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The relationship between brain lesions and stroke outcomes is crucial for advancing patient prognosis and developing effective therapies. Stroke is a leading cause of disability worldwide, and it is important to understand the neurological basis of its varied symptomatology. Lesion-symptom mapping (LSM) methods provide a means to identify brain areas that are strongly associated with specific symptoms. However, inner variations in LSM methods can yield different results. To address this, our study aimed to characterize the lesion-symptom mapping variability using three different LSM methods. Specifically, we sought to determine a lesion symptom core across LSM approaches enhancing the robustness of the analysis and removing potential spatial bias. A cohort consisting of 35 patients with either right- or left-sided middle cerebral artery strokes were enrolled and evaluated using the NIHSS at 24 h post-stroke. Anatomical T1w MRI scans were also obtained 24 h post-stroke. Lesion masks were segmented manually and three distinctive LSM methods were implemented: ROI correlation-based, univariate, and multivariate approaches. The results of the LSM analyses showed substantial spatial differences in the extension of each of the three lesion maps. However, upon overlaying all three lesion-symptom maps, a consistent lesion core emerged, corresponding to the territory associated with elevated NIHSS scores. This finding not only enhances the spatial accuracy of the lesion map but also underscores its clinical relevance. This study underscores the significance of exploring complementary LSM approaches to investigate the association between brain lesions and stroke outcomes. By utilizing multiple methods, we can increase the robustness of our results, effectively addressing and neutralizing potential spatial bias introduced by each individual method. Such an approach holds promise for enhancing our understanding of stroke pathophysiology and optimizing patient care strategies. [ABSTRACT FROM AUTHOR]

Recently, it was shown that a detailed reconstruction of urban height maps is possible from single very high resolution (VHR) synthetic aperture radar (SAR) images with deep convolutional neural networks. Being merely a proof-of-concept so far, the potential of this approach has not been fully exploited yet. With this work, we present an optimized deep learning model for height estimation from single VHR SAR images, which incorporates sensor knowledge into the estimation. We embed this model into a SAR-specific processing chain that allows the generation of seamless georeferenced digital surface models (DSMs) with geodetically defined heights in an orthometric map coordinate system. Extensive experiments are carried out with a custom-generated dataset including over 50 TerraSAR-X images from 8 different cities. They confirm that our workflow generalizes well across different locations while being robust to different properties of the input data. Thus, our workflow provides the unique ability to produce elevation models of urban areas quickly, regardless of weather, around the clock, and at low cost. This can be of immense benefit when time is critical, e.g. in disaster response scenarios or in the context of reconnaissance activities. [ABSTRACT FROM AUTHOR]

The European Ground Motion Service (EGMS) uses Sentinel-1 data to monitor and measure land displacement on a European scale, providing accurate and consistent data on ground motion phenomena. Since 2022, three types of EGMS products have been made available to all users in a free and open-access manner via the Copernicus platform. As a result, analysis of spatial gradients of displacements is achievable by using reliable and annually updated information on ground motion, particularly in urban areas. The Geomatics research unit of the Center Tecnològic de Telecomunicacions de Catalunya (CTTC) is working on a project with the main purpose of computing the spatial gradient of land deformation and generating a wide-area Differential Deformation map that allows identifying buildings and urban structures that could potentially be vulnerable to damage. The final output of the project will be made available to the public through a web server. The project involves configuring a self-hosted, low-cost web server using open-source tools; developing software pipelines to compute the spatial gradient of deformation and convert deformation data; and setting up a tailored web visor to display the results. Because the project must manage a large volume of data with millions of Measurement Points (MP) and extensive processing times, automation is critical to its success. The obtained information is not only beneficial for monitoring anthropogenic phenomena, but it is also vital for urban management and risk mitigation planning. [ABSTRACT FROM AUTHOR]

With global climate change and accelerating urbanization, accurate and timely extent information on urban wetlands is extremely important for sustainable urban development and conservation of ecosystem services, supporting the implementation and evaluation of the United Nations Sustainable Development Goals (SDGs). China has experienced the most dramatic urbanization process in recent decades, but accurate and comprehensive information for urban wetland changes in China's major cities are still unavailable. In this study, using 137,779 Landsat images available on the Google Earth Engine platform, we developed a novel approach (MWC-CCDC) integrating historical maximum wetness composition (MWC) and continuous change detection and classification (CCDC), and generated the first annual 30-m resolution urban wetland maps for 71 major cities with populations over 0.5 million across China from 1985 to 2022. The resultant annual urban wetland distribution dataset in China, named China_Urban_Wetland (CUW), achieved over 82.81 % overall classification accuracy. According to the CUW, cities in the Yangtze River basin, Pearl River Delta, and Hangzhou Bay cover the majority of urban wetland area in China's 71 major cities. Wuhan had the largest urban wetland area (168.00 km2), followed by Nanjing (103.19 km2). Jiujiang has the highest wetland coverage rate at 27.23 %, followed by Wuhan (20.68 %) and Xiangyang (18.89 %). Between 1985 and 2022, 52 of 71 cities lost wetland area, with Tianjin having the largest percentage loss (79.40 %), followed by Urumqi (74.86 %) and Guiyang (67.55 %). In contrast, Hohhot experienced the largest increase (90.63 %), followed by Beihai (89.21 %) and Xi'an (71.34 %). In about one-third the cities, urban wetland landscape patterns are becoming more fragmented and less connected. Such consistent annual assessments of urban wetlands is expected to benefit the implementation and evaluation of urban-related targets in the SDGs. [ABSTRACT FROM AUTHOR]

