Latest AI and machine learning research in practice management for healthcare professionals.
Dialogue data has been a key source for understanding learning processes, offering critical insights into how students engage in collaborative discussions and how these interactions shape their knowledge construction. The advent of Large Language Models (LLMs) has introduced promising opportunities for advancing qualitative research, particularly in the automated coding of dialogue data. However...
Among the new techniques of Versatile Video Coding (VVC), the quadtree with nested multi-type tree (QT+MTT) block structure yields significant coding gains by providing more flexible block partitioning patterns. However, the recursive partition search in the VVC encoder increases the encoder complexity substantially. To address this issue, we propose a partition map-based algorithm to pursue fas...
Summary: Long non-coding RNAs (lncRNAs) exert their functions by cooperating with other molecules including proteins and DNA. Triplexes, formed thro...
As semantic communication (SemCom) attracts growing attention as a novel communication paradigm, ensuring the security of transmitted semantic infor...
The delivery of mental healthcare through psychotherapy stands to benefit immensely from developments within Natural Language Processing (NLP), in p...
Traditional predictive coding networks, inspired by theories of brain function, consistently achieve promising results across various domains, exten...
The emergence of generative AI chatbots such as ChatGPT has prompted growing public and academic interest in their role as informal mental health su...
Point cloud compression has become a crucial factor in immersive visual media processing and streaming. This paper presents a new open dataset calle...
Recent advancements in deep learning-based joint source-channel coding (deepJSCC) have significantly improved communication performance, but their h...
Recent advancements in large language models (LLMs) have revolutionized their ability to handle single-turn tasks, yet real-world applications deman...
Learned image compression (LIC) has recently made significant progress, surpassing traditional methods. However, most LIC approaches operate mainly ...
Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabiliti...
Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, is a critical task with applications in pharmacovig...
Extracting medical history entities (MHEs) related to a patient's chief complaint (CC), history of present illness (HPI), and past, family, and soci...
Medical knowledge graphs (KGs) are essential for clinical decision support and biomedical research, yet they often exhibit incompleteness due to kno...
This paper investigates distributed joint source-channel coding (JSCC) for correlated image semantic transmission over wireless channels. In this se...
Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit ev...
While video compression based on implicit neural representations (INRs) has recently demonstrated great potential, existing INR-based video codecs s...
Implicit Neural Representations (INRs) are increasingly recognized as a versatile data modality for representing discretized signals, offering benef...
Traditional image compression methods aim to faithfully reconstruct images for human perception. In contrast, Coding for Machines focuses on compres...