3 accepted papers
We propose a fast and dynamic algorithm for Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that efficiently supports online updates. Traditional DBSCAN algorithms, designed for batch processing, become computationally expensive when applied to dynamic datasets, particularly in…
Graph Neural Networks (GNNs) are a powerful class of machine learning models with applications in recommender systems, drug discovery, social network analysis, and computer vision. One challenge with their implementation is that GNNs often take large-scale graphs as inputs, which imposes significant…
Motivated by DNA-based data storage, we study the erasure shuffling channel. This channel takes as input multiple strings, which are passed through an erasure channel and then shuffled out of order. We show that the capacity of this channel, for a large set of channel parameters, is given by the cap…