Cet article propose une revue de littérature sur la nature, l'utilisation et l'efficacité clinique de la psychothérapie assistée par la MDMA pour le traitement du trouble de stress post-traumatique (TSPT). Nous décrirons d'abord la MDMA, en soulignant ses propriétés pharmacologiques et ses effets psychologiques, puis nous donnerons des informations historiques sur sa synthèse et les premiers usages thérapeutiques. Nous analyserons ensuite l'étude clinique de phase III, ainsi que les évaluations cliniques à long terme de la thérapie. Les résultats mettent en évidence la grande efficacité de la thérapie assistée par la MDMA pour traiter le TSPT. L'étude de phase III montre que sur un échantillon de 90 participants, 67 % des participants ne répondent plus aux critères de diagnostic du TSPT après la thérapie, et que 88 % des participants ont une réduction statistiquement significative de leurs symptômes. La MDMA agit principalement au niveau des émotions et des interactions sociales, ce qui lui confère un potentiel thérapeutique inédit ; elle facilite l'accès aux souvenirs traumatiques là où, en temps normal, la charge émotionnelle dépasse les capacités d'intégration et de symbolisation du sujet. Dans ce nouvel état transitoire, le patient, soutenu par la présence de deux thérapeutes, peut alors revisiter ses expériences traumatiques afin de les traiter avec des outils thérapeutiques classiques. La psychothérapie assistée par la MDMA est une approche innovante qui peut être étendue bien au-delà du traitement du TSPT. Elle s'inscrit dans le domaine en plein développement des thérapies assistées par les psychédéliques, dont la spécificité est une prise de substance ponctuelle qui se distingue de l'usage prolongé de molécules ayant des vertus thérapeutiques en psychiatrie. This paper provides a review of the literature on the nature, use and clinical effectiveness of MDMA-assisted psychotherapy for the treatment of patients with post-traumatic stress disorder (PTSD). We first describe MDMA, highlighting its pharmacological properties and psychological effects, followed by historical informations on its synthesis and early therapeutic uses. We will then analyse the phase III clinical study, as well as the long-term clinical evaluations of the therapy. The results show that MDMA-assisted therapy is highly effective in treating PTSD. A phase III study showed that in a 90-participant sample, 67% of participants no longer met the diagnostic criteria for PTSD after therapy, and that 88% of participants had a statistically significant reduction in their symptoms. MDMA acts mainly at the level of emotions and social interactions, and it helps the patient to feel confident with his therapists while encouraging the expression of emotions. MDMA facilitates access to traumatic memories where, under normal circumstances, the emotional charge exceeds the subject's capacity for integration and symbolisation. In this new transitory state, the patient, supported by the presence of two therapists, can then revisit his or her traumatic experiences in order to process them with classic therapeutic tools. MDMA-assisted psychotherapy is an innovative approach that can be extended far beyond the treatment of PTSD. It is part of the rapidly developing field of psychedelic-assisted therapies, the specificity of which is a one-time intake of a substance that differs from the prolonged use of molecules with therapeutic properties in psychiatry. [ABSTRACT FROM AUTHOR]

Cameras and LiDARs are currently two types of sensors commonly used for 3D mapping. Vision-based methods are susceptible to textureless regions and lighting, and LiDAR-based methods easily degenerate in scenes with insignificant structural features. Most current fusion-based methods require strict synchronization between the camera and LiDAR and need auxiliary sensors, such as IMU. All of these lead to an increase in device cost and complexity. To address that, in this paper, we propose a low-cost mapping pipeline called PanoVLM that only requires a panoramic camera and a LiDAR without strict synchronization. First, camera poses are estimated by a LiDAR-assisted global Structure-from-Motion, and LiDAR poses are derived with the initial camera-LiDAR relative pose. Then, line-to-line and point-to-plane associations are established between LiDAR point clouds, which are used to further refine LiDAR poses and remove motion distortion. With the initial sensor poses, line-to-line correspondences are established between images and LiDARs to refine their poses jointly. The final step, joint panoramic Multi-View Stereo, estimates the depth map for each panoramic image and fuses them into a complete dense 3D map. Experimental results show that PanoVLM can work on various scenarios and outperforms state-of-the-art (SOTA) vision-based and LiDAR-based methods. Compared with the current SOTA LiDAR-based techniques, namely LOAM, LeGO-LOAM, and F-LOAM, PanoVLM manifests a reduction in the absolute rotation error and absolute translation error by 20% and 35%, respectively. Our code and dataset are available at https://github.com/3dv-casia/PanoVLM. [ABSTRACT FROM AUTHOR]

Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recall@top1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. [ABSTRACT FROM AUTHOR]

We consider a hyperbolic quartic curve F (x , y , z) = 0 associated with a 4-by-4 nilpotent matrix which has one ordinary double point satisfying F (x , y , z) = F (x , − y , z). Let R be the Riemann surface determined as a non-singular model of the curve F (x , y , z) = 0. In this situation, the genus of R is 2. We express the period matrix of R using explicit line integrals depending on a simple symplectic transformation of the canonical cycles which Maple algcurves package provided, and determine the Riemann constant of the Riemann vanishing theorem. We also provide a pictorial visualization of the real parts of the image of an Abel map and theta divisor. [ABSTRACT FROM AUTHOR]

