Crowd-sourced mapping offers a scalable alternative to creating maps using traditional survey vehicles. Yet, existing methods either rely on prior high-definition (HD) maps or neglect uncertainties in the map fusion. In this work, we present a complete pipeline for HD map generation using production vehicles equipped only with a monocular camera, consumer-grade GNSS, and IMU. Our approach includes on-cloud localization using lightweight standard-definition maps, on-vehicle mapping via an extended object trajectory (EOT) Poisson multi-Bernoulli (PMB) filter with Gibbs sampling, and on-cloud multi-drive optimization and Bayesian map fusion. We represent the lane lines using B-splines, where each B-spline is parameterized by a sequence of Gaussian distributed control points, and propose a novel Bayesian fusion framework for B-spline trajectories with differing density representation, enabling principled handling of uncertainties. We evaluate our proposed approach, B$^2$F-Map, on large-scale real-world datasets collected across diverse driving conditions and demonstrate that our method is able to produce geometrically consistent lane-level maps.
Omar Angelo Ibrahim, Henri Trang, Qianlan Chen
et al.
Thalamic atrophy is a sensitive imaging marker of neurodegeneration in multiple sclerosis (MS) and related disorders, though thalamus segmentation remains method-dependent. Quantitative magnetic resonance imaging (qMRI) may enhance thalamic boundary contrast, particularly in the context of deep learning. We benchmarked thalamic segmentations from two atlas-constrained algorithms, FreeSurfer and FIRST, and two deep learning algorithms, DBSegment and MindGlide (an MS-trained model), against ground truth (GT) labels, tested whether quantitative R1 maps improve performance, and evaluated clinical validity cross-sectionally and longitudinally. We generated thalamus masks using each algorithm from T1-weighted data in a single-scanner cohort (baseline n = 321; 1-year follow-up n = 234) including patients with MS/related disorders and healthy controls. Using MindGlide, we also produced FLAIR- and R1-based masks and ensembles. Manual GT labels were obtained for 50 MS patients using T1w and FLAIR scans. For voxel-wise GT agreement, DBSegment yielded the highest Dice-similarity coefficient; atlas-constrained methods showed the highest sensitivity but lowest precision, while MindGlide balanced both. Volumetrically, MindGlide showed the most accurate estimates; DBSegment and FreeSurfer showed proportional bias, and both atlas-constrained methods overestimated thalamic volumes. Adding R1 input to MindGlide produced modest or no gains in GT agreement. Additionally, MindGlide volumes were most consistently associated with disability and cognitive scores cross-sectionally, and longitudinally showed the largest effects between thalamic volume change and EDSS worsening. Incorporating R1 maps offered no cross-sectional benefit but strengthened longitudinal associations. Higher-resolution qMRI and multi-contrast deep learning architectures may further enhance thalamic segmentation and monitoring in neuroinflammatory diseases.
Computer applications to medicine. Medical informatics, Neurology. Diseases of the nervous system
Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas
et al.
Background/Objectives: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment. Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.
In regular dynamics, discrete maps are model presentations of discrete dynamical systems, and they may approximate continuous dynamical systems. Maps are used to investigate general properties of dynamical systems and to model various natural and socioeconomic systems. They are also used in engineering. Many natural and almost all socioeconomic systems possess memory which, in many cases, is power-law-like memory. Generalized fractional maps, in which memory is not exactly the power-law memory but the asymptotically power-law-like memory, are used to model and investigate general properties of these systems. In this paper we extend the definition of the notion of generalized fractional maps of arbitrary positive orders that previously was defined only for maps which, in the case of integer orders, converge to area/volume-preserving maps. Fractional generalizations of H'enon and Lozi maps belong to the newly defined class of generalized fractional maps. We derive the equations which define periodic points in generalized fractional maps. We consider applications of our results to the fractional and fractional difference H'enon and Lozi maps.
Gravity is the driving force of star formation. Although gravity is caused by the presence of matter, its role in complex regions is still unsettled. One effective way to study the pattern of gravity is to compute the accretion it exerts on the gas by providing gravitational acceleration maps. A practical way to study acceleration is by computing it using 2D surface density maps, yet whether these maps are accurate remains uncertain. Using numerical simulations, we confirm that the accuracy of the acceleration maps $\mathbf a_{\rm 2D}(x,y)$ computed from 2D surface density are good representations for the mean acceleration weighted by mass. Due to the under-estimations of the distances from projected maps, the magnitudes of accelerations will be over-estimated $|\mathbf a_{\rm 2D}(x,y)| \approx 2.3 \pm 1.8 \; |\mathbf a_{\rm 3D}^{\rm proj}(x,y)|$, where $\mathbf a_{\rm 3D}^{\rm proj}(x,y)$ is mass-weighted projected gravitational acceleration, yet $\mathbf a_{\rm 2D}(x,y)$ and $ \mathbf a_{\rm 3D}^{\rm proj}(x,y)$ stay aligned within 20$^{\circ}$. Significant deviations only occur in regions where multiple structures are present along the line of sight. The acceleration maps estimated from surface density provide good descriptions of the projection of 3D acceleration fields. We expect this technique useful in establishing the link between cloud morphology and star formation, and in understanding the link between gravity and other processes such as the magnetic field. A version of the code for calculating surface density gravitational potential is available at \url{https://github.com/zhenzhen-research/phi_2d}.
