This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning capabilities for the task.We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the critical role of the knowledge injection module,as the removal of this module resulted in a significant drop in F1 score. The experimental results demonstrate that the proposed method not only effectively improves the accuracy of disease diagnosis but also enhances the interpretability of the predictions, providing more reliable support and evidence for clinical diagnosis.

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.

How can we mine frequent path regularities from a graph with edge labels and vertex attributes? The task of association rule mining successfully discovers regular patterns in item sets and substructures. Still, to our best knowledge, this concept has not yet been extended to path patterns in large property graphs. In this paper, we introduce the problem of path association rule mining (PARM). Applied to any \emph{reachability path} between two vertices within a large graph, PARM discovers regular ways in which path patterns, identified by vertex attributes and edge labels, co-occur with each other. We develop an efficient and scalable algorithm PIONEER that exploits an anti-monotonicity property to effectively prune the search space. Further, we devise approximation techniques and employ parallelization to achieve scalable path association rule mining. Our experimental study using real-world graph data verifies the significance of path association rules and the efficiency of our solutions.

The complex nature of disease mechanisms and the variability of patient symptoms present significant obstacles in developing effective diagnostic tools. Although machine learning has made considerable advances in medical diagnosis, its decision-making processes frequently lack transparency, which can jeopardize patient outcomes. This underscores the critical need for Explainable AI (XAI), which not only offers greater clarity but also has the potential to significantly improve patient care. In this literature review, we conduct a detailed analysis of analyzing XAI methods identified through searches across various databases, focusing on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. The literature search revealed the application of 9 trending XAI algorithms in the field of healthcare and highlighted the pros and cons of each of them. Thus, the article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.

Multimodal Large Language Models (MLLMs) have demonstrated great zero-shot performance on visual question answering (VQA). However, when it comes to knowledge-based VQA (KB-VQA), MLLMs may lack human commonsense or specialized domain knowledge to answer such questions and require obtaining necessary information from external knowledge sources. Previous works like Retrival-Augmented VQA-v2 (RAVQA-v2) focus on utilizing as much input information, such as image-based textual descriptions and retrieved knowledge, as possible to improve performance, but they all overlook the issue that with the number of input tokens increasing, inference efficiency significantly decreases, which contradicts the demands of practical applications. To address this issue, we propose Retrieval-Augmented MLLM with Compressed Contexts (RACC). RACC learns to compress and aggregate retrieved contexts, from which it generates a compact modulation in the form of Key-Value (KV) cache. This modulation is then used to adapt the downstream frozen MLLM, thereby achieving effective and efficient inference. RACC achieves a state-of-the-art (SOTA) performance of 62.9% on OK-VQA. Moreover, it significantly reduces inference latency by 22.0%-59.7% compared to the prominent RAVQA-v2. Abundant experiments show RACC's broad applicability. It is compatible with various off-the-shelf MLLMs and can also handle different knowledge sources including textual and multimodal documents.

Background: Health 3.0 allows decision making to be based on longitudinal data from multiple institutions, from across the patient's healthcare journey. In such a distributed setting, blockchain smart contracts can act as neutral intermediaries to implement trustworthy decision making. Objective: In a distributed setting, transmitted data will be structured using standards (such as HL7 FHIR) for semantic interoperability. In turn, the smart contract will require interoperability with this standard, implement a complex communication setup (e.g., using oracles), and be developed using blockchain languages (e.g., Solidity). We propose the encoding of smart contract logic using a high-level semantic Knowledge Graph, using concepts from the domain standard. We then deploy this semantic KG on blockchain. Methods: Off-chain, a code generation pipeline compiles the KG into a concrete smart contract, which is then deployed on-chain. Our pipeline targets an intermediary bridge representation, which can be transpiled into a specific blockchain language. Our choice avoids on-chain rule engines, with unpredictable and likely higher computational cost; it is thus in line with the economic rules of blockchain. Results: We applied our code generation approach to generate smart contracts for 3 health insurance cases from Medicare. We discuss the suitability of our approach - the need for a neutral intermediary - for a number of healthcare use cases. Our evaluation finds that the generated contracts perform well in terms of correctness and execution cost ("gas") on blockchain. Conclusions: We showed that it is feasible to automatically generate smart contract code based on a semantic KG, in a way that respects the economic rules of blockchain. Future work includes studying the use of Large Language Models (LLM) in our approach, and evaluations on other blockchains.

The rapid advancement of artificial intelligence systems has brought the challenge of AI alignment to the forefront of research, particularly in complex decision-making and task execution. As these systems surpass human-level performance in sophisticated problems, ensuring their alignment with human values, intentions, and ethical guidelines becomes crucial. Building on previous work in explanation generation for human-agent alignment, we address the more complex dynamics of multi-agent systems and human-AI teams. This paper introduces a novel approach to model alignment through weak-to-strong generalization in the context of language models. We present a framework where a strong model facilitates the improvement of a weaker model, bridging the gap between explanation generation and model alignment. Our method, formalized as a facilitation function, allows for the transfer of capabilities from advanced models to less capable ones without direct access to extensive training data. Our results suggest that this facilitation-based approach not only enhances model performance but also provides insights into the nature of model alignment and the potential for scalable oversight of AI systems.

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as ``hallucination'', lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field. In order to track recent progress in this field, we set up a repository at \url{https://github.com/pengboci/GraphRAG-Survey}.

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, a novel user interface that bridges the gap between non-expert users and complex database querying processes. SQLucid addresses existing limitations by integrating visual correspondence, intermediate query results, and editable step-by-step SQL explanations in natural language to facilitate user understanding and engagement. This unique blend of features empowers users to understand and refine SQL queries easily and precisely. Two user studies and one quantitative experiment were conducted to validate SQLucid's effectiveness, showing significant improvement in task completion accuracy and user confidence compared to existing interfaces. Our code is available at https://github.com/magic-YuanTian/SQLucid.

This paper presents an adaptive visual servoing framework for robotic on-orbit servicing (OOS), specifically designed for capturing tumbling satellites. The vision-guided robotic system is capable of selecting optimal control actions in the event of partial or complete vision system failure, particularly in the short term. The autonomous system accounts for physical and operational constraints, executing visual servoing tasks to minimize a cost function. A hierarchical control architecture is developed, integrating a variant of the Iterative Closest Point (ICP) algorithm for image registration, a constrained noise-adaptive Kalman filter, fault detection and recovery logic, and a constrained optimal path planner. The dynamic estimator provides real-time estimates of unknown states and uncertain parameters essential for motion prediction, while ensuring consistency through a set of inequality constraints. It also adjusts the Kalman filter parameters adaptively in response to unexpected vision errors. In the event of vision system faults, a recovery strategy is activated, guided by fault detection logic that monitors the visual feedback via the metric fit error of image registration. The estimated/predicted pose and parameters are subsequently fed into an optimal path planner, which directs the robot's end-effector to the target's grasping point. This process is subject to multiple constraints, including acceleration limits, smooth capture, and line-of-sight maintenance with the target. Experimental results demonstrate that the proposed visual servoing system successfully captured a free-floating object, despite complete occlusion of the vision system.