Landmark Nodes Research Articles

Microsurgery in periodontics and oral implantology: a systematic review of current clinical applications and outcomes.

The aim of this systematic review was to comprehensively explore the current trends and therapeutic approaches in which an operating microscope (OM) is used in periodontics and dental implant surgeries. A systematic search strategy was built to detect studies including various surgical techniques performed under an OM. PubMed, EMBASE, and SCOPUS databases were searched. No limitations in terms of time and language were applied. The data regarding the study design, type of procedure, treatment groups, and surgical outcomes were collected and analyzed descriptively. In addition, a bibliometric analysis was performed concerning the co-authorship and keyword co-occurrence network. Out of 1985 articles, finally, 55 met the inclusion criteria. Current periodontal and implant microsurgery trends consist of: periodontal therapy, dental implant microsurgery, soft tissue grafting and periodontal plastic surgery, bone augmentation, ridge preservation, and ortho-perio microsurgery. The bibliometric analysis revealed "guided tissue regeneration", "periodontal regeneration" and "root coverage" being the most repeated keywords (landmark nodes). 132 authors within 29 clusters were identified, publishing within the frameworks of "periodontal and implant microsurgery". Within its limitations, this systematic review provides an overview of the latest trends in periodontal and implant microsurgery when considering the use of an OM as the magnification tool. Also, it discusses the reported success and outcomes of the mentioned procedures.

Efficient and Provable Effective Resistance Computation on Large Graphs: An Index-based Approach

Effective resistance (ER) is a fundamental metric for measuring node similarities in a graph, and it finds applications in various domains including graph clustering, recommendation systems, link prediction, and graph neural networks. The state-of-the-art algorithm for computing effective resistance relies on a landmark technique, which involves selecting a node that is easy to reach by all the other nodes as a landmark. The performance of this technique heavily depends on the chosen landmark node. However, in many real-life graphs, it is not always possible to find an easily reachable landmark node, which can significantly hinder the algorithm's efficiency. To overcome this problem, we propose a novel multiple landmarks technique which involves selecting a set of landmark nodes V l such that the other nodes in the graph can easily reach any one of a landmark node in V l . Specifically, we first propose several new formulas to compute ER with multiple landmarks, utilizing the concept of Schur complement. These new formulas allow us to pre-compute and maintain several small-sized matrices related to V l as a compact index. With this powerful index technique, we demonstrate that both single-pair and single-source ER queries can be efficiently answered using a newly-developed V l -absorbed random walk sampling or V l -absorbed push technique. Comprehensive theoretical analysis shows that all proposed index-based algorithms achieve provable performance guarantees for both single-pair and single-source ER queries. Extensive experiments on 5 real-life datasets demonstrate the high efficiency of our multiple landmarks-based index techniques. For instance, our algorithms, with a 1.5 GB index size, can be up to 4 orders of magnitude faster than the state-of-the-art algorithms while achieving the same accuracy on a large road network.

Efficient Resistance Distance Computation: The Power of Landmark-based Approaches

Resistance distance is a fundamental metric to measure the similarity between two nodes in graphs which has been widely used in many real-world applications. In this paper, we study two problems on approximately computing resistance distance: (i) single-pair query which aims at calculating the resistance distance r(s, t) for a given pair of nodes (s, t); and (ii) single-source query which is to compute all the resistance distances r(s, u) for all nodes u in the graph with a given source node s. Existing algorithms for these two resistance distance query problems are often costly on large graphs. To efficiently solve these problems, we first establish several interesting connections among resistance distance, a new concept called v-absorbed random walk, random spanning forests, and a newly-developed v-absorbed push procedure. Based on such new connections, we propose three novel and efficient sampling-based algorithms as well as a deterministic algorithm for single-pair query; and we develop an online and two index-based approximation algorithms for single-source query. We show that the two index-based algorithms for single-source query take almost the same running time as the algorithms for single-pair query with the aid of a linear-size index. The striking feature of all our algorithms is that they are allowed to select an easy-to-hit node by random walks on the graph. Such an easy-to-hit landmark node v can make the v-absorbed random walk sampling, spanning tree sampling, as well as the v-absorbed push more efficient, thus significantly improving the performance of our algorithms. Extensive experiments on 5 real-life datasets show that our algorithms substantially outperform the state-of-the-art algorithms for two resistance distance query problems in terms of both running time and estimation errors.