Photorealistic 3D models are important data sources for digital twin cities and smart city applications. These models are usually generated from data collected by aerial or ground-based platforms (e.g., mobile mapping systems (MMSs) and backpack systems) separately. Aerial and ground-based platforms capture data from overhead and ground surfaces, respectively, offering complementary information for better 3D mapping in urban areas. Particularly, backpack mapping systems have gained popularity for 3D mapping in urban areas in recent years, as they offer more flexibility to reach regions (e.g., narrow alleys and pedestrian routes) inaccessible by vehicle-based MMSs. However, integration of aerial and ground data for 3D mapping suffers from difficulties such as tie-point matching among images from different platforms with large differences in perspective, coverage, and scale. Optimal fusion of the results from different platforms is also challenging. Therefore, this paper presents a novel method for the fusion of aerial, MMS, and backpack images and point clouds for optimized 3D mapping in urban areas. A geometric-aware model for feature matching is developed based on the SuperGlue algorithm to obtain sufficient tie-points between aerial and ground images, which facilitates the integrated bundle adjustment of images to reduce their geometric inconsistencies and the subsequent dense image matching to generate 3D point clouds from different image sources. After that, a graph-based method considering both geometric and texture traits is developed for the optimal fusion of point clouds from different sources to generate 3D mesh models of better quality. Experiments conducted on a challenging dataset in Hong Kong demonstrated that the geometric-aware model could obtain sufficient accurately matched tie-points among the aerial, MMS, and backpack images, which enabled the integrated bundle adjustment of the three image datasets to generate properly aligned point clouds. Compared with the results obtained from state-of-the-art commercial software, the 3D mesh models generated from the proposed point cloud fusion method exhibited better quality in terms of completeness, consistency, and level of detail. [ABSTRACT FROM AUTHOR]

A depth map fusion algorithm fuses depth maps from different perspectives into a unified coordinate framework and performs surface calculations to generate dense point clouds of the entire scene. The existing algorithms ensure the quality of these dense point clouds by eliminating inconsistencies between depth maps, but the problem of many redundant calculations often arises. In this paper, a depth map fusion algorithm based on pixel region prediction is proposed. First, the image combination is calculated to select a set of candidate neighbor images for each reference image in the scene. Second, voxels and measure estimates are constructed on a coarse scale, and an inference strategy and a corrector are proposed to merge pixel regions at a fine scale guided by the coarse scale. Finally, the deduced pixel regions at the fine scale are used as the image-space constraints for depth fusion. Public and actual oblique images datasets are used for experimental verification. Compared with the famous COLMAP, OPENMVS, Gipuma and ACMP methods, the number of redundant calculations is significantly reduced; according to Data1 ∼ Data9 in the experiment, as the number of images increases, the fusion efficiency is increased by 47.5% to 156.6%; at the same time, the point cloud accuracy is comparable to other methods. [ABSTRACT FROM AUTHOR]

Most supervised geographic mapping methods with very-high-resolution (VHR) images are designed for a specific task, leading to high label-dependency and inadequate task-generality. Additionally, the lack of socio-economic information in VHR images limits their applicability to social/human-related geographic studies. To resolve these two issues, we propose an unsupervised multi-modal geographic representation learning framework (MMGR) using both VHR images and points-of-interest (POIs), to learn representations (regional vector embeddings) carrying both the physical and socio-economic properties of the geographies. In MMGR, we employ an intra-modal and an inter-modal contrastive learning module, in which the former deeply mines visual features by contrasting different VHR image augmentations, while the latter fuses physical and socio-economic features by contrasting VHR image and POI features. Extensive experiments are performed in two study areas (Shanghai and Wuhan in China) and three relevant while distinctive geographic mapping tasks (i.e., mapping urban functional distributions, population density, and gross domestic product), to verify the superiority of MMGR. The results demonstrate that the proposed MMGR considerably outperforms seven competitive baselines in all three tasks, which indicates its effectiveness in fusing VHR images and POIs for multiple geographic mapping tasks. Furthermore, MMGR is a competent pre-training method to help image encoders understand multi-modal geographic information, and it can be further strengthened by fine-tuning even with a few labeled samples. The source code is released at https://github.com/bailubin/MMGR. [ABSTRACT FROM AUTHOR]

Designing new discrete maps attracts numerous studies in the literature. This work introduces a way to build discrete maps by combining a memristor and multiple nonlinear terms. Different approaches for building memristive maps have been reviewed. We present a general model for constructing discrete maps with a memristor and two nonlinear terms. New memristive maps are reported. In addition, we have studied the MNFM 1 map in more details to discovery its specific features. It is possible to extend our approach to obtain a memristive map with multiple nonlinear terms. Memristive maps can be used in civil aviation applications. • A general approach to construct memristor-based maps is proposed. • A gallery of new chaotic maps without fixed points is introduced. • Complex dynamics are investigated and discussed. • Hardware implementation of a chaotic map is reported. [ABSTRACT FROM AUTHOR]

Development of microneedle array patches (MAPs) for potential use in immunization is ongoing, but the cost of manufacturing is expected to be higher than that of existing needle-and-syringe vial systems. The potential benefits of MAPs in reaching previously unvaccinated populations have been touted, but affordability, especially in low- and middle-income countries, remains an open question. In this study, we quantify the expected impact on operational costs of switching to MAPs for immunization for measles-rubella, human papilloma virus, and typhoid in both routine and campaign-based delivery modes. We endeavor to make a comprehensive estimate, including the costs of labor, syringes, waste management (i.e., sharps and trash), wastage (unused vaccine), freight and in-country cold chain transportation. We examined five potential use cases and our results show that in total, operational cost savings from a switch to MAPs are expected to range from a low of $0.24 per dose delivered (HPV, 1-dose vial, campaign) up to $0.61 per dose delivered (MR, 10-dose vial, routine). Excluding the allocated cost of labor, the estimated range of cost savings are $0.18 and $0.43, respectively. Confidence intervals are wide, due to the uncertainty in the assumptions, but in all five use cases tested, there was at least an 87 % probability of savings. These results show that operational savings from a switch to MAPs may offset at least part of the expected incremental manufacturing costs, which will make the transition more viable in settings with limited budget space. With this in mind, development agencies should continue to invest in MAPs technology and, if the product does come to market, use this evidence as part of total value of vaccines assessments and to inform investment strategies for implementation of vaccine MAPs, including alignment with policy makers. • Switching to microneedle patch (MAP) immunizations may save operational costs. • Savings are most likely in use cases with high wastage rates or labor costs. • Continued development of MAPs for MR, TCV, and HPV has potential value. • Cost-sharing policies between countries and donors should consider MAPs savings. [ABSTRACT FROM AUTHOR]

