially a...
Graph Batch Coarsening framework for scalable graph neural networks.
Neural networks : the official journal of the International Neural Network Society
Nov 22, 2024
Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as node-wise, layer-wise, and subgraph sampling, have been proposed to allevia...
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