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Most feds to get 1% pay raise in 2026

A yet-to-be-determined number of law enforcement officers will see larger increases in line with the 2026 military pay raise. The post Most feds to get 1% pay raise in 2026 first appeared on Federal News Network.
http://news.poseidon-us.com/TMnhbW

pdf-parser: All Streams, (Sun, Aug 31st)

A user reported a bug in pdf-parser: when dumping all filtered streams, an error would occur:
http://news.poseidon-us.com/TMnhHc

Transforming scientific discovery with Microsoft Azure and NVIDIA

Scientific innovation speeds up with Azure’s cloud and NVIDIA’s GPUs—see how researchers are transforming discovery. The post Transforming scientific discovery with Microsoft Azure and NVIDIA appeared first on Microsoft Azure Blog.
http://news.poseidon-us.com/TMnPX1

Protecting Azure Infrastructure from silicon to systems

At Microsoft, secure design begins at the foundation of our computing stack—the silicon level—and extends through every layer of the cloud. The post Protecting Azure Infrastructure from silicon to systems appeared first on Microsoft Azure Blog.
http://news.poseidon-us.com/TMnPWX

Week in review: 300k+ Plex Media Server instances still vulnerable to attack, exploited Git RCE flaw

Here’s an overview of some of last week’s most interesting news, articles, interviews and videos: 300k+ Plex Media Server instances still vulnerable to attack via CVE-2025-34158 Over 300,000 internet-facing Plex Media Server instances are still vulnerable to attack via CVE-2025-34158, a critical vulnerability for which Plex has issued a fix for earlier this month, Censys has warned. Git vulnerability leading to RCE is being exploited by attackers (CVE-2025-48384) CVE-2025-48384, a recently patched vulnerability in the … More → The post Week in review: 300k+ Plex Media Server instances still vulnerable to attack, exploited Git RCE flaw appeared first on Help Net Security.
http://news.poseidon-us.com/TMnMHy

New AI model predicts which genetic mutations truly drive disease

Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions of electronic health records and routine lab tests like cholesterol or kidney function, the system produces “ML penetrance” scores that place genetic risk on a spectrum rather than a simple yes/no. Some variants once thought dangerous showed little real-world impact, while others previously labeled uncertain revealed strong disease links.
http://news.poseidon-us.com/TMmvGL