'People say I come across as incredibly boring!' How to find love on the dating apps - whatever the obstacles Sick of swiping and messaging but never meeting anyone you like and who likes you back? Here's what worked for some lucky couples U sing dating apps to find love is commonplace these days - and yet, for many singles, it has become a double-edged sword. The perks of having a never-ending supply of potential matches at your fingertips are obvious - but the appeal of connecting and meeting with strangers is time-limited. It can be especially frustrating to feel as if you're stuck at the swiping stage. In 2023, US jeweller Shane Company found that the average American will spend about eight months using dating apps - swiping on around 3,960 profiles - before finding a partner.

U.N. calls for probe after alleged drone attack on Gaza-bound aid flotilla Activists wave Palestinian flags as they gather to support a flotilla carrying humanitarian aid in Ajaccio, on the French Mediterranean island of Corsica, on Sept 12. | AFP-JIJI Rome - The United Nations called Wednesday for an investigation into alleged drone attacks against a Gaza-bound aid flotilla that prompted Italy and Spain to send naval ships to help. The Global Sumud Flotilla, carrying activists including Swedish environmentalist Greta Thunberg, blamed Israel for more than a dozen explosions heard around its vessels off Greece late on Tuesday. U.N. Human Rights Office spokesperson Thameen Al-Kheetan said anyone responsible for the violations should be held accountable, and called for an independent, impartial and thorough investigation. 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.

On Wednesday, officials and diplomats sounded the alarm on A.I.'s ability to undermine the integrity of information and fabricate fake voice and video tapes. They also warned that it posed a threat to cybersecurity and would enable the rise of autonomous weapons. Still, some argued that, if used responsibly and with guardrails, A.I. potentially could also help foster peace and stability. Secretary General António Guterres, who for the past year has championed efforts to regulate A.I., said that the Council had a responsibility to ensure the military use of artificial intelligence complies with international law and the U.N. Charter. "From design to deployment to decommissioning, A.I. systems must always comply with international law; military uses must be clearly regulated," Mr. Guterres said, before ending his speech with a warning and a call to action.

Denmark's Aalborg airport in the country's north has been closed after unauthorised drones were seen in its airspace, according to local authorities. Three other smaller airports in the country's southern region - Esbjerg, Sønderborg and Skrydstrup - also reported drone activity, but were not closed. The incident comes after the country's Copenhagen airport was forced to close earlier this week due to a drone incursion, which the prime minister described as the most severe attack on Danish infrastructure so far. Police said the devices could be seen from the ground, adding they couldn't rule out the activity being a prank. They were investigating who was controlling them and their motive.

The accuracy, resilience, and affordability of localisation are fundamental to autonomous robotic inspection within aircraft maintenance and overhaul (MRO) hangars. Hangars typically feature tall ceilings and are often made of materials such as metal. Due to its nature, it is considered a GPS-denied environment, with extensive multipath effects and stringent operational constraints that collectively create a uniquely challenging environment. This persistent gap highlights the need for domain-specific comparative studies, including rigorous cost, accuracy, and integration assessments, to inform a reliable and scalable deployment of a localisation system in the Smart Hangar. This paper presents the first techno-economic roadmap that benchmarks motion capture (MoCap), ultra-wideband (UWB), and a ceiling-mounted camera network across three operational scenarios: robot localisation, asset tracking, and surface defect detection within a 40 50 m hangar bay. A dual-layer optimisation for camera selection and positioning framework is introduced, which couples market-based camera-lens selection with an optimisation solver, producing camera layouts that minimise hardware while meeting accuracy targets. The roadmap equips MRO planners with an actionable method to balance accuracy, coverage, and budget, demonstrating that an optimised vision architecture has the potential to unlock robust and cost-effective sensing for next-generation Smart Hangars.

We prove the first guarantees of sparse recovery for ReLU neural networks, where the sparse network weights constitute the signal to be recovered. Specifically, we study structural properties of the sparse network weights for two-layer, scalar-output networks under which a simple iterative hard thresholding algorithm recovers these weights exactly, using memory that grows linearly in the number of nonzero weights. We validate this theoretical result with simple experiments on recovery of sparse planted MLPs, MNIST classification, and implicit neural representations. Experimentally, we find performance that is competitive with, and often exceeds, a high-performing but memory-inefficient baseline based on iterative magnitude pruning.

Imitation Learning (IL) algorithms offer an efficient way to train an agent by mimicking an expert's behavior without requiring a reward function. IL algorithms often necessitate access to state and action information from expert demonstrations. Although expert actions can provide detailed guidance, requiring such action information may prove impractical for real-world applications where expert actions are difficult to obtain. To address this limitation, the concept of learning from observation (LfO) or state-only imitation learning (SOIL) has recently gained attention, wherein the imitator only has access to expert state visitation information. In this paper, we present a framework for LfO and use it to survey and classify existing LfO methods in terms of their trajectory construction, assumptions and algorithm's design choices. This survey also draws connections between several related fields like offline RL, model-based RL and hierarchical RL. Finally, we use our framework to identify open problems and suggest future research directions.

We propose a non-autoregressive framework for the Travelling Salesman Problem where solutions emerge directly from learned permutations, without requiring explicit search. By applying a similarity transformation to Hamiltonian cycles, the model learns to approximate permutation matrices via continuous relaxations. Our unsupervised approach achieves competitive performance against classical heuristics, demonstrating that the inherent structure of the problem can effectively guide combinatorial optimization without sequential decision-making. Our method offers concrete evidence that neural networks can directly capture and exploit combinatorial structure.

Abstract--Large Language Models (LLMs) often struggle with maintaining coherent multi-step reasoning traces, particularly in tasks that require a structured logical flow. This work introduces a quantum-inspired approach to address the challenge by incorporating a fidelity-based reward derived from Projected Entangled Pair States (PEPS) into Proximal Policy Optimization. Unlike prior approaches that use direct supervision or contrastive objectives, the proposed method guides learning through structural consistency, offering a novel approach to enforce global coherence in generated reasoning traces. The proposed framework is evaluated using multiple coherence-determining metrics on diverse datasets such as GSM8K, StrategyQA, and EntailmentBank spanning arithmetic, intuitive, and entailment-based reasoning. Results show that the proposed quantum-inspired approach offers significant improvements over supervised, contrastive, and pretrained baseline approaches, highlighting the effectiveness of quantum-inspired fidelity as a foundation to improve reasoning trace coherence in LLMs. In recent times, large language models (LLMs) have made substantial progress in their ability to perform diverse natural language tasks such as open-domain question answering [1] [2], code generation [3] [4], summarization [5], and dialogue systems [6].

Estimating hidden states in dynamical systems, also known as optimal filtering, is a long-standing problem in various fields of science and engineering. In this paper, we introduce a general filtering framework, \textbf{LLM-Filter}, which leverages large language models (LLMs) for state estimation by embedding noisy observations with text prototypes. In various experiments for classical dynamical systems, we find that first, state estimation can significantly benefit from the reasoning knowledge embedded in pre-trained LLMs. By achieving proper modality alignment with the frozen LLM, LLM-Filter outperforms the state-of-the-art learning-based approaches. Second, we carefully design the prompt structure, System-as-Prompt (SaP), incorporating task instructions that enable the LLM to understand the estimation tasks. Guided by these prompts, LLM-Filter exhibits exceptional generalization, capable of performing filtering tasks accurately in changed or even unseen environments. We further observe a scaling-law behavior in LLM-Filter, where accuracy improves with larger model sizes and longer training times. These findings make LLM-Filter a promising foundation model of filtering.