Map Representation Research Articles

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

A fusion framework with multi-scale convolution and triple-branch cascaded transformer for underwater image enhancement

Acquiring high-quality underwater images is critical for various marine applications. However, light absorption and scattering problems in underwater environments severely degrade image quality. To address these issues, this study proposes a Fusion Framework with Multi-Scale Convolution and Triple-Branch Cascaded Transformer for Underwater Image Enhancement(FMTformer). This innovative framework incorporates multi-scale convolution and three-branch cascade transformer to enhance underwater images effectively. The FMTformer framework adds in the Multi-Conv Multi-Scale Fusion (MCMF) mechanism, which utilizes a spectrum of convolutional kernels to adeptly extract multi-scale features from both the base and detail layers of the decomposed image. This method ensures the capture of both high- and low-frequency information. Furthermore, this research introduces the Tri-Branch Self-Attention Transformer (TBSAT), designed to get cross-dimensional interactions via its Tri-Branch structure, significantly refines image processing quality. The framework also embedded the Value Reconstruct Cascade Transformer (VRCT), which refines feature map representation through mixed convolution, yielding enriched attention maps. Empirical evidence indicates that FMTformer achieves parity with the state-of-the-art in both subjective and objective evaluation metrics, outperforming extant methodologies.

Developing a combined mapping representation of air and noise pollutions

Airparif and Bruitparif, observatories for air and noise pollutions in the Ile-de-France region, have worked together to develop a combined mapping representation of air and noise pollutions. On the one hand, Bruitparif has tested different approaches to propose a noise indicator taking into account the various exposures to road, air and traffic noise, according to the strategic noise maps. On the other hand, Airparif has developed an air quality indicator taking into account the main regulated air pollutants. They have finally put together an original method of mapping representation of the double exposure to noise and air pollutions. Mapping combining air and noise pollutions is an efficient diagnostic tool for municipalities. This makes it possible to identify the areas at stake: those that are preserved from these pollutions and should be protected, and those that are highly exposed and where action must be taken. It offers a wide range of uses, like cross-referencing of air-noise data with the location of establishments receiving vulnerable groups, or making comparison with socio-economic data and health indicators.

UAT:Unsupervised object tracking based on graph attention information embedding

An excellent unsupervised tracker includes a powerful base tracker and an effective unsupervised tracking strategy. However, most base trackers lack internal feature representations for information embedding processes. Most unsupervised trackers are not robust enough in complex environments and lack an effective template update strategy. We propose an unsupervised object tracking based on graph attention information embedding (UAT) to solve the above problems. UAT combines graph attention mechanism with multi-scale features to construct a multi-scale graph attention module (MGA). MGA module dynamically and efficiently completes the information embedding between the template branch and the search area branch. The response map obtained by fusing the feature maps of the two branches is more informative about the location of the target. An attention based information reinforcement update module (RUM) improves the robustness of the tracker. RUM enhances the representation of the feature map in both the spatial dimension and the channel dimension. Template features are also updated indirectly through information transfer between the two branches. RUM suppresses background interference and improves network perception during tracking. Experiments on challenging benchmarks such as VOT2018, VOT2019, TrackingNet, OTB100, LaSOT and UAV123 demonstrate that the proposed UAT achieves state-of-the-art performance in unsupervised trackers.

Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis

This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.

Gender and sexuality in knowledge organization systems

This article addresses the terms and concepts in knowledge organization systems related to individuals and social groups with sexual and gender identities that do not conform to cis-heteronormativity. Preliminary insights suggest that these concepts are not represented adequately in knowledge organization systems. Although advancements in gender and sexuality studies have occurred, these systems have not developed accordingly and as a result, hinder the recognition of the diverse range of sexual and gender identities thereby risking the perpetuation of prejudiced and discriminatory discourses about the respective individuals and groups. This exploratory qualitative study investigates terms and concepts reflecting terminological, social, and rights advancements relating to sexual and gender-diverse individuals. A bibliographic search was conducted in books, scientific articles, guides, and manuals addressing these advancements to support the construction of a conceptual map representation of the concepts. The study systematically structured representations hierarchically, beginning with overarching concepts and gradually delving into more specific ones. This approach was guided by a comprehensive understanding of the domain under analysis, ensuring clarity and accessibility for the reader. The organization of knowledge needs to incorporate gender and sexuality in the construction of knowledge organization systems so that diversity is adequately represented in these systems.

