Union-find Research Articles

Project Evaluation and Review Technique (PERT) Analysis in the renovation project of the Church of St. John the Evangelist, Jakarta

The renovation of the St. Yohanes Evangelist Church project located on 197, Melawai Street Kebayoran Baru, Jakarta with a building area about 1480.9 m2 was chosen as a research object because the Church Construction Committee wanted to accelerate the renovation project from 2 years 5 months targeted to 1 year 5 months. This is because the Church of St. John the Evangelist as a place of worship can be immediately used again. The PERT method can be used to manage projects more efficiently and effectively; to be able to know the possibility of accelerating the duration of the project carried out on activities in the critical path and the shortening of the duration of each work on each alternative is equalized. In this study, the critical path compression step of the network using Microsoft Project and the results of the Project Evaluation and Review Technique (PERT) method, obtained a probability of 99.88%, so the renovation project of Saint John Evangelist Church can be an accelerated project to 1 year 5 months.

Concurrent disjoint set union

We develop and analyze concurrent algorithms for the disjoint set union (“union-find” ) problem in the shared memory, asynchronous multiprocessor model of computation, with CAS (compare and swap) or DCAS (double compare and swap) as the synchronization primitive. We give a deterministic bounded wait-free algorithm that uses DCAS and has a total work bound of Obiggl ( m cdot left( log {left( frac{np}{m} + 1 right) } + alpha {left( n, frac{m}{np} right) } right) biggr ) for a problem with n elements and m operations solved by p processes, where alpha is a functional inverse of Ackermann’s function. We give two randomized algorithms that use only CAS and have the same work bound in expectation. The analysis of the second randomized algorithm is valid even if the scheduler is adversarial. Our DCAS and randomized algorithms take O(log n) steps per operation, worst-case for the DCAS algorithm, high-probability for the randomized algorithms. Our work and step bounds grow only logarithmically with p, making our algorithms truly scalable. We prove that for a class of symmetric algorithms that includes ours, no better step or work bound is possible. Our work is theoretical, but Alistarh et al (In search of the fastest concurrent union-find algorithm, 2019), Dhulipala et al (A framework for static and incremental parallel graph connectivity algorithms, 2020) and Hong et al (Exploring the design space of static and incremental graph connectivity algorithms on gpus, 2020) have implemented some of our algorithms on CPUs and GPUs and experimented with them. On many realistic data sets, our algorithms run as fast or faster than all others.

Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis.

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.

Non-perfect maze generation using Kruskal algorithm

A non-perfect maze is a maze that contains loop or cycle and has no isolated cell. A non-perfect maze is an alternative to obtain a maze that cannot be satisfied by perfect maze. This paper discusses non-perfect maze generation with two kind of biases, that is, horizontal and vertical wall bias and cycle bias. In this research, a maze is modeled as a graph in order to generate non-perfect maze using Kruskal algorithm modifications. The modified Kruskal algorithm used Fisher Yates algorithm to obtain a random edge sequence and disjoint set data structure to reduce process time of the algorithm. The modification mentioned above are adding edges randomly while taking account of the edge’s orientation, and by adding additional edges after spanning tree is formed. The algorithm designed in this research constructs an non-perfect maze with complexity of where and denote vertex and edge set of an grid graph, respectively. Several biased non-perfect mazes were shown in this research by varying its dimension, wall bias and cycle bias.

Computational Techniques for Investigating Information Theoretic Limits of Information Systems

Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their computation efficiency and applicability is the reduction of variables, based on problem-specific symmetry and dependence relations. In this work, we propose using the disjoint-set data structure to algorithmically identify the reduction mapping, instead of relying on exhaustive enumeration in the equivalence classification. Based on this reduced linear program, we consider four techniques to investigate the fundamental limits of information systems: (1) computing an outer bound for a given linear combination of information measures and providing the values of information measures at the optimal solution; (2) efficiently computing a polytope tradeoff outer bound between two information quantities; (3) producing a proof (as a weighted sum of known information inequalities) for a computed outer bound; and (4) providing the range for information quantities between which the optimal value does not change, i.e., sensitivity analysis. A toolbox, with an efficient JSON format input frontend, and either Gurobi or Cplex as the linear program solving engine, is implemented and open-sourced.

