'I've applied for 500 jobs in two months since graduating' You have to work 10 times harder to work for a role that 10 years ago you could have got very easily straight out of university, says 22-year-old business management graduate Charlotte Briggs. Within two months she had applied for 500 roles. It's quite upsetting because I've worked really hard for the last three years to achieve a 2:1 just to be rejected for not having experience. Although her job search sounds extreme, it may not be that unusual. According to latest ONS figures, 22.5% of people aged 16 to 24 cannot find work, putting London as the UK region with the second highest rate of youth unemployment.

Iran has fired missiles and drones at several Gulf Arab nations, which have sought to intercept them, in a now-daily fallout from the United States-Israel war launched on Iran nearly three weeks ago that has engulfed the Middle East with deaths, destruction, assassinations, and an energy crisis spreading far beyond the region. Early Tuesday, Qatar's Ministry of Defence said its armed forces intercepted a missile attack against the country. The statement came hours after the Kuwaiti army said it was intercepting hostile missile and drone attacks. The UAE, Saudi Arabia and Bahrain have also reported intercepting missiles and drones in recent hours. Saudi Arabia's Ministry of Defense reported the interception and destruction of a drone in the Eastern Region.

A standard pick-up truck is mounted with a launching system for eight Cobra-3120 loitering munitions. TAIPEI - After years of sourcing drones from a wide range of international suppliers, including China, Ukraine has a new entrant supporting its battlefield needs: Taiwan. The self-ruled island has quietly been ramping up exports of domestically produced drones to war-torn Ukraine, underscoring how its homegrown industry has advanced in recent years, evolving from a largely experimental sector into a burgeoning supplier of battlefield-relevant technology. The move, which also helps expand Taiwan's defense-industrial base, has seen the island sell well over 100,000 drones to Ukraine since last year alone, mainly via Poland and the Czech Republic, according to data provided by the Taipei-based Research Institute for Democracy, Society and Emerging Technology (DSET). In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Recommending out-of-vocabulary (OOV) items is a challenging problem since the in-vocabulary (IV) items have well-trained behavioral embeddings but the OOV items only have content features. Current OOV recommendation models often generate'makeshift' embeddings for OOV items from content features and then jointly recommend with the makeshift' OOV item embeddings and the behavioral IV item embeddings. However, merely using the'makeshift' embedding will result in suboptimal recommendation performance due to the substantial gap between the content feature and the behavioral embeddings. To bridge the gap, we propose a novel User Sequence IMagination (USIM) fine-tuning framework, which first imagines the user sequences and then refines the generated OOV embeddings with the user behavioral embeddings. Specifically, we frame the user sequence imagination as a reinforcement learning problem and develop a recommendation-focused reward function to evaluate to what extent a user can help recommend the OOV items.

Follow-the-Regularized-Leader (FTRL) is a powerful framework for various online learning problems. By designing its regularizer and learning rate to be adaptive to past observations, FTRL is known to work adaptively to various properties of an underlying environment. However, most existing adaptive learning rates are for online learning problems with a minimax regret of $\Theta(\sqrt{T})$ for the number of rounds $T$, and there are only a few studies on adaptive learning rates for problems with a minimax regret of $\Theta(T^{2/3})$, which include several important problems dealing with indirect feedback. To address this limitation, we establish a new adaptive learning rate framework for problems with a minimax regret of $\Theta(T^{2/3})$. Our learning rate is designed by matching the stability, penalty, and bias terms that naturally appear in regret upper bounds for problems with a minimax regret of $\Theta(T^{2/3})$. As applications of this framework, we consider three major problems with a minimax regret of $\Theta(T^{2/3})$: partial monitoring, graph bandits, and multi-armed bandits with paid observations. We show that FTRL with our learning rate and the Tsallis entropy regularizer improves existing Best-of-Both-Worlds (BOBW) regret upper bounds, which achieve simultaneous optimality in the stochastic and adversarial regimes. The resulting learning rate is surprisingly simple compared to the existing learning rates for BOBW algorithms for problems with a minimax regret of $\Theta(T^{2/3})$.

Labeling articulated objects in unconstrained settings has a wide variety of applications including entertainment, neuroscience, psychology, ethology, and many fields of medicine. Large offline labeled datasets do not exist for all but the most common articulated object categories (e.g., humans). Hand labeling these landmarks within a video sequence is a laborious task. Learned landmark detectors can help, but can be error-prone when trained from only a few examples. Multi-camera systems that train fine-grained detectors have shown significant promise in detecting such errors, allowing for self-supervised solutions that only need a small percentage of the video sequence to be hand-labeled.

Has Trump failed to sell the Iran war to the world? Are US-Israeli attacks against Iran legal? The administration of United States President Donald Trump has said in a court filing that the Pentagon's blacklisting of Anthropic was justified and lawful, opposing the artificial intelligence company's high-stakes lawsuit challenging the decision. The administration made its comments in a court filing on Tuesday. The Trump administration's filing says Anthropic is unlikely to succeed in its claims that the US government's action violated speech protections under the US Constitution's First Amendment, asserting that the dispute stems from contract negotiations and national security concerns, not retaliation.

Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology.

Fires have broken out in Baghdad's Green Zone after a drone swarm, believed to have been launched by groups aligned with Iran. Araghchi: Iran's system holds despite targeted leaders Experts discuss'Israeli strategy' in killing of senior Iran official Ali L'We'll be doing something with Cuba very soon', Trump says Pakistan'strongly' rejects claim it struck Kabul hospital

Chinese President Xi Jinping, Russian President Vladimir Putin and North Korean leader Kim Jong Un arrive for a reception marking the 80th anniversary of the end of World War II, at the Great Hall of the People in Beijing on Sept. 3, 2025. Moscow and Beijing are driving closer collaboration between authoritarian states and such networks help advance repression globally, according to researchers who used artificial intelligence to drill into the activities. The U.S.-based nonprofit Action for Democracy said in a report Wednesday that its researchers built an index to track seven types of cooperation, including on funding, diplomatic activities, propaganda and tech sharing. It found that China and Russia sit at the center of global authoritarian collaboration" and were jointly involved in around half of all recorded activity. The report's authors said that such cooperation generated compound returns because, for example, surveillance infrastructure exported to one regime becomes a template for the next." In a time of both misinformation and too much information, quality journalism is more crucial than ever.