The US Built a Site to Ensure Fair Access to Public Lands. Recreation.gov was supposed to make access to public lands more equitable and streamlined. It's a few minutes before 8 am Mountain Time on March 16, the day that river permit cancellations are released on Recreation.gov, the federal website for public land reservations. Rec.gov, as it's commonly called, administers everything from river permits and timed entrance fees at the most popular national parks to campground reservations on remote sites belonging to the Bureau of Land Management, and a lot of people are recreating on public land these days. There were 11 million reservations on the site in 2024, up significantly from 3.5 million reservations reported in 2019. At the center of it all is an unlikely player in the outdoor recreation space: The site is operated by the government contractor Booz Allen Hamilton, a corporation known more for cybersecurity than rafting trips. Early each year, outdoor enthusiasts gear up for Recreation.gov's annual lotteries for some of the most iconic experiences in the country: a river trip down Idaho's Middle Fork of the Salmon River, which flows through the Frank Church River of No Return Wilderness. Backcountry permits to hike into the Wave, an otherworldly rock formation in Arizona's Paria Canyon-Vermilion Cliffs Wilderness. Overnight stays in the rugged, lake-studded Enchantments, in Washington's Okanogan-Wenatchee National Forest. Odds of getting a desirable Middle Fork permit are around 2 percent.

This Game Boy clone is an uncompromising approach to the ultimate gamer nostalgia. But there are better options out there. A tad pricey for an old game console. There are better options for tinkerers. Buying it puts money in the pocket of the killer chatbots guy.

Deep Neural Networks (DNNs) rely on inherent fluctuations in their internal parameters (weights and biases) to effectively navigate the complex optimization landscape and achieve robust performance. While these fluctuations are recognized as crucial for escaping local minima and improving generalization, their precise relationship with fundamental hyperparameters remains underexplored. A significant knowledge gap exists concerning how the learning rate, a critical parameter governing the training process, directly influences the dynamics of these neural fluctuations. This study systematically investigates the impact of varying learning rates on the magnitude and character of weight and bias fluctuations within a neural network. We trained a model using distinct learning rates and analyzed the corresponding parameter fluctuations in conjunction with the network's final accuracy. Our findings aim to establish a clear link between the learning rate's value, the resulting fluctuation patterns, and overall model performance. By doing so, we provide deeper insights into the optimization process, shedding light on how the learning rate mediates the crucial exploration-exploitation trade-off during training. This work contributes to a more nuanced understanding of hyperparameter tuning and the underlying mechanics of deep learning.

In 1907, Duncan MacDougall put dying patients on a scale. William Blake's 1805 illustration for Scottish poet Robert Blair's poem The Grave imagines the soul rising from the body at death. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a little complicated to weigh a dying person on a hospital bed, but that didn't matter to Duncan MacDougall. In the early 20th century, MacDougall's unique, purpose-built scale was ready to receive test subjects.

Believe me when I say: I truly thought I knew the story of Tetris. The puzzle game's journey from behind the iron curtain in 1980s Moscow to multi-million-selling video game has been the subject of countless articles, a greatly entertaining book and a recent film. I have played Tetris in various forms for more than 30 years, from the Game Boy to the Nintendo Switch, even in VR. So when I loaded up Tetris Forever, an interactive documentary on Tetris's 40-year history from the developers-slash-archivists at Digital Eclipse, I wasn't expecting to learn anything new. I was proven very wrong.

If anyone could make the Dark Side sound good, it was James Earl Jones. The actor, who died Monday at the age of 93, provided the voice for Darth Vader in more than a dozen Star Wars properties, from A New Hope to Star Tours. He made the Force sound ominous in a way that made it appealing. With his passing, it feels as though all the power and gravitas and respect he brought to the character is gone. A few years ago, when Jones provided a few lines of dialog as Vader for The Rise of Skywalker, he'd expressed interest in wrapping up his time as the Sith Lord, according to Vanity Fair.

Today, a group of senators introduced the NO FAKES Act, a law that would make it illegal to create digital recreations of a person's voice or likeness without that individual's consent. Amy Klobuchar (D-Minn.) and Thom Tillis (R-N.C.), fully titled the Nurture Originals, Foster Art, and Keep Entertainment Safe Act of 2024. If it passes, the NO FAKES Act would create an option for people to seek damages when their voice, face or body are recreated by AI. Both individuals and companies would be held liable for producing, hosting or sharing unauthorized digital replicas, including ones made by generative AI. We've already seen many instances of celebrities finding their imitations of themselves out in the world.

Numerous studies on adversarial attacks targeting self-driving policies fail to incorporate realistic-looking adversarial objects, limiting real-world applicability. Building upon prior research that facilitated the transition of adversarial objects from simulations to practical applications, this paper discusses a modified gradient-based texture optimization method to discover realistic-looking adversarial objects. While retaining the core architecture and techniques of the prior research, the proposed addition involves an entity termed the 'Judge'. This agent assesses the texture of a rendered object, assigning a probability score reflecting its realism. This score is integrated into the loss function to encourage the NeRF object renderer to concurrently learn realistic and adversarial textures. The paper analyzes four strategies for developing a robust 'Judge': 1) Leveraging cutting-edge vision-language models. 2) Fine-tuning open-sourced vision-language models. 3) Pretraining neurosymbolic systems. 4) Utilizing traditional image processing techniques. Our findings indicate that strategies 1) and 4) yield less reliable outcomes, pointing towards strategies 2) or 3) as more promising directions for future research.

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.

After Disney releases a new film in English, the company will go back and localize it in as many as 46 global languages to make the movie accesible to as wide an audience as possible. This is a massive undertaking, one for which Disney has an entire division -- Disney Character Voices International Inc -- to handle the task. And it's not like you're getting Chris Pratt back in the recording booth to dub his GotG III lines in Icelandic and Swahili -- each version sounds a little different given the local voice actors. But with a new "AI dubbing" system from ElevenLabs, we could soon get a close recreation of Pratt's voice, regardless of the language spoken on-screen. ElevenLabs is an AI startup that offers a voice cloning service, allowing subscribers to generate nearly identical vocalizations with AI based on a few minutes worth of audio sample uploads.