Soon-to-be-laid-off Meta contractors say they're being treated differently than Mark Zuckerberg's full-time employees, who stand to receive more generous severance packages. Now we're being left behind," chanted a horde of contract workers who gathered outside Meta's offices in Dublin, Ireland, on Friday afternoon. Waving flags, brandishing signs, and armed with whistles and vuvuzelas, they were out to protest a round of planned layoffs. The workers are employed by Dublin-based company Covalen, which handles content moderation and data labeling services that help Meta to fine-tune its AI products. In April, Covalen told 700 employees that their jobs were at risk, citing "reduced demand," WIRED reported . A large swath of the affected workers won't receive any severance because they've been employed for less than two years. The rest are being offered the minimum payout required under local labor laws--two weeks' pay for every year of employment--according to the Communications Workers' Union (CWU), whose members include Covalen employees. "We're just getting the crumbs here," Aadel Obaid, a team manager at Covalen who is part of the planned layoffs, tells WIRED. "Give us a little bit of the pie." To try to compel Covalen into revising the severance package, workers voted to strike outside the company's corporate office, before marching to Meta's nearby European headquarters. According to John Bohan, an organizer at the CWU, Meta could use its leverage as an anchor client to pressure Covalen into offering its employees an enhanced severance package. The workers are asking for double what's currently being offered--and at least some form of payment for workers who don't meet the two-year threshold. The company could also release Covalen workers from a "cooldown period" preventing them from working on another Meta account for six months after being laid off, Bohan says. At 1 pm local time on Friday, the striking workers began to gather outside Covalen's corporate headquarters, a red-brick office building on an otherwise largely residential street in the heart of Dublin. The protests began with a wall of sound: the workers beat drums, booed, whistled, shouted, and catcalled. Then came a volley of call-and-response chants led by a worker with a megaphone. The building's security guard watched, bemused, from inside the lobby, hands on his hips. Two hours later, the group--now more than 150 people--began to march down the center of the mile-long stretch of road to Meta's campus, slowing the trailing traffic to a crawl. Dubliners enjoying the early onset of summer stopped to gawp; some applauded. When the protesters arrived at Meta's complex, two security guards stood with crossed arms, blocking the way. The group set up at the gates and began another round of chants: "We scrub the feed.

Datacentre industry representatives disputed the findings and said the sector boosted the economy. Datacentre industry representatives disputed the findings and said the sector boosted the economy. 'Hidden datacentre tax' costing Irish households millions, report says Datacentres used 22% of country's electricity last year, pushing up household bills, study suggests Thu 28 May 2026 09.01 EDTLast modified on Thu 28 May 2026 09.32 EDT Energy demand by datacentres in Ireland has added hundreds of euros to household electricity bills in a pattern that could be replicated across Europe, according to a report. Ireland's growing number of datacentres last year used 22% of the country's electricity, more than all urban homes combined, according to the Central Statistics Office. The equivalent figure in the US and UK is 6%.

The folds in the tilted rock layers and differences in their erosion rate gives the limestone the step-like appearance we see today. Breakthroughs, discoveries, and DIY tips sent six days a week. While Ireland's natural landscape is known for every shade of green imaginable, a different color dominates one part of Ireland. Along the Burren Region on the country's western coast, gray limestone pavement covers the rocky and treeless landscape. NASA's Operational Land Imager (OLI) on the Landsat 8 satellite captured a view of Burren, showing the rocky landscape and an 860-foot-tall limestone hill called Moneen Mountain.

Plastic pollution is one of those problems everyone can see, yet few know how to tackle it effectively. I grew up walking the beaches around Tramore in County Waterford, Ireland, where plastic debris has always been part of the coastline, including bottles, fragments of fishing gear and food packaging. According to the UN, every year 19-23 million tonnes of plastic lands up in lakes, rivers and seas, and it has a huge impact on ecosystems, creating pollution and damaging animal habitats. Community groups do tremendous work cleaning these beaches, but they're essentially walking blind, guessing where plastic accumulates, missing hot spots, repeating the same stretches while problem areas may go untouched. Years later, working in marine robotics at the University of Limerick, I began developing tools to support marine clean-up and help communities find plastic pollution along our coastline.

