Decomposition is a proven way to shrink deep networks without changing I/O. We bring this idea to hyperdimensional computing (HDC), where footprint cuts usually shrink the feature axis and erode concentration and robustness. Prior HDC decompositions decode via fixed atomic hypervectors, which are ill-suited for compressing learned class prototypes. We introduce DecoHD, which learns directly in a decomposed HDC parameterization: a small, shared set of per-layer channels with multiplicative binding across layers and bundling at the end, yielding a large representational space from compact factors. DecoHD compresses along the class axis via a lightweight bundling head while preserving native bind-bundle-score; training is end-to-end, and inference remains pure HDC, aligning with in/near-memory accelerators. In evaluation, DecoHD attains extreme memory savings with only minor accuracy degradation under tight deployment budgets. On average it stays within about 0.1-0.15% of a strong non-reduced HDC baseline (worst case 5.7%), is more robust to random bit-flip noise, reaches its accuracy plateau with up to ~97% fewer trainable parameters, and -- in hardware -- delivers roughly 277x/35x energy/speed gains over a CPU (AMD Ryzen 9 9950X), 13.5x/3.7x over a GPU (NVIDIA RTX 4090), and 2.0x/2.4x over a baseline HDC ASIC.

Social and collaborative platforms emit multivariate time-series traces in which early interactions--such as views, likes, or downloads--are followed, sometimes months or years later, by higher impact like citations, sales, or reviews. We formalize this setting as Lead-Lag Forecasting (LLF): given an early usage channel (the lead), predict a correlated but temporally shifted outcome channel (the lag). Despite the ubiquity of such patterns, LLF has not been treated as a unified forecasting problem within the time-series community, largely due to the absence of standardized datasets. To anchor research in LLF, here we present two high-volume benchmark datasets--arXiv (accesses citations of 2.3M papers) and GitHub (pushes/stars forks of 3M repositories)--and outline additional domains with analogous lead-lag dynamics, including Wikipedia (page-views edits), Spotify (streams concert attendance), e-commerce (click-throughs purchases), and LinkedIn profile (views messages). Our datasets provide ideal testbeds for lead-lag forecasting, by capturing long-horizon dynamics across years, spanning the full spectrum of outcomes, and avoiding sur-vivorship bias in sampling. We documented all technical details of data cura-tion and cleaning, verified the presence of lead-lag dynamics through statistical and classification tests, and benchmarked parametric and non-parametric baselines for regression. Our study establishes LLF as a novel forecasting paradigm and lays an empirical foundation for its systematic exploration in social and usage data. The success of human activities is often measured by their collective impact, ranging from music streams and movie box office revenues to product sales and social media popularity. These impact metrics typically follow heavy-tailed distributions (Clauset et al., 2009) and slow decay patterns across timescales (Candia et al., 2019), making early identification of future hits fundamentally challenging (Cheng et al., 2014; Martin et al., 2016). At the same time, digital platforms increasingly log online user interactions--searches, views, downloads, likes, and shares--that often precede these long-term dynamics. These temporal lead-lag dynamics are remarkably ubiquitous, spanning domains as diverse as science (Haque & Ginsparg, 2009), economics (Wu & Brynjolfsson, 2015), arts (Goel et al., 2010), culture (Gruhl et al., 2005), and social movements (Johnson et al., 2016). A systematic understanding of such lead-lag dynamics is not only crucial for anticipating and optimizing impact in digital ecosystems, but also essential for designing effective strategies that identify and promote emerging innovations and products.

This paper examines how decision makers in academia, government, business, and civil society navigate questions of power in implementations of artificial intelligence (AI). The study explores how individuals experience and exercise "levers of power," which are presented as social mechanisms that shape institutional responses to technological change. The study reports on the responses of personalized questionnaires designed to gather insight on a decision maker's institutional purview, based on an institutional governance framework developed from the work of Neo Institutionalists. Findings present the anonymized, real responses and circumstances of respondents in the form of twelve fictional personas of high-level decision makers from North America and Europe. These personas illustrate how personal agency, organizational logics, and institutional infrastructures may intersect in the governance of AI. The decision makers' responses to the questionnaires then inform a discussion of the field level personal power of decision-makers, methods of fostering institutional stability in times of change, and methods of influencing institutional change in the field of AI. The final section of the discussion presents a table of the dynamics of the levers of power in the field of AI for change makers and 5 testable hypotheses for institutional and social movement researchers. In summary, this study provides insight on the means for policymakers within institutions and their counterparts in civil society to personally engage with AI governance.

