Path Vectors Research Articles

Fall migration, oceanic movement, and site residency patterns of eastern red bats (Lasiurus borealis) on the mid-Atlantic Coast

Along the mid-Atlantic coast of the United States, eastern red bats (Lasiurus borealis) are present during fall mating and migration, though little is currently known about most aspects of bat migration. To reveal migration patterns, and understand drivers of over-water flight, we captured and radio-tagged 115 eastern red bats using novel technology, and subsequently tracked and described their movements throughout the region. We compared over-water flight movements to randomly generated patterns using a use-availability framework, and subsequently used a generalized linear mixed effects model to assess the relationship of over-water flight to atmospheric variables. We used hidden Markov models to assess daily activity patterns and site residency. Most bats with long-distance movements traveled in a southwesterly direction, however path vectors were often oriented interior toward the continental landmass rather than along the coastline. We observed that some bats transited wide sections of the Chesapeake and Delaware bays, confirming their ability to travel across large water bodies. This over-water flight typically occurred in the early hours of the night and during favorable flying conditions. If flight over large water bodies is a proxy for over-ocean flight, then collision risk at offshore wind turbines – a major source of migratory bat fatalities – may be linked nightly to warm temperatures that occur early in the fall season. Risk, then, may be somewhat predictable and manageable with mitigation options linking wind-energy operation to weather conditions and seasonality.

Improvements to code2vec: Generating path vectors using RNN

Source code analysis has many application scenarios, such as code plagiarism detection and software vulnerability search. Source code analysis can benefit from machine learning, but it typically requires a standard vector representation and cannot be directly applied to the source code. Thus, we are required to embed source code into vector representation while maintaining the semantics of the code as much as possible. Code2vec proposes a code embedding method that converts source code into code vector through Abstract Syntax Tree(AST). However, we found that code2vec uses a hashing algorithm to generate the identifier for the path in the path context, which leads to the loss of node information in the path and also causes the model training parameters to be very large. Therefore, we present a new path representation which utilizes RNN to generate vectors for paths. We also proposed alternative model designs and evaluated their impact on the model in the experiments. The results we obtained in a challenging source code classification task suggest that, compared to code2vec, the RNN-based paths representation can produce a better embedding model with fewer training parameters.

Reconfigurable Intelligent Surface Enabled Interference Nulling and Signal Power Maximization in mmWave Bands

Reconfigurable intelligent surface (RIS) has emerged as a promising mean to enhance wireless transmission. The effective reflected paths provided by RIS are able to alleviate the susceptibility to blockage effects, especially in high-frequency band communications, where signals experience severe path loss and high directivity. This paper is concerned with an RIS-assisted system over the millimeter wave (mmWave) channel characterized by sparse propagation paths. A base station tries to connect with the desired user through an RIS, while the undesired user can also receive the signal transmitted from BS unavoidably, which is treated as the interference signal. All terminals are assumed to be equipped with a single antenna for the sake of simplicity. The paper aims to propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power transmitted from the base station (BS) at the desired user, while nulling the received interference signal power at the undesired user. The proposed reflecting design relies on the decomposition of the reflecting beamforming vectors and all channel path vectors into Kronecker product of factors being uni-modulus vectors. By exploiting characteristics of Kronecker mixed products, different factors of the reflecting are designed for either nulling the interference signal at the undesired user, or coherently combining data paths at the desired user. Furthermore, a channel estimation strategy is proposed to enable the proposed reflecting beamforming design. The magnitude, azimuth, and elevation arrival and departure angles of desired and undesired paths are estimated by an efficient 2-dimension (2-D) line spectrum optimization technique based on the atomic norm minimization (ANM) framework. The performance of the reflecting designs and channel estimation scheme is analyzed and demonstrated by simulation results.

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model.

In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to “information trek.” Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities.

Evaluating multi-state systems reliability with a new improved method

The computation of network reliability for a system with many states is an NP-hard issue. Finding all the minimum path vectors (d-MPs) lower boundary points for each level d is one of the few approaches for computing such dependability. This research proposed enhancements to the technique described in Chen's "Searching for d-MPs with rapid enumeration" paper. We propose additional adjustments to the method that creates the flow vector F in this enhancement. This decreases the number of required steps and the temporal complexity of the method. Comparing the newly suggested approach to the old algorithm reveals that the adjustment has increased the enumeration's efficiency and degree of complexity.

