Acyclic Networks Research Articles

Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes.

Temporal event sequence alignment has been used in many domains to visualize nuanced changes and interactions over time. Existing approaches align one or two sentinel events. Overview tasks require examining all alignments of interest using interaction and time or juxtaposition of many visualizations. Furthermore, any event attribute overviews are not closely tied to sequence visualizations. We present Sequence Braiding, a novel overview visualization for temporal event sequences and attributes using a layered directed acyclic network. Sequence Braiding visually aligns many temporal events and attribute groups simultaneously and supports arbitrary ordering, absence, and duplication of events. In a controlled experiment we compare Sequence Braiding and IDMVis on user task completion time, correctness, error, and confidence. Our results provide good evidence that users of Sequence Braiding can understand high-level patterns and trends faster and with similar error. A full version of this paper with all appendices; the evaluation stimuli, data, and analysis code; and source code are available at [Formula: see text].

Secure Non-Linear Network Code Over a One-Hop Relay Network

When there exists a malicious attacker in the network, we need to consider the possibilities of eavesdropping and the contamination simultaneously. Under an acyclic broadcast network, the optimality of linear codes was shown when Eve is allowed to attack any $r$ edges. The optimality of linear codes is not shown under a different assumption for Eve. As a typical example of an acyclic unicast network, we focus on the one-hop relay network under the single transmission scheme by assuming that Eve attacks only one edge in each level. Surprisingly, as a result, we find that a non-linear code significantly improves the performance on the one-hop relay network over linear codes. That is, a non-liner code realizes the imperfect security on this model that cannot be realized by linear codes. This kind of superiority of a linear code still holds even with considering the effect of sequential error injection on information leakage.

WITHDRAWN: Neutrosophic numbers in finding the shortest path using dynamic programming

This paper proposed a method to find the shortest path in Neutrosophic environment using a dynamic programming algorithm. Applying Triangular, Trapezoidal and Pentagonal Neutrosophic numbers determine the shortest path. The shortest route is estimated for the acyclic network. Furthermore, we compared the results of Neutrosophic numbers and proved that the results are same.

Robust DOA Estimation Method for MIMO Radar via Deep Neural Networks

It is a serious problem that the performance loss is suffered by traditional Direction-of-Arrival (DOA) estimation methods in non-ideal environment, such as mutual coupling of array elements, coherent sources, colored noise and plethora targets. A data-driven robust DOA estimation framework is proposed for MIMO radar via deep neural networks (DNN), so as to overcome the problems mentioned before. The framework consists of an autoencoder, a feedforward network, a network parameters database and a series of parallel directed acyclic graph networks (DAGN). Assisted with feedforward network for target-number determination, matching parameters of networks will be loaded from database. The autoencoder acts like a noise filter, it reconstructs the noise-free covariance from the noisy signal and thus the generalization burden of the subsequent DOA estimation DAGN will be decreased. Each sub-network of the parallel DAGN consists of a convolutional neural network (CNN) and two bidirectional long short-term memory (BiLSTM) networks, from which the estimation of DOA will be obtained by regression. The simulation results show that the proposed method is superior to the traditional methods in a non-ideal environment, and can also perform well when the number of targets reaches the upper limitation of the degrees of freedom of MIMO radar.

A multiperiod drayage problem with customer-dependent service periods

We investigate a multiperiod drayage problem in which customers request transportation services over several days, possibly leaving the carrier some flexibility to change service periods. We compare three approaches for the problem: a path-based model with all feasible routes, a “Price-and-Branch” algorithm in which the pricing is formulated as a collection of shortest path problems in a cunningly constructed acyclic network, and a compact arc-flow formulation based on this network. The experiments shows that the latter formulation is the most efficient, and can solve to optimality instances of real-world size (and beyond) in time compatible with typical operational constraints. Also, the models allow us to assess that limited amounts of flexibility from customers can significantly improve routing costs for the carrier while decreasing customers’ cost as well.

AI and Machine Learning for Industrial Security With Level Discovery Method

Protecting enterprise information security is a main task of Internet of Things system. The interaction between employees in same enterprise is based on level structures. So it is important to discover levels of employees for urban developers to protect enterprise information security. In this article, we propose a level discovery method for employees (LDME) from the records of employees using mobile phones named LDME. The call behavior between employees are expressed as several weighted directed complex networks, LDME represent edges in these weighted directed complex networks as vectors to exact both direction and weight information of the edges. Combined with supervised learning method, LDME prune these weighted directed networks into directed acyclic networks, which accurately reflect levels information between employees. At the same time, LDME mines the maximal frequent directed acyclic substructure from the above directed acyclic networks with efficient way, which indicate the stable levels information. We use real data to verify the performance of our method. The experimental result shows that the level of employees mined with our method is accurate and stable.

WITHDRAWN: Triangular neutrosophic network with shortest path

Abstract In this article, we're concerned with symmetry with Triangular Neutrosophic Fuzzy Number. The acyclic network decides the shortest path. Because of more than one home, it's the best decision. Multi-attribute community decision making (MAGDM), in particular, allows the most appropriate set of items based on data collected by more than one decision-maker.

