At a diner just off the freeway north of Sacramento, a mostly white crowd listened intently as it learned how to "save America" by leaning on the same laws that enshrined the rights of Black voters 60 years ago. Over mugs of coffee and plates of pot roast smothered in gravy, attendees in MAGA and tea party gear took notes about the landmark Voting Rights Act and studied the U.S. Constitution. They peppered self-proclaimed "election integrity" activist Marly Hornik with questions about how to become skilled citizen observers monitoring California poll workers. The nearly 90 people gathered in the diner in February were there to understand how they can do their part in a plan to sue California to block certification of the 2024 election results unless the state can prove that ballots were cast only by people eligible to vote. If any votes are found to be ineligible, Hornik explained, then all voters are being disenfranchised -- just like those decades ago who couldn't vote because of their race.

This paper presents a new method for image compression by neural networks. First, we show that we can use neural networks in a pyramidal framework, yielding the so-called PCA pyramids. Then we present an image compression method based on the PCA pyramid, which is similar to the Laplace pyramid and wavelet transform. Some experimental results with real images are reported. Finally, we present a method to combine the quantization step with the learning of the PCA pyramid. 1 Introduction In the past few years, a lot of work has been done on using neural networks for image compression, d. e.g.

This paper presents a new method for image compression by neural networks. First, we show that we can use neural networks in a pyramidal framework, yielding the so-called PCA pyramids. Then we present an image compression method based on the PCA pyramid, which is similar to the Laplace pyramid and wavelet transform. Some experimental results with real images are reported. Finally, we present a method to combine the quantization step with the learning of the PCA pyramid. 1 Introduction In the past few years, a lot of work has been done on using neural networks for image compression, d. e.g.

First, we show that we can use neural networks in a pyramidal framework,yielding the so-called PCA pyramids. Then we present an image compression method based on the PCA pyramid, which is similar to the Laplace pyramid and wavelet transform. Some experimental results with real images are reported. Finally, we present a method to combine the quantization step with the learning of the PCA pyramid. 1 Introduction In the past few years, a lot of work has been done on using neural networks for image compression, d .

Although they then cite a "slow, rigorous proof" by K. Hornik et al. that implies the superiority of neural systems Put our 27 years experience placing technical In practice, since neural nets and Turing-equivalent systems professionals to work for you. If you earn over $35,000, we have a better, more rewarding job for you... right depend on the arithmetic of real numbers rather now. Call (301) 231-9000 or send your resume in infinite precision fashion. These computations can be confidence to: Dept. The details of what this superiority entails remain unclear.