Set Of Paths Research Articles

SNIFF: A Scalable Network Inference Framework for Measuring End-to-End Performance

For Internet measurement, a key challenge is how to measure end-to-end performance accurately and effectively. To achieve this goal, several recent studies suggest applying advanced matrix completion techniques to recover the complete matrix using a small percentage of samples. While the low-rank features of matrices are generally utilized in existing studies, important features, which are unique in our domain context and can be used to significantly reduce path sampling overhead with guaranteed recovery accuracy, have not been fully explored. To fill the gap, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable network inference framework</i> , namely, SNIFF, to monitor the end-to-end performance of a set of paths over time. In our framework, we extensively investigate real datasets and identify key features in measurement data, including the low-rank feature of the path-time matrix, the smoothness of performance over a short period, and the pair-wise linear correlation of paths. Based on these features, we develop three non-uniform sampling strategies that increase the sampling rate of important paths while reducing the rate if two paths are highly correlated. Moreover, we formulate two types of optimization problems for matrix completion that take into account important characteristics of data and we develop efficient algorithms to solve the corresponding problems. To evaluate the proposed framework, we conduct extensive numerical experiments for measuring end-to-end delay and throughput performance. The results confirm the feasibility and advantages of the proposed framework and algorithms.

Branch-and-cut-and-price for multi-agent path finding

The Multi-Agent Path Finding problem aims to find a set of collision-free paths that minimizes the total cost of all paths. The problem is extensively studied in artificial intelligence due to its relevance to robotics, video games and logistics applications, but is seldom considered in the mathematical optimization community. This paper tackles the problem using a branch-and-cut-and-price algorithm that incorporates a shortest path pricing problem for finding paths for every agent independently and thirteen classes of constraints for resolving different types of conflicts. Experimental results show that this mathematical approach solves 2402 of 4430 instances compared to 2039 and 1939 by the state-of-the-art solvers Lazy CBS and CBSH2-RTC published in artificial intelligence venues.

Bound Inference and Reinforcement Learning-Based Path Construction in Bandwidth Tomography

Inferring the bandwidth of internal links from the bandwidth of end-to-end paths, so-termed bandwidth tomography, is a long-standing open problem in the network tomography literature. The difficulty is due to the fact that no existing mathematical tool is directly applicable to solve the inverse problem with a set of min-equations. We systematically tackle this challenge by designing a polynomial-time algorithm that returns the exact bandwidth value for all identifiable links and the tightest error bound for unidentifiable links for a given set of measurement paths. When measurement paths are not given in advance, we prove the hardness of building measurement paths that can be used for deriving the global tightest error bounds for unidentifiable links. Accordingly, we develop a reinforcement learning (RL) approach for measurement path construction, that utilizes the special knowledge in bandwidth tomography and integrates both offline training and online prediction. Evaluation results with real-world ISP as well as simulated networks demonstrate that compared to other path construction methods, Random and Diversity Preferred, our RL-based path construction method can build measurement paths that result in much smaller average error bound of link bandwidth.

Study on the Covering Problem in Network Reliability for Precision Business Management

A new research area namely, precision business management, is quietly blooming through network theory. In precision business management, several new management tools can be developed to precisely adjust the behavior of the entities in the company to satisfy the management requirement. This paper deals with the situation when specific business process pattern should be recognized, and the related entities in the process have to be identified. It is referred to a kind of covering problem in network reliability. Minimal path set can be considered as a set of flow combinations. However, in some cases, a subset of the flow combination may be required for applications. Therefore, techniques for dealing such cases are needed. This paper proposes a heuristic approach to solve the covering problem in network reliability. Some numerical examples are presented for explanation. The results show that the cases are interesting in the discussion.

Decomposition of (2k + 1)-regular graphs containing special spanning 2k-regular Cayley graphs into paths of length 2k + 1

A Pℓ-decomposition of a graph G is a set of paths with ℓ edges in G that cover the edge set of G. Favaron, Genest, and Kouider (2010) conjectured that every (2k+1)-regular graph that contains a perfect matching admits a P2k+1-decomposition. They also verified this conjecture for 5-regular graphs without cycles of length 4. In 2015, Botler, Mota, and Wakabayashi verified this conjecture for 5-regular graphs without triangles. In this paper, we verify it for (2k+1)-regular graphs that contain the kth power of a spanning cycle; and for 5-regular graphs that contain special spanning 4-regular Cayley graphs.

Bilateral birth and death process in q-calculus

In this paper, we shall give the complete solution of the equations governing the bilateral birth and death process on the path set in which the birth and death rates and where 0<q<1 and . The mathematical methods employed here are based on q-Bessel Fourier analysis.