A Primal Adjacency-Based Algorithm for the Shortest Path Problem with Resource Constraints

The shortest path problem with resource constraints (SPPRC) is often used as a subproblem within a column-generation approach for routing and scheduling problems. It aims to find a least-cost path between the source and the destination nodes in a network while satisfying the resource consumption limitations on every node. The SPPRC is usually solved using dynamic programming. Such approaches are effective in practice, but they can be inefficient when the network is large and especially when the number of resources is high. To cope with this major drawback, we propose a new exact primal algorithm to solve the SPPRC defined on acyclic networks. The proposed algorithm explores the solution space iteratively using a path adjacency–based partition. Numerical experiments for vehicle and crew scheduling problem instances demonstrate that the new approach outperforms both the standard dynamic programming and the multidirectional dynamic programming methods.

Globally optimal synthesis of heat exchanger networks. Part I: Minimal networks

AbstractThis article introduces the concept of minimal structure (MSTR) and presents an enumeration algorithm for the synthesis of heat exchanger networks based on MSTR. Minimal Structures refer to a class of heat exchanger networks featuring acyclic heat transfer networks without energy loops. The enumerations used are either exhaustive or smart with a stopping criterion. Without loss of generality we use the isothermal mixing Synheat model, that is, the method applies identically to other superstructures, with likely variations in the optimization models associated to each step. A conjecture is used to state that the algorithm renders solutions that are globally optimal. Literature examples are used to demonstrate the capabilities of the enumeration algorithm. Most of our solutions compare favorably with the best reported ones in literature, with exceptions where the reported solution is not minimal.

Universal stability in multi-hop radio networks

We study stability of routing in multi-hop wireless networks in the framework of adversarial queueing. A routing algorithm consists of three components: a transmission policy to decide on immediate transmissions, a scheduling policy to select a packet to transmit, and a hearing control to coordinate transmissions with scheduling. We consider two kinds of hearing control: proactive and reactive. We compare these policies and show relationships between the wired-network adversarial model and the multi-hop radio network model. In particular, we introduce a family of scheduling policies that are universally stable when using reactive hearing control, and obtain some stability results by using regular transmission oracles. We also prove that networks in which there is at least one node with an out-degree greater than 1 are unstable when using reactive hearing control, but all directed acyclic networks are universally stable, regardless of their out-degree, if using proactive hearing control.

A Blockchain-Based Framework for Lightweight Data Sharing and Energy Trading in V2G Network

The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for micro-transactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

Global Systemic Financial Risk Spillovers and Their External Shocks

The shock of the global financial crisis sparked widespread concern across the world about systemic financial risk and led to the reexamination of regulatory mechanisms. The traditional principle of “too big to fail” underwent a transformation into the new idea of “too interconnected to fail.” We used Directed Acyclic Graph (DAG) technology and network topology analysis to examine the dynamic evolution of global systemic financial risk and the risk trends in global financial markets from the perspective of network connectivity. Our findings show that financial markets in the Chinese Mainland are net receivers of risk spillovers and that systemic financial risk has a clear cross-market contagion effect due to a global volatility spillover scale of 64 percent. To maintain the stability and security of China’s financial markets, consideration should be given to the regulatory precept of “too interconnected to fail” in establishing macro-prudential risk prevention mechanisms.

Dynamic aspects of domination networks

A dynamic quantitative theory and measurement of power or dominance structures are proposed. Such power structures are represented as directed networks. A graph somewhat similar to the Lorenz curve for inequality measurement is introduced. The changes in the graph resulting from network dynamics are studied. Dynamics are operationalized in terms of added nodes and links. Study of dynamic aspects of networks is essential for potential applications in many fields such as business management, politics, and social interactions. As such, we provide examples of a dominance structure in a directed, acyclic network. We calculate the change in the D-measure, which is a measure expressing the degree of dominance in a network when nodes are added to an existing simple network.

Design exploration predicts designer creativity: a deep learning approach.

This study examined brain activation in graphic designers responding to pictorial stimulation during exploration tasks and determined the predictive effects of design exploration on designer creativity through a deep learning approach. The top and bottom 25% (10 each participants) were assigned high-creativity and low-creativity groups, respectively. The results provided the following indications. (i) Shallow architectures had higher prediction accuracy than deeper architectures. (ii) The prediction accuracy of shallow long short-term memory networks was higher than that of convolution neural networks. (iii) Bandpower exhibited increased prediction accuracy, and shallow LSTM networks with differing power spectra among independent components outperformed other deep learning methods. (iv) Direct acyclic graph networks did not improve prediction accuracy. (v) Design exploration could effectively predict designer creativity.

