Devices and Vaccines

Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions

The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerging paradigm: collaborative learning between on-device small model and cloud-based large model, which promises low-latency, cost-efficient, and personalized intelligent services while preserving user privacy. We provid...

Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection

Personalization of Large Language Models (LLMs) is important in practical applications to accommodate the individual needs of different mobile users. Due to data privacy concerns, LLM personalization often needs to be locally done at the user's mobile device, but such on-device personalization is constrained by both the limitation of on-device compute power and insufficiency of user's personal d...

A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports

Timing of clinical events is central to characterization of patient trajectories, enabling analyses such as process tracing, forecasting, and causal...

Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning

Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller...

Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance

Small language models (SLMs) support efficient deployments on resource-constrained edge devices, but their limited capacity compromises inference pe...

Exploring Backdoor Attack and Defense for LLM-empowered Recommendations

The fusion of Large Language Models (LLMs) with recommender systems (RecSys) has dramatically advanced personalized recommendations and drawn extens...

Graph-Based Prediction Models for Data Debiasing

Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthc...

An Early Experience with Confidential Computing Architecture for On-Device Model Protection

Deploying machine learning (ML) models on user devices can improve privacy (by keeping data local) and reduce inference latency. Trusted Execution E...

Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare

Generative agents have been increasingly used to simulate human behaviour in silico, driven by large language models (LLMs). These simulacra serve a...

Automated identification of fall-related injuries in unstructured clinical notes.

Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses cha...

Evaluating the Impact of the EU AI Act on Medical Device Regulation.

Artificial Intelligence (AI) is increasingly incorporated into medical devices, revolutionizing diagnostics, treatment planning, and patient monitorin...

Evaluation of the Performance of a Large Language Model to Extract Signs and Symptoms from Clinical Notes.

Large language models (LLMs) have increasingly been used to extract critical information from unstructured clinical notes, which often include importa...

Inter-event Interval Microscopy for Event Cameras

Event cameras, an innovative bio-inspired sensor, differ from traditional cameras by sensing changes in intensity rather than directly perceiving in...

Enhancing Leaf Disease Classification Using GAT-GCN Hybrid Model

Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. As innovative agricultural p...

Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller

The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the treatment and diagnosis of cardiovascular diseases (CV...

Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models

Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, is a critical task with applications in pharmacovig...

Sim-is-More: Randomizing HW-NAS with Synthetic Devices

Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approxima...

A Scalable Predictive Modelling Approach to Identifying Duplicate Adverse Event Reports for Drugs and Vaccines

The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associatio...

Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy

Purpose: Autonomous systems in mechanical thrombectomy (MT) hold promise for reducing procedure times, minimizing radiation exposure, and enhancing ...

RECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains u...