How to visualize the spatial uncertainty of landmark representations in maps?

Landmark representations in maps play an important role in spatial tasks such as self-localization, spatial orientation, and navigation. By matching landmarks to their map representations, people can identify their current location and orientation within a map. However, spatial inaccuracies of landmark representations like these of pictograms in maps have been found to affect the ability to successfully match spatial environments to their map representations. These map matching difficulties could negatively affect the performance in spatial tasks. Due to the increasing number of data sources used in modern map services, for example provided by volunteers, map data quality has become more heterogeneous and uncertain. Therefore, solutions for counteracting the negative effects of spatially inaccurate landmark representations on map matching are required. Here, we report two studies that investigate whether visualizing spatial landmark uncertainty can improve map matching. Although suggestions for visualizing uncertainty exist, there is no empirical evidence whether they can intuitively communicate spatial uncertainty of landmark representations. If map users are made aware of potential spatial inaccuracies in a map, they may observe small or moderate spatial inaccuracies of landmark representations but still be able to match these to the represented landmarks. Our findings support this assumption. Using pictogram size, transparency, or circular uncertainty areas around pictograms to communicate spatial uncertainty increased the likelihood of a perceived match between 3D environments or 360° images and a corresponding map representation when landmark representations were spatially inaccurate. Furthermore, our findings indicate that, in addition to landmarks, people use spatial reference points such as road segments for map matching. Thus, if sufficient other spatial reference points are available both in the environment and its map representation, negative effects of spatially inaccurate landmark representations on map matching may be less severe.

Representation of multi-cubic mappings and some approximations in 2-Banach spaces

Representation of multi-cubic mappings and some approximations in 2-Banach spaces

Improved Detection of Multi-Class Bad Traffic Signs Using Ensemble and Test Time Augmentation Based on Yolov5 Models

Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison.

Hematopoietic Endothelial Progenitor cells enhance motor function and cortical motor map integrity following cerebral ischemia.

Hematopoietic stem cells (HSC) are recruited to ischemic areas in the brain and contribute to improved functional outcome in animals. However, little is known regarding the mechanisms of improvement following HSC administration post cerebral ischemia. To better understand how HSC effect post-stroke improvement, we examined the effect of HSC in ameliorating motor impairment and cortical dysfunction following cerebral ischemia. Baseline motor performance of male adult rats was established on validated motor tests. Animals were assigned to one of three experimental cohorts: control, stroke, stroke + HSC. One, three and five weeks following a unilateral stroke all animals were tested on motor skills after which intracortical microstimulation was used to derive maps of forelimb movement representations within the motor cortex ipsilateral to the ischemic injury. Stroke + HSC animals significantly outperformed stroke animals on single pellet reaching at weeks 3 and 5 (28±3% and 33±3% versus 11±4% and 17±3%, respectively, p < 0.05 at both time points). Control animals scored 44±1% and 47±1%, respectively. Sunflower seed opening task was significantly improved in the stroke + HSC cohort versus the stroke cohort at week five-post stroke (79±4 and 48±5, respectively, p < 0.05). Furthermore, Stroke + HSC animals had significantly larger forelimb motor maps than animals in the stroke cohort. Overall infarct size did not significantly differ between the two stroked cohorts. These data suggest that post stroke treatment of HSC enhances the functional integrity of residual cortical tissue, which in turn supports improved behavioral outcome, despite no observed reduction in infarct size.

