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WPEC
'Secret' CIA bulk data collection raises questions about surveillance laws
HUNT VALLEY, Md. (TND) — The Central Intelligence Agency has a “secret” bulk data collection program that could “incidentally” include information about Americans, according to two U. S. senators. ‘WARRANTLESS BACKDOOR SEARCHES OF AMERICANS’ What kind of information? That’s classified. But concerns are being raised the CIA has hidden details about the program.
HTTPS://CBS12.COM/SECRET-CIA-BULK-DATA-COLLECTION-RAISES-QUESTIONS-ABOUT-SURVEILLANCE-LAWS-CENTRAL-INTELLIGENCE-AGENCY-FBI-SPYING-SPY-ON-AMERICANS-NATIONAL-SECURITY-AGENCY-NSA-EDWARDS-SNOWDEN-RUSSIA-WYDEN-HEINRICH-REAGAN-1981Tags: bulk collection
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ZDNET
NSA reform report: End bulk metadata program, no more software backdoors
Two of the 40 recommendations — which the White House can still ignore — hints that private companies should be allowed to report data access figures.
HTTPS://WWW.ZDNET.COM/ARTICLE/NSA-REFORM-REPORT-END-BULK-METADATA-PROGRAM-NO-MORE-SOFTWARE-BACKDOORS/Tags: NSA, bulk collection
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bioRxiv
High-resolution image reconstruction with latent diffusion models from human brain activity
Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector of image Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straight-forward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at this https URL. ### Competing Interest Statement The authors have declared no competing interest.
HTTPS://WWW.BIORXIV.ORG/CONTENT/10.1101/2022.11.18.517004V1Tags: brain imaging, fmri, stable diffusion, ai, cognitive neuroscience, Freedom of thought, privacy law
THIS Article is 10 years old but the reforms it mentions are still relevant, just like how Flint, MI still does not have clean drinking water.
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