Mapping Problem Research Articles

Anti Tai mapping for unordered labeled trees

The well-studied Tai mapping between two rooted labeled trees T1=(V1,E1) and T2=(V2,E2) defines a one-to-one mapping between nodes in T1 and T2 that preserves ancestor relationship [1]. For unordered trees the problem of finding a maximum-weight Tai mapping is known to be NP-complete [2]. In this work, we define an anti Tai mapping M⊆V1×V2 as a binary relation between two unordered labeled trees such that any two (x,y),(x′,y′)∈M violate ancestor relationship and thus cannot be part of the same Tai mapping, i.e. (x≤x′⇔y≰y′)∨(x′≤x⇔y′≰y), given an ancestor order x<x′ meaning that x is an ancestor of x′. Finding a maximum-weight anti Tai mapping arises in the cutting plane method for solving the maximum-weight Tai mapping problem via integer programming. We give an efficient polynomial-time algorithm for finding a maximum-weight anti Tai mapping for the case when one of the two trees is a path and further show how to extend this result in order to provide a polynomially computable lower bound on the optimal anti Tai mapping for two unordered labeled trees. The latter result stems from the special class of anti Tai mappings defined by the more restricted condition x∼x′⇔y≁y′, where ∼ denotes that two nodes belong to the same root-to-leaf path. For this class, we give an efficient algorithm that solves the problem exactly on two unordered trees in O(|V1|2|V2|2).

Understanding the Snow Cover Climatology over Turkey from ERA5-Land Reanalysis Data and MODIS Snow Cover Frequency Product

Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types.

Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

A map construction method based on a collision probability model and an improved A* algorithm is proposed to address the issues of insufficient security in mobile robot map construction and path planning in complex environments. The method is based on modeling the asymmetry of paths, which complicates problem solving. Firstly, this article constructs a collision probability function model, and based on this model it is fused with the obstacle grid map, which is based on the grid method, to draw a collision probability grid map (CPGM) containing collision probability information. Secondly, incorporating the collision probability values from the CPGM into the actual cost function of the traditional A* algorithm improves the security of path planning in complex environments. The experimental results show that the improved A* algorithm decreases the percentage of dangerous nodes in complex environments by 69.23%, shortens the path planning length by 19.52%, reduces the search time by 16.8%, and reduces the number of turns by 46.67%. Therefore, the method in this paper solves the problem of traditional grid maps lacking security information and can plan a path with higher security and which is smoother, improving the security and robustness of mobile robot autonomous navigation in complex environments.

Deep orientated distance-transform network for geometric-aware centerline detection

The detection of structure centerlines from imaging data plays a crucial role in the understanding, application and further analysis of many diverse problems, such as road mapping, crack detection, medical imaging and biometric identification. In each of these cases, pixel-wise segmentation is not sufficient to understand and quantify overall graph structure and connectivity without further processing that can lead to compound error. We thus require a method for automatic extraction of graph representations of patterning. In this paper, we propose a novel Deep Orientated Distance-transform Network (DODN), which predicts the centerline map and an orientated distance map, comprising orientation and distance in relation to the centerline and allowing exploitation of its geometric properties. This is refined by jointly modeling the relationship between neighboring pixels and connectivity to further enhance the estimated centerline and produce a graph of the structure. The proposed approach is evaluated on a diverse range of problems, including crack detection, road mapping and superficial vein centerline detection from infrared/ color images, improving over the state-of-the-art by 2.1%, 10.9% and 17.3%/ 4.6% respectively in terms of quality, demonstrating its generalizability and performance in a wide range of mapping problems.

Kernel-GPA: A globally optimal solution to deformable SLAM in closed-form

We study the generalized Procrustes analysis (GPA), as a minimal formulation to the simultaneous localization and mapping (SLAM) problem. We propose Kernel-GPA, a novel global registration technique to solve SLAM in the deformable environment. We propose the concept of deformable transformation which encodes the entangled pose and deformation. We define deformable transformations using a kernel method and show that both the deformable transformations and the environment map can be solved globally in closed-form, up to global scale ambiguities. We solve the scale ambiguities by an optimization formulation that maximizes rigidity. We demonstrate Kernel-GPA using the Gaussian kernel and validate the superiority of Kernel-GPA with various datasets. Code and data are available at https://bitbucket.org/FangBai/deformableprocrustes.

Association detection between multiple traits and rare variants based on family data via a nonparametric method.

The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits. In this article, we construct a new non-parametric statistic by the generalized Kendall's τ theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases. We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect.

Approximate variational inequalities and equilibria

We study relations between the solution sets of Variational Inequalities, Minty Variational Inequalities, Natural Map problems and Nash Equilibrium Problems. Moreover, motivated by the inherent relevance of inexactness both in modeling non-cooperative games and in algorithms for variational inequalities, we consider inexact versions of such problems and we establish relations to quantify how inexactness propagates from one problem to the other.

