 reality product.
An attempt by a Just Stop Oil activist to glue their own head to Johannes Vermeer’s classic portrait, "Girl with a Pearl Earring," is the latest in a string of viral stunts by the U.K.-based environmental rights group.
The organization — whose goal is to end oil production and consumption — has been involved in several controversial and attention-grabbing protests in the U.K. and Europe.
Here's a roundup of their latest stunts.
To date, none of the paintings targeted by the group have been directly harmed, though the frame of Van Gogh's "Sunflowers" did sustain minor damage.
A Just Stop Oil activist attempted to glue his head to "Girl with a Pearl Earring" at The Hague's Mauritshuis museum Thursday, while a second demonstrator poured tomato soup over him.
The painting was behind glass and the museum said it was not damaged as a result of the stunt. Three people were arrested in connection with the protest.
Soup and "Sunflowers"
Two Just Stop Oil activists splashed tomato soup on Van Gogh's masterpiece — "Sunflowers" — at London's National Gallery on Oct. 14. The pair then glued their hands to the wall, as one shouted, "What is worth more, art or life?"
The duo were identified in The Guardian as Phoebe Plummer and Anna Holland. Both were arrested following the protest. The painting was unharmed but there was minor damage to the frame.
Inspired by Just Stop Oil's tomato-forward protest, climate change activists in Germany splashed liquified mashed potatoes on Claude Monet's "Grainstacks" on Oct. 23.
Two demonstrators with Letzte Generation, which translates to "last generation," glued their hands to the wall of Potsdam's Museum Barberini.
"We are in a climate catastrophe, and all you are afraid of is tomato soup or mashed potatoes on a painting," one protester said. "Grainstacks" was also behind protective glass.
On Oct. 24, Just Stop Oil activists smashed a piece of cake into the wax figure of King Charles III on display at Madame Tussauds in London. Four people were arrested following the incident.
In a recent interview with Euronews, Just Stop Oil spokesperson Alex De Koning said, "We’re peaceful protesters. We will cause disruption, we will attack paintings and art, but we’re only doing this until the government make a meaningful statement about ending new fossil fuel assets in the UK. As soon as that happens, we’re off and we move out of the way."DMK President M K Stalin on Saturday vowed to quit politics if he failed to prove the corruption charges against Tamil Nadu Municipal Administration Minister S P Velumani.
Responding to Velumani’s challenge to prove the corruption charges levelled against him or quit politics, Stalin said, “I am ready to quit politics. Are you (Velumani) ready?” The DMK Chief was addressing a people’s gram sabha meeting at Devarayapuram in Thondamuthur constituency, represented by the minister.
Referring to the memorandum submitted by the DMK to the Governor last month listing corruption charges against the CM and his colleagues, Velumani had said he would step down from his post if the charges were proved and sought to know whether Stalin was ready to quit politics if he failed to do so.
“We will not only prove the charges against the minister, but also drag him to the court of law and get him punished for his corrupt deeds…” Stalin said.
Stalin promised to order an inquiry into the ‘mysterious death’ of late Chief Minister J Jayalalithaa, since the judicial commission constituted by the government has not submitted its report even after three years, immediately after assuming power.
The DMK leader also mocked AIADMK for announcing K Palaniswami as the chief ministerial candidate, as its alliance partner BJP was yet to endorse it.
Though the state government has banned gram sabha meetings, the party was holding people’s gram sabha meetings and lauded the participation in large numbers, as they had given a big mandate to DMK in the last Lok Sabha and local body elections.
This superb book includes a full commercial directory for the entire country. Organised by Province, and then town, it lists all the principal office holders, gentry, professionals, trades, hotels, schools, public institutions, churches, and even pubs for each town in Ireland. Slater took over Pigot's important publication of commercial directories of Ireland, and this was the first installment. It has almost twice as much detail as its predecessor (published in 1824), and is now an extremely rare item.
This title includes the Introductory sections, and the Ulster and Belfast sections only.Spatially relaxed inference on high-dimensional linear models

