The course introduces basic data structures and algorithmic techniques that allow students to solve computational problems on the most important data types, such as sequences, sets, trees, and graphs.

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...

This paper presents a theoretical framework for modeling cloud resources and their financial relationships as weighted graphs, with a modified Dijkstra's algorithm to identify cost-efficient resource ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

To cite the full Handbook online, please use: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editor(s). Cochrane Handbook for Systematic Reviews ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

This study presents a useful deep learning-based inter-protein contact prediction method named PLMGraph-Inter which combines protein language models and geometric graphs. The evidence supporting the ...

TL;DR: Normally vector indexing is thought of as a common implementation for Generative AI. While this is true at a very rudimentary level, this blog discusses how to use vector indexes in Neo4j to ...

Researchers in the field of human-technology interaction have demonstrated how a custom-built 'data-to-music' algorithms can help to better understand complex data. The transformation of digital data ...

Evolution has led to natural algorithms that regulate collective behavior in many biological systems. Here, we investigate natural algorithms that solve the shortest path problem, a basic optimization ...