Efficient Mapping Research Articles

Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps

Abstract Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extragalactic microlensing rely on computationally expensive magnification map generation. With large data sets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of κ, γ, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the broad-line region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.

Fast and reliable quantitative measures of white matter development with magnetic resonance fingerprinting

Abstract Developmental cognitive neuroscience aims to shed light on evolving relationships between brain structure and cognitive development. To this end, quantitative methods that reliably measure individual differences in brain tissue properties are fundamental. Standard qualitative MRI sequences are influenced by scan parameters and hardware-related biases, and also lack physical units, making the analysis of individual differences problematic. In contrast, quantitative MRI can measure physical properties of the tissue but with the cost of long scan durations and sensitivity to motion. This poses a critical limitation for studying young children. Here, we examine the reliability of an efficient quantitative multiparameter mapping method - Magnetic Resonance Fingerprinting (MRF) - in children scanned longitudinally. We focus on T1 values in white matter, since quantitative T1 values are known to primarily reflect myelin content, a key factor in brain development. Forty-nine children aged 8-13y (mean 10.3y ±1.4) completed two scanning sessions 2-4 months apart. In each session, two 2-minute 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan duration on image quality and scan-rescan reliability. A separate calibration scan was used to measure B0 inhomogeneity and correct for bias. We examined the impact of scan time and B0 inhomogeneity correction on scan-rescan reliability of values in white matter, by comparing single 2-min and combined two 2-min scans, with and without B0-correction. Whole-brain voxel-based reliability analysis showed that combining two 2-min MRF scans improved reliability (pearson’s r=0.87) compared with a single 2-min scan (r=0.84), while B0-correction had no effect on reliability in white matter (r=0.86 and 0.83 4-min vs 2-min). Using diffusion tractography, we segmented major white matter fiber tracts and examined the profiles of MRF-derived T1 values along each tract. We found that T1 values from MRF showed similar or greater reliability compared with diffusion parameters. Lastly, we found that R1 (1/T1) values in multiple white matter tracts were significantly correlated with age. In sum, MRF-derived T1 values were highly reliable in a longitudinal sample of children and replicated known age effects. Reliability in white matter was improved by longer scan duration but was not affected by B0-correction, making it a quick and straightforward scan to collect. We propose that MRF provides a promising avenue for acquiring quantitative brain metrics in children and patient populations where scan time and motion are of particular concern.

Labeling Network Intrusion Detection System (NIDS) Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models

Analysts in Security Operations Centers (SOCs) are often occupied with time-consuming investigations of alerts from Network Intrusion Detection Systems (NIDSs). Many NIDS rules lack clear explanations and associations with attack techniques, complicating the alert triage and the generation of attack hypotheses. Large Language Models (LLMs) may be a promising technology to reduce the alert explainability gap by associating rules with attack techniques. In this paper, we investigate the ability of three prominent LLMs (ChatGPT, Claude, and Gemini) to reason about NIDS rules while labeling them with MITRE ATT&CK tactics and techniques. We discuss prompt design and present experiments performed with 973 Snort rules. Our results indicate that while LLMs provide explainable, scalable, and efficient initial mappings, traditional machine learning (ML) models consistently outperform them in accuracy, achieving higher precision, recall, and F1-scores. These results highlight the potential for hybrid LLM-ML approaches to enhance SOC operations and better address the evolving threat landscape. By utilizing automation, the presented methods will enhance the analysis efficiency of SOC alerts, and decrease workloads for analysts.

A Multi-Drone System Proof of Concept for Forestry Applications

This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones.

Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+

Road extraction is a sub-domain of remote sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. Convolutional neural networks (CNNs), especially the DeepLab series known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects’ features, address some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in the place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP with a CNN backbone network and a Squeeze-and-Excitation block will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from remote sensing images. The Results Section presents a comparison of our model’s performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach.

Optimized Airborne Millimeter-Wave InSAR for Complex Mountain Terrain Mapping.

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system's potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy.