High School Students’ Perceptions towards COVID-19 Vaccination: Benefits, Barriers and Influencing Factors

After the outbreak of the Corona pandemic and its conquest of the world, it became necessary to find a vaccine against the Corona virus in order to break the cycle of its spread, but the acceptance or rejection of the vaccination by the citizens may be affected by the mental and representational maps that have stuck to the epidemic through the emergence of several trends. Some researchers have observed that the large population has a relatively high reluctance to current vaccinations and that vaccination coverage is not adequate (van Doremalen et al 2020; Harapan et al. 2019). In Morocco, the elderly have, it seems, readily accepted to participate in the vaccination campaign launched by the government. But what is the attitude of young people towards vaccination against COVID 19? This study aims to understand the perceptions towards the COVID-19 vaccination and the targeted subject is the high school students (17-18 years old). The research used five-point Likert scale instruments to examine student’s perceptions of the COVID-19 vaccination. The sample for this analysis is N=263 participants. Through the analysis that targeted the students’ answers about the advantages of vaccination, it was found that vaccination against the Corona virus has several advantages for students of the second year of the baccalaureate, as both scientific and literary students acknowledge that it combats the disease, but the percentage of importance varies from scientific to literary. Statistics from both categories of students indicated that literary people consider the vaccine against the Corona virus to be ineffective, while scientists do not. The results also showed that there are more risks of unwanted negative side effects than benefits. Statistics have also shown that among the obstacles that would prevent students from getting vaccinated against the virus, in addition to fear of the vaccine, doubt about its safety. Both science and literary students indicated that the school, the free vaccination, the media, the Internet, and the family are some important factors that may affect students' receipt of the vaccination. The comparison between the two groups of students showed that there are no significant differences between the students regarding the COVID-19 vaccination process, and from this we can assume that the information obtained by the science students did not affect their perceptions.

Machine learning unravels inherent structural patterns in Escherichia coli Hi-C matrices and predicts chromosome dynamics.

High dimensional nature of the chromosomal conformation contact map ('Hi-C Map'), even for microscopically small bacterial cell, poses challenges for extracting meaningful information related to its complex organization. Here we first demonstrate that an artificial deep neural network-based machine-learnt (ML) low-dimensional representation of a recently reported Hi-C interaction map of archetypal bacteria Escherichia coli can decode crucial underlying structural pattern. The ML-derived representation of Hi-C map can automatically detect a set of spatially distinct domains across E. coli genome, sharing reminiscences of six putative macro-domains previously posited via recombination assay. Subsequently, a ML-generated model assimilates the intricate relationship between large array of Hi-C-derived chromosomal contact probabilities and respective diffusive dynamics of each individual chromosomal gene and identifies an optimal number of functionally important chromosomal contact-pairs that are majorly responsible for heterogenous, coordinate-dependent sub-diffusive motions of chromosomal loci. Finally, the ML models, trained on wild-type E. coli show-cased its predictive capabilities on mutant bacterial strains, shedding light on the structural and dynamic nuances of ΔMatP30MM and ΔMukBEF22MM chromosomes. Overall our results illuminate the power of ML techniques in unraveling the complex relationship between structure and dynamics of bacterial chromosomal loci, promising meaningful connections between ML-derived insights and biological phenomena.

Assessing maps for social topic representation: a qualitative content analysis of maps for sustainable mobility

ABSTRACT Cartographic representations are increasingly used to communicate sustainable mobility topics. Various formats of maps, such as static maps, mobile maps, map-based dashboards, story maps, and 3D map scenes, are created to illustrate mobility planning, traffic status, emissions, and travel behaviors. These maps are rich in cross-disciplinary information, target to diverse audiences, adopt various design technologies, and enable knowledge exchange among stakeholders of sustainable mobility. This paper examines how maps are used as communication tools for traffic and infrastructure to support decision-making and engage people to actively adapt their behavior towards green mobility. In this study, we (1) identified 48 maps that actively communicate sustainable mobility topics, (2) proposed a rigorous approach to assess maps that are created for communicating sustainable mobility topics, and (3) derived design recommendations for map makers to achieve more effective and efficient communication.

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions.

Investigating Active Site Binding of Ligands to High and Low Activity Carbonic Anhydrase Enzymes Using Native Mass Spectrometry.

