Within days of being released from his seventh prison term for romance fraud, Raymond McDonald was back on a dating app looking for his next victim. Over more than 20 years he had racked up 58 convictions, mostly for fraud and theft, while telling lies on an industrial scale and taking thousands of pounds from women for holidays and weddings which were never going to happen. This time when he went looking, the BBC was waiting. He thought he was having a date with Kaye, but instead found himself being approached by a BBC reporter and camera crew. He had met Kaye online and, calling himself Rob, told her he was a deep-sea diver looking for a wife.

Several attempts have been made to explore local significant differences in the past years.

Hundreds of illegal dumps are operating across England, including at least 11 so-called super sites containing tens of thousands of tonnes of rubbish, a BBC investigation has found. Drone footage showed one of the waste dumps in Over, Gloucestershire. Most sites are in countryside locations, often hidden, and on what should be agricultural land. Police say many are run by organised crime gangs, who are making cash by charging much less than legitimate operators to take and bury waste. How the great outdoors went from an escape from the nine to five to a full-time social media job.

The UK lacks a plan to defend itself from military attack, a committee of MPs has warned. In a highly critical report, the defence committee says the UK is over-reliant on US resources and that preparations to defend itself and overseas territories in the event of attack are nowhere near where they need to be. The committee's chair, Labour MP Tan Dhesi, said: Putin's brutal invasion of Ukraine, unrelenting disinformation campaigns, and repeated incursions into European airspace mean that we cannot afford to bury our heads in the sand. It comes as the Ministry of Defence (MoD) identified parts of the country where six or more new munitions factories could be built. In June, Defence Secretary John Healey announced plans to move the UK to war-fighting readiness, including £1.5bn to support the construction of new munitions factories, which will be built by private contractors.

Men with suspected prostate cancer will be able to get a diagnosis from the NHS within a day, under a new trial hailed as a potential game changer for identifying and treating the disease. The 15 hospitals taking part will use AI technology to interpret MRI scans and spot areas of abnormal tissue within minutes, according to NHS England. Scans showing a high-cancer risk will be triaged as priority review for a radiologist and patients will be booked for a same-day biopsy. Around one in eight men will develop prostate cancer in their lives, according to Prostate Cancer UK, with research showing it has overtaken breast cancer as the most commonly diagnosed form of the disease in the UK. But unlike breast cancer, there is currently no national screening programme for prostate cancer.

The relative similarity testing aims to determine which of the distributions, P or Q, is closer to an anchor distribution U. Existing kernel-based approaches often test the relative similarity with a fixed kernel in a manually specified alternative hypothesis, e.g., Q is closer to U than P. Although kernel selection is known to be important to kernel-based testing methods, the manually specified hypothesis poses a significant challenge for kernel selection in relative similarity testing: Once the hypothesis is specified first, we can always find a kernel such that the hypothesis is rejected. This challenge makes relative similarity testing ill-defined when we want to select a good kernel after the hypothesis is specified. In this paper, we cope with this challenge via learning a proper hypothesis and a kernel simultaneously, instead of learning a kernel after manually specifying the hypothesis. We propose an anchor-based maximum discrepancy (AMD), which defines the relative similarity as the maximum discrepancy between the distances of (U, P) and (U, Q) in a space of deep kernels. Based on AMD, our testing incorporates two phases. In Phase I, we estimate the AMD over the deep kernel space and infer the potential hypothesis. In Phase II, we assess the statistical significance of the potential hypothesis, where we propose a unified testing framework to derive thresholds for tests over different possible hypotheses from Phase I. Lastly, we validate our method theoretically and demonstrate its effectiveness via extensive experiments on benchmark datasets. Codes are publicly available at: https://github.com/zhijianzhouml/AMD.

Several attempts have been made to explore local significant differences in the past years.