Reliability estimation and optimisation of multistate flow networks using a conditional Monte Carlo method

The Monte Carlo (MC) method is a practical approach to estimating the reliability of large multistate flow networks (MSFNs) in reality, e.g. transportation systems and computer networks. However, deriving an accurate reliability estimate using the crude MC method is computational expensive. This research proposes a conditional MC method to estimate the reliability of a MSFN using the minimal path vectors to level d (d-MPs) and minimal cut vectors to level d (d-MCs). A recursive method is developed to select d-MPs and d-MCs that incur a narrow gap between upper and lower reliability bounds. Then, state vectors are conditionally sampled in a recursive manner using matrix operations. The conditional MC method is embedded in the genetic algorithm (GA) to optimise system reliability. A ranking and selection procedure is used in GA to allocate simulation efforts to different solutions. Numerical studies validate that the proposed conditional MC method can obtain a more accurate reliability estimate than the crude MC method within the same computation time. The improved GA that includes the conditional MC method also outperforms the original GA in reliability optimisation.

BLE-Based Indoor Tracking System with Overlapping-Resistant IoT Solution for Tourism Applications.

In this paper, an overlapping-resistant Internet of Things (IoT) solution for a Bluetooth Low Energy (BLE)-based indoor tracking system (BLE-ITS) is presented. The BLE-ITS is a promising, inexpensive alternative to the well-known GPS. It can be used in human traffic analysis, such as indoor tourist facilities. Tourists or other customers are tagged by a unique MAC address assigned to a simple and energy-saving BLE beacon emitter. Their location is determined by a distributed and scalable network of popular Raspberry Pi microcomputers equipped with BLE and WiFi/Ethernet modules. Only simple triggered messages in the form of login records (LRs) are sent to a server, where the so-called path vectors (PVs) and interest profile (IPr) are set. The authors implemented the prototype and demonstrated its usefulness in a controlled environment. As it is shown in the paper, the solution is highly overlap-resistant and mitigates the so-called multilocation problem.

Searching for d-MPs for All Level d in Multistate Two-Terminal Networks Without Duplicates

The reliability evaluation of multistate networks is primarily using minimal path (cut) vectors, namely d -MPs ( d -MCs), which are the lower (upper) boundary vectors that satisfy the system demand d . The generation of all d- MPs ( d- MCs) is an NP-hard problem. Thus, it is important to develop an efficient algorithm to search for d- MPs ( d- MCs). Existing algorithms searching for d- MPs based on MPs all generate duplicate d- MP candidates, and extra steps are required to detect and remove those duplicates. In this article, we propose an improved algorithm to search for d- MPs for all d levels without generating duplicate d- MP candidates for two-terminal multistate networks. First, we discover and prove the mechanism of generating duplicate d- MP candidates based on MPs. Second, a novel method is proposed to prevent generating duplicate d- MP candidates. Third, an improved algorithm is developed by combining the proposed method to search for d- MPs without generating duplicate d- MP candidates for all d levels. Through computational experiments, it is found that the proposed algorithm is more efficient than existing algorithms for finding all d- MPs for all possible d values.

Analysis of minimal path and cut vectors in multistate monotone systems and use it for detection of binary type multistate monotone systems

Theorem specifying how all minimal cut (path) vectors to level j can be obtained from all minimal path (cut) vectors to level j in any multistate monotone system is proven. Characterizations of binary type multistate monotone systems and binary type multistate strongly coherent systems by minimal cut and path vectors and corresponding to them, binary type cut and path sets are also demonstrated.

An improved algorithm for reliability bounds of multistate networks

Indirect approaches based on minimal path vectors (d-MPs) and/or minimal cut vectors (d-MCs) are reported to be efficient for the reliability evaluation of multistate networks. Given the need to find more efficient evaluation methods for exact reliability, such techniques may still be cumbersome when the size of the network and the states of component are relatively large. Alternatively, computing reliability bounds can provide approximated reliability with less computational effort. Based on Bai’s exact and indirect reliability evaluation algorithm, an improved algorithm is proposed in this study, which provides sequences of upper and lower reliability bounds of multistate networks. Novel heuristic rules with a pre-specified value to filter less important sets of unspecified states are then developed and incorporated into the algorithm. Computational experiments comparing the proposed methods with an existing direct bounding algorithm show that the new algorithms can provide tight reliability bounds with less computational effort, especially for the proposed algorithm with heuristic L1.

