Cutting-edge artificial intelligence technology is poised to supercharge offensive hacking capabilities, and urgent action is needed to face up to the threat, US, UK, Canadian, Australian and New Zealand officials have said. "Frontier AI models are anticipated to exceed current industry expectations, fundamentally transforming both offensive and defensive cyber capabilities," the intelligence alliance commonly known as the Five Eyes said in a three-page statement on Monday. The statement was light on detail and mostly restated core cybersecurity advice, such as swiftly patching faulty software and not putting systems online unless necessary. The officials also urged defenders to use AI "to strengthen defence", for example by identifying weaknesses sooner or responding more quickly to incidents. The warning was another indication of officials' increasing concerns over models such as Anthropic's Mythos or OpenAI's GPT-5.5-Cyber, which are said to allow users to quickly execute complex - and potentially devastating - hacks.

Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, progress in the field is hindered by inconsistent evaluation practices: studies rely on oversimplified synthetic data generators, adopt incompatible metrics, and lack transparency in hyperparameter selection, making fair comparisons difficult. We address this gap with a novel benchmarking framework comprising three contributions: (1) a drift simulation method that injects controlled distributional changes into real-world datasets via Monte Carlo trials, enabling supervised evaluation while preserving real-world data complexity; (2) an evaluation protocol for drift detection with timing-aware criteria, including the derivation of new metrics (e.g., F1 detection score, normalized detection time) that are comparable across streams; and (3) we advocate for a leave-one-dataset-out hyperparameter optimization protocol for drift detection methods that promotes configuration robustness across heterogeneous stream dynamics. We benchmark 14 widely used drift detection methods on 7 realworld datasets across 4 drift types (class prior, label swap, feature permutation, feature filtering), each under both abrupt and gradual transitions. Our experimental results provide insights into the strengths and weaknesses of current drift detection approaches while establishing baseline performance metrics for future research in this area. All code and experiments are publicly available.

Defense Minister Shinjiro Koizumi speaks to reporters as New Zealand defense chief Chris Penk (left) and Australian Defense Minister Richard Marles listen on the sidelines of the Shangri-La Dialogue security summit in Singapore on Saturday. Held on the margins of the Shangri-La Dialogue security forum in Singapore on Saturday, the meeting came as Wellington actively evaluates purchasing the same class of advanced warships that Japan recently sold to Australia, in order to maintain interoperability with its sole formal defense ally. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories. However, before writing your first comment, please create a display name in the Profile section of your subscriber account page.

A Philippine Navy band plays music to welcome the Royal New Zealand Navy frigate HMNZS Te Kaha upon arrival at the South Harbor, for a four-day goodwill visit in metro Manila in April 2017. New Zealand intends to spend about 1.6 billion New Zealand dollars ($936 million) on drones, ship maintenance and naval upgrades to bolster the island nation's maritime security at a time of increasing concern about supply routes. Defense Minister Chris Penk said Saturday that the government will invest in two types of drones: one for the southwest Pacific to provide long-duration intelligence, surveillance and reconnaissance; the other is a polar-capable vehicle that can operate from naval vessels in the Southern Ocean. "New Zealand's prosperity and security depend on the sea," Penk said in a statement. "Recent events have served as a reminder of how quickly disruptions to international shipping routes can affect economies and supply chains across the globe. The oceans are not a barrier to danger, but a vital national interest that must be actively secured."

Learning dynamical systems through operator-theoretic representations provides a powerful framework for analyzing complex dynamics, as spectral quantities such as eigenvalues and invariant structures encode characteristic time scales and long-term behavior. However, dynamical operators are typically estimated independently for each system, preventing the discovery of shared structure across related dynamics. To address this limitation, we posit that related dynamical systems lie near a low-dimensional manifold in spectral operator space. Based on this hypothesis, we introduce DOODL (Dynamical OperatOr Dictionary Learning), a framework that learns a dictionary of characteristic spectral dynamics whose combinations approximate this manifold and yield compact, interpretable embeddings of individual systems. Beyond representation learning, DOODL enables fast and interpretable operator estimation from short and partially observed trajectories by constraining the estimation to the learned operator manifold. Experiments on metastable Langevin dynamics and turbulent plasma simulations demonstrate that DOODL scales to highly complex multiscale regimes while capturing characteristic spectral structure governing the dynamics rather than merely fitting trajectories, achieving errors one to two orders of magnitude lower than independent operator estimation methods in challenging low-data regimes.