The challenge of map construction involves creating a representation of a travel network using data from the paths traveled by entities within the network. Although numerous algorithms for constructing maps can effectively piece together the overall layout of a network, accurately capturing smaller details like the positions of intersections and turns tends to be more difficult. This difficulty is especially pronounced when the data is noisy or collected at irregular intervals. In this paper we present Roadster , a map construction system that combines efficient cluster computation and a sophisticated method to construct a map from a set of such clusters. First, edges are extracted by producing a number of subtrajectory clusters, of varying widths, which naturally correspond to paths in the network. Second, representative paths are extracted from the candidate clusters. The geometry of each representative path is improved in a process involving several stages, that leads to map edges. The rich information obtained from the clustering process is also used to compute map vertices, and to finally connect them using map edges. An experimental evaluation of Roadster , using vehicle and hiking GPS data, shows that the system can produce maps of higher quality than previous methods. • We present a system to build maps from GPS tracks, based on subtrajectory clustering. • Our method uses a fast Fréchet clustering algorithm to identify potential roads. • Roadster features enhanced intersection placement and cluster selection algorithms. • We include a detailed experimental evaluation comparing to the state of the art. • Our results outperform previous methods in both global and local evaluations. [ABSTRACT FROM AUTHOR]

The correlation map of the longitudinal relaxation time (T 1) and the transverse relaxation time (T 2) offers valuable information of different proton contributions and is served as a significant data for identifying, quantifying and characterizing complex relaxation components in unconventional reservoirs. However, the acquisition speed and spectral resolution for existing pulse sequences cannot be guaranteed simultaneously. We designed an improved pulse sequence which integrates the inversion recovery-balanced steady-state free precession (IR-BSSFP) sequence and the Carr-Purcell-Meiboom-Gill (CPMG) sequence, allowing for the measurement of two-dimensional T 1 -T 2 spectrums. The IR-BSSFP sequence adopts a low refocusing angle and reduced flip angle pulse transmission power, eliminating the need for complete polarization. Moreover, the parameters of the improved pulse sequence such as the value and the number of the flip angle, the repetition time are discussed by numerical simulations. The result showed that the new pulse sequence achieves high-precision measurement performance, and the T 1 -T 2 spectrum can be achieved within a few seconds. • The IR-BSSFP-CPMG is developed for the measurement of the T 1 -T 2 correlation map. • Both the signal quality and acquisition efficiency can be guaranteed by the new pulse sequence. • Proper selection of the pulse sequence parameters is necessary for the precise measurement. [ABSTRACT FROM AUTHOR]

There are 22 nontrivial periodic maps on the orientable closed surface of genus 2, up to conjugacy. We determine whether they can extend to periodic maps on the 3-torus, orientation-preservingly or orientation-reversingly. [ABSTRACT FROM AUTHOR]

We propose an unsupervised probabilistic framework for learning a human-centred representation of a person's environment from first-person video. Specifically, non-geometric maps modelled as hierarchies of probabilistic place graphs and view graphs are learned. Place graphs model a user's patterns of transition between physical locations whereas view graphs capture an aspect of user behaviour within those locations. Furthermore, we describe an implementation in which the notion of place is divided into stations and the routes that interconnect them. Stations typically correspond to rooms or areas where a user spends time. Visits to stations are temporally segmented based on qualitative visual motion. We describe how to learn maps online in an unsupervised manner, and how to localise the user within these maps. We report experiments on two datasets, including comparison of performance with and without view graphs, and demonstrate better online mapping than when using offline clustering. • Unsupervised map learning and localisation from first-person (wearable) video. • Proposed map representation using place graphs and view graphs. • Evaluation of online mapping and localisation using two datasets. • Demonstrated effectiveness of hierarchical map representation. [ABSTRACT FROM AUTHOR]

The three innermost layers of the ALICE Inner Tracking System (ITS2) will be replaced by a truly cylindrical tracker, the ITS3, to be ready for LHC Run 4 (2029–2032). The ITS3 will be composed of three layers, each made by two self-supporting, ultra-thin (≤ 50 μ m) flexible Monolithic Active Pixel silicon Sensors (MAPS) of large area (O(10 × 26 cm 2)). The final sensor will be realized using the 65 nm CMOS imaging process and stitching technology. Multiple small-scale test structures were included in the first production run (Multiple Layer Reticle 1 - MLR1) to validate the 65 nm CMOS imaging technology. First large-scale stitched MAPS were included in the second production run (Engineering Run 1 - ER1). The pixel cell performance has been qualified on the MLR1 Digital Pixel Test Structures (DPTS) in laboratory and with in-beam measurements. The large-area (1.4 × 25.9 cm 2) ER1 MOnolithic Stitched Sensor (MOSS) prototype has been used to prove the stitching principle and evaluate the detection efficiency and spatial resolution. This contribution will give an overview of the most recent results of the digital prototype tests. [ABSTRACT FROM AUTHOR]

