Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate, or sacrifice detection sensitivity in order to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) has been shown to be a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. We have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The Adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Human language production exhibits remarkable richness and variation, reflecting diverse communication styles and intents. However, this variation is often overlooked in summarization evaluation. While having multiple reference summaries is known to improve correlation with human judgments, the impact of the reference set on reference-based metrics has not been systematically investigated. This work examines the sensitivity of widely used reference-based metrics in relation to the choice of reference sets, analyzing three diverse multi-reference summarization datasets: SummEval, GUMSum, and DUC2004. We demonstrate that many popular metrics exhibit significant instability. This instability is particularly concerning for n-gram-based metrics like ROUGE, where model rankings vary depending on the reference sets, undermining the reliability of model comparisons. We also collect human judgments on LLM outputs for genre-diverse data and examine their correlation with metrics to supplement existing findings beyond newswire summaries, finding weak-to-no correlation. Taken together, we recommend incorporating reference set variation into summarization evaluation to enhance consistency alongside correlation with human judgments, especially when evaluating LLMs.

Abstract-- Traditional soil sampling and analysis methods are labor-intensive, time-consuming, and limited in spatial resolution, making them unsuitable for large-scale precision agriculture. T o address these limitations, we present a robotic solution for real-time sampling, analysis and mapping of key soil properties. Our system consists of two main sub-systems: a Sample Acquisition System (SAS) for precise, automated in-field soil sampling; and a Sample Analysis Lab (Lab) for real-time soil property analysis. The system's performance was validated through extensive field trials at a large-scale Australian farm. Experimental results show that the SAS can consistently acquire soil samples with a mass of 50g at a depth of 200mm, while the Lab can process each sample within 10 minutes to accurately measure pH and macronutrients. These results demonstrate the potential of the system to provide farmers with timely, data-driven insights for more efficient and sustainable soil management and fertilizer application. I. INTRODUCTION Achieving sustainable agricultural resource management requires accurate, high-resolution, and up-to-date data on soil properties such as pH and macronutrients [1], [2]. However, conventional soil sampling and testing methods fail to address this need at scale.

Real-world decision-making often begins with identifying which modality contains the most relevant information for a given query. While recent multimodal models have made impressive progress in processing diverse inputs, it remains unclear whether they can reason contrastively across multiple modalities to select the one that best satisfies a natural language prompt. We argue this capability is foundational, especially in retrieval-augmented and decision-time contexts, where systems must evaluate multiple signals and identify which one conveys the relevant information. To evaluate this skill, we introduce Contra4, a dataset for contrastive cross-modal reasoning across four modalities: image, audio, video, and 3D. Each example presents a natural language question alongside multiple candidate modality instances, and the model must select the one that semantically aligns with the prompt. Contra4 combines human-annotated captions with a mixture-of-models round-trip-consistency filter to ensure high-quality supervision, resulting in 174k training examples and a manually verified test set of 2.3k samples. While task-specific fine-tuning helps improve performance by 56% relative to baseline, state-of-the-art models still achieve only an absolute of 56% accuracy overall and 42% in four-modality settings, underscoring a significant limitation in current multimodal models.

Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and limited training data. Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data. However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for various downstream tasks. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To investigate these issues, we augment WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. While these narratives may have variable effects on downstream tasks, they consistently improve cultural distinctiveness than survey data alone. Our work highlights the inherent complexity of aligning cultural values with the goal of guiding task-specific behavior. We release our code at https://github.com/faridlazuarda/from-surveys-to-narratives.

Charlie Kirk suspect Tyler Robinson makes stony-faced first court appearance as he's charged with capital murder: Live updates Texas AG's mistress' shock new life after sex scandal was exposed Astonishing moment Charlie Kirk's wife Erika loses Miss USA pageant to pro-trans rival... as Trump watches on Michael Keaton slammed for saying there was'irony' in Charlie Kirk's shooting Extraordinary measures jail put in motion to keep Charlie Kirk assassin suspect alive: 'It's severe' Dark truth about why Taylor Swift really hid behind that bulletproof screen at Travis Kelce's game Former TV anchor is arrested again after causing'serious injury' as his downward spiral continues AMANDA PLATELL: I'm so disgusted with myself for the cruel thing I said about Kate... I'm choking on my words now I know the truth. Hollywood heartthrob Robert Redford had to'protect himself' from lusting costars... but only had eyes for his wife Hallmark star Paula Shaw dead at 84: Tributes pour in for the beloved actress who'touched countless lives' Ivanka Trump's'inappropriate' demand that left Melania reeling on the last UK state visit... this time, insiders tell how the First Lady has triumphed over the'thorn in her side' The Mounjaro mums are out of control. First it was for weight loss, then it silenced booze cravings. Now there's another miracle'benefit' they won't shut up about - but I fear it will end in disaster Idyllic small town in Maine hit with largest HIV outbreak in state's history Amanda Seyfried faces furious backlash after resharing comments alluding to Charlie Kirk's death being expected Bargain-filled Nevada casino town is branded the'new Las Vegas' as tourists flock there instead of rip-off Sin City AI set to offer women'two for one' heart and breast screening Women could get'two for one' screening for breast cancer and heart problems using AI, a study suggests. The technology can be trained to examine mammograms images to detect both tumours as well as gauging the potential risk of heart attacks and stroke.

President Donald Trump has arrived in the UK for his historic second state visit, which will see a crowded mix of royal pageantry, trade talks and international politics. Before making the trip from the US on Air Force One, Trump sent positive signals, describing the visit as an honour and saying: My relationship is very good with the UK. They want to see if they can refine the trade deal a little bit I'm into helping them, said Trump, with a multi-billion US technology investment deal being announced as the president's visit got underway. But Trump said the main purpose of the visit was to see my friend King Charles: He represents the country so well, such an elegant gentleman. Landing at Stansted airport, President Trump received an official welcome from a line-up on the runway including Foreign Secretary Yvette Cooper. The president is spending the night in the US ambassador's residence, Winfield House, before a day of royal ceremony and lavish spectacle in Windsor Castle on Wednesday - with the president describing Windsor as the ultimate in settings.

Microsoft says its new $30bn (£22bn) investment in the UK's AI sector - its largest outside of the US - should significantly boost Britain's economy in the next few years. Its package forms a major part of a £31billion agreement made between the UK government and various other US tech giants, including Nvidia and Google, to invest in British-based infrastructure to support AI technology, largely in the form of data centres. Microsoft will also now be involved in the creation of a powerful new supercomputer in Loughton, Essex. Speaking exclusively to the BBC Microsoft CEO Satya Nadella told the BBC of the tech's potential impact on economic growth. It may happen faster, so our hope is not ten years but maybe five.

Microsoft and Nvidia unveiled plans to invest up to $45 billion in the UK during US President Donald Trump's state visit. Microsoft and Nvidia have unveiled plans to invest up to $45 billion dollars into the UK economy, in a move that will bolster the building of more data centers as well as research and development into artificial intelligence . The investment comes as US president Donald Trump travels to Britain, where he is expected to announce a US-UK tech deal alongside UK prime minister Keir Starmer. As part of the agreement, Microsoft has committed to invest $30 billion in AI infrastructure over the next four years. The company claims this is the largest financial commitment it has ever made in the UK and will make up more than two thirds of the total investment announced into the UK this week, timed to Trump's visit.

Terror charges against Luigi Mangione were dismissed in Manhattan court while supporters gathered outside, including one claiming marriage to his AI.