Adaptive Real-Time Clustering Method for Dynamic Visual Tracking of Very Flexible Wings

Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, ) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system.

A Gradient-Based Clustering for Multi-Database Mining

Multinational corporations have multiple databases distributed throughout their branches, which store millions of transactions per day. For business applications, identifying disjoint clusters of similar and relevant databases contributes to learning the common buying patterns among customers and also increases the profits by targeting potential clients in the future. This process is called clustering, which is an important unsupervised technique for big data mining. In this article, we present an effective approach to search for the optimal clustering of multiple transaction databases in a weighted undirected similarity graph. To assess the clustering quality, we use dual gradient descent to minimize a constrained quasi-convex loss function whose parameters will determine the edges needed to form the optimal database clusters in the graph. Therefore, finding the global minimum is guaranteed in a finite and short time compared with the existing non-convex objectives where all possible candidate clusterings are generated to find the ideal clustering. Moreover, our algorithm does not require specifying the number of clusters a priori and uses a disjoint-set forest data structure to maintain and keep track of the clusters as they are updated. Through a series of experiments on public data samples and precomputed similarity matrices, we show that our algorithm is more accurate and faster in practice than the existing clustering algorithms for multi-database mining.

Distributed Environment Modeling using Path Compression Algorithm

In distributed environment, some operations related to objects are performed. For example, objects can be accessed or they can be moved. In our study, events related to objects (object-access, object-move) were created as independent events. In this study, the distributed environment simulation was performed and the effectiveness and success of the path compression algorithm, which we proposed as a result of the experimental study, was demonstrated. The purpose of this study is to show the effectiveness and benefits of the path compression algorithm. Path compression algorithm is an efficient algorithm whose runtime is linear. With the path compression, the long node chain that is formed while data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast and the cost of access is reduced. This result is shown with experimental study by modeling the distributed environment. It is shown comparative the results of the distributed environment simulation according to the various Access/Move (%) rates using binomial distribution. When we use the path compression, the maximum length and mean length of the chain decreases. Thus, with the path compression algorithm, the long node chain created by the objects is broken, the cost of accessing the objects is reduced, and fast access to the objects is ensured. In short, with our study, fast access to data is ensured in a distributed environment.

Information Flow Control with Decentralized Labeling Model in Information Security

Data security aims to prevent the use, modification, and spread of data by unauthorized people. In this study, our purpose was to provide data privacy and confidentiality with information flow control in distributed databases. In particular, a decentralized label model was developed that maintained confidentiality, including privacy, with data flow control. This model consists of an actor, an object, and a label. The owners of the objects are actors, and they need to share their data objects with others. Actors label the data objects and then send them out. A label contains the policy statements of data security issued by each of the owners. Each owner sets its own security and privacy policy independently of the other owners. The confidentiality of data in unsecured transport channels is ensured for all the actors in the system by means of labels while the data are in flow. Data objects are spread and shared securely among actors within unsecured environments. In addition, with the path compression, the long node chain that is formed while the data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast, and the cost of access is reduced. This result was shown experimentally by modeling the distributed environment.

Money Mules and Tumblers Money Laundering During the Cryptocurrency Era

Money laundering activities related to the cryptocurrency market have seen an exponential increase over the last fifteen years as a consequence of technological developments and economic distresses, as the 2008 crisis and the 2020 pandemic. This essay will analyse the European Union legislation created in order to tackle this phenomenon, dwelling on the Fifth Anti-Money Laundering Directive and its similarities among international laws. In particular, it will be displayed the importance of intermediaries, such as money mules and mixing services, to ease money laundering and increase the anonymity. In this framework, the European Union finds itself almost powerless: the legality of the virtual currency source is assessed only when entering and exiting in the virtual market and not during in-market transactions as well as a complete lack of legislation on mixing services activities. Therefore, how can the European Union steam the misuse of such intermediaries with ex-ante and ex-post interventions? And, finally, are the European privacy policies so important to outrank the risk related to money laundering activities? This paper shows that one way to prevent cryptocurrency money laundering pullulation is launching sensitization and awareness programmes since young age through educational institutions and, most importantly, a narrower legislation is required, implementing those laws that proved to be effective in other countries, in defiance of privacy policies.