Методика использования базы знаний производящих функций двух переменных

Mathematical knowledge bases are the development of classical mathematical reference books and encyclopedias, which, in turn, makes them an important tool for conducting various research in mathematical sciences and related fields. At present, there are various knowledge bases of mathematical objects. In this paper, we consider the knowledge base of the generating functions of two variables, which allows us to operate with multivariate objects. The relevance and significance of the work lies in solving various problems related to the mathematical apparatus of generating functions. In the paper we consider the use of the knowledge base of generating functions of two variables for solving problems of operating generating functions and obtaining coefficients for composition, reciprocal and compositional inverse generating functions of two variables and their powers, as well as obtaining explicit expressions for the coefficients of logarithmic derivatives of the generating functions. In addition, an inverse problem is considered aimed at obtaining generating functions for explicit expressions describing their coefficients. The use of the knowledge base of generating two-variable functions contributes to the process of constructing combinatorial generation algorithms for combinatorial objects defined by generating functions of many variables. As an example, the construction of combinatorial generation algorithms for sets defined by the generalized Narayana numbers is shown. The Narayana numbers describe classes of subsets for combinatorial sets defined by the Catalan numbers. In this paper, one of the combinatorial interpretations for the Narayana numbers is chosen - the set of the Dyck paths of length n, which have m peaks.

Retail store layout optimization for maximum product visibility

It is well-established that increased product visibility to shoppers leads to higher sales for retailers. In this study, we propose an optimization methodology which assigns product categories and subcategories to store locations and sublocations to maximize the overall visibility of products to shoppers. The methodology is hierarchically developed to meet strategic and tactical layout planning needs of brick-and-mortar retailers. Layouts in both levels of planning are optimized considering eligibility requirements and complete set of shopper paths, thus, they successfully capture the unique shopping behaviour of consumers in a store’s region. The resulting mathematical optimization problem is recognized as a special instance of the well-known Quadratic Assignment Problem, which is considered computationally as one of the hardest optimization problems. We adopt a linearization technique and demonstrate via a real-world numerical example that our linearized optimization models substantially improve the store layout, hence, can be used in practical applications as a vital decision support model for store layout planning.

Promoting ecological solutions for sustainable infrastructure

Linear infrastructure networks such as roads, railways, navigation and irrigation canals, and power lines have grown exponentially since the mid-20th century. Most of these networks built before the 1990s have a significant impact on the environment. While there is no doubt that humanity needs infrastructure to ensure safe, secure and sufficient access to food, water and energy, it is essential to prevent the loss of biodiversity and ecosystems which are also at the basis of the provision of such fundamental services. Those complex, interconnected issues cannot be tackled without research and innovation, both in the fields of biodiversity and of infrastructure. IENE (Infrastructure Ecology Network Europe) was set up in 1996 to meet this need. Its mission is to promote the exchange of knowledge, experience and best practice in safe and sustainable pan-European transport infrastructure. With a status of an association today, this independent network has more than 400 members consisting of researchers, engineers, decision makers and infrastructure operators. IENE functions as an international and interdisciplinary forum. It supports cross-border cooperation in research, mitigation, planning, design, construction and maintenance in the field of biodiversity and transport infrastructure. Every two years, IENE organises an international conference to present cutting-edge research, identify pressing issues and problems, discuss effective solutions and map out future activities in the field of transport ecology and infrastructure. We are very glad to present you in this special issue some of the best scientific outcomes of the IENE 2020 conference, hoping that it will contribute to further breakthroughs in science and uptake in policy-making and practices on the ground. We commend the organising team of the University of Evora, Portugal, for their excellent programming of the conference and for having gathered exceptional scientists on the topic of biodiversity and infrastructure. They managed to host a high-quality event, despite the many adjustments that had to be done because of the covid-19, including postponing the conference to January 2021 and holding it entirely online. The topic of IENE conference 2020 was “Linear Infrastructure Networks with Ecological Solutions” and the motto was “working together”. This means that every stakeholder has a role to play, and that biodiversity should be considered at all governance scales and during all phases of the set-up of infrastructure. The papers selected here are of particular interest to follow the path set forth in the conference’s final declaration, that is included in this issue.

