'Guerilla' liberals form a'Fight Club' to oust Schumer after walking right into Trump's Oval Office trap Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY'Canceled' comedian Louis CK devours Hollywood legend's widow on streets of NYC as steamy romance is revealed Troubled 350lbs son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion'Dementia gene' now linked to another devastating neurological disease, study shows Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Tourists warned against visiting 8 popular destinations in 2026 - including European hotspot where locals don't want you Dawson's Creek star James Van Der Beek looks healthy in new social media video as his wife gushes'he's bouncing back' amid cancer battle Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Anna Kepner's grim cause of death aboard Carnival cruise ship confirmed, as homicide investigation continues Brigitte Bardot, 91, is rushed to hospital again as she battles a'serious illness' after undergoing surgery One of America's best-known billionaire's secret thoughts about Trump's state of mind revealed World's coolest streets revealed - as two UK high streets make the top 31 Disney brings Olaf to life! AI-powered snowman robot can walk and talk just like the Frozen character - as delighted fans say'it's like he jumped right off the screen' READ MORE: Inventor is forced to cut robot open to prove there's no-one inside Disney has brought one of its most legendary characters to life - and he's seriously worth melting for. Measuring just three feet (one metre) tall, Olaf the robot can walk and talk just like the delightful eternally optimistic snowman from the Frozen movies.

'Guerilla' liberals form a'Fight Club' to oust Schumer after walking right into Trump's Oval Office trap Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY'Canceled' comedian Louis C.K. devours Hollywood legend's widow on streets of NYC as steamy romance is revealed Troubled 350lbs son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Brigitte Bardot, 91, is rushed to hospital again as she battles a'serious illness' after undergoing surgery'Dementia gene' now linked to another devastating neurological disease, study shows Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Anna Kepner's grim cause of death aboard Carnival cruise ship confirmed, as homicide investigation continues Dawson's Creek star James Van Der Beek looks healthy in new social media video as his wife gushes'he's bouncing back' amid cancer battle Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Pam Bondi's furious response after beauty queen prosecutor who upstaged her has Comey and James indictments thrown out by judge Google Maps blunder turns tiny village into shortcut route, causing it to be'bombarded' by lorries that are damaging people's Grade II-listed homes READ MORE: Scientists issue warning over mind-altering'brain weapons' There are five key stages of the human brain, a new study has revealed. Researchers from the University of Cambridge compared brain scans of 3,802 people aged between 0 and 90. Their analysis revealed that the average human life is split up by four pivotal'turning points' between five key stages - childhood, adolescence, adulthood, early ageing, and late ageing.

The brain goes through five distinct phases in life, with key turning points at ages nine, 32, 66 and 83, scientists have revealed. Around 4,000 people up to the age of 90 had scans to reveal the connections between their brain cells. Researchers at the University of Cambridge showed that the brain stays in the adolescent phase until our early thirties when we peak. They say the results could help us understand why the risk of mental health disorders and dementia varies through life. The brain is constantly changing in response to new knowledge and experience - but the research shows this is not one smooth pattern from birth to death.

Scaling laws describe how learning performance improves with data, compute, or training time, and have become a central theme in modern deep learning. We study this phenomenon in a canonical nonlinear model: phase retrieval with anisotropic Gaussian inputs whose covariance spectrum follows a power law. Unlike the isotropic case, where dynamics collapse to a two-dimensional system, anisotropy yields a qualitatively new regime in which an infinite hierarchy of coupled equations governs the evolution of the summary statistics. We develop a tractable reduction that reveals a three-phase trajectory: (i) fast escape from low alignment, (ii) slow convergence of the summary statistics, and (iii) spectral-tail learning in low-variance directions. From this decomposition, we derive explicit scaling laws for the mean-squared error, showing how spectral decay dictates convergence times and error curves. Experiments confirm the predicted phases and exponents. These results provide the first rigorous characterization of scaling laws in nonlinear regression with anisotropic data, highlighting how anisotropy reshapes learning dynamics.

Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for how their risk scores relate, especially at the extremes. Using the METABRIC breast cancer cohort, we studied whether directly modeling the joint relationship between clinical and genomic machine learning risk scores could improve risk stratification for 5-year cancer-specific mortality. We created a binary 5-year cancer-death outcome and defined two sets of predictors: a clinical set (demographic, tumor, and treatment variables) and a genomic set (gene-expression $z$-scores). We trained several supervised classifiers, such as Random Forest and XGBoost, and used 5-fold cross-validated predicted probabilities as unbiased risk scores. These scores were converted to pseudo-observations on $(0,1)^2$ to fit Gaussian, Clayton, and Gumbel copulas. Clinical models showed good discrimination (AUC 0.783), while genomic models had moderate performance (AUC 0.681). The joint distribution was best captured by a Gaussian copula (bootstrap $p=0.997$), which suggests a symmetric, moderately strong positive relationship. When we grouped patients based on this relationship, Kaplan-Meier curves showed clear differences: patients who were high-risk in both clinical and genomic scores had much poorer survival than those high-risk in only one set. These results show that copula-based fusion works in real-world cohorts and that considering dependencies between scores can better identify patient subgroups with the worst prognosis.