Data Compression and Protection Through Representation With Combinations and Spiral Path

We are living in Industry 4.0 era where enormous data need to be stored and processed. Though hardware is also becoming more abundant, its limitation in medium transmission speed and memory storage still beats this need. Therefore, data compression seems always an emerging necessity. Another big threat to data is also the unauthorized access especially while dealing with sensitive data. In this paper, we introduce an enhancement of a 2D signals compression with security tools through cryptography. Spiral path compression technique uses data representation through combinations to achieve satisfactory lossless compression rate but it also offers intrinsic encryption of data. Instead of representing an image as a matrix of pixels, we represent it through a group of index numbers, each belonging to a part of the image called mini-images. Every index is performed through a spiral path inside the mini-image starting from the most repeated pixel value. The histogram not only helps on defining these starting points of spirals but also decreases the number of bits needed to represent the index. Since there are many starting points possible for each mini-image, we use a random distribution to decide which of them to be selected. We also use a matrix of private keys to make possible the protection of the image from unauthorized use. We conclude that using this technique, we can achieve satisfactory compression rates compared to actual compression rates used nowadays and many other cryptographic possibilities are available for future studies.

Efficient VLSI routing algorithm employing novel discrete PSO and multi-stage transformation

For routing industrial circuits, the Steiner minimal tree (SMT) model can be applied in different routing problems, such as wirelength optimization, congestion reduction, and delay optimization. In this paper, an efficient VLSI routing algorithm employing novel discrete particle swarm optimization (PSO) and multi-stage transformation is proposed to build two types of SMT, including X-architecture Steiner minimal tree and rectilinear Steiner minimal tree. Firstly, to simultaneously handle two types of SMT problems, an effective encoding strategy is proposed to be more suitable for PSO and thus it can overcome the difficulty of designing different algorithms for different routing architectures. Secondly, a multi-stage transformation strategy is presented to expand the search space of the proposed algorithm and accelerate the convergence speed of the proposed algorithm. Various combinations of multi-stage transformation strategies have been tested to highlight the best combination. Furthermore, the various genetic operators combined with union-find data structure strategy are proposed to construct the novel and effective discrete particle update formula. Experimental simulation results on industrial circuits show that the proposed algorithm can get the best solutions among the existing algorithms.

The distribution of union size: Canada, 1913–2014

I study the size distribution of Canadian trade unions and whether it is consistent with power law (Pareto) or other heavy tailed distributions (the lognormal and stretched exponential), using data from selected years between 1913 and 2014. I assess the fit of these distributions and undertake some model comparison tests. I also discuss some mechanisms that could give rise to these distributions in trade union size as well as the implications of my findings for unions and future research.

Disjoint-Set Data Structure-Aided Structured Gaussian Elimination for Solving Sparse Linear Systems

Structured Gaussian elimination (SGE) is a class of methods for efficiently solving sparse linear systems. The key idea is to first triangulate the original linear systems. The maximum component (MC)-based strategies are widely used to implement the triangulation process. The most straightforward way to find the MC is through exhaustive search. Instead, in this letter, we propose to use a disjoint-set data structure (DSDS) to efficiently maintain the components. The extra storage and time complexity introduced by the DSDS are respectively linear to the number of unknowns and constraints involved in a linear system. Simulation results show that using the DSDS can be several times faster than doing the exhaustive search to find the components.

N-cluster correlations in four- and five-dimensional percolation

We study $N$-cluster correlation functions in four- and five-dimensional (4D, 5D) bond percolation by extensive Monte Carlo simulation. We reformulate the transfer Monte Carlo algorithm for percolation [Phys. Rev. E {\bf 72}, 016126 (2005)] using the disjoint-set data structure, and simulate a cylindrical geometry $L^{d-1}\times \infty$, with the linear size up to $L=512$ for 4D and $128$ for 5D. We determine with a high precision all possible $N$-cluster exponents, for $N \! =\!2$ and $3$, and the universal amplitude for a logarithmic correlation function. From the symmetric correlator with $N \! = \!2$, we obtain the correlation-length critical exponent as $1/\nu \! =\! 1.4610(12)$ for 4D and $1/\nu \! =\! 1.737 (2)$ for 5D, significantly improving over the existing results. Estimates for the other exponents and the universal logarithmic amplitude have not been reported before to our knowledge. Our work demonstrates the validity of logarithmic conformal field theory and adds to the growing knowledge for high-dimensional percolation.

