Look Up Say More Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Trending Now Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series If you thought I was only this, I'm gonna do something completely different now! 'Lanterns' stars Aaron Pierre and Kyle Chandler's'Hot Ones Versus' is hilariously competitive This is what I'd put in my smart home of the future 6:32 On Mashable's, hosts Kristy Puchko (Mashable's Entertainment Editor) and Mark Stetson (Senior Creative Producer) bring humor and their trusted insights to the biggest shows, films, digital trends, and cultural moments. From viral-worthy rants and passionate raves to smart recaps and first-look teasers, they cover what everyone is talking about. Celebrity guests join the conversation for real talk about their careers, upcoming projects, and what's trending online. Comedian and viral creator Kyle Gordon sits down for an interview with Mashable Executive Producer, Mark Stetson, on to discuss his comedy roots, musical parodies, and creative process. From childhood Chris Farley impressions and Christopher Guest influences to shooting the music video in South Africa and Eswatini, Kyle opens up about his journey, live shows, and balancing music with comedy.

Taiwan President Lai Ching-te has cancelled a presidential trip to the African nation of Eswatini, accusing Beijing of putting pressure on its neighbours to bar his aircraft from flying over their territories. Seychelles, Mauritius and Madagascar revoked Lai's overflight permits after intense pressure and economic coercion from China, said a Taiwan official. China denied coercion, while praising the three African countries saying it had high appreciation for them. This is the first publicly known instance where a Taiwanese leader has had to cancel a foreign trip due to revoked flight permits. Eswatini, formerly known as Swaziland, is Taiwan's only diplomatic ally in Africa.

Its low-rank QTT structure arises from intrinsic spectral smoothness in continuous models, or from spectral energy concentration as the number of components D grows in discrete models. We demonstrate this on weighted sums of Bernoulli and lognormal random variables. In the latter, the approach reaches high-resolution discretizations of N = 230 frequency modes on standard hardware, far beyond the N =224 ceiling of dense implementations. These compressed representations enable efficient computation of Value at Risk (VaR) and Expected Shortfall (ES), supporting applications in quantitative finance and beyond. I. INTRODUCTION Weighted sums of independent random variables constitute a basic probabilistic model, describing macroscopic behavior arising from the aggregation of microscopic stochastic components. These models arise in a wide range of applications. Their probability distribution generally lacks a closed-form expression, and their evaluation involves multidimensional convolution integrals that are susceptible to the curse of dimensionality. Consequently, evaluating these models relies on specializednumericalmethods. Whilethese methods have been adapted for discrete settings [18, 19], they are frequently hampered by persistent Gibbs oscillations, which arise from distributional discontinuities and preclude uniform convergence [20, 21]. No existing method simultaneously achieves an accurate approximation of the exact, fully non-Gaussian target distribution while remaining scalable to larger, practically relevant system sizes. In this work, we introduce a new algorithm that combines the Fourier spectral method with tensor-network techniques.

We present an ultra-fast and flexible search algorithm that enables search over trillion-scale natural language corpora in under 0.3 seconds while handling semantic variations (substitution, insertion, and deletion). Our approach employs string matching based on suffix arrays that scales well with corpus size. To mitigate the combinatorial explosion induced by the semantic relaxation of queries, our method is built on two key algorithmic ideas: fast exact lookup enabled by a disk-aware design, and dynamic corpus-aware pruning. We theoretically show that the proposed method suppresses exponential growth in the search space with respect to query length by leveraging statistical properties of natural language. In experiments on FineWeb-Edu (Lozhkov et al., 2024) (1.4T tokens), we show that our method achieves significantly lower search latency than existing methods: infini-gram (Liu et al., 2024), infini-gram mini (Xu et al., 2025), and SoftMatcha (Deguchi et al., 2025). As a practical application, we demonstrate that our method identifies benchmark contamination in training corpora, unidentified by existing approaches. We also provide an online demo of fast, soft search across corpora in seven languages.