Strain-minimizing hyperbolic network embeddings with landmarks

Abstract We introduce L-hydra (landmarked hyperbolic distance recovery and approximation), a method for embedding network- or distance-based data into hyperbolic space, which requires only the distance measurements to a few ‘landmark nodes’. This landmark heuristic makes L-hydra applicable to large-scale graphs and improves upon previously introduced methods. As a mathematical justification, we show that a point configuration in $d$-dimensional hyperbolic space can be perfectly recovered (up to isometry) from distance measurements to just $d+1$ landmarks. We also show that L-hydra solves a two-stage strain-minimization problem, similar to our previous (unlandmarked) method ‘hydra’. Testing on real network data, we show that L-hydra is an order of magnitude faster than the existing hyperbolic embedding methods and scales linearly in the number of nodes. While the embedding error of L-hydra is higher than the error of the existing methods, we introduce an extension, L-hydra+, which outperforms the existing methods in both runtime and embedding quality.

Exploring a dynamic ring without landmark

Consider a group of autonomous mobile computational entities, called agents, arbitrarily placed at some nodes of a dynamic but always connected ring. The agents neither have any knowledge about the size of the ring nor have a common notion of orientation. We consider the Exploration problem where the agents have to collaboratively explore the graph and terminate, with the requirement that each node has to be visited by at least one agent. It has been shown by Luna et al. (2020) [22] that the problem is solvable by two anonymous agents if there is a single observably different node in the ring called landmark node. The problem is unsolvable by any number of anonymous agents in absence of a landmark node. We consider the problem with non-anonymous agents (agents with distinct identifiers) in a ring with no landmark node. The assumption of agents with distinct identifiers is strictly weaker than having a landmark node as the problem is unsolvable by two agents with distinct identifiers in absence of a landmark node. This setting has been recently studied by Mandal et al. (2020) [6]. There it is shown that the problem is solvable in this setting by three agents assuming that they have edge crossing detection capability. Edge crossing detection capability is a strong assumption which enables two agents moving in opposite directions through an edge in the same round to detect each other and also exchange information. In this paper we give an algorithm that solves the problem with three agents without the edge crossing detection capability.

On the robustness of the metric dimension of grid graphs to adding a single edge

The metric dimension (MD) of a graph is a combinatorial notion capturing the minimum number of landmark nodes needed to distinguish every pair of nodes in the graph based on graph distance. We study how much the MD can increase if we add a single edge to the graph. The extra edge can either be selected adversarially, in which case we are interested in the largest possible value that the MD can take, or uniformly at random, in which case we are interested in the distribution of the MD. The adversarial setting has already been studied by Eroh et al., (2015) for general graphs, who found an example where the MD doubles on adding a single edge. By constructing a different example, we show that this increase can be as large as exponential. However, we believe that such a large increase can occur only in specially constructed graphs, and that in most interesting graph families, the MD at most doubles on adding a single edge. We prove this for d-dimensional grid graphs, by showing that 2d appropriately chosen corners and the endpoints of the extra edge can distinguish every pair of nodes, no matter where the edge is added. For the special case of d=2, we show that it suffices to choose the four corners as landmarks. Finally, when the extra edge is sampled uniformly at random, we conjecture that the MD of 2-dimensional grids converges in probability to 3+Ber(8/27), and we give an almost complete proof.