A novel static deformation measurement and visualization method for wind turbine blades using home-made LiDAR and processing program

Wind turbine blades play a key role in the wind energy collection system. Deformation measurement for turbine blades is an important and efficient way to prevent blade damages. Here, a novel and low-cost measurement method of static deformation for wind turbine blades using home-made LiDAR was proposed, and then verified by measuring the flap-wise load bending of a wind turbine blade model. The horizontal and vertical angular resolution of the proposed system are 0.09° and 0.225°, meaning a high resolution of 500 × 200 distance points in a 45° × 45° field of view of each depth image. The visualization and processing of depth images was realized using Point Cloud Library. To fast segment the 3D wind turbine blade model from background data, we also proposed a segmentation approach based on Euclidean clustering and union-find algorithm. The experimental results show that this method can segment the blade model from acquired data effectively, reducing the time-cost by nearly 50%. Furthermore, the bending degrees and curvatures of the blade in different buckling loads can be calculated using processed data. The proposed method can realize low-cost, on-site, portable and visualized deformation measurement for wind turbine blades and would be a promising way to test blades and prevent blade damages.

Public Awareness and the Behavior of Unpopular Courts

AbstractScholars have long debated the positive and negative consequences of an aware public for the quality of governance in modern liberal democracies. This article extends this debate to the context of constitutional review by exploring how public awareness can limit the effective exercise of review by courts lacking strong public support. Incorporating aspects of both the legitimacy and separation of powers theories on judicial power, the author argues that public awareness weakens the efficacy of such unpopular courts by creating an electoral incentive for governments to defy adverse rulings, even when doing so may lead to punishment from other institutional stakeholders. The article develops a simple formal model that identifies how and under what conditions public awareness can influence an unpopular court's decision making. An analysis of rulings issued by the Court of Justice of the European Union finds support for the model's empirical implications.

Novel Fast Networking Approaches Mining Underlying Structures From Investment Big Data

Mining the relationship structures among the investors plays a vital role in promoting economic development as well as preventing financial risks, especially in the context of big data. This article proposes fast networking approaches from investment big data to explore three underlying structures, namely, investment pedigrees, investment groups, and structural holes. Inspired by disjoint sets and path compression, we first present a pedigree classification algorithm to identify investment pedigrees. Second, through introducing a pruning strategy and a data structure termed as “2-tuple list,” we develop a novel linear-time structure mining algorithm in network (SMAN) for investigating investment groups and structural holes from the investment pedigree. Finally, we show that our SMAN has higher clustering accuracy and efficiency than other existing algorithms on a variety of real-world tasks in terms of normalized mutual information (NMI) values. Our method is particularly well suited for mining the underlying structures from investment big data.

Program extraction for mutable arrays

We present a lightweight method to represent, verify, and extract efficient programs involving mutable arrays in the Coq proof assistant. Our method mainly consists of a library for handling mutable arrays and an improved extraction plugin. Our library provides a monadic domain specific language for modeling computations involving mutable arrays, a simple reasoning method based on the Ssreflect extension and the Mathematical Components library, and an extraction method to efficient OCaml programs using in-place updates. Our extraction plugin improves the performance of our extracted programs, or more appropriately, through the application of simple program transformations for purely functional programs, it reduces both construction and destruction costs of inductive and coinductive objects and function call costs. As concrete applications for our method, we provide efficient implementations, correctness proofs, and benchmarks of the union–find data structure and the quicksort algorithm.

On the K\xf6nig deficiency of zero-reducible graphs

A confluent and terminating reduction system is introduced for graphs, which preserves the number of their perfect matchings. A union-find algorithm is presented to carry out reduction in almost linear time. The König property is investigated in the context of reduction by introducing the König deficiency of a graph G as the difference between the vertex covering number and the matching number of G. It is shown that the problem of finding the König deficiency of a graph is NP-complete even if we know that the graph reduces to the empty graph. Finally, the König deficiency of graphs G having a vertex v such that G-v has a unique perfect matching is studied in connection with reduction.