In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes domains such as healthcare, employment, and criminal justice, raising concerns about the fairness behavior of these systems. Existing works in fair ML cover tasks such as bias detection, fair prediction, and fair decision-making, but largely focus on static settings. At the same time, fairness in temporal contexts, particularly survival/time-to-event (TTE) analysis, remains relatively underexplored, with current approaches to fair survival analysis adopting statistical fairness definitions, which, even with unlimited data, cannot disentangle the causal mechanisms that generate disparities. To address this gap, we develop a causal framework for fairness in TTE analysis, enabling the decomposition of disparities in survival into contributions from direct, indirect, and spurious pathways. This provides a human-understandable explanation of why disparities arise and how they evolve over time. Our non-parametric approach proceeds in four steps: (1) formalizing the necessary assumptions about censoring and lack of confounding using a graphical model; (2) recovering the conditional survival function given covariates; (3) applying the Causal Reduction Theorem to reframe the problem in a form amenable to causal pathway decomposition; (4) estimating the effects efficiently. Finally, our approach is used to analyze the temporal evolution of racial disparities in outcome after admission to an intensive care unit (ICU).

In this work, we revisit the problem of active sequential prediction-powered mean estimation, where at each round one must decide the query probability of the ground-truth label upon observing the covariates of a sample. Furthermore, if the label is not queried, the prediction from a machine learning model is used instead. Prior work proposed an elegant scheme that determines the query probability by combining an uncertainty-based suggestion with a constant probability that encodes a soft constraint on the query probability. We explored different values of the mixing parameter and observed an intriguing empirical pattern: the smallest confidence width tends to occur when the weight on the constant probability is close to one, thereby reducing the influence of the uncertainty-based component. Motivated by this observation, we develop a non-asymptotic analysis of the estimator and establish a data-dependent bound on its confidence interval. Our analysis further suggests that when a no-regret learning approach is used to determine the query probability and control this bound, the query probability converges to the constraint of the max value of the query probability when it is chosen obliviously to the current covariates. We also conduct simulations that corroborate these theoretical findings.

Outside of Japan, Google's chatbot is accessible on both desktop and iOS. Google has added a new sidebar to Chrome that allows users to access Gemini from any of their tabs. After debuting in the US, Gemini in Chrome is making its way to more markets. Starting today, Google is rolling out Chrome's built-in chatbot to users in Asia and the Pacific, including Australia, Indonesia, Japan, the Philippines, Singapore, South Korea and Vietnam. The expansion comes after Google earlier this year made Gemini in Chrome available to people in Canada, India and New Zealand .

Inside the bird eat bird world of'beak jousting.' More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Even without an upper beak, one bird in New Zealand is defying odds at the top of the pecking order. Bruce is a rescued kea () parrot that is the alpha male among his species living at the Willowbank Wildlife Reserve in Christchurch, New Zealand.

The world's most family-friendly landmarks revealed - with six UK spots making the top 50 The UK's best staycations revealed by Daily Mail Travel - from a Gara Rock beach proposal to an £80-a-night mansion retreat This sun-drenched European coast offers great value - and it's just a two-hour flight away Don't get caught out by Ryanair's small bag restrictions - I've tested the carry-on suitcases and underseat bags that beat the strict requirements Why heading to Salcombe, one of Britain's most expensive seaside towns, in the shoulder season is an off-peak treat - and what to do there Tired of fun! Middle class families who turn their noses up at Butlin's are missing out Luxury hotel owner in Cornwall offers to foot British tourists' petrol bills to ease financial pain of staycation With flights disrupted amid Iran war, these are Europe's easiest countries to navigate by train - and how it compares to flying for price and time How to retire to the seaside for as little as £90,000 - and Britain's best hidden beach home spots New business class seats with IMAX-style wrap-around screens revealed - making passengers feel like they're in the cinema How the cost of your staycation REALLY compares with a'cheap' holiday abroad - when you factor in everything from food to fuel Why the Lake District shouldn't introduce tourism tax, says Cumbria tourism boss How Marseille became Europe's Capital of Cool - with 20 degree sunshine, sea views and amazing seafood The world's best food markets revealed - and a UK spot comes in second place READ MORE: The best hotels in the UK for 2026 revealed - does YOUR favourite make the list? Ready to hit the mute button on reality? Deep in the Pembrokeshire countryside lies a cosmic retreat that feels almost light years away from Earth. The awe-inspiring Spodnic UFO is one of three standout stays at Melin Mabes, a four-acre glamping site owned and ran by Martin Johnson and his wife, CarolAnne. 'It looks like it's just landed from outer space and aliens could come out,' Martin notes as he showcases his brainchild during the first episode of Channel's World's Most Secret Hotels.