Doctors from Scotland and the US have completed what is thought to be a world-first stroke procedure using a robot. Prof Iris Grunwald, of the University of Dundee, performed the remote thrombectomy - the removal of blood clots after a stroke - on a human cadaver that had been donated to medical science. The professor was at Ninewells Hospital in Dundee, while the body she was operating on while using the machine was across the city at the university. Hours later, Ricardo Hanel - a neurosurgeon in Florida - used the technology to carry out the first transatlantic surgery from his Jacksonville base on a human body in Dundee over 4,000 miles (6,400km) away. The team has called it a potential game changer if it becomes approved for use on patients.

A woman who was plunged into sudden menopause after surgery to remove both ovaries is spearheading efforts to change NHS policy. Kate Dyson, 44, from Hastings, East Sussex, underwent the surgery six months ago after having a subtotal hysterectomy in 2021 to remove her uterus - a procedure which leaves the cervix in place. The mum-of-three says she was completely unprepared for the impact of surgical menopause, which is triggered by both ovaries being removed. Honestly, it was like falling off a hormonal cliff edge, she told BBC Radio Sussex. Within hours of the surgery I was home the same day.

'Neuralink is doing legitimate technology development for neuroscience, and then Elon Musk comes along and starts talking about telepathy and stuff.' 'Neuralink is doing legitimate technology development for neuroscience, and then Elon Musk comes along and starts talking about telepathy and stuff.' Investors' 'dumb transhumanist ideas' setting back neurotech progress, say experts I t has been an excellent year for neurotech, if you ignore the people funding it. In August, a tiny brain implant successfully decoded the inner speech of paralysis patients. In October, an eye restored sight to patients who had lost their vision. It would just be better, say experts, if the most famous investors in the space - tech magnates such as Elon Musk and OpenAI's Sam Altman - were less interested in uploading their brains to computers or merging with AI. "It's distorting the debate a lot," said Marcello Ienca, a professor of neuroethics at the Technical University of Munich.

You probably don't give much thought to the device that you're reading this article on, as long as it looks good and keeps working. But the elements that power and run it are the subject of an escalating struggle between the world's two biggest economies - the US and China - with African countries in the eye of the storm. The African continent is rich in critical minerals and metals - like lithium, rare earths, cobalt and tungsten - which are vital to making and running our personal tech. Such materials are also essential for everything from electric vehicles, to AI data centres, and weapon systems. China has long been the biggest player in the global market for critical minerals and metals.

Abstract--Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks, where malicious actors inject fake user profiles, including a group of well-designed fake ratings, to manipulate recommendations. Due to security and privacy constraints in practice, attackers typically possess limited knowledge of the victim system and thus need to craft profiles that have transferability across black-box RSs. T o maximize the attack impact, the profiles often remains imperceptible. However, generating such high-quality profiles with the restricted resources is challenging. Some works suggest incorporating fake textual reviews to strengthen the profiles; yet, the poor quality of the reviews largely undermines the attack effectiveness and imperceptibility under the practical setting. T o tackle the above challenges, in this paper, we propose to enhance the quality of the review text by harnessing in-context learning (ICL) capabilities of multimodal foundation models. T o this end, we introduce a demonstration retrieval algorithm and a text style transfer strategy to augment the navie ICL. Specifically, we propose a novel practical attack framework named RAGAN to generate high-quality fake user profiles, which can gain insights into the robustness of RSs. The profiles are generated by a jailbreaker and collaboratively optimized on an instructional agent and a guardian to improve the attack transferability and imperceptibility. Comprehensive experiments on various real-world datasets demonstrate that RAGAN achieves the state-of-the-art poisoning attack performance. Impact Statement--Recommender systems play a vital role across e-commerce, online content, and social media platforms, benefiting both users and businesses through personalized suggestions and improved engagement. These advantages also create incentives for malicious actors to exploit them. Recent studies reveal that modern recommender systems are vulnerable to data poisoning attacks, leading to unfair competition and loss of user trust. However, existing attack methods often have limited practicality, overestimating system robustness under real-world constraints.

