How AI Coding Tools Crushed the Endpoint Security Fortress
Security vendors have spent years building up defenses around the endpoint, but one researcher says AI coding tools have brought the walls down.
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Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work
arXiv:2505.24246v4 Announce Type: replace Abstract: As AI systems are increasingly tested and deployed in open-ended and high-stakes domains, crowdworkers are often tasked with responsible AI (RAI) content work. These tasks include labeling violent content, moderating disturbing text, or simulating harmful behavior for red teaming exercises to shape AI system behaviors. While prior research efforts have highlighted the risks to worker well-being associated with RAI content work, far less attention has been paid to how these risks are communicated to workers by task designers or individuals who design and post RAI tasks. Existing transparency frameworks and guidelines, such as model cards, datasheets, and crowdworksheets, focus on documenting model information and dataset collection process
The surprise winners of Trump's immigration wars
<p>If you're an immigrant worker in the U.S. right now, you've got a better shot landing a visa as a farmer than a tech worker, researcher, <a href="https://www.axios.com/2026/03/24/trump-visa-policy-immigrant-doctors" target="_blank">doctor or nurse</a>.</p><p><strong>Why it matters</strong>: The Trump administration's crackdown on <a href="https://www.axios.com/2025/10/04/trump-h1b-visa-immigration-147" target="_blank">H-1B visas</a> is crushing sectors that rely on high-skilled immigrant workers, while seasonal programs for farm workers have gotten a pass.</p><hr><ul><li>"There was all of this hype that Trump would be friendly toward high-skilled immigration and harder on low-skilled immigration," said Sam Peak, an immigration expert at the Economic Innovation Group. </li><li>The opposi
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A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments
arXiv:2412.12646v3 Announce Type: replace Abstract: Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstructions and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve fully coherent distributed dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and t
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