Energetic and Valuable Path Compendium Routing Using Frustration Free Communication Dimension Extension Algorithm in MANET

In the mobile ad hoc network (MANET), nodes are unenergetic nodes; also, it does not provide valuable routing, since it has the limited size for routing information storage for every node, and node multiple path takes more energy for small size of information sharing from sender node to destination node. It maximizes energy consumption and end-to-end delay and reduces network lifetime. In the proposed Energetic and Valuable Path Compendium Routing (EVPC) technique for obtaining energy saving enrichment in mobile ad hoc network process by separating the network into groups and chosen as heads within the groups by using path compendium technique also referred as arbitrary group head chosen depends on communication scheme. Path compendium is known to play an essential task to contain the issues of routing scalability in the network communication process. Through the increasing amount of nodes linked to the network surroundings, emerges the requirement to improve the communication table dimension to hold the improved nodes. To overcome this path compendium, a transmitter scheme is applied. The frustration free communication dimension extension algorithm is used by overriding set of paths and altering advertising node to energetic node with shortest distance path. The frustration free communication dimension extension procedure offers more effectiveness in enhancing the different metrics and principally minimizes the energy consumption by 25% and end-to-end delay by 15% and improves the network lifetime by 35%.

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network.

Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists' route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists' preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

TSPS: A Topic based Shortest Path Set algorithm for influence maximization

This paper focuses on the influence maximization problem in social networks, which aims to find some influence nodes that maximize the spread of information. Most existing achievements usually adopt a uniform propagation probability, without considering the topic information. Moreover, the classic Independent Cascade Model and its approximations have suffered from much running time. To overcome this limitation, this paper proposed a Topic based Shortest Path Set algorithm (TSPS). Additionally, a comprehensive set of experiments are conducted on large real-world networks, showing that our proposal provides more impressive results in the aspects of influence spread and running time.

Reliability Uncertainty Analysis Method for Aircraft Electrical Power System Design Based on Variance Decomposition

As a safety critical system, affected by cognitive uncertainty and flight environment variability, aircraft electrical power system proves highly uncertain in its failure occurrence and consequences. However, there are few studies on how to reduce the uncertainty in the system design stage, which is of great significance for shortening the development cycle and ensuring flight safety during the operation phrase. For this reason, based on the variance decomposition theory, this paper proposes an importance measure index of the influence of component failure rate uncertainty on the uncertainty of power supply reliability (system reliability). Furthermore, an algorithm to calculate the measure index is proposed by combining with the minimum path set and Monte Carlo simulation method. Finally, the proposed method is applied to a typical series-parallel system and an aircraft electrical power system, and a criteria named as “quantity and degree optimization criteria” is drawn from the case study. Results demonstrate that the proposed method indeed realizes the measurement of the contribution degree of component failure rate uncertainty to system reliability uncertainty, and combined with the criteria, proper solutions can be quickly determined to reduce system reliability uncertainty, which can be a theoretical guidance for aircraft electrical power system reliability design.

On the generating functions of pattern-avoiding Motzkin paths

Using a recursive approach, we show that the generating function for sets of Motzkin paths avoiding a single (not necessarily consecutive) pattern is rational over x and the Catalan generating function C(x)=1−1−4x22x2, where x keeps track of the length of the path. Moreover, an algorithm is provided for finding the generating function in the more general case of an arbitrary set of patterns. In addition, this algorithm allows us to find a combinatorial specification for pattern-avoiding Motzkin paths, which can be used not only for enumeration, but also for exhaustive and random generation.

Path decompositions of triangle-free graphs

Let G be a graph with n vertices. A path decomposition of G is a set of edge-disjoint paths containing all the edges of G. Let p(G) denote the minimum number of paths needed in a path decomposition of G. Gallai Conjecture asserts that if G is connected, then p(G)≤⌈n/2⌉. If G is allowed to be disconnected, then the upper bound ⌊34n⌋ for p(G) was obtained by Donald [7], which was improved to ⌊23n⌋ independently by Dean and Kouider [6] and Yan [14]. For graphs consisting of vertex-disjoint triangles, ⌊23n⌋ is reached and so this bound is tight. If triangles are forbidden in G, then p(G)≤⌊g+12gn⌋ can be derived from the result of Harding and McGuinness [11], where g denotes the girth of G. In this paper, we also focus on triangle-free graphs and prove that p(G)≤⌊3n/5⌋, which improves the above result with g=4.

Motion Planning Solution with Constraints Based on Minimum Distance Model for Lane Change Problem of Autonomous Vehicles

Lane change is one of the important operations in motion of an autonomous vehicle. When encountering obstacles or wanting to overtake the vehicle ahead, the autonomous vehicle will make a decision and choose the best path to control the trajectory of motion to perform lane change. In this article, we will present solutions for lane change trajectories, including general path setting, building nonlinear models with states of vehicle speed, acceleration and jerk; building a constraint set to avoid collisions with a minimum safe distance model, which takes into account the potentially collision angle positions during lane change. Simulation results are performed in Matlab simulation environment to demonstrate an effective proposed solution and addressed the disadvantages in the modeling process for lane-changing operations, in order to improve the proactive safety of the motion planning for autonomous vehicles.