Efficient sparsity-promoting MAP estimation for Bayesian linear inverse problems

Abstract Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed linear inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown augmented by a generalized gamma hyper-prior model for the variance hyper-parameters. This investigation generalizes such models and their efficient maximum a posterior estimation using the iterative alternating sequential algorithm in two ways: (1) general sparsifying transforms: diverging from conventional methods, our approach permits use of sparsifying transformations with nontrivial kernels; (2) unknown noise variances: the noise variance is treated as a random variable to be estimated during the inference procedure. This is important in applications where the noise estimate cannot be accurately estimated a priori. Remarkably, these augmentations neither significantly burden the computational expense of the algorithm nor compromise its efficacy. We include convexity and convergence analysis and demonstrate our method’s efficacy in several numerical experiments.

Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation.

Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. This paper introduces a novel medical image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed to facilitate rapid and accurate multi-organ labeling. To enhance the model's capability to comprehend spatial information, we propose the Global Spatial-Channel Attention Mechanism (GSCA). This mechanism enables the model to concentrate more effectively on regions of interest. Additionally, we have integrated Efficient Mapping Convolutional Blocks (EMCB) into the feature-learning process, allowing for the extraction of multi-scale spatial information and the adjustment of feature map channels through optimized weight values. Moreover, the proposed framework progressively enhances its performance by utilizing a generative-adversarial learning strategy, which contributes to improvements in segmentation accuracy. Consequently, EGAUNet demonstrates exemplary segmentation performance on public multi-organ datasets while maintaining high efficiency. For instance, in evaluations on the CHAOS T2SPIR dataset, EGAUNet achieves approximately 2% higher performance on the Jaccard metric, 1% higher on the Dice metric, and nearly 3% higher on the precision metric in comparison to advanced networks such as Swin-Unet and TransUnet.

Radio-frequency cavity field measurements through free falling bead

This paper introduces a novel bead-falling measurement method for the precise and efficient mapping of electromagnetic fields within radio-frequency (rf) cavities, which are crucial components in the design of accelerators. The traditional bead-pull method, while effective, involves mechanical complexities and is prone to errors from wire perturbations. The innovative method reported here leverages the simplicity and accuracy of free-falling beads to mitigate these issues. This technique eliminates the need for a wire-pulley system, thereby simplifying the experimental setup and reducing potential mechanical errors. We detail the development and operational principles of this new method, including the design of a compact, portable measurement device that integrates a bead/droplet release system and a bead detection system linked to a vector network analyzer. The device has been tested with a three-gap buncher cavity and a scaled Alvarez-type cavity, demonstrating its ability to perform rapid, reliable field measurements under various challenging conditions, including low signal-to-noise ratios and environmental vibrations. The results confirm the method’s superiority in precision and operational efficiency, potentially setting a new standard for rf cavity diagnostics and tuning. Published by the American Physical Society 2025

Elementary-level intrusive coupling of machine learning for efficient mechanical analysis of variable stiffness composite laminates: a spatially-adaptive fidelity-sensitive computational framework

AbstractMechanical analysis of the complex configurations of composite laminates can be computationally prohibitive based on accurate higher-order theories, especially when the analyses involve multiple realizations corresponding to different sets of input parameters such as uncertainty quantification, optimization, reliability and sensitivity analysis. Efficient lower-order theories should not be adopted in such situations since the error accumulates with multiple realizations, leading to poor outcomes. We propose an elementary-level coupling of machine learning for efficient, yet accurate mechanical analysis of laminated composites based on finite element simulations coupled with gaussian process regression. The generic parameter space of material properties, mesh size, number of layers, and ply angle in composite laminates are accounted for forming an efficient mapping with the augmentation of lower-order theory-based elementary-level structural matrices. The computationally efficient machine learning models predict the difference in the elements of the stiffness matrix for higher-order zigzag theory (HOZT) and first-order shear deformation theory (FSDT) at the first stage. Based on such machine learning-based difference mapping, we augment the elementary stiffness matrices obtained using FSDT efficiently to the equivalent of HOZT theory without any additional computational expenses (referred to here as augmented FSDT, or aFSDT). However, it is not necessary to augment all the elements in the analysis domain which might otherwise lead to unnecessary computational expenses and loss in accuracy. To achieve an optimal level of computational efficiency and accuracy, we further propose spatially-adaptive fidelity-sensitive coupling of machine learning, only for the elements within the analysis domain where it is necessary to adopt higher-order theories. The selective augmentation strategy essentially brings in a scope of integrating physics-based insights of critical stress resultant distribution into the algorithm based on best theory diagram. Subsequently, the global structural matrices are computed exploiting such adaptive criteria containing a mixed set of elements formed using FSDT and aFSDT, which leads to an accuracy equivalent to HOZT in the mechanical analysis of composite laminates almost at the computational expense of FSDT. The proposed spatially-adaptive fidelity-sensitive scheme ensures optimal performance in terms of computational efficiency by augmenting selective elements while minimizing the loss of accuracy due to the involvement of surrogates. Detailed numerical results are presented for static, dynamic and stability characterization of composite laminates including the demonstration for variable stiffness composite configurations based on the efficient machine learning-assisted elementary-level intrusive computational framework, wherein the notion of engineering judgement is introduced concerning the trade-off between computational efficiency and required level of accuracy.

