The new Apple Watch includes several "intelligent" listening features that have privacy and security baked in. But the protections can't change the facts of what the tools do. The new Apple Watch Series 12 and Ultra 4 come not just with better fitness tracking and upgraded noise reduction, but a whole new way to listen. Apple announced on Wednesday that the two smartwatches come with four opt-in "audio intelligence" tools that are powered by audio gathered by the watches' microphones. They include sound and music recognition, a conversation recap feature, and "Live Rewind" so a user can see a transcription of anything that was said in their environment in the previous 15 seconds.

If you work regularly with PDF documents then the chances are you're already very familiar with PDFelement by Wondershare . This hugely popular software is the go-to for many people when they want to create, edit, sign or convert PDFs. Now, with the arrival of Version 13, there are powerful new AI tools that will make life even easier for those that need to work with multiple forms of documentation or have to manage agreements across a team. So, here's how PDFelement V13 can streamline your workload and help you study, research or prepare presentations, all while saving you time. Spaces is one of the standout new features in PDFelement V13. This is a simple, organized workspace where you can gather together information from various sources (PDFs, YouTube URLs, text documents, Powerpoint files, spreadsheets, notes and other reference materials) then ask the AI contextual questions about the content.

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We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on which a non-linear scalar activation function is applied, and then returns the output into a Banach space via some linear readouts. By proving a weighted Nachbin theorem, we establish a universal approximation theorem for differentiable maps, which goes beyond the usual formulation on compact sets and also includes the approximation of the derivatives. This leads us to approximation results for non-anticipative functionals including the horizontal and vertical derivatives. As a further application, we show that linear functions of the signature are able to approximate path space functionals including their directional derivatives.

When you purchase through links in our articles, we may earn a small commission. PDF Editify makes browser-based markups and utility tasks easy, but it remains limited as a full document editor. Doesn't appear to directly edit existing PDF text, images, or layout PDF Editify is a simple, affordable browser tool for quick PDF markups and low-sensitivity utility tasks. It's easy to use, but its limited editing depth and web-only workflow keep it from being a full Acrobat replacement. PDF Editify is built for people who need to make quick changes to a PDF without installing full desktop software.

We introduce CLEVER1, a high-quality, curated benchmark of 161 problems for end-to-end verified code generation in Lean. Each problem consists of (1) the task of generating a specification that matches a held-out ground-truth specification, and (2) the task of generating a Lean implementation that provably satisfies this specification. Unlike prior benchmarks, CLEVER avoids test-case supervision, LLM-generated annotations, and specifications that leak implementation logic or allow vacuous solutions. All outputs are verified post-hoc using Lean's type checker to ensure machine-checkable correctness. We use CLEVER to evaluate several few-shot and agentic approaches based on state-of-the-art language models. These methods all struggle to achieve full verification, establishing it as a challenging frontier benchmark for program synthesis and formal reasoning. Our benchmark can be found on GitHub as well as HuggingFace. All our evaluation code is also available online.

A painting bought for less than $100 (£75) in a US charity shop in the 1960s has sold for almost £190,000 at auction. Art teacher Helene Plotkin bought the work by Scottish Colourist FCB Cadell in White Plains, New York in 1966, unaware of its true value. The painting, Interior: The Lady in Black, hung in her living room for 60 years - but the artist's signature was illegible and was only recently identified. It sold for £189,200, including buyer's premium, in Edinburgh as part of Lyon & Turnbull's Scottish painting and sculpture auction. The background to the painting only became clear when Helene's son Barry began his own research into it and took it for a valuation last year.

Multi-agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol to invoke, which agent role should perform a subtask, which model to bind to each role, how roles should interact, when to use retrieval or verification, and when to omit a step entirely. These choices interact with task regime and operational constraints, so static pipelines and one-off model comparisons provide only a limited view of the design space. This paper introduces AgensFlow, an open-source framework that treats multi-agent coordination as an online policy-learning problem under partial observability. The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology, and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed-systems incident tasks and security-advisory tasks. The evaluation shows three main results: learned routing reaches a higher-quality operating point than a fixed pipeline baseline on coordination-heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm-started policy graphs can reduce exploration cost while preserving plateau quality. Overall, the results support that learned, auditable routing can improve coordination-heavy multi-agent workflows over static wiring.

In the era of precision medicine, genome-wide epigenetic modifications offer rich data that could inform risk prediction. However, these data are high-dimensional and exhibit complex dependence structures, which makes it difficult to jointly model them with low-dimensional covariates when the goal is to obtain interpretable effect estimates for covariate adjustment. Standard Bayesian additive regression trees (BART) provide strong predictive performance but treat all predictors uniformly within the tree ensemble, obscuring the contributions of significant covariates and complicating variable selection in high-dimensional settings. We propose a semi-parametric BART model (spBART) that addresses this limitation by modeling low-dimensional covariates through a parametric component with interpretable coefficients, while capturing complex nonlinear associations among high-dimensional predictors through the tree ensemble. To perform stable variable selection, we develop a cross-validation-based procedure that aggregates posterior inclusion probabilities across folds and applies Bayesian false discovery rate control. We apply the proposed method to a pooled case--control analysis of high-dimensional genome-wide 5-hydroxymethylcytosine profiles derived from circulating cell-free DNA in two multiple myeloma studies ($N = 869$). The approach identifies a parsimonious set of candidate loci and achieves strong out-of-sample discrimination (AUC $= 0.96$) in a held-out validation set. Overall, spBART provides a unified framework for combining interpretable covariate inference with flexible modeling and variable selection in high-dimensional biomedical studies.