Greece
Daily Show recaps all the excuses Republicans are making not to attend Trumps convention
Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Look Up Back to School Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net Versus All Series'Daily Show' recaps all the excuses Republicans are making not to attend Trump's convention Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Jon Stewart recaps everything Trump has done in the month he's been away Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said'Daily Show' host Ronny Chieng reacts to Trump saying'Islamic Republic of Japan' 'Daily Show's Josh Johnson gives hilarious breakdown of Trump's reflecting pool saga Trump held a big midterm convention on Wednesday, but many Republicans apparently had to miss out. In the clip above Michael Kosta gleefully recaps the various excuses given, from Rep. Mike Flood saying his son has a high school football game (Oh yeah, classic mid-week high school football game, says Kosta, That's why they call it Wednesday night lights) to Rep. Rich McCormick's son getting married in Greece (Look son, I need you to get married in Greece this week, Kosta says. I know you don't love her, but this is important.).
Ryanair boss says 'foreign object' may have caused plane window incident
Ryanair broken window may have been caused by'foreign object' To play this video you need to enable JavaScript in your browser. An incident on a Ryanair flight which saw a passenger partially sucked out of a plane window may have been caused by foreign object damage to an engine, said the airline's boss. Michael O'Leary said an initial investigation into the incident on 10 July between Greece and Germany suggests it looks like a foreign object damage to the engine on takeoff at Thessaloniki but ... we can't say that definitively. He insisted that the engine on the 18-year-old Boeing 737 plane had been fully serviced and overhauled in the past two years. Serbian national Ljubisa Karović's head and right shoulder were sucked out of the plane's window, leaving him seriously injured and in shock.
Greece's 'war on Roma' is Europe's new blueprint for discrimination
Jonathan Lee is a Romani activist from Wales, working at the European Roma Rights Centre. For the Romani families living in Nea Zoi, an informal neighbourhood near Aspropyrgos, Greece, the pre-dawn hum of surveillance drones has become a regular soundtrack to their lives. By daybreak, K-9 units and tactical police have blocked narrow dirt roads, police in riot gear have formed a perimeter around the neighbourhood, and armed officers are breaking through doors to makeshift homes, all under the banner of "public order". Since late 2025, this routine has repeated with terrifying regularity: at least 76 raids in six months, involving 473 officers, targeting 152 Romani communities across Greece. Documented by the European Roma Rights Centre as the most extensive anti-Roma police operation in decades, these actions are presented by Greek politicians as a tactical response to organised crime.
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision (Appendix)
B.1 Complexity Analysis Denote the number of nodes and edges in the graph as N and E, the number of latent factors as K, the number of operation choices as |O|, the dimensionality of hidden representations as d. The time complexity of the disentangled super-network is O(K|E|d+K|V|d2), where the computation for each factor is fully parallelizable and amenable to GPU acceleration, and K is usually a small constant. The time complexity of the self-supervised training and contrastive search modules is both O(K2d2). As architectures under different factors share the parameters, the number of learnable parameters is the same as classical graph super-network, i.e., O(|O|d2). Therefore, the complexity of our method is comparable to classical GNAS methods.
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
Bermudez, Yaiza, Perlaza, Samir, Esnaola, Iñaki
In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients adopt an empirical risk minimization with relative-entropy regularization (ERM-RER) learning framework and a forward-backward communication between clients is established, it suffices to share the locally obtained Gibbs measures to achieve the same performance as that of a centralized ERM-RER with access to all the datasets. The core idea is that the Gibbs measure produced by client~$k$ is used, as reference measure, by client~$k+1$. This effectively establishes a principled way to encode prior information through a reference measure. In particular, achieving centralized performance in the decentralized setting requires a specific scaling of the regularization factors with the local sample sizes. Overall, this result opens the door to novel decentralized learning paradigms that shift the collaboration strategy from sharing data to sharing the local inductive bias via the reference measures over the set of models.
Curiosity rover finds signs of ancient life on Mars
Martian clay may have held water billions of years ago. 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. NASA's Curiosity Mars rover took this selfie at a location nicknamed Mary Anning after a 19th century English paleontologist. This was the site of the chemical experiment uncovering diverse organic molecules on Mars, in the Glen Torridon region, which scientists believe was a site where ancient conditions would have been favorable to supporting life, if it ever was present. Breakthroughs, discoveries, and DIY tips sent six days a week.
Spurious Predictability in Financial Machine Learning
Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability.
Inside the UFO hotel in Wales - with 'spacecraft' door, NASA-designed interiors and Doctor Who TARDIS bathroom
The world's most family-friendly landmarks revealed - with six UK spots making the top 50 The UK's best staycations revealed by Daily Mail Travel - from a Gara Rock beach proposal to an £80-a-night mansion retreat This sun-drenched European coast offers great value - and it's just a two-hour flight away Don't get caught out by Ryanair's small bag restrictions - I've tested the carry-on suitcases and underseat bags that beat the strict requirements Why heading to Salcombe, one of Britain's most expensive seaside towns, in the shoulder season is an off-peak treat - and what to do there Tired of fun! Middle class families who turn their noses up at Butlin's are missing out Luxury hotel owner in Cornwall offers to foot British tourists' petrol bills to ease financial pain of staycation With flights disrupted amid Iran war, these are Europe's easiest countries to navigate by train - and how it compares to flying for price and time How to retire to the seaside for as little as £90,000 - and Britain's best hidden beach home spots New business class seats with IMAX-style wrap-around screens revealed - making passengers feel like they're in the cinema How the cost of your staycation REALLY compares with a'cheap' holiday abroad - when you factor in everything from food to fuel Why the Lake District shouldn't introduce tourism tax, says Cumbria tourism boss How Marseille became Europe's Capital of Cool - with 20 degree sunshine, sea views and amazing seafood The world's best food markets revealed - and a UK spot comes in second place READ MORE: The best hotels in the UK for 2026 revealed - does YOUR favourite make the list? Ready to hit the mute button on reality? Deep in the Pembrokeshire countryside lies a cosmic retreat that feels almost light years away from Earth. The awe-inspiring Spodnic UFO is one of three standout stays at Melin Mabes, a four-acre glamping site owned and ran by Martin Johnson and his wife, CarolAnne. 'It looks like it's just landed from outer space and aliens could come out,' Martin notes as he showcases his brainchild during the first episode of Channel's World's Most Secret Hotels.
Massively Parallel Exact Inference for Hawkes Processes
Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes process admits a faster $O(N)$ recurrence, but prior work evaluates this recurrence sequentially, without exploiting parallelization on modern GPUs. We show that the Hawkes process intensity can be expressed as a product of sparse transition matrices admitting a linear-time associative multiply, enabling computation via a parallel prefix scan. This yields a simple yet massively parallelizable algorithm for maximum likelihood estimation of linear exponential Hawkes processes. Our method reduces the computational complexity to approximately $O(N/P)$ with $P$ parallel processors, and naturally yields a batching scheme to maintain constant memory usage, avoiding GPU memory constraints. Importantly, it computes the exact likelihood without any additional assumptions or approximations, preserving the simplicity and interpretability of the model. We demonstrate orders-of-magnitude speedups on simulated and real datasets, scaling to thousands of nodes and tens of millions of events, substantially beyond scales reported in prior work. We provide an open-source PyTorch library implementing our optimizations.