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Gene body methylation buffers noise in gene expression in plants.
Full text linkNon-genetic variability in gene expression is an inevitable consequence of the stochastic nature of processes driving transcription and translation. While previous studies demonstrated that gene expression noise is negatively correlated with gene body methylation, the function of this correlation remains poorly understood in multicellular systems. Here, we provide a first functional link between gene body methylation and transcription noise in plants. We investigated a mutant with partial loss of CG methylation (met1-1) and 10 epigenetic recombinant inbred lines (epiRILs) generated by a cross between Col-0 and met1-3 plants, and observed an increase in gene expression noise, but this was not the case in met1-3 with complete loss of CG methylation. Loss of CG methylation in met1-3 could be compensated by a low but significant gain of non-CG methylation that buffers the noise in gene expression. Overall, our results show that gene body methylation has a functional role in reducing variability in transcription in a large subset of housekeeping genes, which require precise expression patterns to meet metabolic requirements. Genes lacking this noise-buffering effect are mainly enriched in stress response, where variability in gene expression can be seen as highly beneficial. [Abstract copyright: © The Author(s) 2026. Published by Oxford University Press.
On the Design Fundamentals of Diffusion Models: A Survey
Full text linkDiffusion models are learning pattern-learning systems to model and sample from data distributions with three functional components namely the forward process, the reverse process, and the sampling process. The components of diffusion models have gained significant attention with many design factors being considered in common practice. Existing reviews have primarily focused on higher-level solutions, covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review of seminal designable factors within each functional component of diffusion models. This provides a finer-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the design factors for different purposes, and the implementation of diffusion models
AlUla: Wonder of Arabia at the Palace Museum, Beijing: Bridging Global Cultural and Archaeological Engagement Between China and the Arab World
Full text linkITA-AITES − BIM in tunnelling – Guideline for mechanised and conventional tunnels
No full textThis guideline has been initiated by the International Tunnelling Association (ITA) Working Group (WG) 22 to support BIM implementation in the tunnelling industry. It provides recommendations which are to be adapted according to the availability of corresponding best practice experiences for all Project parties in order to support the adoption of BIM within a tunnelling Project.This guideline is intended to be used by all engineers and owners to provide a reference framework for the implementation of BIM for tunnel Projects. It focuses on the implementation of BIM for segmentally lined mechanically bored tunnels and conventional tunnels. This guideline primarily covers the implementation of heavy civil elements for a tunnel Project. For more general information concerning BIM and its use in underground construction, the reader is referred to more general guidelines such as The German Tunnelling Committee’s (DAUB’s) “Building and Operation of Underground Structures – BIM in Tunnelling” or similar documents as provided in Section 16.Specific recommendations concerning the modelling of non-civil works, e.g., mechanical, electrical, automation, or control systems, or Project-specific internal structures, e.g., concrete infills, plenum walls, duct-banks, smoke ducts, etc. are not provided.This guideline is not intended to contest Employer’s Information Requirements or local best practise. It is only intended to alleviate ambiguity that may exist due to general definitions in the Employer’s Information Requirements or provide reference to owners for the development of their Requirements.It is expected that the BIM’s capabilities will continue to expand as new BIM technology is developed. This version of this guideline is, however, based on a review of current international practise of BIM in tunnelling. As such, this document is subject to updates in consequent versions
Adaptation in Target-based International Environmental Agreements
No full textIn this paper, we examine the impact of adaptive investments on international environmental agreements that, like the current Paris Agreement, have a bottom-up nature, with signatory countries committing to a common emission target (T-agreement). Furthermore, we contrast the effectiveness of the T-agreement with that of an agreement in which signatory countries act as a coalition and make their decisions jointly (J-agreement). Under a T-agreement, the strategic relationships between countries’ emissions, as well as between adaptation and mitigation, align with the results found in the literature on J-agreements. However, the impact of adaptation and of the number of countries participating to the agreement differ significantly. These results offer new insights into the interplay between emissions, adaptation, and participation in the context of international environmental agreements, and they highlight the critical role played by the agreement’s structure and substance
Securing UAV Communications by Fusing Cross-Layer Fingerprints
Full text linkThe open nature of wireless communications renders unmanned aerial vehicle (UAV) communications vulnerable to impersonation attacks, under which malicious UAVs can impersonate authorized ones with stolen digital certificates. Traditional fingerprint-based UAV authentication approaches rely on a single modality of sensory data gathered from a single layer of the network model, resulting in unreliable authentication experiences, particularly when UAVs are mobile and in an openworld environment. To transcend these limitations, this paper proposes SecureLink, a UAV authentication system that is among the first to employ cross-layer information for enhancing the efficiency and reliability of UAV authentication. Instead of using single modalities, SecureLink fuses physical-layer radio frequency (RF) fingerprints and application-layer micro-electromechanical system (MEMS) fingerprints into reliable UAV identifiers via multimodal fusion. SecureLink first aligns fingerprints from channel state information measurements and telemetry data, such as feedback readings of onboard accelerometers, gyroscopes, and barometers. Then, an attention-based neural network is devised for in-depth feature fusion. Next, the fused features are trained by a multi-similarity loss and fed into a one-class support vector machine for open-world authentication. We extensively implement our SecureLink using three different types of UAVs and evaluate it in different environments. With only six additional data frames, SecureLink achieves a closed-world accuracy of 98.61% and an open-world accuracy of 97.54% with two impersonating UAVs, outperforming the existing approaches in authentication robustness and communication overheads. Finally, our datasets collected from these experiments are available on GitHub: https://github.com/PhyGroup/SecureLink_data
