Since September, residents on South Korea's Jeju island have been spotting small packs of what appear to be bags of Chinese tea washed ashore. Upon closer inspection, however, they were found to contain ketamine. Some 28kg (62 lbs) of the drug, wrapped in foil and labelled with the Chinese character for tea, have been found on at least eight occasions, police say. Ketamine is used as an anaesthetic in medical procedures, but its recreational use is illegal in South Korea. It can cause severe physical and mental damage, including to the heart and lungs, when misused.

Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.

Breakthroughs, discoveries, and DIY tips sent every weekday. Ancient Egyptians may have used opium a . Based on recent examinations, archaeologists now say the drug may even have been a near-daily recreational habit. Opium might have even been widely used across socio-economic classes as long as 3,000 years ago. The evidence is detailed in a study recently published in the, and offers a glimpse into the daily lives of regular Egyptians and royalty alike.

Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as role-playing agents, we examine whether LLMs exhibit emotional stereotypes when assigned nationality-specific personas. Specifically, we investigate how different countries are represented in pre-trained LLMs through emotion attributions and whether these attributions align with cultural norms. To provide a deeper interpretive lens, we incorporate four key cultural dimensions, namely Power Distance, Uncertainty Avoidance, Long-Term Orientation, and Individualism, derived from Hofstedes cross-cultural framework. Our analysis reveals significant nationality-based differences, with emotions such as shame, fear, and joy being disproportionately assigned across regions. Furthermore, we observe notable misalignment between LLM-generated and human emotional responses, particularly for negative emotions, highlighting the presence of reductive and potentially biased stereotypes in LLM outputs.

The Forward-Forward (FF) learning algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function with well-designed negative samples for contrastive learning. Existing goodness functions are typically defined as the sum of squared postsynaptic activations, neglecting correlated variability between neurons. In this work, we propose a novel goodness function termed dimensionality compression that uses the effective dimensionality (ED) of fluctuating neural responses to incorporate second-order statistical structure. Our objective minimizes ED for noisy copies of individual inputs while maximizing it across the sample distribution, promoting structured representations without the need to prepare negative samples.We demonstrate that this formulation achieves competitive performance compared to other non-BP methods. Moreover, we show that noise plays a constructive role that can enhance generalization and improve inference when predictions are derived from the mean of squared output, which is equivalent to making predictions based on an energy term. Our findings contribute to the development of more biologically plausible learning algorithms and suggest a natural fit for neuromorphic computing, where stochasticity is a computational resource rather than a nuisance. The code is available at https://github.com/ZhichaoZhu/StochasticForwardForward

Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in extracting meaningful insights from complex data. We introduce Non-negative Stiefel Approximating Flow (NSA-Flow), a general-purpose matrix estimation framework that unifies ideas from sparse matrix factorization, orthogonalization, and constrained manifold learning. NSA-Flow enforces structured sparsity through a continuous balance between reconstruction fidelity and column-wise decorrelation, parameterized by a single tunable weight. The method operates as a smooth flow near the Stiefel manifold with proximal updates for non-negativity and adaptive gradient control, yielding representations that are simultaneously sparse, stable, and interpretable. Unlike classical regularization schemes, NSA-Flow provides an intuitive geometric mechanism for manipulating sparsity at the level of global structure while simplifying latent features. We demonstrate that the NSA-Flow objective can be optimized smoothly and integrates seamlessly with existing pipelines for dimensionality reduction while improving interpretability and generalization in both simulated and real biomedical data. Empirical validation on the Golub leukemia dataset and in Alzheimer's disease demonstrate that the NSA-Flow constraints can maintain or improve performance over related methods with little additional methodological effort. NSA-Flow offers a scalable, general-purpose tool for interpretable ML, applicable across data science domains.

