Large language models (LLMs) often reflect Western-centric biases, limiting their effectiveness in diverse cultural contexts. Although some work has explored cultural alignment, the potential for cross-cultural transfer, using alignment in one culture to improve performance in others, remains underexplored. This paper investigates cross-cultural transfer of commonsense reasoning in the Arab world, where linguistic and historical similarities coexist with local cultural differences. Using a culturally grounded commonsense reasoning dataset covering 13 Arab countries, we evaluate lightweight alignment methods such as in-context learning and demonstration-based reinforcement (DITTO), alongside baselines like supervised fine-tuning and direct preference optimization. Our results show that merely 12 culture-specific examples from one country can improve performance in others by 10\% on average, within multilingual models. In addition, we demonstrate that out-of-culture demonstrations from Indonesia and US contexts can match or surpass in-culture alignment for MCQ reasoning, highlighting cultural commonsense transferability beyond the Arab world. These findings demonstrate that efficient cross-cultural alignment is possible and offer a promising approach to adapt LLMs to low-resource cultural settings.

Proper initialization is crucial for any system, particularly in multi-agent systems (MAS), where it plays a pivotal role in determining both the system's efficiency and effectiveness. However, existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages. Inspired by the principles of effective team composition, we propose AgentInit, which aims to optimize the structure of agent teams. Specifically, in addition to multi-round interactions and reflections between agents during agent generation, AgentInit incorporates a Natural Language to Format mechanism to ensure consistency and standardization. Balanced team selection strategies using Pareto principles are subsequently applied to jointly consider agent team diversity and task relevance to promote effective and efficient collaboration and enhance overall system performance. Experiments show that AgentInit consistently outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks, achieving an overall performance improvement of up to 1.2 and 1.6, respectively, while also significantly reducing token consumption. Further analysis confirms its strong transferability to similar tasks and verifies the effectiveness of its key components, demonstrating its capability and adaptability as a reliable MAS initialization method. Source code and models are available at https://github.com/1737423697/AgentInit.

Federated learning in practice must contend with heterogeneous feature spaces, severe non-IID data, and scarce labels across clients. We present FedFusion, a federated transfer-learning framework that unifies domain adaptation and frugal labelling with diversity-/cluster-aware encoders (DivEn, DivEn-mix, DivEn-c). Labelled teacher clients guide learner clients via confidence-filtered pseudo-labels and domain-adaptive transfer, while clients maintain personalised encoders tailored to local data. To preserve global coherence under heterogeneity, FedFusion employs similarity-weighted classifier coupling (with optional cluster-wise averaging), mitigating dominance by data-rich sites and improving minority-client performance. The frugal-labelling pipeline combines self-/semi-supervised pretext training with selective fine-tuning, reducing annotation demands without sharing raw data. Across tabular and imaging benchmarks under IID, non-IID, and label-scarce regimes, FedFusion consistently outperforms state-of-the-art baselines in accuracy, robustness, and fairness while maintaining comparable communication and computation budgets. These results show that harmonising personalisation, domain adaptation, and label efficiency is an effective recipe for robust federated learning under real-world constraints.

Abstract--Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges from non-IID data, client unreliability, and adversarial manipulation. This paper introduces F edFiTS, a trust-and fairness-aware selective FL framework that advances the FedFaSt line by combining fitness-based client election with slotted aggregation. FedFiTS implements a three-phase participation strategy--free-for-all training, natural selection, and slotted team participation--augmented with dynamic client scoring, adaptive thresh-olding, and cohort-based scheduling to balance convergence efficiency with robustness. A theoretical convergence analysis establishes bounds for both convex and non-convex objectives under standard assumptions, while a communication-complexity analysis shows reductions relative to FedA vg and other baselines. Experiments on diverse datasets--medical imaging (X-ray pneumonia), vision benchmarks (MNIST, FMNIST), and tabular agricultural data (Crop Recommendation)--demonstrate that FedFiTS consistently outperforms FedA vg, FedRand, and FedPow in accuracy, time-to-target, and resilience to poisoning attacks. By integrating trust-aware aggregation with fairness-oriented client selection, FedFiTS advances scalable and secure FL, making it well suited for real-world healthcare and cross-domain deployments. The digitisation of healthcare and advances in artificial intelligence (AI) have reshaped medical data usage, enabling improved decision-making. Federated Learning (FL) represents a pivotal development, allowing collaborative model training across institutions without compromising data privacy, making it a crucial aspect in medical contexts. However, FL in healthcare must address challenges related to trust, transparency, security, and fairness.

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Why is Jimmy Kimmel returning to ABC? What is the H-1B visa programme? Inside Story What are the risks of a global digital divide in AI? UN warns 118 states are way behind leaders in Artificial Intelligence. Artificial intelligence is the tech revolution of our times. But more than 100 countries are falling way behind those leading the surge in AI, and are losing out. That is what the UN General Assembly will hear this week.

It took a while for humans to climb the food chain. Breakthroughs, discoveries, and DIY tips sent every weekday. Most paleobiologists believe humanity truly began around 2 million years ago with a species known as . Part of this evolutionary demarcation stems from the theory that the early hominins were some of the first primates to consistently shift from the role of "prey" to that of "predator." But according to an analysis of tiny injuries on two fossilized jaw fragments, some researchers now believe our ancestors required a bit more time to ascend the food chain.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' MORE: Gabrielle surging into major hurricane as forecasters warn of'life-threatening' impact to East Coast Hurricane forecasters warn that the US East Coast could feel the wrath of two tropical cyclones at the same time next week. The National Hurricane Center (NHC) revealed their updated tracks for a pair of tropical disturbances brewing in the Atlantic, giving both a high likelihood of turning into named storms within seven days . One of these threatening weather systems, currently dubbed Invest 94L, is expected to form right off the coast of Florida . The other potential storm, called Invest 93L, has been developing in the middle of the Atlantic and forecasters unveiled an initial path that could take it towards the Carolinas by the end of the week.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' Why Amazon Fresh failed: As all 19 remaining UK stores close, experts blame'dystopian' concept that stoked fears of Big Brother surveillance READ MORE: How artificial intelligence is ALREADY patrolling Britain's shops It was supposed to trigger a revolution in how we shop on the high street. But Amazon's'Fresh' shopping concept - which uses AI to track customers - may instead be remembered as a failed experiment in mass surveillance. The tech giant has announced that it has made the'difficult decision' to close all 19 Amazon Fresh stores in the UK - putting up to 250 workers at risk. Amazon claims the closures are the result of a'thorough evaluation of business operations and the very substantial growth opportunities in online delivery'.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' Shake It Off! How Taylor Swift has ditched her Southern drawl in favour of a northern American accent, revealed In the 19 years since she released her first song, Taylor Swift's music been through several transitions, changing from country, to pop, to indie folk, and almost every genre in between. Now, a study has revealed how it's not just Swift's music that has evolved. Scientists from the University of Minnesota say the chart-topping singer's accent has also changed over time. In their study, the team analysed years of Swift's recorded interviews to track how her dialect has transformed.