We consider the inference problem for high-dimensional linear models, when covariates have an underlying spatial organization reflected in their correlation. A typical example of such a setting is high-resolution imaging, in which neighboring pixels are usually very similar. Accurate point and confidence intervals estimation is not possible in this context with many more covariates than samples, furthermore with high correlation between covariates. This calls for a reformulation of the statistical inference problem, that takes into account the underlying spatial structure: if covariates are locally correlated, it is acceptable to detect them up to a given spatial uncertainty. We thus propose to rely on the $\delta$-FWER, that is the probability of making a false discovery at a distance greater than $\delta$ from any true positive. With this target measure in mind, we study the properties of ensembled clustered inference algorithms which combine three techniques: spatially constrained clustering, statistical inference, and ensembling to aggregate several clustered inference solutions. We show that ensembled clustered inference algorithms control the $\delta$-FWER under standard assumptions for $\delta$ equal to the largest cluster diameter. We complement the theoretical analysis with empirical results, demonstrating accurate $\delta$-FWER control and decent power achieved by such inference algorithms.


Introduction
High-dimensional setting. High-dimensional regression corresponds to a setting where the number of covariates (or features) p exceeds the number of samples n. It notably occurs when searching for conditional associations among some high-dimensional observations and some outcome of interest: the target. Typical examples of the highdimensional setting include inference problems on high-resolution images, where one aims at pixel-or voxel-level analysis, e.g., in neuroimaging [Norman et al., 2006, De Martino et al., 2008, astronomy [Richards et al., 2009], but also in other fields where covariates display a spatial structure e.g., in genomics [Balding, 2006, Dehman et al., 2015. In all these examples, it actually turns out that not only n < p but even n p and the covariates are spatially structured because of the physics of the problem or the measurements process. Because such high-dimensional data lead to high-variance results, probing statistical significance is important to give a level of confidence in the reported association. For this reason, the present analysis departs from traditional sparse modeling methods such as the Lasso [Tibshirani, 1996], that simply aim at selecting a good set of predictive covariates without considering statistical significance. In this context, a first approach is to consider the multivariate linear model: where the target is denoted by y ∈ R n, the design matrix by X ∈ R n×p, the parameter vector by β * ∈ R p and the random error vector by ε ∈ R n. The aim is to infer β *, with statistical guarantees on the estimate, in particular regarding the support, i.e., the set of covariates with non-zero importance.

Statistical inference on individual parameters.
In high-dimensional settings, standard statistical inference methodology does not apply, but numerous methods have recently been proposed to recover the non-zero parameters of β * with statistical guarantees. Many methods rely on resampling: bootstrap procedures [Bach, 2008, Chatterjee and Lahiri, 2011, Liu and Yu, 2013, perturbation resampling-based procedures [Minnier et al., 2011], stability selection procedures [Meinshausen and Bühlmann, 2010] and randomized sample splitting [Wasserman andRoeder, 2009, Meinshausen et al., 2009]. All of these approaches suffer from limited power. Contrarily to the screening/inference procedure, post-selection inference procedures generally merge the screening and inference steps into one and then use all the samples [Berk et al., 2013, Lockhart et al., 2014, Lee et al., 2016, resulting in potentially more powerful tests than sample splitting. Yet, these approaches do not scale well with large p. Another family of methods rely on debiasing procedures: the most prominent examples are corrected ridge [Bühlmann, 2013] and desparsified Lasso [Zhang and Zhang, 2014, van de Geer et al., 2014, Javanmard and Montanari, 2014 which is an active area of research [Javanmard and Montanari, 2018, Bellec and Zhang, 2019, Celentano et al., 2020. Additionally, knockoff filters [Barber andCandès, 2015, Candès et al., 2018] consist in creating noisy "fake" copies of the original variables, and checking which original variables are selected prior to the fake ones. Finally, a general framework for statistical inference in sparse high-dimensional models has been proposed recently [Ning and Liu, 2017].
Failure of existing statistical inference methods. In practice, in the n p setting we consider, the previous methods are not well adapted as they are often power