An ellipse-guided routing algorithm in wireless sensor networks

In wireless sensor networks, sensor nodes are deployed to collect data, perform calculations, and forward information to either other nodes or sink nodes. Recently, geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very efficient. However, the local minimum phenomenon, which hinders greedy forwarding, is a major problem in geographic routing. This phenomenon is attributed to an area called a hole that lacks active sensors, which either prevents the packet from being forwarded to a destination node or produces a long detour path. In order to solve the hole problem, mechanisms to detect holes and determine landmark nodes have been proposed. Based on the proposed mechanisms, landmark-based routing was developed in which the source node first sends a packet to the landmark node, and the landmark node then sends the packet to the destination. However, this approach often creates a constant node sequence, causing nodes that perform routing tasks to quickly run out of energy, thus producing larger holes. In this paper, a new approach is proposed in which two virtual ellipses are created with the source, landmark, and destination nodes. Then guide the forwarding along the virtual ellipses. Furthermore, a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple landmarks. Thus, the proposed approach improves both geographic routing and energy efficiency routing. Simulation experiments show that the proposed approach increases the battery life of sensor nodes, lowers the end-to-end delay, and generates a short path.

A Bibliometric Review of Two Decades’ Research on Closed-Loop Supply Chain: 2001-2020

The closed-loop supply chain (CLSC) is generally regarded as an environmentally friendly approach that can help in reducing environmental impacts and achieving sustainable development of society and economics. In recent years, the popularity research of CLSC has been widely concerned by both business and academia practitioners. It is observed that most of the literatures have focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades’ research on CLSC. This study contributes in fulfilling this gap. A comprehensive bibliometric analysis was conducted based on 1,155 articles in Web of Science Core Collection Database from 2001 to 2020. In order to track research frontiers and hotspots, visualization software VOSviewer and CiteSpace are used for analysis. Initially, a descriptive analysis was carried out to identify the trends of number of publications, the leading journals, top authors and regions. A thematic cluster analysis was then carried out to identify the research domains. Subsequently, based on the analyses of co-keywords, dominant categories and co-citation, hot issues and research trends are summarized. “game theory”, and “remanufacturing” are emerging research trends for CLSC. “Dual channel”, “quality” and “circular economy” had become hot topics. This review also finds the landmark nodes and pivot nodes in the research of CLSC. Finally, some research gaps are revealed to shed light on future directions.

ParkLoc

Locating a vehicle indoors (e.g., underground parking garages) has been a difficult problem to tackle, due to the unavailability of GPS and/or Wi-Fi signals. Current GPS-free indoor localization efforts often rely on infrastructure supports such as Wi-Fi or BLE beacons, whereas the smartphone-only proposals mostly require significant data collection and training efforts per garage. In this context, we propose ParkLoc, a novel lightweight smartphone-only solution for vehicular localization in GPS/Wi-Fi-deprived environments such as indoor parking garages. ParkLoc exploits the inherent planar graph structure of the navigable paths in parking facilities, in order to match a vehicle trajectory onto a sub-section of the map, by modeling these as sparse directed graphs. Exploiting an approximate graph matching method, ParkLoc is able to track a vehicle in real-time with a median error of 4.8m and localize a parked vehicle with a median error of 2m from the nearest parking space. Furthermore, ParkLoc adopts the popular GraphSLAM algorithm from robotics research; it learns the map graph from the observed trajectory graphs and a given set of bootstrap (seed) landmark nodes, in a semi-supervised manner. A key benefit of our approach is that ParkLoc works off-the-shelf without any expensive on-site training or sensor data collection per garage. A comprehensive evaluation of ParkLoc through extensive experiments performed in 4 different parking facilities reveals the promising performance of our graph-based approach for both localization and mapping.

An Indoor Wayfinding System Based on Geometric Features Aided Graph SLAM for the Visually Impaired.

