With the rapid development of the big data era, Vertical Federated Learning (VFL) has been widely applied to enable data collaboration while ensuring privacy protection. However, the ultrahigh dimensionality of features and the sparse data structures inherent in large-scale datasets introduce significant computational complexity. In this paper, we propose the Vertical Federated Feature Screening (VFS) algorithm, which effectively reduces computational, communication, and encryption costs. VFS is a two-stage feature screening procedure that proceeds from coarse to fine: the first stage quickly filters out irrelevant feature groups, followed by a more refined screening of individual features. It significantly reduces the resource demands of downstream tasks such as secure joint modeling or federated feature selection. This efficiency is particularly beneficial in scenarios with ultrahigh feature dimensionality or severe class imbalance in the response variable. The statistical and computational properties of VFS are rigorously established. Numerical simulations and real-world applications demonstrate its superior performance.

Trump's Border Crackdown Is Wreaking Havoc on the World Cup Travel bans and other visa issues are creating problems for World Cup participants even before the whistle blows. Even before the first whistle blows, the 2026 World Cup --taking place from June 11 to July 19 across the United States, Canada, and Mexico--already has winners and losers away from the field. Here, amidst denied visas, prolonged checks, and contested entries, a parallel competition is emerging where human rights are at stake. This World Cup was meant to be a global celebration of soccer in North America. For the first time in history, the tournament is being held in three different countries, a move meant to unite the entire continent and turn the World Cup into an even more inclusive event.

Loud eaters and phones nearly spoiled my cinema trip - and it's not just me The cinema lights are low and you're cocooned in your seat, ready for the film to transport you to another world. But just as you settle in, you're jolted back to reality. Audience members around you are scrolling on their phones, talking and munching loudly. Cinemas do clearly ask everyone not to disturb those around them - through the use of adverts, announcements and signs - but is behaviour in getting worse? I experienced disruption a few weeks ago while watching Ryan Gosling's sci-fi movie, Project Hail Mary, at a cinema in London.

We develop methods for rapidly identifying important components of a convex optimization problem for the purpose of achieving fast convergence times. By considering a novel problem formulation--the minimization of a sum of piecewise functions--we describe a principled and general mechanism for exploiting piecewise linear structure in convex optimization. This result leads to a theoretically justified working set algorithm and a novel screening test, which generalize and improve upon many prior results on exploiting structure in convex optimization. In empirical comparisons, we study the scalability of our methods. We find that screening scales surprisingly poorly with the size of the problem, while our working set algorithm convincingly outperforms alternative approaches.

If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as Tab. These datasets appear in MoleculeNet as well. As mentioned in the introduction in the main paper, there are also issues with inconsistent representations and undefined stereochemistry. We list an example for each in Figure 1 and Figure 1.

Fairness and robustness are two important goals in the desig n of modern distributed learning systems. Despite a few prior works attemp ting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and un addressed questions that are of paramount significance to this topic: (i) What mak es jointly satisfying fairness and robustness difficult?

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely tobind toamolecular target inalargelibraryofcompounds.

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Silicon Valley's power brokers spent the past year currying favor with President Trump. Two deadly shootings in Minneapolis are now exposing the price of that bargain. It was November 12, 2016, four days after Donald Trump won his first presidential election. Aside from a few outliers (looking at you, Peter Thiel), almost everyone in the tech world was shocked and appalled. At a conference I attended that Thursday, Facebook CEO Mark Zuckerberg said it was " a pretty crazy idea " to think that his company had anything to do with the outcome.