The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods, particularly heuristic-based divergence attacks, often exhibit limited success and offer limited insight into the fundamental drivers of memorization leakage. This paper introduces Confusion-Inducing Attacks (CIA), a principled framework for extracting memorized data by systematically maximizing model uncertainty. We empirically demonstrate that the emission of memorized text during divergence is preceded by a sustained spike in token-level prediction entropy. CIA leverages this insight by optimizing input snippets to deliberately induce this consecutive high-entropy state. For aligned LLMs, we further propose Mismatched Supervised Fine-tuning (SFT) to simultaneously weaken their alignment and induce targeted confusion, thereby increasing susceptibility to our attacks. Experiments on various unaligned and aligned LLMs demonstrate that our proposed attacks outperform existing baselines in extracting verbatim and near-verbatim training data without requiring prior knowledge of the training data. Our findings highlight persistent memorization risks across various LLMs and offer a more systematic method for assessing these vulnerabilities.

Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world. Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and Yoruba (i.e <1% of the languages spoken in Nigeria). This is in part due to the unavailability of textual data in these languages to train and apply NLP algorithms. In this work, we introduce ibom -- a dataset for machine translation and topic classification in four Coastal Nigerian languages from the Akwa Ibom State region: Anaang, Efik, Ibibio, and Oro. These languages are not represented in Google Translate or in major benchmarks such as Flores-200 or SIB-200. We focus on extending Flores-200 benchmark to these languages, and further align the translated texts with topic labels based on SIB-200 classification dataset. Our evaluation shows that current LLMs perform poorly on machine translation for these languages in both zero-and-few shot settings. However, we find the few-shot samples to steadily improve topic classification with more shots.

An observatory detected the first radio signal from the interstellar object 3I/Atlas. An image of the interstellar comet 3I/Atlas, captured by the Hubble telescope on July 21, 2025. More evidence has emerged to support the natural origin of comet 3I/Atlas . After several weeks of conspiracy theories, social media debates, and speculation on popular podcasts such as Joe Rogan's, this interstellar object is still a comet . The most recent confirmation came from an observatory in South Africa that detected the first radio signal from 3I/Atlas.

Newsom blasts'pathetic' Democrats for'surrendering' to Trump as'gang of eight' senators join Republicans to end longest government shutdown in US history Olympics set to ban ALL transgender athletes and Imane Khelif'DSD' competitors from female events after'finding scientific evidence of advantages to being born male' The REAL story of how Meghan lost her best friend: They've not spoken in years... but now insiders reveal'aggravation' and tensions that go'deeper than anyone knows' Scientists are baffled to discover mysterious'voids' in the third-largest pyramid of Giza - as scans suggest they could be a secret entrance Jordon Hudson appears to dodge encounter with Bill Belichick's daughter-in-law at UNC game after social media dig PayPal billionaire delivers chilling warning about spread of Communism as eerily prescient comment comes to light in wake of Mamdani's win Has Sydney Sweeney become too toxic for Hollywood? Star suffers box office flop with new film Christy after THAT controversial ad, Zendaya'feud' and backlash over her political views Dark side of Danielle Bernstein: She is America's most hated influencer... but now insiders reveal claims of behavior so outrageous they'kind of respect her' for getting away with it My brother was ALIVE on the operating table as surgeons tried to harvest his organs. Donald Trump launches new broadside at'corrupt' BBC journalists as director-general Tim Davie and news boss both quit in disgrace over doctored video of US President Meghan Markle wealthy pal's bookshop'is reported to council for serving her As Ever wine without a licence' after duchess used it as promotional pop-up Sussexes attended charity gala with Serena Williams before Kris Jenner's birthday party - while Royal Family marked Remembrance Sunday NFL announcer Tony Romo slammed by fans after outrageous'DTF' sexual reference live on air Donald Trump makes stunning flyover for first NFL visit of the season... hours after it emerged he wants $3.7bn new stadium named after him Jay Leno makes touching remark about caring for wife Mavis after 45 years of marriage amid heartbreaking'advanced' dementia diagnosis Barbara Bach captured America's hearts as a Bond girl... see her now after 44 years as a Beatle's wife Humanoid robot moves so smoothly, its inventor is forced to cut it open to prove there's not a person hiding inside READ MORE: Nike launches the world's first powered footwear A humanoid robot has reached new depths of the uncanny valley with its smooth, humanlike movements. Chinese electric vehicle manufacturer, Xpeng, revealed its latest robot dubbed the Xpeng IRON, at an event last week. The bot proved so eerily lifelike that its inventors were forced to cut it open on stage to prove there wasn't a person hiding inside.