Abstract--As black-box models become foundational to mod-solution that can be applied across a wide range of scientific ern research, ensuring their stability is paramount for the realiza-and industrial domains. The inherent diversity of inputs--ranging from structured Gaussian distributions to Notwithstanding its widespread application, the framework complex data with unknown structures--poses a significantexhibits certain shortcomings when dealing with complex challenge: how to stabilize black-box outputs while effectivelydatasets. First, standard resampling schemes often fail to leveraging available prior information. This paper introduces aaccount for the underlying data structures; as a result, the task-oriented randomization methodology that adaptively tailorsdrawn samples cannot reflect the true data distribution, thereby its strategy to the underlying generative mechanisms of the input data, specifically addressing unstructured complexities. Second, effective sampling requires prior comprehensive suite of stability guarantees is proposed. Beyondknowledge of the distribution, which is often unattainable establishing rigorous theoretical foundations for stability, thein practical environments.

The extraction of pharmacological knowledge from regulatory documents has become a key focus in biomedical natural language processing, with applications ranging from adverse event monitoring to AI-assisted clinical decision support. However, research in this field has predominantly relied on English-language corpora such as DrugBank, leaving a significant gap in resources tailored to other healthcare systems. To address this limitation, we introduce DART (Drug Annotation from Regulatory Texts), the first structured corpus of Italian Summaries of Product Characteristics derived from the official repository of the Italian Medicines Agency (AIFA). The dataset was built through a reproducible pipeline encompassing web-scale document retrieval, semantic segmentation of regulatory sections, and clinical summarization using a few-shot-tuned large language model with low-temperature decoding. DART provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions. To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions. Experimental results show that instruction-tuned LLMs can accurately infer potential interactions and their clinical implications when grounded in the structured textual fields of DART. We publicly release our code on GitHub: https://github.com/PRAISELab-PicusLab/DART.

Opposition parties in Italy have complained to the communications watchdog about a series of AI-generated images published on social media by deputy prime minister Matteo Salvini's far-right party, calling them "racist, Islamophobic and xenophobic", the Guardian has learned. The centre-left Democratic party (PD), with the Greens and Left Alliance, filed a complaint on Thursday with Agcom, the Italian communications regulatory authority, alleging the fake images used by the League contained "almost all categories of hate speech". Over the past month, dozens of apparently AI‑generated photos have appeared on the League's social channels, including on Facebook, Instagram and X. The images frequently depict men of colour, often armed with knives, attacking women or police officers. Antonio Nicita, a PD senator, said: "In the images published by Salvini's party and generated by AI there are almost all categories of hate speech, from racism and xenophobia to Islamophobia. They are using AI to target specific categories of people – immigrants, Arabs – who are portrayed as potential criminals, thieves and rapists. "These images are not only violent but also deceptive: by blurring the faces of the victims it is as if they want to protect the identity of the person attacked, misleading users into believing the photo is real.

Veritone CEO Ryan Steelberg says the Writers Guild of America needs to make sure their writers are protected as AI becomes more popular. Nearly two weeks into the national writers' strike spearheaded by the Writers Guild of America (WGA), little progress has been made between both sides. The WGA has a litany of requests for the Alliance of Motion Picture and Television Producers (AMPTP). Per its website, the WGA has specific proposals with regard to artificial intelligence, including the "regulation of AI on minimum basic agreement (MBA) -covered projects; AI can't write or rewrite literary material; can't be used as source material; and MBA-covered material can't be used to train AI." When it comes to these provisions that surround artificial intelligence, studios have put the kibosh on writers' requests, instead suggesting annual meetings to review evolving technology.

In science and technology, there has been a long and steady drive toward improving the accuracy of measurements of all kinds, along with parallel efforts to enhance the resolution of images. An accompanying goal is to reduce the uncertainty in the estimates that can be made, and the inferences drawn, from the data (visual or otherwise) that have been collected. Yet uncertainty can never be wholly eliminated. And since we have to live with it, at least to some extent, there is much to be gained by quantifying the uncertainty as precisely as possible. Expressed in other terms, we'd like to know just how uncertain our uncertainty is.

The number of materials with the potential for use in each of the many layers in a solar cell is enormous. And even once they have chosen one to work with, scientists need to understand its interactions with the other materials present, and the effects of changing parameters such as layer thickness, dopant concentration and a wealth of others in order to get the best out of the cells they are working on. With so many possibilities, this can be a time-consuming process. And scientists today are increasingly able to turn to artificial intelligence to guide them in the next steps to take in practical lab work. And developing a system to do just that for solar cell design was the focus of a group of researchers at the Massachusetts Institute of Technology (MIT), who worked with experts at Google Brain to develop a system to evaluate the potential of different solar cell designs, and also predict which changes would provide improved performance characteristics.

In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow. Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.

Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FO-CuS), which is equivalent to running these earlier methods simultaneously for all sizes of window, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different change in mean scenarios, and demonstrate its practical utility through its state-of-the art performance at detecting anomalous behaviour in computer server data.

The Institute of Chartered Accountants of Trinidad and Tobago (ICATT), earlier this month, hosted a business forum comprising an audience of financial executives from various sectors including energy, banking and finance at the KPMG Headquarters in Port of Spain. The event themed "Artificial Intelligence (AI) – the Future of Accounting" exposed professional accountants to global developments, good practice guidance and knowledge-sharing that will enhance their roles and domain across the economy. In delivering the opening remarks, ICATT's president, Stacy-Ann Golding, praised the ICATT Professional Accountants in Business (PAIB) Committee for organising the forum, the topic of which, she noted, was critical to improving the readiness of today's accounting professionals to deal with AI and its implications. Bring a depth of insight and experience were featured speakers Nigel Romano, managing director and chief executive officer, JMMB Bank and Leslie Lee Fook, director of Artificial Intelligence, Automation and Analytics at Incus Services Ltd. Romano spoke on the use of AI, "I can recall the now obsolete, clunky computerised systems used in accounting during the 1970s and how they helped speed up work processes at that time. Today a similar shift is happening as current systems will soon be overshadowed by those powered by self-learning / machine learning capabilities."

Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made their mathematical analysis difficult, recent work has established important statistical properties like consistency and asymptotic normality by considering subsampling in lieu of bootstrapping. Though such results open the door to traditional inference procedures, all formal methods suggested thus far place severe restrictions on the testing framework and their computational overhead precludes their practical scientific use. Here we propose a permutation-style testing approach to formally assess feature significance. We establish asymptotic validity of the test via exchangeability arguments and show that the test maintains high power with orders of magnitude fewer computations. As importantly, the procedure scales easily to big data settings where large training and testing sets may be employed without the need to construct additional models. Simulations and applications to ecological data where random forests have recently shown promise are provided.