DAG-based workflows scheduling using Actor–Critic Deep Reinforcement Learning

High-Performance Computing (HPC) is essential to support the advance in multiple research and industrial fields. Despite the recent growth in processing and networking power, the HPC Data Centers (DCs) are finite, and should be carefully managed to host multiple jobs. The scheduling of tasks (composing a job) is a crucial and complex task, once the reflexes of the scheduler’s decisions are perceptible both for users (e.g., slowdown) and for infrastructure administrators (e.g., use of resources and queue length). In fact, the process of scheduling workflows atop a DC can be modeled as a graph mapping problem. While an undirected graph is used to represent the DC, a Directed Acyclic Graph (DAG) is used to express the tasks dependencies. Each vertex and edge from both graphs can have weights associated with them, denoting the residual capacities for DC resources, as well as computing and networking demands for workflows. Motivated by the combinatorial explosion of the aforementioned scheduling problem, the integration of Machine Learning (ML) for generating or improving scheduling policies is a reality, however the proposals in the specialized literature opt, mostly, for using simplified models to reduce the search space or are trained to specific scenarios, which leads to policies that eventually fall short of real DCs expectations. Given this challenge, this work applies Actor–Critic (AC) Reinforcement Learning (RL) to schedule DAG-based workflows. Instead of proposing a new policy, the AC RL is used to select the appropriated scheduling policy from a pool of consolidated algorithms, guided by the DAGs workload and DC usage. The AC RL-based scheduler analyzes the DAGs queue and the DC status to define which algorithms are better suited to improve the overall performance indicators in each scenario instance. The simulation protocol comprises multiple analysis with distinct workload configurations, number of jobs, queue ordering polices and strategies to select the target DC servers. The results demonstrated that the AC RL selects the scheduling policy which fits the current workload and DC status.

A comprehensive modeling approach for the task mapping problem in heterogeneous systems with dataflow processing units

SummaryWe introduce a new model for the task mapping problem to aid in the systematic design of algorithms for heterogeneous systems including, but not limited to, CPUs, GPUs, and FPGAs. A special focus is set on the communication between the devices, its influence on parallel execution, as well as on device‐specific differences regarding parallelizability and streamability. We give a comprehensive description on how a given task mapping can be abstractly evaluated including mappings to dataflow‐based hardware accelerators. We show how this model can be utilized in different system design phases and present two novel mixed‐integer linear programs to demonstrate the usage of the model, showing significant improvements compared to pure CPU mapping for randomly generated task graphs. To the best of our knowledge, we present the first ILP for task mapping that considers pipelining effects when streaming tasks on an FPGA.

Global urban high-resolution land-use mapping: From benchmarks to multi-megacity applications

Timely and reliable land-use maps are of great importance in urban planning and environmental monitoring. With the rich spatial structure information, very high resolution (VHR) imagery is an important data source for identifying complex urban land use. However, the existing scene datasets and land-use mapping products based on VHR images have the following three problems: 1) accurate geographic boundaries of urban land parcels are lacking; 2) the category systems are inconsistent with the definitions in urban land use; and 3) it is difficult to achieve efficient and fully automated mapping in multiple cities. To tackle these problems, the GlobalUrbanNet-based automatic multi-city mapping and analysis (GAMMA) framework is proposed in this article. The GAMMA framework is made up of the GlobalUrbanNet (GUN) dataset, the multi-city fully automatic urban land-use mapping (AutoULUM) method, and the analysis of urban development patterns. Specifically, the large-scale 42-category fine-grained VHR urban land-use dataset—the GUN dataset—was constructed to deal with the above global urban land-use mapping problems, which contains 1,846,151 samples and 42 land-use categories covering six continents. The GUN dataset samples with land-use semantics and parcel boundaries were generated automatically based on the open-source area of interest (AOI) data from OpenStreetMap (OSM). In addition, the AutoULUM method is proposed to automate the process of OSM road network rectification and land parcel generation. On this basis, efficient and complete multi-city land-use maps can be produced using the GUN-pretrained scene classification models. To establish a benchmark for urban land-use classification, the representative urban land-use classification methods were evaluated on the GUN dataset. For further application, eight megacities from six continents were selected for automatic land-use mapping and analysis, i.e., Shanghai, Wuhan, and Chengdu in Asia, Helsinki in Europe, Nairobi in Africa, New York in North America, Rio de Janeiro in South America, and Sydney in Oceania. The results show that the models trained on the proposed GUN dataset have good generalizability in global urban areas, the AutoULUM method achieves efficient and fully automatic land-use mapping, and the GAMMA framework will help boost the coordinated development of multiple cities around the world.

Exact Sketch-Based Read Mapping.