Carbonic anhydrases (CAs) are a family of enzymes that play an important pH regulatory role in health and disease. While different CA isozymes have a high degree of structural similarity, they have variable enzymatic activity, with CA III being the least active and having less than 1% of the activity of CA II, the most active. Furthermore, ligand binding studies for CA III are limited, and a resulting lack of chemical probes impedes understanding of this CA isozyme in comparison to other CA family members where studies are abundant. Therefore, we employed native mass spectrometry (nMS), also known as intact mass spectrometry, to assess ligand binding to CA II and CA III and discovered two novel compounds that for the first time display strong binding to CA III. We present a new data visualization and quantification tool developed to display native mass spectra as an intuitive stacked heat map representation for rapidly interpreting the results of ligand-protein binding from nMS screening.

Food city Ho Chi Minh City: designing relational mapping of the culture of place

ABSTRACT This research investigates the novel design and implementation of a relational mapping of city spaces, based on research conducted in Ho Chi Minh City, Vietnam. The experimental mapping employs relational methods of exploration to document and understand the city. A special Memefest mapping workshop used food as a key medium for some of the relationships that underpin the culture, community, and the city itself. This paper outlines the design of a sensory based exploration and creative mapping of food culture in Ho Chi Minh City and puts special emphasis in the atmospheres that are created through the relations between food, community, and the structures that underlie these. Lastly the paper shows an example of installations and explains how they work as (post) representational maps of relations and atmospheres.

SuPrNet: Super Proxy for 4D occupancy forecasting

Spacetime (4D) occupancy forecasting explicitly models the future development of self-driving vehicles (SDVs) themselves and their surrounding environment, which is highly beneficial for their downstream tasks. Currently, using point cloud forecasting as a proxy to self-supervisedly optimize the 4D occupancy forecasting framework has significant application prospects due to its independence from labeled data. However, existing methods have the following shortcomings: (1) Insufficient mining of spatiotemporal information in the scene. (2) Neglecting the extraction of regional deep features. (3) Inadequate learning ability for spatiotemporal predictive learning. Based on this, we propose SuPrNet, constructing a ”Super Proxy” to address the aforementioned issues. Specifically, SuPrNet first embeds the spatial and temporal information of the current frame into point-wise features through a 4D hybrid representation combined with multi-planar and grid features (4DPG). Then, a Hierarchical Encoder–Decoder Module (HED) with sparsity consideration is used to structure the point cloud and extract deep features. Thanks to the structured feature map representation, SuPrNet proceeds to use a spatiotemporal predictive learning module (STL) to implicitly predict future feature maps. Finally, we decode these feature maps to a specific size as the forecasting of future occupancy. The 4DPG, HED, and STL in the model are designed to address the aforementioned issues, and experiments on Nuscene and KITTI-Odometry show that our method achieves the best 4D occupancy forecasting and point cloud forecasting accuracy, surpassing previous state-of-the-art models by approximately 30%. Additionally, we have expanded and validated the application prospects of SuPrNet through various downstream tasks. Our project is available at https://github.com/AlanLiangC/4DPCF.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain’s ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks’ dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Eulerian-Lagrangian Fluid Simulation on Particle Flow Maps

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation naturally embodies a perfect flow map. Centered on this concept, we have developed an Eulerian-Lagrangian framework comprising four essential components: Lagrangian particles for a natural and precise representation of bidirectional flow maps; a dual-scale map representation to accommodate the mapping of various flow quantities; a particle-to-grid interpolation scheme for accurate quantity transfer from particles to grid nodes; and a hybrid impulse-based solver to enforce incompressibility on the grid. The efficacy of PFM has been demonstrated through various simulation scenarios, highlighting the evolution of complex vortical structures and the details of turbulent flows. Notably, compared to NFM, PFM reduces computing time by up to 49 times and memory consumption by up to 41%, while enhancing vorticity preservation as evidenced in various tests like leapfrog, vortex tube, and turbulent flow.