Some variants and extensions of dominance network analysis

The aim of this paper is to extend Dominance Network (DN) analysis, a methodology that evaluates the dominance relationships among a sample of homogeneous operating units (OUs) based on the inputs they consume and the outputs they produce. The relative efficiencies of the OUs are commonly assessed using Data Envelopment Analysis (DEA). DN complements DEA providing indicators to assess the performance of not only each OU individually but also the whole set of OUs. DN also allows visualisation of multidimensional datasets that in conventional DEA can only be done for low-dimensional datasets. The methodology builds a weighted directed acyclic network, the nodes of which are the observed OUs and the links represent all the dominance relationships among the OUs. The weight of these links measures the relative inefficiency among the linked OUs. Complex Network Analysis (CNA) techniques can be applied to this type of layered network. This provides many interesting measures and indexes to characterise the individual nodes as well as the whole sample. However, existing DN applications only involve the existing OUs and hence use a non-convex DEA technology. In this paper, an approach to overcome this limitation and use other DEA technologies is proposed. Also, as a secondary objective, how the use of different inefficiency metrics affects the results of the analysis is studied.

A Novel Convolution-Based Algorithm for the Acyclic Network Symbolic Reliability Function Problem

Acyclic (binary-state) networks are commonly implemented in many diverse disciplines and applications, including information systems, processes management, project management. These networks owe their popularity to the fact that they do not use directed cycles. Network reliability is the most commonly used tool for evaluating and managing systems modeled on acyclic networks, and minimal paths (MPs) play a significant role in evaluating this reliability. This study therefore proposes a new algorithm based on a novel convolution concept for evaluating acyclic network reliability. The proposed algorithm is able to find all convolution-based MP sets within polynomial time, and then obtain the symbolic function of the acyclic network reliability in terms of those convolution-based MP sets based on the pivotal decomposition. Its total time complexity is O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), which is the best among all existing MP algorithms which are at least O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|P|</sup> ), where O(|P|) = O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), and n and |P| are the number of nodes and MPs, respectively. The correctness and time complexity of the proposed algorithm is proven and examined. An example is used to display the novel convolution-based algorithm is implemented to solve the acyclic network reliability problem.

Graph Theoretic Foundations of Cyclic and Acyclic Linear Dynamic Networks

Dynamic Networks are signal flow graphs explicitly partitioning structural information from dynamic or behavioral information in a dynamic system. This paper develops the mathematical foundations underlying this class of models, revealing structural roots for system concepts such as system behavior, well-posedness, causality, controllability, observability, minimality, abstraction, and realization. This theory of abstractions uses graph theory to systematically and rigorously relate LTI state space theory, developed by Kalman and emphasizing differential equations and linear algebra, to the operator theory of Weiner, emphasizing complex analysis, and Willem’s behavioral theory. New systems concepts, such as net effect, complete abstraction, and extraneous realization, are introduced, and we reveal conditions when acyclic abstractions exist for a given network, opening questions about their use in network reconstruction and other applications.

DAG-Coder: Directed Acyclic Graph-Based Network Coding for Reliable Wireless Sensor Networks

Wireless sensor devices are usually powered with limited non-rechargeable batteries. This energy can easily be depleted when no wise consumption procedures are used, leaving the sensor non-functional. As most of the energy is consumed during the transmission of data, developing efficient data manipulation and transmission approaches is crucial and still an open problem that attracts the attention of many research groups. In this article a directed acyclic graph (DAG) based dissemination approach, where clustering and network coding techniques are applied, is proposed. As a main goal, this work aims at improving the network reliability (ensure recovery of lost packets), while minimizing energy consumption and balancing load at gateways. The proposed approach is compared to state-of-art approaches in terms of network reliability and energy saving trade-off. Experimental results demonstrate the strong performance of the proposed work.

A Probabilistic Clustering Approach for Identifying Primary Subnetworks of Discrete Fracture Networks with Quantified Uncertainty

Fractures form the main pathways for flow of fluids and transport of constituents carried by these fluids in fractured subsurface media. The majority of flow and transport occurs in the primary subnetwork or backbone, which is a subset of the fracture network. Understanding characteristics of the fracture network flow and transport backbones is thus of great importance towards improving efficiency of subsurface applications, such as aquifer management, hydrocarbon extraction, and long- term storage of spent nuclear fuel. We propose a method for identifying these backbones that also quantifies uncertainty over all possible backbones. Our approach treats the backbone identification problem as a probabilistic clustering problem. We develop a probabilistic generative model to simulate backbones that are induced acyclic flow networks. A user-controlled parameter determines the size of the identified backbones, with small backbones having good agreement with early particle arrival times and larger backbones having good agreement with early and late particle arrival times. We demonstrate our method on a representative fracture network with 264 fractures. Our method discovers flow channelization in backbones roughly 10%--50% the size of the original network, supporting previous experiments and discrete fracture network simulations. To the best of our knowledge, our method is the first backbone identification method to quantify uncertainty over backbones.

A Factor Graph-Based Distributed Consensus Kalman Filter

The Kalman filter as an effective tool to solve the state estimation problem for linear dynamic systems can be derived from a generalized perspective by applying the sum-product message passing over a factor graph. This viewpoint encourages us to visualize the state estimation problem over a network where all the nodes aspire to obtain consensus-based state estimates of a dynamic system by collecting sequential measurements over time. In this work, nodes process in a distributed and cooperative fashion and exchange Gaussian messages among neighbors resulting in a Gaussian belief propagation algorithm. We discuss and illustrate the performance of our proposed method under acyclic and cyclic network typologies.