Abstract

Abstract
Text that accumulates over time does not only drift: topics are born, they die, they fragment into competing sub-narratives (splits), and they fuse into broader frames (merges). Our companion system paper, BERTilda, detects these events with transparent similarity and flow rules and validates them against human annotators; the present article is its mathematical companion. We show that the forward (outflow) and backward (inflow) coverages used by BERTilda are two semi-couplings between the topics of adjacent windows, both one-sided limits of a single unbalanced optimal-transport problem. The transport view separates the modeling layer from the readout layer: the plan is governed by three interpretable transport parameters, a blur and two marginal fidelities, and by a cost matrix fixed by the embedding geometry, while lifecycle labels are read from two explicit decision levels for survival and dominance. Because the plan is a stable function of that geometry and the topic centroids concentrate around their latent positions, we obtain finite-sample guarantees that account for the noise in the centroids themselves, not just in document attribution. We prove that the recovered transport structure converges to the latent geometry and that labels are recovered once their margin exceeds the plan’s perturbation radius; a lower bound for an edge-decision subproblem shows that the resulting inverse-square dependence on the decision margin cannot be improved in general. The theory also offers an explanation of BERTilda’s empirical pattern: continuation and disappearance are easy, while split and merge have the smallest attainable margins.

Abstract
Solar photovoltaic (PV) power generation has become one of the most widely adopted forms of clean energy worldwide. In large-scale PV farm operation and maintenance, unmanned aerial vehicles equipped with thermal infrared (TIR) cameras are increasingly used to enable automated fault detection and classification. However, the long imaging distance and the inherently low resolution of TIR images often lead to fault patterns appearing with low contrast, making subtle discriminative features difficult to extract and posing significant challenges to achieving highly accurate fault identification and classification. To address these challenges, we propose GEPFNet, a network that exploits Group Equivariant Convolutions to explicitly model the geometric structures of faults, incorporates multi-scale processing with unified local-global contextual representations, and adopts a parallel feature fusion strategy to integrate multi-level features and enhance contextual utilization effectively. The design of feature extraction and fusion mechanisms ensures the proposed GEPFNet achieves strong robustness and generalization under complex operational conditions. The effectiveness of GEPFNet was validated on two public datasets with distinct resolutions, class distributions, and feature characteristics: PVF-10 and the Infrared Solar Module (ISM) dataset. Extensive experiments and statistical analyses demonstrate that the proposed GEPFNet achieves state-of-the-art performance on the PVF-10 dataset, obtaining an accuracy of 96.05 %+/- 0.42 for the 2-Class task and 94.64 %+/- 0.35 for the 10-Class task. On the ISM dataset, GEPFNet achieves an improvement of approximately 5 % over the baseline models. Moreover, under highly imbalanced data distributions, the proposed GEPFNet achieves average accuracy improvements of 5.83% and 3.82% on PVF-10 and ISM, respectively, further demonstrating its capability to enhance class-wise performance. With only 9.51 GFLOPs, GEPFNet also exhibits notable computational efficiency, making it well suited for PV fault classification in TIR imagery.

Abstract
Methane (CH4) detection plays a crucial role in atmospheric monitoring, industrial safety, global climate assessments, and environmental sensing. Laser absorption spectroscopy techniques have become the gold standard for achieving fast, selective, and highly sensitive CH4 measurements across a wide range of conditions. This review summarizes the main spectroscopic methods used for methane detection, their operating principles, performance characteristics, and practical implementation considerations. Emphasis is placed on tunable diode laser absorption spectroscopy (TDLAS), wavelength modulation spectroscopy (WMS), dual-comb spectroscopy (DCS), cavity-enhanced methods such as cavity ring-down spectroscopy (CRDS), photoacoustic and photothermal techniques including quartz-enhanced photoacoustic spectroscopy (QEPAS) and hollow-core fiber photothermal interferometry, and emerging mid-infrared (MIR)/quantum-cascade-based approaches.

Abstract
Similarity is a key property across two or more interactive systems. Indeed, interacting with similar systems can bring benefits (e.g., reduced learning efforts and training time) but may also raise issues (e.g., interference errors and higher cognitive load). Interactive systems may be similar, as they correspond to the same work and tasks performed with different underlying systems, such as the pilots’ tasks in the cockpit of an Airbus A320 and a Boeing 737. They may also be similar by design, as they belong to the same suite as, for instance, the Microsoft Office software suite, where several tools are designed to be used concurrently by the same users. Previous work on similarity has focused on the visual presentation of interactive applications, highlighting commonalities and differences in terms of layout, shape, colours and other features of the user interface. This paper proposes a systematic and formal approach to analyse and assess the similarity between several interactive systems, focusing on their behaviours. To this end, we propose a tool-supported process that exploits both interactive formal system behaviour models and user task models. These models are analysed with the help of formal tools (model checking) to identify commonalities and differences in their exhibited behaviours. Beyond, we compare the specific and generic behavioural properties of each model, which provide semantic and meaningful information about how similar they are and how they differ. The approach is applied to two similar critical command and control interactive systems for flight safety operations in the space domain. © 2026 Copyright held by the owner/author(s).

Abstract
Wikipedia is the largest and most globally well-known online encyclopedia, but its collaborative nature leads to a significant disparity in article quality. In this work, we explore real-time and automatic quality assessment within Wikipedia through machine-learning. We first constructed a dataset of 36,000 English articles and 145 features, then compared the performance of multiple classification and regression algorithms and studied how the number of classes and features affects the model's performance. The six-class experiments achieved a classifier accuracy of 64% and a mean absolute error of 0.09 in regression methods, which matches or beats most state-of-the-art approaches. Our model produces similar results on some non-English Wikipedias, but the error is slightly higher on other versions. We have also determined that the features measuring the article's content and revision history bring the largest performance boost.