Density estimation in high-dimensional settings is an important and challenging statistical problem.Traditional methods based on kernel smoothing are inefficient in high dimensions due to the difficulties in specifying appropriate location-adaptive kernels. In this work, we introduce pre-training, a key idea behind many cutting-edge AI technologies, to the context of non-parametric density estimation. By establishing a pre-trained neural network that can recommend an appropriate location-adaptive kernel for each sample point, efficient density estimation with adaptive kernels is achieved in high dimensions. A wide range of numerical experiments show that this strategy is highly effective for improving density-estimation accuracy, when the target distribution is close to the distribution family for pre-training. When the target distribution is substantially different from the pre-training distribution family, the benefit from the proposed pre-training strategy may be diluted, but can be reactivated by an additional fine-tuning procedure.

This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networks to approximate the optimal partitioning rule from simulated data, the proposed framework reduces reliance on conventional clustering methods, such as $K$-means, $K$-medoids, or hierarchical clustering, and their associated objective functions and heuristics. Leveraging statistical features, such as autocorrelations and quantile autocorrelations, the approach learns a data-driven affinity structure from which clustering partitions can be recovered, without requiring explicit prior specification of cluster shapes or structures. In addition, one version of the method can automatically determine the number of clusters, avoiding ad-hoc selection procedures. Comprehensive empirical studies show that the proposed framework achieves competitive or superior clustering accuracy relative to traditional methods, even in challenging scenarios where competing techniques are provided with the true number of clusters. An application to financial time series of stock returns illustrates its practical utility. By reducing the need for algorithm selection and calibration, the proposed framework opens new possibilities for automated, adaptive, and data-driven clustering of temporal data across scientific and industrial domains.

Training large language models (LLMs) such as GPT-5 and Qwen-3 (Singh et al., 2025; Yang et al., 2025) on massive text corpora aims at capturing the underlying distribution of natural language. Yet, it remains challenging for the trained model to extrapolate to out-of-distribution or out-of-domain settings beyond the support of its training data. The literature has seen the development of various data perturbation techniques, such as synonym replacement, random insertion, deletion, and swap, that modify training instances into semantically similar variants to effectively expose LLMs to a broader range of inputs and improve their ability to generalize beyond the training data (Feng et al., 2019, 2020; Li et al., 2024; Cen et al., 2026). However, their approach remains grounded in the discrete, word-level augmentation procedures mentioned previously, which may restrict its adaptivity across diverse domains. While discrete perturbations are simple to use, they could be too coarse and hard to refine due to the complexity of natural language (Park et al., 2022; Li et al., 2023). Meanwhile, fixed perturbations apply the same transformations to the data regardless of the contexts, thus failing to generalize appropriately (Ismailov and Asanova, 2025).

Some of the world's biggest and most powerful brands are attempting to be more cute and cuddly. Tech giants Microsoft and Apple are among a wave of businesses who have recently introduced new cartoon character mascots, a tactic experts say is often used to make a brand seem more human and friendly, and to build a stronger connection with customers. Apple's character, a blue and white figure with an outsized head, has become unofficially known as Little Finder Guy. Introduced in March in social media videos to promote a new laptop, it has gained some positive coverage. Microsoft, which years ago shelved its widely-disliked Clippy paperclip virtual assistant, has also unveiled a new cartoon character for its AI assistant Copilot.

A Qatar-based professor has pioneered a non-invasive eye scan to detect neurodegenerative diseases years before symptoms appear. The technology uses AI to analyse the eye and can identify early signs of dementia, Parkinson's disease, and other diseases within minutes. Church leaders killed in latest ethnic violence in India's Manipur

AI chatbots are giving out people's real phone numbers People report that their personal contact info was surfaced by Google AI--and there's apparently no easy way to prevent it. A Redditor recently wrote that he was "desperate for help": for about a month, he said, his phone had been inundated by calls from "strangers" who were "looking for a lawyer, a product designer, a locksmith." Callers were apparently misdirected by Google's generative AI. In March, a software developer in Israel was contacted on WhatsApp after Google's chatbot Gemini provided incorrect customer service instructions that included his number. And in April, a PhD candidate at the University of Washington was messing around on Gemini and got it to cough up her colleague's personal cell phone number. AI researchers and online privacy experts have long warned of the myriad dangers generative AI poses for personal privacy.

The Metropolitan Police has warned that it is preparing for potential violence and hate speech crimes across two protests in London this Saturday. More than 4,000 officers will be drafted in to police the rival events - possibly one of the largest protest deployment in decades - amid fears that far-right demonstrators could clash with pro-Palestine marchers if the two groups are not kept apart. In addition, tens of thousands of football fans are also expected at Wembley Stadium for the FA Cup Final, adding further pressures on the capital's police. Scotland Yard said the risks meant it had to impose the highest degree of control. Measures the Met is planning include the first authorisation of live facial recognition cameras at a demonstration.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' 'One of the longest' Russian attacks kills at least six people in Ukraine At least six people have been killed and dozens injured in "one of the longest, massive Russian attacks against Ukraine", according to Ukrainian President Volodymyr Zelenskyy, despite renewed claims from the Russian and United States presidents that the war may be nearing an end. Zelenskyy said the barrage began on Wednesday morning and lasted for hours, striking Kyiv, the western city of Lviv near the Polish border and the Black Sea port of Odesa, among other areas. In the southern region of Kherson, Governor Oleksandr Prokudin said a woman was killed when a Russian drone struck a bus in the town of Bilozerka. Another drone attack in the western region of Rivne killed three people and injured four, according to Governor Oleksandr Koval. In the Kharkiv region in northeastern Ukraine, authorities said a 60-year-old man was killed when Russian forces attacked a community near the city of Zolochiv with first-person view drones.

The 10th International Workshop on Health Intelligence (W3PHIAI-26) celebrated a decade of bringing AI and health research together, building on a lineage that began with the AAAI-W3PHI workshops focused on population health (2014-2016), the AAAI-HIAI workshops focused on personalized health (2013-2016), and the subsequent joint W3PHIAI workshops held annually from 2017 through 2025. Over this decade, the series has produced hundreds of talks and high-impact publications that have collectively received thousands of citations, shaping the research agenda in both population health intelligence and personalized healthcare AI. This year's special theme, "Foundation Models and AI Agents," reflected the field's rapidly evolving frontier: the emergence of autonomous and semi-autonomous AI systems reshaping clinical workflows, patient management, health system operations, and public health surveillance. Day 1 of the workshop focused on medical imaging and the translation of AI for clinical ...

Humanoid robots are trained in China. The court ruled that the company in Hangzhou had been wrong to fire the worker because AI could do his job. Humanoid robots are trained in China. The court ruled that the company in Hangzhou had been wrong to fire the worker because AI could do his job. A court in China has ruled in favour of a worker whose company replaced him with artificial intelligence (AI), awarding him more than £28,000 in compensation.