Given a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a "similar sequence". Traditionally, "similar sequence" was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold. In this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in time and space, where is the number of -mers inside the sketch of the reference, is the number of -mers inside the read's sketch and is the number of times that -mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm's performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.

Development of a Hardware Demonstration Platform for Multispacecraft Reconnaissance of Small Bodies

The next frontier in space exploration involves visiting some of the 2 million small bodies scattered throughout the solar system. However, these missions are expected to be challenging due to the surface irregularities of these bodies and the very low gravity, which makes steps like getting into orbit very complex. For these reasons, reconnaissance is crucial for small body exploration before taking on ambitious orbital, surface, and sample-return missions. Our previous work developed IDEAS, an automated design software for small body reconnaissance mission development using spacecraft swarms. A critical challenge to furthering such designs is the lack of hardware demonstration platforms for interplanetary spacecraft operations. In this paper, we present MAPS (Multi-Agent Photogrammetry of Small bodies), a hardware platform to demonstrate critical reconnaissance operations of multi-spacecraft missions identified by the IDEAS framework. MAPS uses UAVs as the autonomous agents that perform reconnaissance operations. The UAVs use their visual feed to generate a 3D surface map of a small body mockup, which is encountered along their flight path. In this paper, we examine the various design elements of a small body surface reconstruction mission inside the MAPS testbed. These elements are used for designing reference trajectories of the participating UAVs, which is enforced using a tracking feedback control law. We then formulate the small-body mapping problem as an MINLP problem, which is handled by the Automated Swarm Designer module of the IDEAS framework. The solutions are implemented inside the MAPS, and shape models generated from the UAV feeds are compared.

Penyuluhan Pencegahan Pernikahan Dini pada Usia Remaja di Sekolah Menengah Pertama

This service aims to provide education as an effort to prevent early marriage at a young age for students at SMP Negeri 4 Selong, East Lombok. This service is carried out using the Participatory Action Research (PAR) method with the following stages: (1) initial mapping, (2) building communication with teachers and students, (3) determining service topics and mapping problems, (5) compiling technical activities, ( 6) carry out community service activities, (7) form discussion groups, (8) reflect. The results of the participant satisfaction survey for this community service activity showed very positive results. One of them is shown by the number of questions asked by the participants, related to the material presented. In addition, the participants also shared their personal experiences with those closest to them who were under direct pressure from their parents to marry early. The satisfaction of the participants was also inseparable from the speakers presented who were competent and focused on social issues, humble in nature so that they could place themselves well in the midst of teenage students.

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

Inertial Iterative Algorithms for Split Variational Inclusion and Fixed Point Problems

This paper aims to present two inertial iterative algorithms for estimating the solution of split variational inclusion (SpVIsP) and its extended version for estimating the common solution of (SpVIsP) and fixed point problem (FPP) of a nonexpansive mapping in the setting of real Hilbert spaces. We establish the weak convergence of the proposed algorithms and strong convergence of the extended version without using the pre-estimated norm of a bounded linear operator. We also exhibit the reliability and behavior of the proposed algorithms using appropriate assumptions in a numerical example.

Machine learning based inversion for earth rock dam compaction density

In water resources and hydropower engineering and highway engineering, the compactness test of earth-rock mixed materials of the earth-rock dam and embankment plays an important role for construction quality assessment. The key is how to accurately and quickly evaluate the compaction density, which requires advanced methods. Meanwhile, machine learning has become an important and powerful tool for parameter prediction. In this work, a method to obtain the compactness of an earth-rock dam using machine learning algorithms is developed and verified. In so doing, first, by analyzing and processing the measured time history signal from a new fast Portable Falling Weight Deflectometer (PFWD), including time domain, frequency domain and mechanical impedance analyses, six optimized characteristic parameters are obtained to invert for the compactness, including the stiffness coefficient, the maximum time difference, the maximum amplitude ratio, the central frequency difference, the maximum stiffness impedance and the resonance frequency. Pearson coefficient was used to analyze the correlation among the six parameters and Gini index was used to analyze the influence degree of the parameters on the relative wet density. Then, the Decision Tree (DT) model, Random Forest (RF) model and Gradient Boosting Decision Tree (GBDT) model were used to predict the wet density and dry density of materials, respectively, and compared. PFWD was applied on a dam to acquire the time history signal, which was then preprocessed following a series of procedures including integrity check, outlier handling and normalization etc, and machine learning algorithms were used for training the data and parameter prediction. Examples show that, considering the generalization and optimization ability, for the GBDT model, the root-mean-square error (RMSE) of the predicted wet density of the earth-rock material is within 0.041 g/cm3 and that of the predicted dry density is within 0.032 g/cm3, which meet the requirements for construction detection and quality control. The proposed method based on PFWD signal analysis and machine learning algorithm models help better solve the nonlinear mapping problem between the PFWD signal and material density, and can accurately invert for the wet density and dry density of materials. It is an effective way for rapid detection and control of earth-rock dam construction quality.