Automated multi-category tunnel damage detection and report generation from ultra-high-resolution panoramic laser images
Full text linkIn the realm of ageing tunnel infrastructure, accurately assessing structural damage remains a pressing challenge due to the inherent subjectivity and time demands of manual inspections. Although reality capture technology allows for digital representation of as-is condition of assets, converting these rich data sources into actionable risk assessments demands still requires innovative solutions. In this paper, we introduce a comprehensive, web-based automated framework that uses ultra-high-resolution (UHR) panoramic tunnel images to automatically generate detailed damage records and risk assessment reports. A significant challenge in this domain is the observation that damage regions often lack sharply defined boundaries; instead, they exhibit gradual, blurred transitions, which is not well-suited to conventional segmentation evaluation. To address this, we formally define the challenge of inconsistency of damage annotation in complex real-world scenarios and propose a novel evaluation metric: Intersection over Union with buffer zone (IoUb). This metric relaxes the rigid boundary precision requirements of traditional evaluation methods, focusing more on capturing the overall damage. We evaluated several instance segmentation algorithms and recommend adopting a lower confidence threshold, as it reduces missed detections without significantly increasing false positives. We introduce post-processing methods that aggregate the predictions from multiple inferences to meet the demands of processing UHR panoramic images, resulting in a 3% improvement in Macro IoU and IoUb, along with a 90% damage recall. Experimental results on Italian road tunnels demonstrate that our framework enhances automated damage detection. We then categorize damage severity using a statistically grounded methodology, enable natural language queries of statistical damage results, and handle visualization and report export, all within a single end-to-end web-based platform. The proposed framework significantly enhances the efficiency of professionals in planning and monitoring ageing tunnel assets. Our code is available at https://github.com/zxy239/Auto-damage-report-generatio
Robust tube localization for Mars Sample Return: Lightweight YOLO-segmentation with angle-guided PnP
No full textOne considered approach in the planned Mars Sample Return (MSR) campaign involves accurately identifying and retrieving sample tubes from the Martian surface. This paper presents an innovative approach that utilises lightweight computer’vision techniques to enhance the efficiency and accuracy of the Sample Transfer Arm (STA) aboard the MSR lander. Our methodology employs the YOLOv8 deep learning model for image segmentation, and centroid detection of tubes in the challenging dusty Martian environment. These detected masks and centroids provide the foundation for constructing an outlined representation of the tubes, which is critical for precise spatial orientation. We exploit the knowledge of the object geometry to find key points and match them using their relative positions with respect to the geometry. Subsequently, a Perspective-n-Point (PnP) algorithm with RANSAC utilizes this outline and pre-computed 3D coordinates to ascertain the tube’s pose. This enables the STA’s camera-equipped gripper to locate and retrieve the samples accurately. This process is meticulously tailored for the constrained computational resources available on Martian missions, addressing limitations in processing speed and lack of parallelization capabilities. Extensive simulations under Martian-like conditions demonstrate the robustness and reliability of our approach, which would be a necessary technology to enable a backup tube retrieval concept for a MSR campaign using a robotic arm by ensuring precise and efficient sample collection. This method can achieve sub-degree and sub-centimeter accuracy with a single image
AttenCraft: Attention-based Disentanglement of Multiple Concepts for Text-to-Image Customization
No full textText-to-image (T2I) customization empowers users to adapt the T2I diffusion model to new concepts absent in the pre-training dataset. On this basis, capturing multiple new concepts from a single image has emerged as a new task, allowing the model to learn multiple concepts simultaneously or discard unwanted concepts. However, multiple-concept disentanglement remains a key challenge. Existing disentanglement models often exhibit two main issues: feature fusion and asynchronous learning across different concepts. To address these issues, we propose AttenCraft, an attention-based method for multiple-concept disentanglement. Our method uses attention maps to generate accurate masks for each concept in a single initialization step, aiding in concept disentanglement without requiring mask preparation from humans or specialized models. Moreover, we introduce an adaptive algorithm based on attention scores to estimate sampling ratios for different concepts, promoting balanced feature acquisition and synchronized learning. AttenCraft also introduces a feature-retaining training framework that employs various loss functions to enhance feature recognition and prevent fusion. Extensive experiments show that our model effectively mitigates these two issues, achieving state-of-the-art image fidelity and comparable prompt fidelity to baseline models
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