This paper presents a 6-degree of freedom (DOF) pose estimation (PE) method and an indoor wayfinding system based on the method for the visually impaired. The PE method involves two-graph simultaneous localization and mapping (SLAM) processes to reduce the accumulative pose error of the device. In the first step, the floor plane is extracted from the 3-D camera's point cloud and added as a landmark node into the graph for 6-DOF SLAM to reduce roll, pitch, and Z errors. In the second step, the wall lines are extracted and incorporated into the graph for 3-DOF SLAM to reduce X , Y , and yaw errors. The method reduces the 6-DOF pose error and results in more accurate pose with less computational time than the state-of-the-art planar SLAM methods. Based on the PE method, a wayfinding system is developed for navigating a visually impaired person in an indoor environment. The system uses the estimated pose and floor plan to locate the device user in a building and guides the user by announcing the points of interest and navigational commands through a speech interface. Experimental results validate the effectiveness of the PE method and demonstrate that the system may substantially ease an indoor navigation task.

Nonlinear subspace clustering using curvature constrained distances

The massive amount of high-dimensional data in science and engineering demands new trends in data analysis. Subspace techniques have shown remarkable success in numerous problems in computer vision and data mining, where the goal is to recover the low-dimensional structure of data in an ambient space. Traditional subspace methods like PCA and ICA assume that the data is coming from a single manifold. However, the data might come from several (possibly intersected) manifolds (surfaces). This has caused the development of new nonlinear techniques to cluster subspaces of high-dimensional data. In this paper, we propose a new algorithm for subspace clustering of data, where the data consists of several possibly intersected manifolds. To this end, we first propose a curvature constraint to find the shortest path between data points and then use it in Isomap for subspace learning. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and all other nodes in the neighborhood graph. It builds a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithms such as hierarchical clustering. The performed experiments on both synthetic and real data sets confirm the performance of our algorithm.

Multiple Manifold Clustering Using Curvature Constrained Path.

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.

Artificial Landmark based Pose-Graph SLAM for AGVs in Factory Environments

This paper proposes a pose-graph based SLAM method using an upward-looking camera and artificial landmarks for AGVs in factory environments. The proposed method provides a way to acquire the camera extrinsic matrix and improves the accuracy of feature observation using a low-cost camera. SLAM is conducted by optimizing AGV`s explored path using the artificial landmarks installed on the ceiling at various locations. As the AGV explores, the pose nodes are added based on the certain distance from odometry and the landmark nodes are registered when AGV recognizes the fiducial marks. As a result of the proposed scheme, a graph network is created and optimized through a G2O optimization tool so that the accumulated error due to the slip is minimized. The experiment shows that the proposed method is robust for SLAM in real factory environments.

Memory-Efficient Fast Shortest Path Estimation in Large Social Networks

As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmark-based methods have been actively studied in recent years. Landmark-based estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmark-based distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent non-pruned landmark-based methods, while requiring less memory or disk space.

Security Games for Node Localization through Verifiable Multilateration

Most applications of wireless sensor networks (WSNs) rely on data about the positions of sensor nodes, which are not necessarily known beforehand. Several localization approaches have been proposed but most of them omit to consider that WSNs could be deployed in adversarial settings, where hostile nodes under the control of an attacker coexist with faithful ones. Verifiable multilateration (VM) was proposed to cope with this problem by leveraging on a set of trusted landmark nodes that act as verifiers. Although VM is able to recognize reliable localization measures, it allows for regions of undecided positions that can amount to the 40 percent of the monitored area. We studied the properties of VM as a noncooperative two-player game where the first player employs a number of verifiers to do VM computations and the second player controls a malicious node. The verifiers aim at securely localizing malicious nodes, while malicious nodes strive to masquerade as unknown and to pretend false positions. Thanks to game theory, the potentialities of VM are analyzed with the aim of improving the defender's strategy. We found that the best placement for verifiers is an equilateral triangle with edge equal to the power range R, and maximum deception in the undecided region is approximately 0.27R. Moreover, we characterizedain terms of the probability of choosing an unknown node to examine furtherathe strategies of the players.

