The controversial dinosaur species was officially described last year after 80 years of debate. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Almost one year ago, paleontologists finally settled a longstanding debate about, arguably Earth's most famous dinosaur . After reviewing decades of mounting evidence, researchers documented around 150 morphological differences across multiple supposed juvenile skeletons. Instead of young dinosaurs, these were actually adult examples of an entirely separate species now known as .

Science The'Holy Grail' of baseball cards is up for auction The rare T206 Wagner card could sell for over $3 million on October 25. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A similar Wagner baseball card auctioned for over $5 million earlier this year. One of history's most famous baseball cards is up for auction, and bidding has already surpassed $1 million with days left to go. Its official name of the 1909-11 T206 White Border Sweet Caporal 150/25 Honus Wagner PSA Authentic (Restored) is quite the mouthful, but Heritage Auction's nickname for the memorabilia is much easier to remember.

Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series FTC and 22 states sue Amazon for allegedly inflating ad prices for 1.2 million advertisers Amazon denies misleading advertisers and says its system helped businesses save billions of dollars. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Rare'Super Mario Bros.'/'Duck Hunt' variant discovered, to auction this week The NES cartridges appear to have come from a previously unknown run of the games. Amanda Yeo is an Assistant Editor at Mashable, covering entertainment, culture, tech, science, and social good. Based in Australia, she writes about everything from video games and K-pop to movies and gadgets. Almost 100 rare Nintendo cartridges have been discovered in a Wisconsin retro games store, with a handful of them to go up for auction this week. The individually wrapped Duck Hunt NES cartridges appear to have come from a previously unknown run of the games, with collectors having been unaware of their existence until now.

Sotheby's Big Auction Raises Concerns Hype and Wealth Are Upending Science Private buyers are increasingly outbidding museums for fossils. That's making it difficult--or even impossible--for researchers to improve our understanding of the past. Forget the sale of the century. The auction house Sotheby's is gearing up for the sale of the epoch. On July 14 it will open live bidding on assorted fossils, but the pièce de résistance is lot 20, a rare 67-million-year-old skeleton.

Online advertising platforms use automated auctions to connect advertisers with potential customers, requiring effective bidding strategies to maximize profits. Accurate ad impact estimation requires considering three key factors: delayed and long-term effects, cumulative ad impacts such as reinforcement or fatigue, and customer heterogeneity. However, these effects are often not jointly addressed in previous studies. To capture these factors, we model ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards. For efficient estimation, we propose a two-stage maximum likelihood estimator combined with data-splitting strategies, ensuring controlled estimation error based on the first-stage estimator's (in)accuracy. Building on this, we design a reinforcement learning algorithm to derive efficient personalized bidding strategies. This approach achieves a near-optimal regret bound of O(dH2 T), where d is the contextual dimension, H is the number of rounds, and T is the number of customers. Our theoretical findings are validated by simulation experiments.

We study the problem of designing procurement auctions for the strategic uncapacitated facility location problem: a company needs to procure a set of facility locations in order to serve its customers and each facility location is owned by a strategic agent. Each owner has a private cost for providing access to their facility (e.g., renting it or selling it to the company) and needs to be compensated accordingly. The goal is to design truthful auctions that decide which facilities the company should procure and how much to pay the corresponding owners, aiming to minimize the total cost, i.e., the monetary cost paid to the owners and the connection cost suffered by the customers (their distance to the nearest facility). We evaluate the performance of these auctions using the frugality ratio. We first analyze the performance of the classic VCG auction in this context and prove that its frugality ratio is exactly 3. We then leverage the learning-augmented framework and design auctions that are augmented with predictions regarding the owners' private costs. Specifically, we propose a family of learning-augmented auctions that achieve significant payment reductions when the predictions are accurate, leading to much better frugality ratios. At the same time, we demonstrate that these auctions remain robust even if the predictions are arbitrarily inaccurate, and maintain reasonable frugality ratios even under adversarially chosen predictions. We finally provide a family of "error-tolerant" auctions that maintain improved frugality ratios even if the predictions are only approximately accurate, and we provide upper bounds on their frugality ratio as a function of the prediction error.

Repeated multi-unit auctions, where a seller allocates multiple identical items over many rounds, are common mechanisms in electricity markets and treasury auctions. We compare the two predominant formats: uniform-price and discriminatory auctions, focusing on the perspective of a single bidder learning to bid against stochastic adversaries. We characterize the learning difficulty in each format, showing that the regret scales similarly for both auction formats under both fullinformation and bandit feedback, as Θ( T)and Θ(T2/3), respectively. However, analysis beyond worst-case regret reveals structural differences: uniform-price auctions may admit faster learning rates, with regret scaling as Θ( T)in settings where discriminatory auctions remain at Θ(T2/3). Finally, we provide a specific analysis for auctions in which the other participants are symmetric and have unitdemand, and show that in these instances, a similar regret rate separation appears.

ROI constraints and budget constraints, is widely adopted by advertisers. A key challenge lies in designing algorithms for non-truthful mechanisms with ROI constraints. While prior work has addressed truthful auctions or non-truthful auctions with weaker benchmarks, this paper provides a significant improvement: We develop online bidding algorithms for repeated first-price auctions with ROI constraints, benchmarking against the optimal randomized strategy in hindsight. In the full feedback setting, where the maximum competing bid is observed, our algorithm achieves a near-optimal eO( T)regret bound, and in the bandit feedback setting (where the bidder only observes whether the bidder wins each auction), our algorithm attains eO(T3/4)regret bound.

Differentiable economics--the use of deep learning for auction design--has driven progress in multi-item auction design with additive and unit-demand valuations. However, there has been little progress for combinatorial auctions (CAs), even in the simplest and yet important single bidder case, due to exponential growth of the bundle space with the number of items. We address this challenge by introducing a deep network architecture for a menu-based CA, which supports the first dominantstrategy incentive compatible (DSIC), revenue-optimizing single-bidder CA. Our idea is to generate a bundle distribution through an ordinary differential equation (ODE) applied to a tractable initial distribution. Our method, BUNDLEFLOW, learns suitable ODE-based transforms, one for each menu element, to optimize expected revenue. BUNDLEFLOW achieves up to 2.23 higher revenue than baselines on standard CA testbeds and scales up to 500 items.