Geochemical map as a kind of hierarchical color contour map is a basic map in the geochemical survey and exploration. Traditional methods on geochemical mapping are relative and rely on the amount of data which cannot be compared among elements. The fixed-value method presented recently on geochemical mapping can overcome the faults of the traditional methods. This study introduces a novel fixed-value method for Mo classification, addressing gaps in existing approaches. Based on 18 fixed-values, Mo concentrations can be divided into 19 levels, which are further categorized into six types: low background on the level of 1st to 5th in blue tone, high background on the level of 6th to 9th in yellow tone, low anomaly on the level of 10th to 12th in pink tone, high anomaly on the level of 13th to 15th in red tone, mineralization as an associate economic metal on the level of 16th to 18th in gray tone, and mineralization as the main economic metal in rocks on the level of 19th in black color. The Mo 19-level fixed-value method was applied to elemental data of samples from areal stream sediments, a weathering profile, and surface outcropped rocks in the Jianshui area in Yunnan province of China. For the areal data, the result of the Mo geochemical map indicates that the Mo concentrations in the Jianshui area are mainly belong to the high background type in yellow tone without mineralized type which is consistent with the fact that no Mo deposits have been discovered in this area. For the data from the weathering profile and surface rocks, the Mo concentrations can also be divided into different levels on this method. Besides, the results of geochemical maps of Sn and Li on their 19-level fixed-value method in the Jianshui area are consistent with their geological facts that deposits of Sn have been discovered. Therefore, the new method is an objective method which can facilitate not only the evaluation of the elemental concentrations on their classified levels but also the comparison among different elements. • A 19-level fixed-value method is proposed to classify the Mo concentration. • The new method is suitable to geological samples in the rock-soil-sediment system. • The new method to contour geochemical map does not rely on the amount of data. • The new method is suitable to comparison in different areas among different elements. [ABSTRACT FROM AUTHOR]

Semantic image synthesis aims to generate high-fidelity images from a segmentation mask, and previous methods typically train a generator to associate a global random map with the conditioning mask. However, the lack of independent control of regional content impedes their application. To address this issue, we propose an effective approach for Multi-modal conditioning-based Diverse Semantic Image Synthesis, which is referred to as McDSIS. In this model, there are a number of constituent generators incorporated to synthesize the content in semantic regions from independent random maps. The regional content can be determined by the style code associated with a random map, extracted from a reference image, or by embedding a textual description via our proposed conditioning mechanisms. As a result, the generation process is spatially disentangled, which facilitates independent synthesis of diverse content in a semantic region, while at the same time preserving other content. Due to this flexible architecture, in addition to achieving superior performance over state-of-the-art semantic image generation models, McDSIS is capable of performing various visual tasks, such as face inpainting, swapping, local editing, etc. • Multiple constituent generators are proposed for spatially disentangled synthesis. • Multi-modal conditioning mechanisms are designed for precise image editing. • Our method is capable of various conditional visual generation tasks. [ABSTRACT FROM AUTHOR]

• It enables dual-frequency methods to achieve high accuracy with four stripes. • A reliable decoding strategy is proposed to enhance the noise immunity. • It encodes more high-frequency cycles without additional images. Dual-frequency fringe projection techniques have been broadly utilized for three-dimensional (3D) shape recovery. However, a pervasive challenge across these methodologies lies in the precise implementation of high-speed measurements. This paper explores a spatial coding strategy designed for the dual-frequency phase unwrapping method to tackle this. The strategy mandates the utilization of only four patterns, encapsulating the phase information of both high and low frequencies. The codewords are strategically embedded within the low-frequency phase, facilitating the acquisition of an accurate absolute phase map. A robust decoding algorithm engineered to ensure dependable phase unwrapping, particularly in scenarios characterized by a substantial number of high-frequency cycles. The proposed method exhibits superior noise immunity and enhances accuracy when compared with the previous dual-frequency phase-shifting methodology. Simulations and experiments demonstrate the robustness and efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]

• Introduces a novel wide-range performance adaptation method (WPAM) for aero-engine map calibration, enhancing the accuracy of component-level models (CLMs). • Utilizes adaptation factor surfaces to precisely adjust operating points on speed lines, surpassing limitations of current calibration techniques. • Calculates adaptation factors based on both steady-state and transient measurements using the Levenberg-Marquardt method, ensuring comprehensive calibration. • Constructs adaptation factor surfaces using the moving least squares method, facilitating modification of component maps in spool speed and β -value directions. • Validates the proposed method through simulation of engines with distinct maps, demonstrating significant improvements in CLM accuracy under steady-state and transient conditions. • Proven applicability to degraded engines and variable geometry engines, with robustness in handling measurement data with noise. High-fidelity component-level models (CLMs) are essential for aero-engine performance modeling, monitoring, and diagnosis, relying heavily on precise component maps. Inaccuracies in these maps can cause significant deviations between predicted and actual measurements. This study introduces a novel wide-range performance adaptation method (WPAM) utilizing adaptation factor surfaces, enabling precise adjustment of speed line operating points. The adaptation factors for the component maps are calculated based on both steady-state and transient measurements using the Levenberg-Marquardt method, with transient adaptation calculations converted into a steady-state solution. Adaptation factor surfaces are constructed using the moving least squares method to modify component maps in both spool speed and β -value directions. Validated on two engines with distinct maps, WPAM significantly enhances CLM accuracy under steady-state and transient conditions, considering multidimensional engine and CLM map discrepancies. It is applicable to degraded and variable geometry engines, exhibiting robustness in handling noisy measurement data. [ABSTRACT FROM AUTHOR]

