The 2026 World Cup promises a global celebration. Many Arab fans may find themselves excluded. For the first time in World Cup history, eight Arab nations have qualified for this year's tournament, including Morocco, Tunisia, Egypt, Algeria, Saudi Arabia, Qatar, Iraq, and Jordan--double the number of teams that qualified for Qatar in 2022. Yet, the tournament is taking place at an unprecedented moment of heightened geopolitical tension. The US-Israel war with Iran, which began in February of this year, has caused ripple effects across Gulf states and neighboring countries in the Levant, including Lebanon, Palestine, and Jordan, reshaping the security around travel and mobility for fans and players hailing from the region. The US State Department has fully suspended visa issuance for nationals from countries with teams that qualified, including Iran and Haiti--despite it being the first time Haiti has qualified for a World Cup since 1974.

FIFA's discrimination monitor at the World Cup has called for a VAR official to be removed for appearing to make a hand gesture resembling a white supremacist sign. When the official broadcast of Germany's opening game against Curacao on Sunday cut pre-game to show the team of video review analysts, Shaun Evans from Australia made an "OK" symbol with his right hand in front of his right leg. Though the game was played in Houston, video officials work in Dallas at the World Cup broadcast centre. "Advice from our experts is that the gesture used clearly resembles an upside down'OK' hand symbol used as a'white power' symbol in global far-right circles," the Fare network, a long-time partner of FIFA and European football body UEFA to monitor racist and discriminatory chants, flags and symbols at international games, said in a statement. "Clearly this official should have no further role to play in this World Cup," Fare said in a statement, describing the gesture as "neo-Nazi".

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.

Soccer officials already rely on cameras to see who's offside and who sent the ball out of bounds. But during this World Cup, refs will use digital twins of each player to view plays from every angle. At the 2026 World Cup, the refs on the field and the officials on the sidelines will be able to use an abundance of tech to help call penalties, spot offside violations, and make other consequential decisions. The video assistant referee system, known as VAR, and the semi-automated offside technology (SAOT) have been used in soccer for years. But the setup at this summer's World Cup represents some of the most advanced uses of adjudication tech to date--not just in soccer, but across all high-level sports.

Fine-tuning text-to-image diffusion models with human feedback is an effective method for aligning model behavior with human intentions. However, this alignment process often suffers from slow convergence due to the large size and noise present in human feedback datasets. In this work, we propose FiFA, a novel automated data filtering algorithm designed to enhance the fine-tuning of diffusion models using human feedback datasets with direct preference optimization (DPO). Specifically, our approach selects data by solving an optimization problem to maximize three components: preference margin, text quality, and text diversity. The concept of preference margin is used to identify samples that contain high informational value to address the noisy nature of feedback dataset, which is calculated using a proxy reward model. Additionally, we incorporate text quality, assessed by large language models to prevent harmful contents, and consider text diversity through a k-nearest neighbor entropy estimator to improve generalization. Finally, we integrate all these components into an optimization process, with approximating the solution by assigning importance score to each data pair and selecting the most important ones. As a result, our method efficiently filters data automatically, without the need for manual intervention, and can be applied to any large-scale dataset. Experimental results show that FiFA significantly enhances training stability and achieves better performance, being preferred by humans 17% more, while using less than 0.5% of the full data and thus 1% of the GPU hours compared to utilizing full human feedback datasets. Warning: This paper contains offensive contents that may be upsetting. Large-scale models trained on extensive web-scale datasets using diffusion techniques (Ho et al., 2020; Song et al., 2020), such as Stable Diffusion (Rombach et al., 2022), Dall-E (Ramesh et al., 2022), and Imagen (Saharia et al., 2022), have enabled the generation of high-fidelity images from diverse text prompts. However, several failure cases remain, such as difficulties in illustrating text content, incorrect counting, or insufficient aesthetics for certain text prompts (Lee et al., 2023; Fan et al., 2024; Black et al., 2023). Fine-tuning text-to-image diffusion models using human feedback has recently emerged as a powerful approach to address this issue (Black et al., 2023; Fan et al., 2024; Prabhudesai et al., 2023; Clark et al., 2023). Unlike the conventional optimization strategy of likelihood maximization, this framework first trains reward models using human feedback (Kirstain et al., 2024; Wu et al., 2023; Xu et al., 2024) and then fine-tunes the diffusion models to maximize reward scores through policy gradient (Fan et al., 2024; Black et al., 2023) or reward-gradient based techniques (Prabhudesai et al., 2023; Clark et al., 2023).

