Lightweight finite element mesh database in Julia

This paper concentrates on the issue of detecting abnormal flows in distributed monitoring systems, which has many network management applications such as anomaly detection and traffic engineering. Collecting massive network traffic in real‐time remains a large challenge due to the limited system resource. Most existing approaches perform abnormal flow detection at one measurement point, while they cause large computation and memory overhead for recovering abnormal flows. In this paper, we propose a novel data streaming method that supports accurate abnormal flow detection with a low memory requirement. The key idea of our method is that each monitor compresses flow information to summary data structure, sends the generated data structure to the controller; then the controller aggregates the received data structures, recovers candidates of abnormal flows and estimates their size and change to find abnormal flows on the basis of the aggregated data structure. The experimental results based on real network traffic show that the proposed approach can detect up to 97% of abnormal flows with low memory and update requirements in comparison with related approaches.

The rapid computation of random eigenvalue problems of uncertain structures is the key point in structural dynamics, and it is prerequisite to the efficient dynamic analysis and optimal design of structures. In this paper, by combining finite element-transfer matrix method (FE-TMM) with perturbation method, a new method named as perturbation FE-TMM is presented for random eigenvalue problems of uncertain structures. By using the proposed method, the rapid computation of random eigenvalue problems of uncertain structures with complicated shapes and boundaries can be achieved, and the repeated eignvalues and characteristic vectors can be solved conveniently. Compared with stochastic finite element method, this method has the low memory requirement, high computational efficiency and high computational stability. It has more advantages for dynamic design of uncertain structures. Formulations as well as some numerical examples are given to validate the method.

the University of Amsterdam, May 20-31, 1974.This Tract collects the lecture notes of five of the courses given.The sixth course, given by Dr. R. Kowalski on Predicate Logic as a Programming Language in Artificial Intelligence, is scheduled to appear elsewhere.We are very grateful to the Netherlands Organization for the Advancement of Pure Research (Z.W.O.)

One of the major difficulties in structural shape optimal design is to create a design model by parameterizing a geometric model. Shape design parameterization is more complicated and difficult to handle than sizing design parameterization. First, the optimum shape is highly dependent on the design parameterization selected. An inappropriate design parameterization may result in an impractical design. On the other hand, changing the geometric shape of the design model to reflect successive changes in design parameters is a tedious, complicated, and inefficient process. Manual updates of geometric shape and finite element meshes are quite impractical. In this paper, a shape design parameterization method that is built on the geometric modeler PATRAN is presented. Using this method, a number of geometric entities that represent structural design boundaries are parameterized, and a design parameter linking process can be performed to create geometric features from geometric entities. To support the proposed method in structural shape design, a unified velocity computation method using geometric parametric mapping and an elliptic equation solver is developed for shape design sensitivity computation. The design parameterization and associated velocity Held computation methods are general enough to handle a broad range of engineering applications. Finally, automatic finite element mesh and geometric shape update methods are presented, which can be used to easily perform design changes.

Word recognition and generation is a fundamental part of the processing of natural language and it requires computationally effective morphological processors, especially for languages with rich morphology such as Modern Greek. Various models have been proposed for developing computerized systems to accomplish the task of recognition of morphosyntactic features of words. In the work presented here, the lazy tagging approach was examined, in which taggers are expected to work in the simplest possible way. The model of functional decomposition was extended and adapted for Modern Greek as a target language, following the lazy word-parsing approach, in order to cover a number of morphological phenomena that are encountered in Modern Greek, namely inflection, affixation, and long-distance dependencies. To achieve a more efficient word recognition, several automata of different levels of computing power, based on the original model, were introduced and evaluated according to the criteria of complexity, recognition speed, and accuracy of the results. The proposed system was used for processing a large-scale corpus, and the results are presented and discussed. To accomplish their task, taggers can rely upon large lexical databases, which are expected to be organized in such a way as to provide rapid access to the stored data and efficient memory management. Directed graphs can be used to describe and organize a lexical database of large magnitude in a compact manner. These data structures are named here matrix lexica, where the letters are described as nodes of directed graphs and the lemmata as paths (set of edges). It is expected that matrix lexica will support a tagger efficiently by providing a high speed of resolution, sound mathematical foundation, low memory requirements, and ability to handle distorted input in future developments

Large datasets can be screened for sequences from a specific organism, quickly and with low memory requirements, by a data structure that supports time- and memory-efficient set membership queries. Bloom filters offer such queries but require that false positives be controlled. We present BioBloom Tools, a Bloom filter-based sequence-screening tool that is faster than BWA, Bowtie 2 (popular alignment algorithms) and FACS (a membership query algorithm). It delivers accuracies comparable with these tools, controls false positives and has low memory requirements.Availability and implementaion:www.bcgsc.ca/platform/bioinfo/software/biobloomtoolsContact:cjustin@bcgsc.ca or ibirol@bcgsc.caSupplementary information:Supplementary data are available at Bioinformatics online.