Data-driven approach to define mathematical model of a permanent-magnet synchronous motor used in small class electric vehicles

he development of cutting-edge motor technologies for electric vehicles (EVs) has been fueled by the global movement towards sustainable mobility. Because of its great efficiency, power density, and flexibility to operate at various speeds, Permanent-Magnet Synchronous Motors (PMSMs) have become the go-to option for small-class EVs. However, the accuracy and applicability of standard mathematical modeling techniques are limited because they frequently miss real-world nonlinearities and parameter fluctuations. In this paper, a data-driven methodology for PMSM modeling that is tailored for small-class EV applications is presented. The suggested method improves the precision, flexibility, and computational efficiency of PMSM models by utilizing real-world operational data. The efficiency map and peak torque characteristics of the WULING MACARON EV's electric drive were defined using regression analysis, which produced low residual errors and high R-squared values of 0.9647 and 0.9789, respectively. The outcomes show that the model can optimize energy use, forecast performance under various circumstances, and assist real-time control applications.

Atlas for sustainable Egyptian governorates buildings based on wind/solar potential: Power, efficiency, economic, environmental, and thermal comfort maps

Atlas for sustainable Egyptian governorates buildings based on wind/solar potential: Power, efficiency, economic, environmental, and thermal comfort maps

Creation of “patchwork MOLLI Image” from multiple free-breathing MOLLI images: Feasible technique to enable efficient T1 mapping for free-breathing children

Creation of “patchwork MOLLI Image” from multiple free-breathing MOLLI images: Feasible technique to enable efficient T1 mapping for free-breathing children

Phase diagram of invasion patterns in «capillary number, wetting angle, disorder» coordinates: A lattice Boltzmann study

Pore space heterogeneity, numerically described by the disorder parameter, is a factor that strongly influences the displacement mechanics in porous media. This paper presents a systematic study of the simultaneous effects of capillary number, wetting angle, and pore space disorder on the invasion patterns of immiscible displacement: viscous and capillary fingering, compact displacement, and various crossover regimes. The results are based on lattice Boltzmann simulations performed on synthetic micromodels and natural X-ray computed tomography models of natural sandstones. This paper addresses two objectives. The first is to present for the first time a three-dimensional phase diagram in «capillary number, wetting angle, disorder» coordinates, which accurately indicates the regions of invasion patterns. The identification is based on a number of displacement characteristics, such as sweep efficiency map, fractal dimension, and the dynamics of the leading front movement. Based on the phase diagram, the critical wetting angles, which define the boundary of the viscous fingering, capillary fingering, and compact displacement regimes, shift towards imbibition with increasing disorder. A decrease in capillary number shifts the critical wetting angles for the viscous fingering and compact displacement modes towards drainage, and for the capillary fingering mode towards imbibition. The second goal is to identify the maximum effect of pore space disorder on sweep efficiency as a function of capillary number and wetting angle. It has been found that at high capillary numbers the disorder effect is independent of the wetting angle. A decrease in capillary number enhances the maximum disorder effect on sweep efficiency and it becomes strongly dependent on the wetting angle. With increased capillary forces the transition from deep imbibition and drainage regimes to the mode with neutral wettability greatly enhances the effect of disorder. The extremum point of wetting angle, at which the effect of disorder is maximum, shifts towards drainage with decreasing capillary number.

