RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment

Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the huge number of diseases. The complexity of symptoms and the shortage of specialized doctors with relevant experience make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents powered by large language models (LLMs) have demonstrated notable improvements across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical exams. However, current agent frameworks lack adaptation for real-world clinical scenarios, especially those involving the intricate demands of rare diseases. To address these challenges, we present RareAgents, the first multi-disciplinary team of LLM-based agents tailored to the complex clinical context of rare diseases. RareAgents integrates advanced planning capabilities, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents surpasses state-of-the-art domain-specific models, GPT-4o, and existing agent frameworks in both differential diagnosis and medication recommendation for rare diseases. Furthermore, we contribute a novel dataset, MIMIC-IV-Ext-Rare, derived from MIMIC-IV, to support further advancements in this field.

DLM-VMTL:A Double Layer Mapper for heterogeneous data video Multi-task prompt learning

In recent years, the parameters of backbones of Video Understanding tasks continue to increase and even reach billion-level. Whether fine-tuning a specific task on the Video Foundation Model or pre-training the model designed for the specific task, incurs a lot of overhead. How to make these models play other values than their own tasks becomes a worthy question. Multi-Task Learning(MTL) makes the visual task acquire the rich shareable knowledge from other tasks while joint training. It is fully explored in Image Recognition tasks especially dense predict tasks. Nevertheless, it is rarely used in video domain due to the lack of multi-labels video data. In this paper, a heterogenous data video multi-task prompt learning (VMTL) method is proposed to address above problem. It's different from it in image domain, a Double-Layers Mapper(DLM) is proposed to extract the shareable knowledge into visual promptS and align it with representation of primary task. Extensive experiments prove that our DLM-VMTL performs better than baselines on 6 different video understanding tasks and 11 datasets.

Synchronous Multi-modal Semantic CommunicationSystem with Packet-level Coding

Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction of multimodal semantics have not been well studied. Due to the independent design of semantic encoders, synchronizing multimodal features in both the semantic and time domains is a challenging problem. In this paper, we take the facial video and speech transmission as an example and propose a Synchronous Multimodal Semantic Communication System (SyncSC) with Packet-Level Coding. To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics, and we propose a semantic codec that achieves similar quality of reconstruction and synchronization with lower bandwidth, compared to traditional methods. To protect semantic packets under the erasure channel, we propose a packet-Level Forward Error Correction (FEC) method, called PacSC, that maintains a certain visual quality performance even at high packet loss rates. Particularly, for text packets, a text packet loss concealment module, called TextPC, based on Bidirectional Encoder Representations from Transformers (BERT) is proposed, which significantly improves the performance of traditional FEC methods. The simulation results show that our proposed SyncSC reduce transmission overhead and achieve high-quality synchronous transmission of video and speech over the packet loss network.

Query Expansion for Patent Searching using Word Embedding and Professional Crowdsourcing

The patent examination process includes a search of previous work to verify that a patent application describes a novel invention. Patent examiners primarily use keyword-based searches to uncover prior art. A critical part of keyword searching is query expansion, which is the process of including alternate terms such as synonyms and other related words, since the same concepts are often described differently in the literature. Patent terminology is often domain specific. By curating technology-specific corpora and training word embedding models based on these corpora, we are able to automatically identify the most relevant expansions of a given word or phrase. We compare the performance of several automated query expansion techniques against expert specified expansions. Furthermore, we explore a novel mechanism to extract related terms not just based on one input term but several terms in conjunction by computing their centroid and identifying the nearest neighbors to this centroid. Highly skilled patent examiners are often the best and most reliable source of identifying related terms. By designing a user interface that allows examiners to interact with the word embedding suggestions, we are able to use these interactions to power crowdsourced modes of related terms. Learning from users allows us to overcome several challenges such as identifying words that are bleeding edge and have not been published in the corpus yet. This paper studies the effectiveness of word embedding and crowdsourced models across 11 disparate technical areas.