A data structure is history independent if its internal representation reveals nothing about the history of operations beyond what can be determined from the current contents of the data structure. History independence is typically viewed as a security or privacy guarantee, with the intent being to minimize risks incurred by a security breach or audit. Despite widespread advances in history independence, there is an important data-structural primitive that previous work has been unable to replace with an equivalent history-independent alternative---dynamic partitioning. In dynamic partitioning, we are given a dynamic set S of ordered elements and a size-parameter B, and the objective is to maintain a partition of S into ordered groups, each of size Θ(B). Dynamic partitioning is important throughout computer science, with applications to B-tree rebalancing, write-optimized dictionaries, log-structured merge trees, other external-memory indexes, geometric and spatial data structures, cache-oblivious data structures, and order-maintenance data structures. The lack of a history-independent dynamic-partitioning primitive has meant that designers of history-independent data structures have had to resort to complex alternatives. In this paper, we achieve history-independent dynamic partitioning. Our algorithm runs asymptotically optimally against an oblivious adversary, processing each insert/delete with O(1) operations in expectation and O(B log N/loglog N) with high probability in set size N.

A data structure is history independent if its internal representation reveals nothing about the history of operations beyond what can be determined from the current contents of the data structure. History independence is typically viewed as a security or privacy guarantee, with the intent being to minimize risks incurred by a security breach or audit. Despite widespread advances in history independence, there is an important data-structural primitive that previous work has been unable to replace with an equivalent history-independent alternative—dynamic partitioning. In dynamic partitioning, we are given a dynamic set S of ordered elements and a size-parameter B, and the objective is to maintain a partition of S into ordered groups, each of size θ(B). Dynamic partitioning is important throughout computer science, with applications to B-tree rebalancing, write-optimized dictionaries, log-structured merge trees, other external-memory indexes, geometric and spatial data structures, cache-oblivious data structures, and order-maintenance data structures. The lack of a historyindependent dynamic-partitioning primitive has meant that designers of history-independent data structures have had to resort to complex alternatives. In this paper, we achieve history-independent dynamic partitioning. Our algorithm runs asymptotically optimally against an oblivious adversary, processing each insert/delete with O(1) operations in expectation and O(B logN/ loglogN) with high probability in set size N.

The implementation of optimized structural design recommender systems utilizing Artificial Intelligence (AI) offers unprecedented opportunities for enhancing the economic viability of vessels. By representing complex ship structural design data as a graph, one can leverage the unique characteristics of Graph Neural Networks (GNNs) to propose design solutions to naval architects during the basic design process. This approach enables the generation of designs in a reduced time frame while facilitating the immediate identification and investigation of potential errors, thereby impacting the ship's cost, performance, weight, stability, safety, and manufacturability. INTRODUCTION The discovery and design of new structural elements design with specific properties are important for advancing technologies in the maritime industry. The basic design stage is a relatively fast process but an essential part of the ship's lifecycle. It is not straightforward to measure the downstream impact of the quality of basic design. Errors in the detailed engineering stage, costly changes in production, lack of efficiency, predictability, and profitability in operations are all aspects that are not easy to consider when an experienced naval architect defines the configuration of a new ship. These aspects have an immediate impact on the ship cost, performance, weight, stability, safety, and manufacturability. (Florean, Alonso de los Rios, Pietro, & Seppälä, 2022) Traditional approaches to design discovery are time-consuming and expensive, limiting the pace of innovation. Analysis methods, such as CFD calculations, based on optimization algorithms, and the recent advancements in universal data exchange standards like OCX, have significantly accelerated this process. However, even these methods can be constrained by the complexity of the data, the number of stakeholders, and the processes involved. Moreover, traditional approaches draw a clear line between design and engineering, where early design models focus on the vessel's fundamental characteristics - naval architectural and functional aspects – while detailed design and production design deal with the layout intricacies and the generation of production and construction data. (Koelman, Veelo, Seppälä, & Filius, 2024) This separation often results in missing design considerations and leads to potential errors that could have been prevented in a more related system.

