Temporal information is pivotal to real-world events, where events, roles, and relationships are inherently dynamic over time. Neglecting temporal structures in retrieval-augmented systems leads to chronological misalignment of evidence, ultimately compromising the accuracy of fact-checking for time-sensitive data. We present T-HiGra, a temporal graph RAG framework that treats time as a first-class signal across hierarchical knowledge graphs and the retrieval pipeline for open-domain, multi-hop question answering. T-HiGra (i) attaches structured temporal information data and provenance to entities, relations, passages and edges; (ii) preserves historical fidelity via alias validity windows during entity merging; and (iii) applies an adaptive time-aware ranking method that jointly combines temporal proximity and semantic similarity. We evaluate T-HiGra on a benchmark for time-sensitive questions, and report multi-facet RAG diagnostics using LLM-as-judge evaluators alongside traditional QA metrics. Against strong baselines (BM25, HippoRAG,...), we achieve the highest correctness (76.21
Predictive Process Monitoring (PPM) leverages historical event data recorded during historical business process executions, to forecast future scenarios of new ongoing process executions. Traditional PPM methods assume that business processes remain in a steady state over time. However, this is often not the real-world case due to concept drifts. This paper presents a PPM method, named ATLAS, which uses an online learning strategy coupled with a LLM, to develop a LSTM model for predicting the next-activity of ongoing process executions (running cases). It keeps the PPM model continuously updated each time a new event is observed. The evaluation assesses the performance the proposed approach in terms of accuracy and computation time also compared to several related online PPM approaches.
Distributed Acoustic Sensing (DAS) technology has emerged as a powerful tool for large-scale acoustic monitoring, transforming standard fiber optic cables into dense arrays of virtual microphones. When combined with artificial intelligence, particularly deep learning, DAS enables scalable and automated detection of acoustic events, making it a promising solution for whale monitoring across vast marine environments. In the field of marine bioacoustics, DAS provides significant advantages in terms of spatial coverage and robustness compared to traditional hydrophone arrays. This paper presents a novel deep learning-based approach to detect whale vocalizations from DAS data. The proposed method leverages a hybrid architecture combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to extract both spatial and temporal features from waterfall diagrams derived from DAS recordings. A multi-stage preprocessing pipeline including frequency filtering and frequency-wavenumber (f-k) filtering is applied to enhance signal quality and isolate whale calls from background noise. Each DAS recording is treated as a spatio-temporal matrix, and sequences of such matrices are sequentially analyzed capturing temporal dependencies. Experimental evaluations on a public dataset from the Ocean Observatories Initiative (OOI) RCA North Cable demonstrate that the CNN-BiLSTM model outperforms CNN and CNN-LSTM baselines, achieving a F1-score of 96
Aviation safety organizations accumulate large repositories of incident reports whose operational knowledge remains difficult to extract systematically at scale. Standard topic modeling applied to raw documents surfaces distributional patterns anchored to surface form rather than the causal structures relevant to safety analysis, and industrial constraints further limit viable approaches. We propose a pipeline decoupling representation shaping from structure discovery: an open-weight LLM summarizes the cause of each incident before topic discovery on the resulting representations, with a weighted multi-dimensional quality filter acting as an automated safeguard against hallucination and aspect drift. Applied to a large corpus of NTSB incident reports, cause-focused representations outperform raw-document baselines in topic coherence, and alignment with expert taxonomy. These results establish representation shaping as the primary lever for aligning discovered topic structures with operational safety objectives, opening concrete deployment perspectives for aviation knowledge management.
Weather radar mosaics provide high-resolution precipitation estimates but are often degraded by beam-blockage artefacts caused by terrain or infrastructure. These artefacts introduce systematic attenuation of radar reflectivity and distort precipitation patterns, particularly in complex topographic environments. Traditional correction approaches typically rely on geometric modeling or multi-radar fusion and often treat detection and correction as separate problems. In this work, we propose a guided conditional diffusion framework for correcting radar beam-blockage artefacts. The model reconstructs the precipitation field through a conditional reverse diffusion process guided by the corrupted radar observation and its associated quality map. During training, beam-blockage patterns are simulated on clean radar sectors to generate corrupted observations while preserving the original precipitation fields as references. This enables the model to learn both the spatial characteristics of blockage and the structure of precipitation fields. The method is evaluated on real radar mosaics containing both simulated and naturally occurring beam-blockage effects. Quantitative results show that the proposed approach reduces reconstruction errors within blocked regions while preserving precipitation values in unaffected areas. Qualitative analysis further demonstrates the recovery of coherent precipitation structures in degraded regions.