Transcriptome and translatome profiling of Col-0 and grp7grp8 under ABA treatment in Arabidopsis

Abscisic acid (ABA) is a crucial phytohormone that regulates plant growth and stress responses. While substantial knowledge exists about transcriptional regulation, the molecular mechanisms underlying ABA-triggered translational regulation remain unclear. Recent advances in deep sequencing of ribosome footprints (Ribo-seq) enable the mapping and quantification of mRNA translation efficiency. Additionally, RNA-binding proteins (RBPs) play essential roles in translational regulation by interacting with target RNA molecules, making the identification of binding sites via UV crosslinking and immunoprecipitation (CLIP) critical for understanding RBP function. Glycine-rich RNA-binding proteins (GRPs), a prominent class of RBPs in plants, are responsive to ABA. In this study, RNA-seq and Ribo-seq analyses were conducted on 3-day-old Col-0 and grp7grp8 seedlings of Arabidopsis thaliana, treated with either ABA or mock solutions. These analyses facilitated deep sequencing of total mRNA and mRNA fragments protected by translating ribosomes. Additionally, CLIP-seq analysis of pGRP7::GRP7-GFP grp7-1 identified RNA bound by GRP7. This multi-omics dataset allows for a comprehensive investigation of the plant’s response to ABA from various perspectives, providing a significant resource for studying ABA-regulated mRNA translation efficiency.

Bandgap Optimization of Photovoltaic Tandem Cells Based on Spectra Collected over a Full Year

Spectral data collected every minute at the test station in Grimstad, Norway, are used to investigate the impact of spectral variation on tandem cells. The results are displayed as efficiency maps which are compared to equivalent maps for the air mass 1.5 global (AM1.5G) reference spectrum. Most of the maps are calculated for ideal cells and will thus serve as benchmarks for what can possibly be achieved under real conditions, but the impact of non‐radiative recombination is also included. It is found that there is generally good agreement between the efficiency under the AM1.5G spectrum and the efficiency found using the collected spectra. The main difference is that a slight blueshift in the real spectra favors larger bandgaps. Seasonal efficiency maps and maps for different types of conditions are also presented. The largest deviation from the reference spectrum is found for the three darkest months of the year. Optimizing the bandgaps for this period may increase seasonal production by several percent, albeit with a significant accompanying reduction in annual production. For June, the sunniest month, as well as for cloudy and lowlight conditions, it is found that the optimal bandgaps are slightly larger than those found for the AM1.5G.

Optimization-Based Energy Management Algorithm for 2-Stroke Hybrid Ship with Controllable Pitch Propeller

This paper examines the fuel consumption savings of a hybrid ship powertrain with 2-stroke main engine by implementing a novel adaptive equivalent consumption minimization strategy that utilizes a controllable pitch propeller. A non-hybrid powertrain model was developed as a demonstrator and real-world data were used for fuel consumption and efficiency maps. The baseline powertrain model was extended to a hybrid by introducing a shaft generator, a battery, a controllable pitch propeller, and the supervisory control algorithm. The potential benefits of the proposed powertrain are examined over different operation phases including port stay, open sea sailing, and port approach. The result showed that the energy efficiency gains can reach up to 6% under the open sea sailing phase. Furthermore, the controllable pitch propeller offers additional energy efficiency benefits of 2% under the port approach phase, utilizing the proposed algorithm. If the proposed powertrain is produced and the implemented algorithm is adopted, this could lead to substantial carbon dioxide emissions and fuel consumption savings at sea.

Semantic Communications for Efficient Spectrum Maps Construction and Signal Source Location

Semantic Communications for Efficient Spectrum Maps Construction and Signal Source Location

Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction

Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction

Voxel Map to Occupancy Map Conversion Using Free Space Projection for Efficient Map Representation for Aerial and Ground Robots

Voxel Map to Occupancy Map Conversion Using Free Space Projection for Efficient Map Representation for Aerial and Ground Robots