The video assistant referee system, or VAR, has led to some controversial calls at the 2026 World Cup. The penultimate Round of 16 match at the 2026 World Cup between Argentina and Egypt was marked not just by exceptional goals, great saves, and fans devoted to their teams. The match also sparked one of the most widely discussed controversies surrounding the video assistant referee system, known as VAR, a technology designed to assist on-field officials in making fairer decisions, but whose use has been criticized for allegedly favoring certain teams. Egypt was eliminated from the tournament with a 3-2 loss to Argentina, after having held a two-goal lead. The Egyptian Football Association argued that "the failure to properly use VAR" had influenced several refereeing decisions that affected the final score.

Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year's World Cup. To casual soccer viewers, the game may look like it always has--same green field, 22 players, a referee, and the familiar rhythm of play unfolding over 90 minutes. The changes are only visible if you look beneath the familiar surface. What appears to be a traditional match is now supported by layers of tracking systems, automated analysis, and real-time data that run quietly in the background.

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.

I am surprised that 2760 data points were enough for training (without overfitting) and assessing the 5-16 layerednetwork.

Estimating distributions from samples is a fundamental statistical task.

Fair, transparent, and explainable decision-making remains a critical challenge in Olympic and Paralympic combat sports. This paper presents \emph{FST.ai 2.0}, an explainable AI ecosystem designed to support referees, coaches, and athletes in real time during Taekwondo competitions and training. The system integrates {pose-based action recognition} using graph convolutional networks (GCNs), {epistemic uncertainty modeling} through credal sets, and {explainability overlays} for visual decision support. A set of {interactive dashboards} enables human--AI collaboration in referee evaluation, athlete performance analysis, and Para-Taekwondo classification. Beyond automated scoring, FST.ai~2.0 incorporates modules for referee training, fairness monitoring, and policy-level analytics within the World Taekwondo ecosystem. Experimental validation on competition data demonstrates an {85\% reduction in decision review time} and {93\% referee trust} in AI-assisted decisions. The framework thus establishes a transparent and extensible pipeline for trustworthy, data-driven officiating and athlete assessment. By bridging real-time perception, explainable inference, and governance-aware design, FST.ai~2.0 represents a step toward equitable, accountable, and human-aligned AI in sports.

Abstract--Although existing cross-domain continual learning approaches successfully address many streaming tasks having domain shifts, they call for a fully labeled source domain hindering their feasibility in the privacy constrained environments. This paper goes one step ahead with the problem of source-free cross-domain continual learning where the use of source-domain samples are completely prohibited. We propose the idea of rehearsal-free frequency-aware dynamic prompt collaborations (REFEREE) to cope with the absence of labeled source-domain samples in realm of cross-domain continual learning. REFEREE is built upon a synergy between a source-pre-trained model and a large-scale vision-language model, thus overcoming the problem of sub-optimal generalizations when relying only on a source pre-trained model. The domain shift problem between the source domain and the target domain is handled by a frequency-aware prompting technique encouraging low-frequency components while suppressing high-frequency components. This strategy generates frequency-aware augmented samples, robust against noisy pseudo labels. The noisy pseudo-label problem is further addressed with the uncertainty-aware weighting strategy where the mean and covariance matrix are weighted by prediction uncertainties, thus mitigating the adverse effects of the noisy pseudo label. Besides, the issue of catastrophic forgetting (CF) is overcome by kernel linear discriminant analysis (KLDA) where the backbone network is frozen while the classification is performed using the linear discriminant analysis approach guided by the random kernel method. Our rigorous numerical studies confirm the advantage of our approach where it beats prior arts having access to source domain samples with significant margins. HE goal of continual learning (CL) is to deal with lifelong learning environments where a sequence of non-stationary tasks is observed.