Indoor localization systems commonly rely on fingerprinting, which requires extensive survey efforts to obtain location-tagged signal data, limiting their real-world deployability. Recent approaches that attempt to reduce this overhead either suffer from low representation ability, mode collapse issues, or require the effort of collecting data at all target locations. We present LocaGen, a novel spatial augmentation framework that significantly reduces fingerprinting overhead by generating high-quality synthetic data at completely unseen locations. LocaGen leverages a conditional diffusion model guided by a novel spatially aware optimization strategy to synthesize realistic fingerprints at unseen locations using only a subset of seen locations. To further improve our diffusion model performance, LocaGen augments seen location data based on domain-specific heuristics and strategically selects the seen and unseen locations using a novel density-based approach that ensures robust coverage. Our extensive evaluation on a real-world WiFi fingerprinting dataset shows that LocaGen maintains the same localization accuracy even with 30% of the locations unseen and achieves up to 28% improvement in accuracy over state-of-the-art augmentation methods.

Data Flows and Colonial Regimes in Africa: A Critical Analysis of the Colonial Futurities Embedded in AI Recommendation Algorithms Angella Ndaka, University of Witwatersrand, Johannesburg, South Africa Fátima Ávila - Acosta, Berlin Graduate School of Social Sciences at Humboldt University, Berlin, Germany Harnred Mbula, Centre for Epistemic Justice, Nairobi, Kenya Christine Amera, Centre for Epistemic Justice, Nairobi Kenya Sandra Tiyani Chauke University of Pretoria, South Africa Eucabeth Majiwa Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya Abstract In the last few years, Africa has experienced growth in a thriving ecosystem of Artificial Intelligence (AI) technologies and systems, developed and promoted by both local and global technology players. While the sociotechnical imaginaries about these syst ems promote AI as critical to achiev ing Africa's sustainable development agenda, some of them have subtly permeated society, recreating new values, cultures, practices, and histories that threaten to marginalize minority groups in the region. Africa predominantly frames AI as an imaginary solution to address complex social challenges; however, the narrative subtly ignores deeper power - related concerns, including data governance, embedded algorithmic colonialism, and the exploitation that propag ates new digital colonial sites. However, the development of current AI ethics in Africa is in its infancy and predominantly framed through lenses of Western perspective, with the social and ethical impacts of the AI innovations and application on African epistemologies and worldviews not prioritized. To ensure that people on the African continent leverage the benefits of AI, these social and ethical impacts o f AI need to be critically and explicitly considered and addressed. This chapter will therefore seek to frame the elemental and invisible problems of AI and big data in the African context by examining digital sites and infrastructure through the lens of power and interests. It will present reflections on how these sites are using AI recommendation algorithms to recreate new digital societies in the region, how they have the potential to propagate algorithmic colonialism and negative gender norms, and what this means for the regional sustainable development agenda. The chapter proposes adopting business models that embrace response - ability and consider the existence of alternative socio - material worlds of AI. These reflections will mainly come from ongoing discussions with Kenyan social media users in this author's user space talks, which take place every month. Keywords: Artificial Intelligence; algorithmic colonialism; Data; response - ability; digital sites Section 1: Introduction The growing global interest, combined with rising investments in AI skilling and infrastructure development, is a key driver of the expanding landscape of AI technologies and systems across Africa.

Recent advances in deep learning have enabled significant progress in plant disease classification using leaf images. Much of the existing research in this field has relied on the PlantVillage dataset, which consists of well-centered plant images captured against uniform, uncluttered backgrounds. Although models trained on this dataset achieve high accuracy, they often fail to generalize to real-world field images, such as those submitted by farmers to plant diagnostic systems. This has created a significant gap between published studies and practical application requirements, highlighting the necessity of investigating and addressing this issue. In this study, we investigate whether attention-based architectures and zero-shot learning approaches can bridge the gap between curated academic datasets and real-world agricultural conditions in plant disease classification. We evaluate three model categories: Convolutional Neural Networks (CNNs), Vision Transformers, and Contrastive Language-Image Pre-training (CLIP)-based zero-shot models. While CNNs exhibit limited robustness under domain shift, Vision Transformers demonstrate stronger generalization by capturing global contextual features. Most notably, CLIP models classify diseases directly from natural language descriptions without any task-specific training, offering strong adaptability and interpretability. These findings highlight the potential of zero-shot learning as a practical and scalable domain adaptation strategy for plant health diagnosis in diverse field environments.

Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural context. In this work, we introduce FanarGuard, a bilingual moderation filter that evaluates both safety and cultural alignment in Arabic and English. We construct a dataset of over 468K prompt and response pairs, drawn from synthetic and public datasets, scored by a panel of LLM judges on harmlessness and cultural awareness, and use it to train two filter variants. To rigorously evaluate cultural alignment, we further develop the first benchmark targeting Arabic cultural contexts, comprising over 1k norm-sensitive prompts with LLM-generated responses annotated by human raters. Results show that FanarGuard achieves stronger agreement with human annotations than inter-annotator reliability, while matching the performance of state-of-the-art filters on safety benchmarks. These findings highlight the importance of integrating cultural awareness into moderation and establish FanarGuard as a practical step toward more context-sensitive safeguards.

Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large and diverse datasets. This paper presents the first comprehensive empirical study of TSFMs in global financial markets. Using a large-scale dataset of daily excess returns across diverse markets, we evaluate zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models. We find that off-the-shelf pre-trained TSFMs perform poorly in zero-shot and fine-tuning settings, whereas models pre-trained from scratch on financial data achieve substantial forecasting and economic improvements, underscoring the value of domain-specific adaptation. Increasing the dataset size, incorporating synthetic data augmentation, and applying hyperparameter tuning further enhance performance.