The Building of Culture Atmosphere in Urban Design

The building of culture atmosphere is one of the core goals of urban design and an important aspect in the evaluation for urban design project. Based on the interpretation and analysis for the theory of culture district, the theory of city image, and the theory of place, the paper discusses the key points and related technical routes in the building of culture atmosphere with different space location (old town and new city town) or different space level (whole city, cultural blocks and landmark nodes). The building of culture atmosphere can help to improve the quality of urban design, and enhance the city's cultural charm.

Connectivity-based localization of large-scale sensor networks with complex shape

We study the problem of localizing a large sensor network having a complex shape, possibly with holes. A major challenge with respect to such networks is to figure out the correct network layout, that is, avoid global flips where a part of the network folds on top of another. Our algorithm first selects landmarks on network boundaries with sufficient density, then constructs the landmark Voronoi diagram and its dual combinatorial Delaunay complex on these landmarks. The key insight is that the combinatorial Delaunay complex is provably globally rigid and has a unique realization in the plane. Thus an embedding of the landmarks by simply gluing the Delaunay triangles properly recovers the faithful network layout. With the landmarks nicely localized, the rest of the nodes can easily localize themselves by trilateration to nearby landmark nodes. This leads to a practical and accurate localization algorithm for large networks using only network connectivity. Simulations on various network topologies show surprisingly good results. In comparison, previous connectivity-based localization algorithms such as multidimensional scaling and rubberband representation generate globally flipped or distorted localization results.

Estimating network proximity and latency

Network proximity and latency estimation is an important component in discovering and locating services and applications. With the growing number of services and service providers in the large-scale Internet, accurately estimating network proximity/latency with minimal probing overhead becomes essential for scalable deployment. Although there exist a number of network distance estimation schemes, they either rely on extensive infrastructure support, require the IP address of the potential targets, falsely cluster distant nodes, or perform poorly with even few measurement errors. We propose Netvigator , a scalable network proximity and latency estimation tool that uses information obtained from probing a small number of landmark nodes and intermediate routers (termed milestones) that are discovered en route to the landmarks, to identify the closest nodes. With very little additional probing overhead, Netvigator uses distance information to the milestones to accurately locate the closest nodes. We developed a Netvigator prototype and report our performance evaluation on PlanetLab and in the intranet of a large enterprise. Netvigator is a running service on PlanetLab as a part of HP Labs' S 3 (Scalable Sensing Service).

Scalable team multicast in wireless ad hoc networks exploiting coordinated motion

In this paper, we study a new multicast paradigm for large scale mobile ad hoc networks, namely team multicast. In team multicast the multicast group does not consist of individuals, rather, of member teams. For example a team may be a special task force that is part of a search and rescue operation. The message must be broadcast to each member of each team in the multicast group. Team multicast is very common in ad hoc networks set up to accomplish some collective tasks, such as for emergency recovery or battlefield applications. A key problem in several of the above applications is scalability to large membership size as well as network size. Our approach exploits motion affinity (more precisely, team members’ coordinated motion) which is typically present when the set of nodes has a commonality of interests. Each team can be viewed as a logical subnet. Within the team a landmark node is dynamically elected. The addresses of and the paths to the chosen landmarks are propagated into the whole network so that a source of a multicast group can route to the landmark of a subscribed team. Our protocol, Multicast-enabled Landmark Ad Hoc Routing (denoted as M-LANMAR), uses tunneling from multicast sources to each landmark of the subscribed team and restricted flooding within the motion group. Simulation study shows that M-LANMAR provides efficient and reliable multicast compared with the application of a “flat” multicast scheme (e.g., ODMRP) that does not exploit team coordinated motion. This paper contains three contributions: a new model for team multicast, with the definition of team dynamics (join, merge, split); the exploitation of team mobility and of landmarks in order to achieve scalable multicast, and; the implementation and performance evaluation of M-LANMAR, a landmark based team multicast scheme.