This article provides an overview on various conservation laws for polyharmonic maps between Riemannian manifolds. Besides recalling that the variation of the energy for polyharmonic maps with respect to the domain metric gives rise to the stress-energy tensor, we also show how the presence of a Killing vector field on the target manifold leads to a conservation law. For harmonic and biharmonic maps we also point out a number of applications of such conservation laws.
Mohammed Abdilfattah Ali, Sabbar Abdullah Salih, Amera Ismail Hussain
This study included an assessment of the hydrogeological conditions of the Mishraq sulphur fields before production, by measuring groundwater levels in (11) wells in Mishraq-2 in 2021, as well as the information of wells obtained, which are (12) wells in Mishraq-3, (68) wells in Mishraq-1. Groundwater levels ranged between (187.71-205.80) m in Mishraq-1, while in Mishraq-2 it ranged between (189.19-196.26) m, as for Mishraq-3 the levels were between (186.4-194.98) m.
The contour maps were drawn for the movement and levels of groundwater, showing that the direction of groundwater movement in Mishraq field-1 is from the west and northwest to the east, with a slight slope towards the southeast, towards the Tigris River, while in the Mishraq field-2, we notice that the direction of groundwater movement is From the east to the west, that is, toward the Tigris River, as for Mishraq field-3, it was found that the direction of movement is from the southeast toward the northwest, that is, toward the Tigris and Great Zab rivers. So it can be said that the Tigris and Great Zab rivers are the two drainage areas in these three fields. The hydraulic properties were analyzed in (44) wells in the three Al-Mishraq fields. Where the values of Transmissivity (T) in Mishraq field-1 ranged between (24.4-1557.5) m2/day, as for Mishraq-2 it ranged between (23-96.91) m2/day, while in Mishraq-3 it ranged between (10.5-4002) ) m2//day, and the hydraulic conductivity (K) ranged between (0.26 -14.68) m/day in Mishraq field-1, as for Mishraq-2 it ranged between (0.7-4.2) m/day, while in Mishraq-3 It ranged between (0.37-119.09) m/day.
Abstract Premise Although several software packages are available for genotyping insertion/deletion (indel) polymorphisms in genomes using next‐generation sequencing data, simultaneously calling indel genotypes across many individuals for use in genetic mapping remains challenging. Methods and Results We present an integrated pipeline, InDelGT, for the extraction of indel genotypes from a segregating population such as backcross or F2 lines, or from an F1 cross between outbred species. The InDelGT algorithm is implemented in three steps: generating an indel catalog, calling indel genotypes, and analyzing indel segregation. We demonstrated the use of the pipeline with an example data set from an F1 hybrid population of Populus and successfully constructed the two parental genetic linkage maps. Conclusions InDelGT is a practical tool that can quickly genotype a large number of indel markers within a population following Mendelian segregation. The InDelGT pipeline is freely available on GitHub (https://github.com/tongchf/InDelGT).
Abstract The United Nations have announced 17 Sustainable Development Goals and 169 targets, which are indivisible and integrated, and which balance the economic, social, and environmental dimensions of sustainable development. This indicates that the performance of successful nations is generally good across many sustainability indicators. Our results, based on multi-criteria and statistical analysis across 173 countries, suggest an interconnection between a country’s sustainability 12 indicators and success. This article focuses on the Country Success and Sustainability (CSS) Maps and Models of the World, which show that improvements in environmental, social, and economic sustainability indicators lead to improvements in the country's success, and vice versa. The CSS Models explain 98.2% of national success and 80.8% of the three dimensions of average sustainability dispersions. When a nation’s success increases by 1%, the 12 indicators of the three dimensions of sustainability improve by 0.85% on average. The human development index and GDP per capita were the success variables with the most substantial impact on 12 sustainability indicators in 173 countries. Calculations made using equal and different weights of 17 criteria show a deviation of 5.34% for the priorities of these 173 countries.
The GRACE Follow-On satellite mission measures distance variations between its two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a scale of a few 100 km originating from hydrology, seismology, climatology and other sources. This mission hosts two ranging instruments, a conventional microwave system based on K(a)-band ranging (KBR) and a novel laser ranging instrument (LRI), both relying on interferometric phase readout. In this paper, we show how the phase measurements can be converted into range data using a time-dependent carrier frequency (or wavelength) that takes into account potential intraday variability in the microwave or laser frequency. Moreover, we analyze the KBR-LRI residuals and discuss which error and noise contributors limit the residuals at high and low Fourier frequencies. It turns out that the agreement between KBR and LRI biased range observations can be slightly improved by considering intraday carrier frequency variations in the processing. Although the effect is probably small enough to have little relevance for gravity field determination at the current precision level, this analysis is of relevance for detailed instrument characterization and potentially for future more precise missions.