Abstract: Call graphs play an important role in different contexts, such as profiling and vulnerability propagation analysis. Generating call graphs in an efficient manner can be a challenging task ...

A practical roadmap for data science beginners, covering fundamentals, key libraries, projects, and advanced skills. It focuses on real-world learning, avoiding common mistakes, and building job-ready ...

Abstract: Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading to ...

A "coordinated developer-targeting campaign" is using malicious repositories disguised as legitimate Next.js projects and technical assessments to trick victims into executing them and establish ...

Numerov’s numerical method is developed in a didactic way by using Python in its Jupyter Notebook version 6.0.3 for three different quantum physical systems: the hydrogen atom, a molecule governed by ...

This is a PyTorch implementation of the VGRNN model as described in our paper: E. Hajiramezanali*, A. Hasanzadeh*, N. Duffield, K. R. Narayanan, M. Zhou, and X. Qian, Variational Graph Recurrent ...

In the evolving landscape of wireless communication, particularly in Beyond 5G (B5G) systems, channel coding must meet increasingly demanding requirements for reliability, adaptability, and low ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

For the latest documentation, see ReadTheDocs. !! Note: The models must be downloaded and installed separately. See the Installation Instructions. FAA_Aligner (Fast_Align Algorithm), based on the ISI ...

We report an educational tool for the upper level undergraduate quantum chemistry or quantum physics course that uses a symbolic approach via the PySyComp Python library. The tool covers both ...