Latest AI and machine learning research in practice management for healthcare professionals.
Feature Coding for Machines (FCM) aims to compress intermediate features effectively for remote intelligent analytics, which is crucial for future intelligent visual applications. In this paper, we propose a Multiscale Feature Importance-based Bit Allocation (MFIBA) for end-to-end FCM. First, we find that the importance of features for machine vision tasks varies with the scales, object size, an...
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an eff...
We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional commu...
Image compression technology eliminates redundant information to enable efficient transmission and storage of images, serving both machine vision an...
Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex networ...
Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable t...
Context: Generative Artificial Intelligence (GenAI) is transforming much of software development, yet its application in software architecture is st...
The advent of Large Language Models (LLMs) is reshaping education, particularly in programming, by enhancing problem-solving, enabling personalized ...
Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable...
Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the proc...
The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally alt...
Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their...
This paper reflects on the process of performing Thematic Analysis with Large Language Models (LLMs). Specifically, the paper deals with the problem...
The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Pred...
Multimodal in-context learning (ICL) has emerged as a key capability of Large Vision-Language Models (LVLMs), driven by their increasing scale and a...
MOTIVATION: As the biological roles and disease implications of non-coding RNAs continue to emerge, the need to thoroughly characterize previously une...
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthc...
3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation. Much research has been focused on reducing its storage requireme...
BACKGROUND: International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD-...
Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie fro...