An Garda Síochána (Irish police force) has launched an investigation after drones were detected in Irish skies on the night the Ukrainian president arrived in Ireland. Volodymyr Zelensky flew into Dublin late on Monday night for a one-day official visit with his wife, First Lady Olena Zelenska. Senior Irish government figures, including Taoiseach (Irish Prime Minister) Micheál Martin, have been briefed on the issue. Martin confirmed it would be discussed at a National Security Council meeting later this month. In a statement, gardaí said its Special Detective Unit (SDU) is investigating the matter and will be liaising with the Defence Forces and international security partners.

The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.

Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images. While recent advances in multi-modal learning have significantly improved performance, current methods still suffer from limited answer reliability and poor interpretability, impairing the ability of clinicians and patients to understand and trust model outputs. To address these limitations, this work first proposes a Region-Aware Multimodal Chain-of-Thought (RMCoT) dataset, in which the process of producing an answer is preceded by a sequence of intermediate reasoning steps that explicitly ground relevant visual regions of the medical image, thereby providing fine-grained explainability. Furthermore, we introduce a novel verifiable reward mechanism for reinforcement learning to guide post-training, improving the alignment between the model's reasoning process and its final answer. Remarkably, our method achieves comparable performance using only one-eighth of the training data, demonstrating the efficiency and effectiveness of the proposal. The dataset is available at https://www.med-vqa.com/GEMeX/.

Carbapenemase-Producing Enterobacteriace poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and extended length of stay (LOS) remains underexplored, particularly with modern deep learning approaches. This study introduces an eXplainable AI modeling framework to investigate CPE impact on patient outcomes from Electronic Medical Records data of an Irish hospital. We analyzed an inpatient dataset from an Irish acute hospital, incorporating diagnostic codes, ward transitions, patient demographics, infection-related variables and contact network features. Several Transformer-based architectures were benchmarked alongside traditional machine learning models. Clinical outcomes were predicted, and XAI techniques were applied to interpret model decisions. Our framework successfully demonstrated the utility of Transformer-based models, with TabTransformer consistently outperforming baselines across multiple clinical prediction tasks, especially for CPE acquisition (AUROC and sensitivity). We found infection-related features, including historical hospital exposure, admission context, and network centrality measures, to be highly influential in predicting patient outcomes and CPE acquisition risk. Explainability analyses revealed that features like "Area of Residence", "Admission Ward" and prior admissions are key risk factors. Network variables like "Ward PageRank" also ranked highly, reflecting the potential value of structural exposure information. This study presents a robust and explainable AI framework for analyzing complex EMR data to identify key risk factors and predict CPE-related outcomes. Our findings underscore the superior performance of the Transformer models and highlight the importance of diverse clinical and network features.

Scientific diagrams are vital tools for communicating structured knowledge across disciplines. However, they are often published as static raster images, losing symbolic semantics and limiting reuse. While Multimodal Large Language Models (MLLMs) offer a pathway to bridging vision and structure, existing methods lack semantic control and structural interpretability, especially on complex diagrams. We propose Draw with Thought (DwT), a training-free framework that guides MLLMs to reconstruct diagrams into editable mxGraph XML code through cognitively-grounded Chain-of-Thought reasoning. DwT enables interpretable and controllable outputs without model fine-tuning by dividing the task into two stages: Coarse-to-Fine Planning, which handles perceptual structuring and semantic specification, and Structure-Aware Code Generation, enhanced by format-guided refinement. To support evaluation, we release Plot2XML, a benchmark of 247 real-world scientific diagrams with gold-standard XML annotations. Extensive experiments across eight MLLMs show that our approach yields high-fidelity, semantically aligned, and structurally valid reconstructions, with human evaluations confirming strong alignment in both accuracy and visual aesthetics, offering a scalable solution for converting static visuals into executable representations and advancing machine understanding of scientific graphics.

Commonaiverse is an interactive installation exploring human emotions through full-body motion tracking and real-time AI feedback. Participants engage in three phases: Teaching, Exploration and the Cosmos Phase, collaboratively expressing and interpreting emotions with the system. The installation integrates MoveNet for precise motion tracking and a multi-recommender AI system to analyze emotional states dynamically, responding with adaptive audiovisual outputs. By shifting from top-down emotion classification to participant-driven, culturally diverse definitions, we highlight new pathways for inclusive, ethical affective computing. We discuss how this collaborative, out-of-the-box approach pushes multimedia research beyond single-user facial analysis toward a more embodied, co-created paradigm of emotional AI. Furthermore, we reflect on how this reimagined framework fosters user agency, reduces bias, and opens avenues for advanced interactive applications.