This paper introduces a new methodology designed to assess the energy performance of urban districts. By integrating building energy performance standards with automated spreadsheet tools, the study provides a framework for quickly analyzing urban energy certificates to identify key areas for energy efficiency improvements. The model considers several factors influencing thermal and energy performance, such as heat transfer through the building envelope, internal heat gains, and energy supply from HVAC systems during both heating and cooling seasons. By evaluating energy needs for heating, cooling, domestic hot water, lighting, and equipment, the methodology offers a comprehensive view of building energy consumption. The adaptable spreadsheet model generates various outputs based on factors like architectural features, expositions, systems for a total of 30 inputs. The tool, designed to be as simple as possible while still ensuring accurate predictions of the energy needs of the analyzed buildings, serves three main purposes. First, it speeds up the energy modeling process of buildings at various scales. Second, it provides a user-friendly system that anyone can use to calculate a building's energy needs and its corresponding energy class. Finally, it suggests optimization strategies by proposing refurbishment solutions for the targeted building stock. Future development includes incorporating this model into geographic information systems for spatial mapping of urban energy districts, offering useful insights for focused interventions and sustainable urban development. [Display omitted] • Comprehensive methodology for building energy efficiency tool development. • Aiming at sustainability goals for various building scale targets. • Validates tool against EnergyPlus simulations for accuracy. • Results show 5 % deviation in winter, 4 % in summer predictions. • Enhances confidence for urban energy planning decisions. [ABSTRACT FROM AUTHOR]

For a certain family of aspherical 2-complexes it is shown that a pair of inequalities, known as hyperbolic index bounds, involving fixed point indices are satisfied for all fixed point minimal self-maps. As a corollary we verify the hyperbolic index bounds for the Nielsen fixed point classes of self-maps f : X → X , when X is a finite wedge of compact surfaces each having non-positive Euler characteristic. [ABSTRACT FROM AUTHOR]

• Integrating UAV LIDAR and RGB optical data. • A cost-effective, time-saving, accurate, and flexible tool for building extraction from UAV data. • ECHO classifier works best for UAV LIDAR and RGB optical data. The focus of this manuscript is on integrating optical images and laser point clouds carried on low-cost UAVs to create an automated system capable of generating urban city models. After pre-processing both datasets, we co-registered both datasets using the DLT transformation model. We estimated structure heights from the LiDAR dataset through a progressive morphological filter followed by removing bare ground. Unsupervised and supervised image classification techniques were applied to a six-band image created from the optical and LiDAR datasets. After finding building footprints, we traced their edges, outlined their borderlines, and identified their geometric boundaries through several image processing and rule-based feature identification algorithms. Comparison between manually digitized and automatically extracted buildings showed a detection rate of about 92.3 % with an average of 7.4 % falsely identified areas with the six-band image in contrast to classifying only the RGB image that detected about 63.2 % of the building pixels with 25.3 % pixels incorrectly identified. Moreover, our building detection rate with the 6-band image was superior to that attained by performing traditional image segmentation for only the LiDAR DEM. Shifts in the horizontal coordinates between corner points identified by a human operator and those detected by the proposed system were in the range of 10–15 cm. This is an improvement over traditional satellite and manned-aerial large mapping systems that have lower accuracies due to sensor limitations and platform altitude. These findings demonstrate the benefits of fusing multiple UAV remote sensing datasets over utilizing a single dataset for urban area mapping and 3D city modeling. [ABSTRACT FROM AUTHOR]

To support the safe and widespread use of unmanned aerial vehicles (UAVs) in urban environments, industry stakeholders and regulatory authorities are partnering to develop urban airspace traffic management systems (UTMs). UTM system providers face strategic decisions in how to design and manage airspace available to UAV flights. We consider a provider that plans to open an urban airspace in which UAV flights are routed above existing roads in 3D corridors corresponding to segmented altitude levels. The provider aims to select a subset of the road network to form an air-network with the goal of providing safe and cost effective service for UAV traffic. The air-network selected must provide routes that respect UAV technology restrictions, and must have adequate capacity to support the expected flight volume. We develop a 3D airspace network design model that selects a subset of roads whose 3D projection into the sky will be used for routing flights. The constrained system optimum (CSO) traffic assignment model is used to evaluate the quality of the network; the CSO user constraints represent battery restrictions while minimizing the total travel time ensures realistic routing in the face of congestion. To incorporate the 3D nature of flights, we use simulation to calibrate a Bureau of Public Roads capacity parameter that reflects the multiple vertical layers of airspace made available when a road is selected for the network. We introduce a methodology to derive candidate maps for urban areas and use it on open-source data to build a case study for Chicago city center. We assess the impact of budget, congestion, minimum-path deviation, and demand patterns on network designs. • We introduce the first network design model for urban UAV air traffic management. • Unlike most literature, UAVs are restricted to fly over existing streets rather than point-to-point. • We use simulation to develop BPR congestion function for UAV traffic in 3D space. • Congestion function incorporates separation standards and multiple altitudes. • We introduce methodology to build test instances from open-source data. [ABSTRACT FROM AUTHOR]