Social media memes are a challenging domain for hate detection because they intertwine visual and textual cues into culturally nuanced messages. To tackle these challenges, we introduce TRACE, a hierarchical multimodal framework that leverages visually grounded context augmentation, along with a novel caption-scoring network to emphasize hate-relevant content, and parameter-efficient fine-tuning of CLIP's text encoder. Our experiments demonstrate that selectively fine-tuning deeper text encoder layers significantly enhances performance compared to simpler projection-layer fine-tuning methods. Specifically, our framework achieves state-of-the-art accuracy (0.807) and F1-score (0.806) on the widely-used Hateful Memes dataset, matching the performance of considerably larger models while maintaining efficiency. Moreover, it achieves superior generalization on the MultiOFF offensive meme dataset (F1-score 0.673), highlighting robustness across meme categories. Additional analyses confirm that robust visual grounding and nuanced text representations significantly reduce errors caused by benign confounders. We publicly release our code to facilitate future research.

Rapid advances in generative AI have led to increasingly realistic deepfakes, posing growing challenges for law enforcement and public trust. Existing passive deepfake detectors struggle to keep pace, largely due to their dependence on specific forgery artifacts, which limits their ability to generalize to new deepfake types. Proactive deepfake detection using watermarks has emerged to address the challenge of identifying high-quality synthetic media. However, these methods often struggle to balance robustness against benign distortions with sensitivity to malicious tampering. This paper introduces a novel deep learning framework that harnesses high-dimensional latent space representations and the Multi-Agent Adversarial Reinforcement Learning (MAARL) paradigm to develop a robust and adaptive watermarking approach. Specifically, we develop a learnable watermark embedder that operates in the latent space, capturing high-level image semantics, while offering precise control over message encoding and extraction. The MAARL paradigm empowers the learnable watermarking agent to pursue an optimal balance between robustness and fragility by interacting with a dynamic curriculum of benign and malicious image manipulations simulated by an adversarial attacker agent. Comprehensive evaluations on the CelebA and CelebA-HQ benchmarks reveal that our method consistently outperforms state-of-the-art approaches, achieving improvements of over 4.5% on CelebA and more than 5.3% on CelebA-HQ under challenging manipulation scenarios.

The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-language, and hardware-software toolchains that lead to dependency hell. We present Pixi, a unified package-management framework that addresses these issues by capturing exact dependency states in project-level lockfiles, ensuring bit-for-bit reproducibility across platforms. Its high-performance SAT solver achieves up to 10x faster dependency resolution than comparable tools, while integration of the conda-forge and PyPI ecosystems removes the need for multiple managers. Adopted in over 5,300 projects since 2023, Pixi reduces setup times from hours to minutes and lowers technical barriers for researchers worldwide. By enabling scalable, reproducible, collaborative research infrastructure, Pixi accelerates progress in robotics and AI.

This paper examines the role of public interest litigation in promoting accountability for AI and automated decision-making (ADM) in Australia. Since ADM regulation faces geopolitical headwinds, effective governance will have to rely at least in part on the enforcement of existing laws. Drawing on interviews with Australian public interest litigators, technology policy activists, and technology law scholars, the paper positions public interest litigation as part of a larger ecosystem for transparency, accountability and justice with respect to ADM. It builds on one participant's characterisation of litigation about ADM as an exercise in legal retrofitting: adapting old laws to new circumstances. The paper's primary contribution is to aggregate, organise and present original insights on pragmatic strategies and tactics for effective public interest litigation about ADM. Naturally, it also contends with the limits of these strategies, and of the Australian legal system. Where limits are, however, capable of being overcome, the paper presents findings on urgent needs: the enabling institutional arrangements without which effective litigation and accountability will falter. The paper is relevant to law and technology scholars; individuals and groups harmed by ADM; public interest litigators and technology lawyers; civil society and advocacy organisations; and policymakers.

Taken in Algeria, 'Last Fireworks' is this year's first place category and open competition overall winner. Breakthroughs, discoveries, and DIY tips sent every weekday. The winners of the 2025 Epson International Pano awards have been announced, showcasing photographs of our great, big, beautiful world in ultra-wide glory. Italy's Alex Wides (Alessandro Cantarelli) won the Open Photographer of the Year and the Nature/Landscape category for his fine-art landscapes (seen above and below). Among this year's 3,423 entries, there were more photographs of the Northern Lights than usual, coinciding with the 11-year solar cycle maximum .