--We introduce a novel wearable textile-garment featuring an innovative electrode placement aimed at minimizing noise and motion artifacts, thereby enhancing signal fidelity in Electrocardiography (ECG) recordings. We present a comprehensive, sex-balanced evaluation involving 15 healthy males and 15 healthy female participants to ensure the device's suitability across anatomical and physiological variations. The assessment framework encompasses distinct evaluation approaches: quantitative signal quality indices to objectively benchmark device performance; rhythm-based analyzes of physiological parameters such as heart rate (HR) and heart rate variability (HRV); machine learning classification tasks to assess application-relevant predictive utility; morphological analysis of ECG features including amplitude and interval parameters; and investigations of the effects of electrode projection angle given by the textile / body shape, with all analyzes stratified by sex to elucidate sex-specific influences. Evaluations were conducted across various activity phases representing real-world conditions. The results demonstrate that the textile system achieves signal quality highly concordant with reference devices in both rhythm and morphological analyses, exhibits robust classification performance, and enables identification of key sex-specific determinants affecting signal acquisition. These findings underscore the practical viability of textile-based ECG garments for physiological monitoring as well as psychophysiological state detection. Moreover, we identify the importance of incorporating sex-specific design considerations to ensure equitable and reliable cardiac diagnostics in wearable health technologies. NTRODUCTION This is a preprint of a manuscript submitted for publication. It has not yet been peer-reviewed, and the final version may differ . The authors acknowledge the funding by the EU TEF-Health project which is part of the Digital Europe Program of the EU (DIGIT AL-2022-CLOUD-AI-02-TEFHEAL TH). LECTROCARDIOGRAPHIC recordings serve as a fundamental diagnostic tool in modern medicine, providing invaluable noninvasive insights into the electrical activity of the heart and therefore the health of the cardiovascular system. Introduced by Willem Einthoven in the early 20th century, Electrocardiography (ECG) remains a cornerstone in clinical cardiology. Einthoven's pioneering work laid the foundation for understanding the principles underlying ECG acquisition and interpretation [1], [2]. ECG signals are acquired through electrodes placed on the skin, capturing the electrical impulses generated by cardiac muscle de-and repolarization. In modern medicine, ECG is used in applications ranging from diagnosing cardiac arrhythmias [4] and ischemic heart disease [5] to monitoring patients during surgery [6] and assessing the effects of pharmacological interventions [7], [8].

Counterfactual Explanations (CEs) are a powerful technique used to explain Machine Learning models by showing how the input to a model should be minimally changed for the model to produce a different output. Similar proposals have been made in the context of Automated Planning, where CEs have been characterised in terms of minimal modifications to an existing plan that would result in the satisfaction of a different goal. While such explanations may help diagnose faults and reason about the characteristics of a plan, they fail to capture higher-level properties of the problem being solved. To address this limitation, we propose a novel explanation paradigm that is based on counterfactual scenarios. In particular, given a planning problem $P$ and an \ltlf formula $ψ$ defining desired properties of a plan, counterfactual scenarios identify minimal modifications to $P$ such that it admits plans that comply with $ψ$. In this paper, we present two qualitative instantiations of counterfactual scenarios based on an explicit quantification over plans that must satisfy $ψ$. We then characterise the computational complexity of generating such counterfactual scenarios when different types of changes are allowed on $P$. We show that producing counterfactual scenarios is often only as expensive as computing a plan for $P$, thus demonstrating the practical viability of our proposal and ultimately providing a framework to construct practical algorithms in this area.

Large Language Models (LLMs) are known to produce hallucinations - factually incorrect or fabricated information - which poses significant challenges for many Natural Language Processing (NLP) applications, such as dialogue systems. As a result, detecting hallucinations has become a critical area of research. Current approaches to hallucination detection in dialogue systems primarily focus on verifying the factual consistency of generated responses. However, these responses often contain a mix of accurate, inaccurate or unverifiable facts, making one factual label overly simplistic and coarse-grained. In this paper, we introduce a benchmark, FineDialFact, for fine-grained dialogue fact verification, which involves verifying atomic facts extracted from dialogue responses. To support this, we construct a dataset based on publicly available dialogue datasets and evaluate it using various baseline methods. Experimental results demonstrate that methods incorporating Chain-of-Thought (CoT) reasoning can enhance performance in dialogue fact verification. Despite this, the best F1-score achieved on the HybriDialogue, an open-domain dialogue dataset, is only 0.75, indicating that the benchmark remains a challenging task for future research. Our dataset and code will be public on GitHub.