National Crime Agency (NCA) officers have arrested an Indian national on suspicion of sanctions offences after the dramatic seizure of a Russian oil tanker in the English Channel on Sunday. He was taken into custody for questioning by investigators following the operation mounted by Royal Marine Commandos along with the NCA. The NCA said 24 Georgian and Indian crew members remained aboard the Smyrtos, anchored off the Dorset coast. Sunday's operation, which saw commandos fast-roping from a helicopter on to the tanker, was the first of its kind carried out by UK armed forces. Russia uses hundreds of oil tankers sanctioned by the UK and other Western states over its invasion of Ukraine.

Continual learning aims to learn multiple tasks sequentially while preserving prior knowledge, but faces the challenge of catastrophic forgetting when adapting to new tasks. Recently, approaches leveraging pre-trained models have gained increasing popularity in mitigating this issue, due to the strong generalization ability of foundation models. To adjust pre-trained models for new tasks, existing methods usually employ low-rank adaptation, which restricts parameter updates to a fixed low-rank subspace. However, constraining the optimization space inherently compromises the model's learning capacity, resulting in inferior performance. To address this limitation, we propose Continuous Subspace Optimization for Continual Learning (CoSO) to fine-tune the model in a series of subspaces rather than a single one. These sequential subspaces are dynamically determined through the singular value decomposition of the gradients.

Crypto token's 50% wipeout shows magnitude of AI-hacking threat The same artificial intelligence tools helping developers audit code in cryptocurrency are also lowering the barriers for attackers, creating an arms race across the industry, researchers say. When Eli Ben-Sasson helped create the Zcash cryptocurrency nearly a decade ago, the cryptographer worried about human adversaries. He didn't expect that machine intelligence would one day expose a flaw that had eluded years of expert human judgment. That reality rattled investors recently after a security researcher working with Zcash used Anthropic's Claude Opus 4.8 to uncover a critical vulnerability that had gone undetected for more than four years. After Zcash disclosed the flaw on June 4, the token -- which traded at far higher levels just weeks earlier -- tumbled about 50% as traders reassessed the security of one of crypto's most prominent privacy networks. The exploit struck at the heart of Zcash's value proposition.

Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability.

Irregular data in the real world are usually organized as heterogeneous graphs consisting of multiple types of nodes and edges. However, current heterogeneous graph research confronts three fundamental challenges: i) Benchmark Deficiency, ii) Semantic Disalignment, and iii) Propagation Degradation. In this paper, we construct a large-scale, universal, and joint multi-domain heterogeneous graph dataset named UniHG to facilitate heterogeneous graph representation learning and cross-domain knowledge mining. Overall, UniHG contains 77.31 million nodes and 564 million directed edges with thousands of labels and attributes, which is currently the largest universal heterogeneous graph dataset available to the best of our knowledge. To perform effective learning and provide comprehensively benchmarks on UniHG, two key measures are taken, including i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes and edges into a common embedding space to facilitate information aggregation; ii) proposing the novel Heterogeneous Graph Decoupling (HGD) framework with a specifically designed Anisotropy Feature Propagation (AFP) module for learning effective multi-hop anisotropic propagation kernels. These two strategies enable efficient information propagation among a tremendous number of multi-attribute entities and meanwhile mine multi-attribute association adaptively through the multi-hop aggregation in large-scale heterogeneous graphs. Comprehensive benchmark results demonstrate that our model significantly outperforms existing methods with an accuracy improvement of 28.93%. And the UniHG can facilitate downstream tasks, achieving an NDCG@20 improvement rate of 11.48% and 11.71%.

Sample-wise learning curves plot performance versus training set size. They are useful for studying scaling laws and speeding up hyperparameter tuning and model selection. Learning curves are often assumed to be well-behaved: monotone (i.e.

Starmer to confirm social media ban for U.K. teens ahead of G7 meet U.K. Prime Minister Keir Starmer is expected to confirm a social media ban on children under 16 on Monday morning. U.K. Prime Minister Keir Starmer will start a crucial week for his premiership by announcing a package of strong restrictions designed to protect British teenagers from online threats. Starmer is expected Monday morning to confirm a ban on children under 16 using major social media platforms, as well as other measures including curfews on older teenagers and tough regulations on chatbots. He will then depart for a Group of Seven summit at Evian-les-Bains, France, where he faces awkward questions following last week's resignation of his defense secretary and uncertainty around the U.K.'s military budget. A ban on young teenagers using social media is popular with the U.K. public despite concerns around how effectively it can be enforced. The Labour government's new range of restrictions -- including some against chatbots and online games -- will go further than laws in Australia, according to a person familiar with the situation, where a ban on social media for teens came into effect last year.

Globally interpretable models are a promising approach for trustworthy AI in safetycritical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human experts during inference. This work proposes the Calibrated Hierarchical QPM (CHiQPM) which offers uniquely comprehensive global and local interpretability, paving the way for human-AI complementarity. CHiQPM achieves superior global interpretability by contrastively explaining the majority of classes and offers novel hierarchical explanations that are more similar to how humans reason and can be traversed to offer a built-in interpretable Conformal prediction (CP) method. Our comprehensive evaluation shows that CHiQPM achieves state-of-the-art accuracy as a point predictor, maintaining 99% accuracy of non-interpretable models. This demonstrates a substantial improvement, where interpretability is incorporated without sacrificing overall accuracy. Furthermore, its calibrated set prediction is competitively efficient to other CP methods, while providing interpretable predictions of coherent sets along its hierarchical explanation.

Recent research shows that large language models (LLMs) are vulnerable to hijacking attacks under the scenario of in-context learning (ICL) where LLMs demonstrate impressive capabilities in performing tasks by conditioning on a sequence of in-context examples (ICEs) (i.e., prompts with task-specific input-output pairs). Adversaries can manipulate the provided ICEs to steer the model toward attackerspecified outputs, effectively "hijacking" the model's decision-making process. Unlike traditional adversarial attacks targeting single inputs, hijacking attacks in LLMs aim to subtly manipulate the initial few examples to influence the model's behavior across a range of subsequent inputs, which requires distributed and stealthy perturbations. However, existing approaches overlook how to effectively allocate the perturbation budget across ICEs. We argue that fixed budgets miss the potential of dynamic reallocation to improve attack success while maintaining high stealthiness and text quality.

In this paper, we study the Karush-Kuhn-Tucker (KKT) points of the associated maximum-margin problem in homogeneous neural networks, including fullyconnected and convolutional neural networks. In particular, We investigates the relationship between such KKT points across networks of different widths generated. We introduce and formalize the KKT point embedding principle, establishing that KKT points of a homogeneous network's max-margin problem (PΦ) can be embedded into the KKT points of a larger network's problem (P Φ) via specific linear isometric transformations. We rigorously prove this principle holds for neuron splitting in fully-connected networks and channel splitting in convolutional neural networks. Furthermore, we connect this static embedding to the dynamics of gradient flow training with smooth losses. We demonstrate that trajectories initiated from appropriately mapped points remain mapped throughout training and that the resulting ω-limit sets of directions are correspondingly mapped, thereby preserving the alignment with KKT directions dynamically when directional convergence occurs. We conduct several experiments to justify that trajectories are preserved. Our findings offer insights into the effects of network width, parameter redundancy, and the structural connections between solutions found via optimization in homogeneous networks of varying sizes.