Climate change is intensifying the occurrence of harmful algal bloom (HAB), particularly cyanobacteria, which threaten aquatic ecosystems and human health through oxygen depletion, toxin release, and disruption of marine biodiversity. Traditional monitoring approaches, such as manual water sampling, remain labor-intensive and limited in spatial and temporal coverage. Recent advances in vision-language models (VLMs) for remote sensing have shown potential for scalable AI-driven solutions, yet challenges remain in reasoning over imagery and quantifying bloom severity. In this work, we introduce ALGae Observation and Segmentation (ALGOS), a segmentation-and-reasoning system for HAB monitoring that combines remote sensing image understanding with severity estimation. Our approach integrates GeoSAM-assisted human evaluation for high-quality segmentation mask curation and fine-tunes vision language model on severity prediction using the Cyanobacteria Aggregated Manual Labels (CAML) from NASA. Experiments demonstrate that ALGOS achieves robust performance on both segmentation and severity-level estimation, paving the way toward practical and automated cyanobacterial monitoring systems.

Tesla chief Elon Musk's $1tn pay package has been approved. Tesla chief Elon Musk's $1tn pay package has been approved. Chants of'Elon' erupt after compensation plan approved despite opposition from several high-profile investors Tesla shareholders approved a $1tn compensation plan for CEO Elon Musk on Thursday, awarding the world's richest person what would be the largest corporate payout in history if he meets the goals necessary to receive it. The pay package, which several high-profile investors opposed, demonstrates that shareholders still believe Musk can lead the automaker in an era dominated by robotics and artificial intelligence. The result of the vote was announced at the annual shareholder event in Austin, Texas, with more than 75% of investors voting in favor of the plan.

The unprecedented payday will go into full effect by 2035--as long as Tesla hits ambitious financial and production targets. On Thursday, Tesla shareholders approved an unprecedented $1 trillion pay package for CEO Elon Musk . The full compensation plan will go into effect by 2035--assuming the company successfully hits ambitious financial and production targets. If that happens, Musk will also get control of some 25 percent of the business, up from the 12 percent he controls currently. More than 75 percent of Tesla shareholders approved the move in a preliminary vote.

Tesla CEO Elon Musk has scored a resounding victory as shareholders have approved a pay package of as much as $878bn over the next decade, endorsing his vision of morphing the electric vehicle (EV) maker into an AI and robotics juggernaut. Shares of Tesla rose more than 3 percent in after-hours trading after the shareholders voted on Thursday. The proposal was approved with more than 75 percent support. "What we are about to embark upon is not merely a new chapter of the future of Tesla, but a whole new book," he said. "This really is going to be quite the story."

Kim Kardashian blames ChatGPT for law school test failures while Miami-Dade Sheriff's Office tests America's first autonomous police vehicle in the latest AI news.

Zohran Mamdani ran an online campaign based on real people and a real message. There are many fair questions following Zohran Mamdani's decisive victory. Will his campaign be a template for others? Will he be able or allowed to follow through on his campaign promises? Will the Democratic establishment accept that its future could look something like this proud 34-year-old democratic socialist?

Science Space Solar System Mars Meet Blue and Gold: NASA's first twin satellites bound for Mars The ESCAPADE mission will help predict space weather for future crewed missions to the Red Planet. Breakthroughs, discoveries, and DIY tips sent every weekday. NASA is readying the first dual-satellite mission to another planet. Currently scheduled to launch no earlier than Sunday November 9 from Cape Canaveral, Florida, the Escape and Plasma Acceleration and Dynamics Explorers (ESCAPADE) are a pair of identical spacecraft tasked with traveling to Mars . Once in the Red Planet's orbit, the spacecraft will create three-dimensional maps of our cosmic neighbor's upper atmosphere, ionosphere, and magnetic fields.