Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …

With the increasing interest in pedestrian detection, pedestrian datasets have also been the
subject of research in the past decades. However, most existing datasets focus on a color …

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud
services, and serverless functions have immediately become a new middleware for building …

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis,
rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

X-Stream is a system for processing both in-memory and out-of-core graphs on a single
shared-memory machine. While retaining the scatter-gather programming model with state …

Memorization is an essential functionality that enables today's machine learning algorithms
to provide a high quality of learning and reasoning for each prediction. Memorization gives …

Many practical computing problems concern large graphs. Standard examples include the
Web graph and various social networks. The scale of these graphs-in some cases billions of …