The Winter Olympics Are Here. Is the Sports Betting World Ready? For the 2026 Winter Games, sportsbooks and betting platforms are watching for illicit activity while testing new ways to get people to bet. For all their prestige and gravitas, the Olympic Games have lately proven to be a hotbed for scandals. From a famous judging controversy in 2002 to bid bribery probes and even the resignation of a top Olympic official who was filmed offering to sell tickets for the 2012 London games on the black market, the modern Games have always felt vulnerable to bad actors.

Sea lampreys--invasive, leechlike creatures that once nearly destroyed the Great Lakes' fishing economy--are kept in check by a small U.S.-Canadian program. Will it survive Trump's slash-and-burn campaign? Ally Porter walked ahead of me as we sidestepped down a steep, loamy embankment. Our path lit only by headlamps, a waning sliver of moon, and what seemed to be thousands of stars, we made our way to a mucky riverbank about twenty feet below. At one point, I lost my footing and ended up wedged against a tree trunk. Porter, who had two tight braids that landed just below her shoulders, kept going. She moved with ease through several inches of sludge, toward a yellow glow stick tied to a tree at the water's edge.

Learning Japanese vocabulary is a challenge for learners from Roman alphabet backgrounds due to script differences. Japanese combines syllabaries like hiragana with kanji, which are logographic characters of Chinese origin. Kanji are also complicated due to their complexity and volume. Keyword mnemonics are a common strategy to aid memorization, often using the compositional structure of kanji to form vivid associations. Despite recent efforts to use large language models (LLMs) to assist learners, existing methods for LLM-based keyword mnemonic generation function as a black box, offering limited interpretability. We propose a generative framework that explicitly models the mnemonic construction process as driven by a set of common rules, and learn them using a novel Expectation-Maximization-type algorithm. Trained on learner-authored mnemonics from an online platform, our method learns latent structures and compositional rules, enabling interpretable and systematic mnemonics generation. Experiments show that our method performs well in the cold-start setting for new learners while providing insight into the mechanisms behind effective mnemonic creation.

Bangla, the seventh most widely spoken language worldwide with 300 million native speakers, faces digital under-representation due to limited resources and lack of annotated datasets. Stemming, a critical preprocessing step in language analysis, is essential for low-resource, highly-inflectional languages like Bangla, because it can reduce the complexity of algorithms and models by significantly reducing the number of words the algorithm needs to consider. This paper conducts a comprehensive survey of stemming approaches, emphasizing the importance of handling morphological variants effectively. While exploring the landscape of Bangla stemming, it becomes evident that there is a significant gap in the existing literature. The paper highlights the discontinuity from previous research and the scarcity of accessible implementations for replication. Furthermore, it critiques the evaluation methodologies, stressing the need for more relevant metrics. In the context of Bangla's rich morphology and diverse dialects, the paper acknowledges the challenges it poses. To address these challenges, the paper suggests directions for Bangla stemmer development. It concludes by advocating for robust Bangla stemmers and continued research in the field to enhance language analysis and processing.

We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historically remained separate. Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs vector space representations and semantic similarity calculations to map framework parameters to heuristic patterns, supported by a computational architecture that combines deep semantic processing with constrained use of Large Language Models. By processing both primary content and secondary elements (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology generalizes to various analytical frameworks and heuristic sets, culminating in a plug-and-play architecture for generating recommender systems that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.

The man in the mask wants you to click the tally counter. So begins Strange Scaffold's short, yet highly distressing new game, Clickolding--a very literal take on clicker games--released on Steam this week. The lore of how video games pitches are conceived is not always exciting. It's often an obfuscated process that involves pitch decks, investor hunts, and a lot of jumping through approval hoops. Clickolding was not one such case.

IN EARLY APRIL 2020, shortly after the British prime minister Boris Johnson had announced the first pandemic lockdown in the United Kingdom, a urologist named Archie Fernando reached out to one of her colleagues, Nadine Hachach-Haram. The two doctors worked at Guy's and St Thomas' hospital, one of the busiest in the country, at a time when nearly a thousand people were dying of Covid-19 every week. Most surgeries were being deferred, except for life-or-limb cases and urgent cancer surgeries, and Hachach-Haram, who is a reconstructive plastic surgeon, recalls how useless she felt. "I would just walk into the wards and ask the nurses what I could do to help," she says. "I started doing everything, like portering and proning, turning patients over to make their breathing slightly better."

An actual, if elderly and ailing, Havanese is looking up at me as I work, and an Avedon portrait book is open on my desk. What could be more beguiling than combining the two? Then my laptop stutters and pauses, and there it is, eerily similar to what Richard Avedon would have done if confronted with a Havanese. The stark expression, the white background, the implicit anxiety, the intellectual air, the implacable confrontational exchange with the viewer--one could quibble over details, but it is close enough to count. My Havedon is, of course, an image produced by an artificial-intelligence image generator--DALL-E 2, in this case--and the capacity of such systems to make astonishing images in short order is, by now, part of the fabric of our time, or at least our pastimes.

Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score.

A rendering of Tesla's humanoid, known as Tesla Bot or Optimus. As I write this, we are all eagerly and skeptically awaiting Elon Musk's update on Tesla's new Optimus robot. Musk said he will unveil a prototype of Optimus at Tesla's AI Day on Friday. Let's be frank, Optimus feels a bit dystopian, as if we're all going to be eminently replaced by a sleek, slender, cold electronic robot. It feels like Optimus inhabits a world of beautiful black and white design, while the rest of us get to drive around in stainless-steel Cybertrucks overseeing our hole-drilling operations on Mars.