We propose a framework to decompose the partisan advantage in a map of electoral districts into four contributing factors: the fairness notion used to evaluate the map, political geography, rules on redistricting criteria that the map must meet, and the discretionary choice of which specific map to adopt. We apply this factor decomposition to the 2021–22 U.S. congressional district map in each state. Such a decomposition can distinguish maps with large partisan advantages that reflect a non-partisan underlying cause, from gerrymandered maps in which the advantage is attributable to the deliberate partisan choices of map-makers, such as the pro-Republican maps in Florida and Texas, or the pro-Democratic map in Illinois. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low. • Enhanced Navigation Capabilities: The SEA enhances navigation using semantic graph maps, improving localization and tasks. • Robust Against Sensor Noise: SEA achieves robust navigation by leveraging semantic knowledge, even with sensor noise. • Localizing with Semantic Knowledge: The SEA boosts object position accuracy by incorporating semantic knowledge in localization. • Adaptability to Environmental Changes: The SEA dynamically updates to environmental changes, adjusting agent navigation effectively. • Computational Efficiency: The SEA is computationally efficient, using fewer resources while matching top methods' performance. [ABSTRACT FROM AUTHOR]

[Display omitted] The aim of this study was to investigate the impact of using microneedle patches in addition to topical therapy for the treatment of psoriasis. Using continuous liquid interface production (CLIP) 3D printing we manufactured round microneedle array patches (MAPs) with a diameter of 14 mm. Needle geometries were varied from square pyramidal, conical, and obelisk, with varied needle lengths of 400 µm, 600 µm, 800 µm, or 1000 µm. MAPs were characterized for force to fracture, skin penetration, skin damage, as well as their ability to deliver a novel oleogel-based corticosteroid (betamethasone dipropionate (BDP) formulation into ex-vivo porcine skin. We found that the obelisk shaped MAPs are more durable compared to the conical and square pyramidal-shaped MAPs. When the obelisk shaped MAPs were used in combination with the oleogel-based BDP formulation, the amount of BDP penetrating the skin was significantly increased with greater needle lengths. [ABSTRACT FROM AUTHOR]

We consider the problem of the existence of heterotic-string and type-II-superstring field theory vertices in the product of spaces of bordered surfaces parameterizing the left- and right-moving sectors of these theories. It turns out that this problem can be solved by proving the existence of a solution to the BV quantum master equation in moduli spaces of bordered spin-Riemann surfaces. We first prove that for arbitrary genus ▪, ▪ Neveu–Schwarz boundary components, and ▪ Ramond boundary components such solutions exist. We also prove that these solutions are unique up to homotopy in the category of BV algebras. Furthermore, we prove that there exists a map in this category under which these solutions are mapped to fundamental classes of Deligne-Mumford stacks of associated punctured spin-Riemann surfaces. These results generalize the work of Costello on the existence of a solution to the BV quantum master equations in moduli spaces of bordered Riemann surfaces which, through the work of Sen and Zwiebach, are related to the existence of bosonic-string vertices, and their relation to fundamental classes of Deligne-Mumford stacks of associated punctured Riemann surfaces. Using the existence of solutions to the BV quantum master equation in moduli spaces of spin-Riemann surfaces, we prove that heterotic-string and type-II-superstring field theory vertices, for arbitrary genus ▪ and an arbitrary number of any type of boundary components, exist. Furthermore, we prove the existence of a solution to the BV quantum master equation in spaces of bordered N = 1 super-Riemann surfaces for arbitrary genus ▪, ▪ Neveu–Schwarz boundary components, and ▪ Ramond boundary components. [ABSTRACT FROM AUTHOR]

Depth map stitching technology is urgently needed in varying fields such as panoramic three-dimensional reconstruction, underwater mapping, autonomous driving, and robot collision avoidance control. Existing image stitching research mainly focuses on three-primary-color images and heavily relies on feature detection quality, while depth map stitching is rarely studied due to the sparse features and low resolution. To address the above challenges, this paper proposes a color image-guided depth map stitching framework consisting of two stages: coarse depth map stitching and depth correction. In the first stage, a color-image-guided coarse depth map stitching network is proposed to accurately estimate the homography matrix, which can further eliminate parallax artifacts in the stitched depth map. In the second stage, a transformer-based depth correction network is designed, which combines saliency detection and regional matching strategies for depth correction, in order to solve the inconsistency in depth value introduced by point-to-point matching and improve the speed of depth correction to some extent. Extensive experiments demonstrate that the proposed method effectively solves the problem of disparity artifacts and mismatched depth values of the same target, and has excellent performance in depth map stitching. [ABSTRACT FROM AUTHOR]

Loop Closure Detection (LCD) is an essential component of visual Simultaneous Localization and Mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint changes, and dynamic objects in scenes. This paper introduced an online appearance based LCD using Local Superpixel Grids Descriptor (LSGD) and Dynamic Nodes (DN), i.e., LSGDDN-LCD, to find similarities between scenes via handcrafted features extracted from the LSGD. Additionally, we proposed the adaptive mechanism to group similar scenes called Dynamic Nodes , which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision–recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on public and our own datasets, indicating its great potential as a generic LCD framework. Our implementation of the LSGDDN-LCD approach and the datasets were open-sourced on GitHub (https://github.com/BaoshengZhang0/LSGDDN-LCD.git). [ABSTRACT FROM AUTHOR]