Discussing tourism during a crisis: resilient reactions and learning paths towards sustainable futures

ABSTRACT This study’s purpose is to conceptualise the relationship between resilience and sustainability from a learning perspective. It asks how a community’s first reactions to a crisis can indicate the possible future development of a destination’s sustainability, and examines the resilience properties of elasticity, hysteresis and malleability in relation to single- and multiple-loop learning. Empirically, this study explores public discussions about tourism in northern Norway immediately before, and during the first months of, the COVID-19 crisis. Such discussions are investigated through a qualitative content analysis of articles from the regional newspaper. The findings identify a variety of perspectives among the participants to the discussions reported in the newspaper, including the coexistence of different views on tourism and sustainability and on responses to a crisis. This study frames the discussions in terms of elastic, hysteretic and malleable reactions, and illustrates three learning paths towards alternative weak and strong sustainable futures in a conceptual model. The originality of this study concerns a conceptualisation of the resilience – sustainability relationship as a set of learning paths that emphasises the dynamic and non-deterministic aspects of tourism development, aiming to evoke a sense of both responsibility and empowerment.

A Research for Executable Path Automatic Generation Method Based on EFSM

Extended finite state machine (EFSM) is currently one of the most widely used model in the field of software testing. EFSM model is an enhanced model based on finite state machine (FSM). Automated test data generation is still a challenging problem due to the complexity of EFSM which extends the input and output parameters, context variables as well as the predicate condition. These reasons lead to conflict of the context variable with the enable conditions in the transition path. In order to avoid infeasible path generation, this paper proposes a method based on modified breadth first search to generate feasible transition path (MBFS-FTP). To solve the problem of state explosion in path generation, this paper converts state diagram to transition diagram on EFSM model. In order to make the EFSM static model can be driven execution, this paper uses UML model and generate executable model, so that implements EFSM execute dynamically. When using breadth-first search algorithm (MBFS) on every target transition to generate an executable transition path, the conflict detection algorithm is utilized to the transition path for conflict decision, avoiding the occurrence of an infeasible transition path. Considering target transition has multiple feasible transition paths, this paper combines suggested penalized value of definition-predicate-use (def-p-use) pair and length of feasible transition path, and then develops the measurement method of feasible transition path, and obtained a set of feasible transition path containing all of transitions. Through the experiment on two actual EFSM model, verifying the effectiveness of MBFS-FTP for feasible transition automatic generation, the experimental results show that MBFS-FTP can reduce the feasible transition path length, and make transition paths are more easily to be triggered at the times of generating test cases lately, furthermore, it can improve the efficiency of generate feasible transition path and save a lot of time.

Differentiated Toll Optimization on Goods Vehicles Based on Prediction of Demand for Large-Scale Network

The area rate of the toll road network is designed to influence the regional road network traffic flow configuration and an important factor of goods vehicle path selection, transportation, and other related departments in formulating the related standard. The toll rate is usually combined with the local passenger and cargo loading conditions. Adjusting and optimizing the charge policy, optimization of processes and methods is still facing many difficulties. In this paper, the model parameters and toll status of various goods vehicles are considered, and an optimization algorithm of toll rates for different vehicles in a large-scale road network is proposed. By constructing a traffic demand prediction model based on multiple truck classification, this paper analyzes the distribution status of network trucks and the final charging under the influence of different rates. At the bottom of the algorithm is an analytical model composed of nonlinear equations whose dimensions scale linearly with the scale of the road network. The scale of the road network is independent of the path set and link attribute. The model is verified and the parameters are calibrated by a small network. Finally, the Road network is selected for empirical analysis in Xinjiang Autonomous Region. A case study shows that the analytical structure information provided by the analytic network model enables the algorithm to quickly identify high quality solutions, and the algorithm has better simulation accuracy and premium rate design advantages.

An approach for solving the three-objective arc welding robot path planning problem

Multi-objective welding robot path planning is becoming an important problem owing to the developing requirements of industrial production intelligence. An approach with path planning and optimization is proposed for solving the problem of arc welding. A rapidly exploring random tree* (RRT*)-based local path search algorithm is applied to generate a set of collision-free paths between any two welding seams, and the global search algorithm based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) framework is introduced to improve welding production efficiency by optimizing the requested contradictory objectives, namely, path length, trajectory smoothness and energy consumption. Both proposed algorithms are tested in different instances and on an actual welding workpiece, and the results prove that the proposed method could be useful in industrial production.