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Verifying Rust cryptography in SymCrypt, from standards to code

Microsoft Research Blog · microsoft.com · By Son Ho, Cédric Fournet, Antoine Delignat-Lavaud, Samuel Lee, Jason Fisher, Jessica Krynitsky · July 13, 2026

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

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GPT-Lab Sep 3, 2026

Adaptive AI Agents in Construction Workflows

Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.

GPT-Lab Aug 5, 2026

How NGINX Vulnerabilities Have Evolved

What 17 years of advisories reveal about recurring weaknesses, risky modules,patching speed, and static-analysis limits The short version: NGINX usually releases small fixes at or before public disclosure. Its biggest recurring problem is memory safety, while newer protocol code such as HTTP/2 and HTTP/3 has become a major area of risk. Why look at NGINX? NGINX runs as a web server, reverse proxy, and gateway across a large part of the Internet. A flaw in request parsing, protocol handling, or m…

Related papers

#machine learning Open access Mar 2024

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.

Yang Yue, Shu Li, Yihua Cheng et al. · 15 citations

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.

Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al. · 13 citations · ⚡1

DRHIN: An Integrated and Interactive Web Server for Drug Repositioning

The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.

Bowei Zhao, Dongxu Li, Yue Yang et al. · 10 citations · ⚡1

Q‐GEM: Quantum Chemistry Knowledge Fusion Geometry‐Enhanced Molecular Representation for Property Prediction

The Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination.

Zhijiang Yang, Liangliang Wang, Tengxin Huang et al. · 6 citations

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