The packaging system is one of the factors influencing the preservation of the nutritional value, microbiological safety, and sensory attributes of meat. The study investigated changes in physicochemical and microbiological properties taking place during 15-day refrigerated storage of two calf muscles, the longissimus lumborum (LL) and semitendinosus (ST), packaged in three systems, respectively, vacuum packing (VP), modified atmosphere packaging (MAP, 80% O 2 + 20% CO 2), and a combined system (VP + MAP, 8 d in VP followed by 7 d in MAP). LL and ST stored in VP had significantly lower levels of lipid oxidation, higher α-tocopherol content, and higher instrumentally measured tenderness in comparison with the samples stored in MAP. On the other hand, the MAP samples had lower purge loss at 5 and 15 days, a higher proportion of oxymyoglobin up to 10 days of storage, and a better microbiological status. Calf muscle samples stored in the VP + MAP system had intermediate values for TBARS and α-tocopherol content and at the same time were the most tender and had the lowest counts of Pseudomonas and Enterobacteriaceae bacteria at 15 days. All packaging systems ensured relatively good quality of veal characteristics up to the last day of storage. However, for MAP at 15 days of storage, unfavourable changes in colour (a high level of metmyoglobin and a decrease in oxymyoglobin, redness and R630/580 ratio) and in the lipid fraction (a high TBARS value and a significant decrease in α-tocopherol content) were observed. • Combined packaging system (VP + MAP) was applied to veal steaks storage. • Veal stored in the combined system was more tender than steaks from VP and MAP. • Veal stored in VP + MAP had intermediate (between VP, MAP) values of lipid oxidation. • The steaks packed in VP had a better α-tocopherol retention than MAP and VP + MAP. • The combined system is an alternative worth considering for packaging of veal. [ABSTRACT FROM AUTHOR]

Depth estimation with sparse reference has emerged recently, and predicts depth map from a monocular image and a set of depth reference samples. Previous works randomly select reference samples by sensors, leading to severe depth bias as this sampling is independent to image semantic and neglects the unbalance of depth distribution in regions. This paper proposes a Coplane-Constrained sparse Depth (CCD) sampling to explore representative reference samples, and design a Local Depth Propagation (LDP) network for complete the sparse point cloud map. This can capture diverse depth information and diffuse the valid points to neighbors with geometry prior. Specifically, we first construct the surface normal map and detect coplane pixels by superpixel segmenting for sampling references, whose depth can be represented by that of superpixel centroid. Then, we introduce local depth propagation to obtain coarse-level depth map with geometric information, which dynamically diffuses the depth from the reference to neighbors based on local planar assumption. Further, we generate the fine-level depth map by devising a pixel-wise focal loss, which imposes the semantic and geometry calibration on pixels with low confidence in coarse-level prediction. Extensive experiments on public datasets demonstrate that our model outperforms SOTA depth estimation and completion methods. • A coplane-constrained depth (CCD) sampling that covers more local regions with diverse depth information. • A Local Depth Propagation (LDP) network to complete the sparse reference map. • A pixel-wise loss to calibrate the coarse-level depth prediction with low confidence. [ABSTRACT FROM AUTHOR]

At present, most existing crowd counting methods use density maps to estimate the number of people, so the quality of density maps is particularly important to the counting results. In practical application, the density map generated by the existing methods is easily affected by the change of head proportion, which cannot truly reflect the size of head in the image and affect the counting accuracy. To effectively overcome the above problems, a crowd counting method via a dynamic-refined density map network is proposed in this paper. This method refines the existing ground-truth density maps by jointly training Counting Net and learnable Refine Net, and counts through Counting Net. Specifically, we design a Refine Net, which adaptively adjusts the relationship between the head sizes at different positions in the dot map through the U-shaped network structure and the regional attention module (RAM), so as to obtain a ground truth that is more in line with the real head size. In addition, we propose a Counting Net based on coprime dilation rate convolution groups to ensure the continuity of information. Extensive experiments on four benchmark datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF and JHU-CROWD++) indicate that our method can achieve state-of-the-art counting performance and high robustness. [ABSTRACT FROM AUTHOR]

Mapping the corn dynamics at a large scale and multiple years is essential for global food security. Traditional mapping approaches by collecting training samples from field surveys are labor-intensive, challenging large-scale mapping of corn dynamics over the long term. This study developed an efficient approach to map large-scale corn dynamics in the main corn districts of the United States (US) using adaptive strategies for collecting high-quality training samples. First, this study proposed an automatic approach to collect stable and representative corn samples from the crop data layers (CDL) product. Then, this study improved the mapping performance of corn at a large scale and in earlier years by using adaptive strategies to collect limited (less than 500) but high-quality training samples. Finally, this study assessed and discussed the model performance across spaces and years using multi-source datasets from 2001 to 2020. Our results indicate the proposed approach with adaptive strategies can generate robust classification models with good performance in mapping large-scale corn dynamics. In our study area, the mean overall accuracy (OA) of corn is about 88% if using the CDL data as a reference. Besides, the R2 of corn areas at the county scale between our result and the surveyed acreage statistics is above 0.9, suggesting the proposed strategy is helpful for mapping corn dynamics across different years. The proposed approach demonstrated that limited but representative samples could map corn dynamics at a large scale with good performance, showing significant improvement compared to the traditional approach. It is feasible to map crop dynamics with multiple types by combining existing crop products with collected field samples, particularly in China, where crop products at the national scale are still lacking. [ABSTRACT FROM AUTHOR]

We present a complete representation theorem for nullnorms on bounded lattices. Explicitly, any nullnorm on a bounded lattice can be represented in terms of two order-preserving maps, a triangular conorm, a triangular norm and a conditionally associative function. As a particular case, we retrieve the known representation of nullnorms only taking values that are comparable with the annihilator (in terms of two order-preserving maps, a triangular conorm and a triangular norm). As an illustration of the representation theorem, we generate construction methods for nullnorms, especially those taking at least one value that is incomparable with the annihilator. [ABSTRACT FROM AUTHOR]