Multi-Spectral Satellite Image Classification Using an Evolving Neural Network Approach

This paper investigates a new evolving neural network classifier based on real coded genetic algorithm for automatic multi-spectral satellite image classification (land cover mapping problem). The evolving neural network classifier is designed using hybrid genetic operators with classification accuracy as a measure of performance. The evolving neural network methodology is implemented in Pentium clusters. The proposed methodology searches for the best neural network architecture and its connection weights for a given set of training patterns. The performance of the proposed evolving neural network based classifier is evaluated for Level-II classifier model using the Landsat 7 Thematic Mapper high resolution imagery. After evolving the neural network at pixel level, the system performance is tested with sites not seen during training. Results are compared with maximum likelihood classifier, gradient based fully connected multilayer perceptron and growing and pruning radial basis function classifier. The proposed classifier is more accurate, robust with respect to the noise in the input spectrum and also overcomes the common limitations of the standard neural based classifier models.

It's not just what we don't know: The mapping problem in the acquisition of negation

How do learners learn what no and not mean when they are only presented with what is? Given its complexity, abstractness, and roles in logic, truth-functional negation might be a conceptual accomplishment. As a result, young children’s gradual acquisition of negation words might be due to their undergoing a gradual conceptual change that is necessary to represent those words’ logical meaning. However, it’s also possible that linguistic expressions of negation take time to learn because of children’s gradually increasing grasp of their language. To understand what no and not mean, children might first need to understand the rest of the sentences in which those words are used. We provide experimental evidence that conceptually equipped learners (adults) face the same acquisition challenges that children do when their access to linguistic information is restricted, which simulates how much language children understand at different points in acquisition. When watching a silenced video of naturalistic uses of negators by parents speaking to their children, adults could tell when the parent was prohibiting the child and struggled with inferring that negators were used to express logical negation. However, when provided with additional information about what else the parent said, guessing that the parent had expressed logical negation became easy for adults. Though our findings do not rule out that young learners also undergo conceptual change, they show that increasing understanding of language alone, with no accompanying conceptual change, can account for the gradual acquisition of negation words.

METHOD OF SIMULTANEOUS LOCALIZATION AND MAPPING FOR CONSTRUCTION OF 2.5D MAPS OF THE ENVIRONMENT USING ROS

SLAM (Simultaneous Localization and Mapping) is a relevant topic of research and development in the field of robotics and computer vision. SLAM finds wide applications in various areas such as autonomous navigation of intelligent robots, solving problems in augmented and virtual reality, UAVs, and other systems. In recent years, SLAM has made significant progress due to the gradual development of its algorithms, the use of advanced sensors, and improvements in computational power of computers. The subject of this study is modern methods of real-time simultaneous localization and mapping. The goal of the research is to model the developed algorithm for constructing maps of the surrounding environment and determining the position and orientation of the intelligent robot in space in real-time using ROS packages. The purpose of this article is to demonstrate the results of combining SLAM methods and developing new approaches to solve simultaneous localization and mapping problems. In order to achieve the set objectives, a collaboration of laser scanning (2D LRF) and depth image reconstruction (RGB-D) methods was utilized for simultaneous localization and mapping of the intelligent robot and construction of a 2.5D environment map. The obtained results are promising and demonstrate the potential of the integrated SLAM methods, which collaborate to ensure accurate execution of simultaneous localization and mapping for intelligent robots in real-time mode. The proposed method allows for considering obstacle heights in constructing the map of the surrounding environment while requiring less computational power. In conclusion, this approach expands technologies without replacing existing working propositions and enables the use of modern methods for comprehensive detection and recognition of the surrounding environment through an efficient localization and mapping approach, providing more accurate results with fewer resources utilized.

Design of Reconfigurable Periodic Structures Based on Machine Learning

The design of periodic structures is a very time-consuming process because it requires many cycles of full-wave simulations to obtain the desired electromagnetic (EM) properties. This challenge is amplified when designing reconfigurable periodic structures. This article proposes an intelligent method for the reconfigurable periodic structure design (RPSD) based on machine learning. First, an improved physical and mathematical modeling approach is proposed for reconfigurable periodic structures, which enables the application of machine learning in RPSD. Thereafter, we set up distributed predictors for different reconfigurable states to avoid the one-to-many mapping problem. In addition, the networks of these predictors are trained using variational autoencoders (VAEs) to reduce the associated learning difficulties. These enable the obtention of accurate predictors with small sample data. Moreover, the training of these predictors can be easily accelerated by parallel computation because of their independence. Finally, reconfigurable periodic structures can be intelligently designed by the combination of the trained predictors and a stochastic optimization technique. Some examples are provided to verify the practicality and accuracy of the proposed method.