The Canadian women's soccer team was dealt a heavy blow Saturday after FIFA announced the women's national team would be deducted six points from the standings in the Paris Olympics after staffers were caught using drones to spy on New Zealand during closed-door training sessions. Following its investigation, the FIFA Appeal Committee announced the Canadian Soccer Association was responsible for failing to ensure its staff members were in compliance with Olympic rules. "CSA was found responsible for failing to respect the applicable FIFA regulations in connection with its failure to ensure the compliance of its participating officials of the Games of the XXXIII Olympiad Paris 2024 Final Competition (OFT) with the prohibition on flying drones over any training sites," the statement said. "The officials were each found responsible for offensive behavior and violation of the principles of fair play in connection with the CSA's Women's representative team's drones usage in the scope of the OFT." Head coach Bev Priestman was removed from her position Thursday night after two staff members were sent home from Paris when an investigation found that analyst Joseph Lombardi had allegedly used a drone to spy on New Zealand's practice sessions.

FIFA deducted six points from Canada in the Paris Olympics women's football tournament and banned three coaches for one year each in a drone spying scandal. The stunning swath of punishments, announced late on Saturday, includes a 200,000-Swiss-franc ( 226,000) fine for the Canadian football federation in a case that has spiralled at the Summer Games. Two assistant coaches were caught using drones to spy on opponent New Zealand's practices before their opening game on Wednesday. Head coach Bev Priestman, who led Canada to the Olympic title in Tokyo in 2021, already was suspended by the national football federation and then removed from the Olympic tournament. She is now banned from all football by FIFA for one year.

Football, like everything else important in life, is about stories. People implant themselves into the narrative: where they were when they saw Maradona's handball, the strangers they hugged when Ole Gunnar Solskjær scored that historic last-minute winner at the 1999 Champions League final. No doubt new tales are already being conjured around Jude Bellingham's scissor kick against Slovakia in the dying seconds of Sunday's Euro 24 match. Sport is a nostalgia machine – and this is as true for video game simulations as it is for the real thing. Every gamer has their favourite footie sim, but for me, and many other players of my … ahem, vintage … it was Pro Evolution Soccer, numbers 3 to 6. This was the early 2000s, the age of the PlayStation 2. I was a writer for hire at Future Publishing, basically hanging out at its office in Bath, working mostly on the Official PlayStation magazine.

Two years ago, the long and lucrative relationship between Electronic Arts and Fifa broke down, with EA taking its ball home and launching EA Sports FC, a new brand for its footie sim series. Fifa president Gianni Infantino made a sulky declaration that he would find a new developer and that, "the only authentic, real game that has the Fifa name will be the best one available for gamers and football fans". This seemed like a ludicrous boast: EA had 20 years of experience making mainstream football sims – an expensive and highly sophisticated endeavour. How could Fifa hope to find a studio capable of competing? Well, it looks as if the global football body may have found its new best friend.

Deep learning methods have led to significant improvements in the performance on the facial landmark detection (FLD) task. However, detecting landmarks in challenging settings, such as head pose changes, exaggerated expressions, or uneven illumination, continue to remain a challenge due to high variability and insufficient samples. This inadequacy can be attributed to the model's inability to effectively acquire appropriate facial structure information from the input images. To address this, we propose a novel image augmentation technique specifically designed for the FLD task to enhance the model's understanding of facial structures. To effectively utilize the newly proposed augmentation technique, we employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss to achieve collective learning of high-level feature representations from two different views of the input images. Furthermore, we employ a Transformer + CNN-based network with a custom hourglass module as the robust backbone for the Siamese framework. Extensive experiments show that our approach outperforms multiple state-of-the-art approaches across various benchmark datasets.