Shortest paths with ordinal weights

We investigate the single-source-single-destination “shortest” path problem in directed, acyclic graphs with ordinal weighted arc costs. We define the concepts of ordinal dominance and efficiency for paths and their associated ordinal levels, respectively. Further, we show that the number of ordinally non-dominated path vectors from the source node to every other node in the graph is polynomially bounded and we propose a polynomial time labeling algorithm for solving the problem of finding the set of ordinally non-dominated path vectors from source to sink.

A Novel Multistate Minimal Cut Vectors Problem and Its Algorithm

Network reliability is always a powerful index for measuring the performance of the multistate flow network (MFN) that is commonly used to model real-world applications. The multistate minimal cut (MC) vector is a special vector, and it is one of the most popular tools in evaluating the reliability of MFNs. Due to its importance, a novel multistate MC vectors problem is proposed by extending the traditional multistate MC vectors problem from a specific fixed flow to all different flows for more applications. To solve this novel problem, a new algorithm is proposed. The time complexity and demonstration of the proposed algorithm are analyzed, and examples are provided. An experiment is also conducted to compare the proposed multistate MC vectors algorithm and the multistate minimal path vectors algorithm (another important method of calculating MFN reliability).

An efficient searching method for minimal path vectors in multi-state networks

Searching for minimal path vectors (MPVs) is an important topic in solving network related problems, especially for the evaluation of network reliability. One of the popular approaches, namely the three-stage method (TSM), is influenced deeply on the efficiency of searching for minimal path vectors in multi-state networks (MSN). TSM consists of three stages, i.e., searching for all minimal path sets, searching for all MPVs, and calculating union probability on MPVs. After reviewing previous works in the literaure, this paper proposes a more efficient method based on cyclic check on the candidates of MPVs, which can do an efficient searching for MPVs in MSNs and even reduce the three-step approach to two-step approach. Benchmarking with the well-known algorithms are made in this paper, and more complicated networks are also examined for verification of the proposed method.

Model Reduction of Networked Multiagent Systems by Cycle Removal

In this paper, we consider the problem of model reduction of consensus networks. We propose a new method of model reduction based on removing edges that close cycles in the network graph. The agent dynamics of the consensus network is given by a symmetric multivariable input-state-output system. In the network, the agents exchange relative output information with their neighbors. We assume that the network graph is connected, unweighted, and undirected. The network used to approximate the original system is defined on the same number of nodes as the original graph, but its edge set is a strict subset of the original edge set. Explicit expressions and upper bounds for the approximation errors are formulated in terms of the signed path vectors of the removed edges and the eigenvalues of the Laplacian matrices of the original and reduced network graphs.

Reliability evaluation of multistate networks: An improved algorithm using state-space decomposition and experimental comparison

This article introduces an improved algorithm using State-Space Decomposition for exact reliability evaluation of multistate networks given all minimal path vectors (d-MPs for short). We make two main contributions to the area. First, during each recursive call for the decomposition process, we find that the set of d-MPs can be decomposed recursively, and only those qualified d-MPs from a previous set of unspecified states are needed. Second, an improved heuristic rule is proposed choose an appropriate d-MP to decompose each set of unspecified states. Then, efficiency investigations of the proposed algorithm are conducted using hypothetical networks by changing one of the following network parameters while fixing the others, namely, the number of components, the number of d-MPs, and the number of states for each component. Efficiency investigations on networks with known structures are also conducted. Based on the computational experiments, it is found that (i) the proposed algorithm is more efficient than existing algorithms using the state-space decomposition method; (ii) the proposed algorithm is more efficient than existing algorithms using the Recursive Sum of Disjoint Products method when the number of d-MPs is not too small; and (iii) the indirect approach incorporating the proposed algorithm is more efficient than existing direct approaches. Guidelines for choosing the appropriate algorithm are provided. In addition, an algorithm is developed for network reliability evaluation given all minimal cut vectors (d-MCs for short).

Improved availability bounds for binary and multistate monotone systems with independent component processes

Abstract Multistate monotone systems are used to describe technological or biological systems when the system itself and its components can perform at different operationally meaningful levels. This generalizes the binary monotone systems used in standard reliability theory. In this paper we consider the availabilities of the system in an interval, i.e. the probabilities that the system performs above the different levels throughout the whole interval. In complex systems it is often impossible to calculate these availabilities exactly, but if the component performance processes are independent, it is possible to construct lower bounds based on the component availabilities to the different levels over the interval. In this paper we show that by treating the component availabilities over the interval as if they were availabilities at a single time point, we obtain an improved lower bound. Unlike previously given bounds, the new bound does not require the identification of all minimal path or cut vectors.

A Merge Search Approach to Find Minimal Path Vectors in Multistate Networks

A new approach namely merge search (MS) is proposed to search for minimal path vectors (MPV) in multistate networks (MSN). Also, a new advance in solving integer programming problems namely fast enumeration (FE) is integrated in this approach. Such an integrated approach can greatly improve the time efficiency of searching for MPV in MSN. Traditionally, searching for MPV in MSN involves three steps: (a) enumerate all feasible flow vectors; (b) transform these vectors to corresponding state vectors; (c) filter out MPV from these state vectors. Steps (a) and (c) are bottlenecks. Explicit enumeration is usually engaged in solving Step (a), and pairwise comparison is usually employed in solving Step (c). The integrated approach uses FE to solve Step (a), and MS to solve Step (c) instead. Some numerical examples are explored to show the superior time efficiency of the proposed approach. The results show that the proposed new approach is valuable in solving the search of MPV in MSN.

Path Reconstruction in Dynamic Wireless Sensor Networks Using Compressive Sensing

This paper presents CSPR, a compressive-sensing-based approach for path reconstruction in wireless sensor networks. By viewing the whole network as a path representation space, an arbitrary routing path can be represented by a path vector in the space. As path length is usually much smaller than the network size, such path vectors are sparse, i.e., the majority of elements are zeros. By encoding sparse path representation into packets, the path vector (and thus the represented routing path) can be recovered from a small amount of packets using compressive sensing technique. CSPR formalizes the sparse path representation and enables accurate and efficient per-packet path reconstruction. CSPR is invulnerable to network dynamics and lossy links due to its distinct design. A set of optimization techniques is further proposed to improve the design. We evaluate CSPR in both testbed-based experiments and large-scale trace-driven simulations. Evaluation results show that CSPR achieves high path recovery accuracy (i.e., 100% and 96% in experiments and simulations, respectively) and outperforms the state-of-the-art approaches in various network settings.

Searching for d-MPs with fast enumeration

Network theories have been developed for decades. They are now widely applied to solve many real-life problems. An active research field is the reliability evaluation of multistate networks. The most popular methods for such evaluation often use minimal paths (MP) or minimal cuts (MC) of the network. In these approaches, three basic steps exist: (a) searching for all MPs/MCs; (b) searching for all d-MPs/d-MCs from these MPs/MCs; (c) calculating union probability from these d-MPs/d-MCs. When the network is live, there are several minimum path vectors with respect to system state for d, namely d-MP, can be found. Steps a and c have great advances recently. This paper focuses on the searching for d-MPs by given MPs in terms of fast enumeration (FE), which was a recent advance in the integer programming problems in the literature. This paper is a first attempt to apply FE to the reliability evaluation of multistate networks. This paper also makes comparison with the well known algorithm for benchmarking. More complicated examples are also examined for illustrative purposes.

Stochastic evaluation methods of a multi-state system via a modular decomposition

Theoretical examinations about multi-state systems started around the publication of Barlow and Proschan [1], which has totally summarised the achievements about binary state systems by many researchers. Recently, a model of a multi-state system is tried to be applied for solving real problems and some authors have published books written from a practical point of view. See Natvig [16], Lisnianski and Levitin [9] and Lisnianski et al. [14], in which we may find real application of the results. In the reliability theory, evaluation of reliability performances of real systems and developing effective evaluation methods are important theme of reliability researchers. In this paper, we propose some evaluation methods of multi-state systems, using basic elements corresponding to the minimal path and cut state vectors of the binary state systems. We especially show convenient stochastic bounds via a modular decomposition. State spaces are assumed to be partially ordered sets, which means that the model proposed in this paper is one of the extremely extended models from the binary case.