PurposeThe fire response of structural reinforced concrete columns is usually justified by the reduction in ultimate load bearing capacity. This is due to the decrease in mechanical strength of steel and concrete upon exposure to a fire. In structural design, it is more desirable to consider the action of load directly. The concept of equivalent accidental load due to a fire might give more convenient structural design data. This paper aims to focus on these issues.Design/methodology/approachA theoretical analysis for the equivalent accidental load imposed on reinforced concrete columns (axially loaded columns, uniaxially loaded columns and biaxially loaded columns) exposed to four‐side fires is carried out. The test results of previous research are used as examples and for checking computations. After determining its temperature field, the equivalent accidental load due to fire is calculated using simplified methods. The fire resistance period of reinforced concrete columns can also be determined.FindingsIf the response of a structural element to a fire can be converted into an accidental load, it can be combined with other components such as wind load and earthquake action to give a total design load. With this method, the equivalent accidental load due to a fire and fire resistance of reinforced concrete columns at elevated temperature can be derived directly, and the process is very simple. The equivalent accidental load and fire resistance of reinforced concrete columns exposed to fire on one, two or three sides can also be derived by the same method. However, the thermal performance of steel and concrete cannot be considered during the calculation.Originality/valueA simplified approach of equivalent accidental load due to fire is proposed. Much simpler guides can be drafted in structural fire design.

Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this article we survey the state of the art in the design and analysis of external memory (or EM) algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs. We consider a variety of EM paradigms for solving batched and online problems efficiently in external memory. For the batched problem of sorting and related problems such as permuting and fast Fourier transform, the key paradigms include distribution and merging. The paradigm of disk striping offers an elegant way to use multiple disks in parallel. For sorting, however, disk striping can be nonoptimal with respect to I/O, so to gain further improvements we discuss distribution and merging techniques for using the disks independently. We also consider useful techniques for batched EM problems involving matrices (such as matrix multiplication and transposition), geometric data (such as finding intersections and constructing convex hulls), and graphs (such as list ranking, connected components, topological sorting, and shortest paths). In the online domain, canonical EM applications include dictionary lookup and range searching. The two important classes of indexed data structures are based upon extendible hashing and B-trees. The paradigms of filtering and bootstrapping provide a convenient means in online data structures to make effective use of the data accessed from disk. We also reexamine some of the above EM problems in slightly different settings, such as when the data items are moving, when the data items are variable-length (e.g., text strings), or when the allocated amount of internal memory can change dynamically. Programming tools and environments are available for simplifying the EM programming task. During the course of the survey, we report on some experiments in the domain of spatial databases using the TPIE system (transparent parallel I/O programming environment). The newly developed EM algorithms and data structures that incorporate the paradigms we discuss are significantly faster than methods currently used in practice.

Modern embedded systems are now capable of executing complex and demanding applications that are often based on large data structures. The design of the critical data structures of the application affects the performance and the memory requirements of the whole system. Dynamic Data Structure Refinement methodology provides optimizations, mainly in list and array data structures, which are based on the application's features and access patterns. In this work, we extend various aspects of the methodology: First, we integrate radix tree optimizations. Then, we provide a set of platform-aware data structure implementations, for performing optimizations based on the hardware features. The extended methodology is evaluated using a wide set of synthetic and real-world benchmarks, in which we achieved performance and memory trade-offs up to 29.6%. Additionally, Pareto optimal data structure implementations that were not available by the previous methodology, are identified with the extended one.

In parallel computing based on finite element analysis, domain decomposition is a key technique for its preprocessing. Generally, a domain decomposition of a mesh can be realized through partitioning of a graph which is converted from a finite element mesh. This paper discusses the method for graph partitioning and the way to actualize mesh partitioning. Relevant softwares are introduced, and the data structure and key functions of Metis and ParMetis are introduced. The writing, compiling, and testing of the mesh partitioning interface program based on these key functions are performed. The results indicate some objective law and characteristics to guide the users who use the graph partitioning algorithm and software to write PFEM program, and ideal partitioning effects can be achieved by actualizing mesh partitioning through the program. The interface program can also be used directly by the engineering researchers as a module of the PFEM software. So that it can reduce the application of the threshold of graph partitioning algorithm, improve the calculation efficiency, and promote the application of graph theory and parallel computing.

This paper outlines a generic algorithm to generate cuts for magnetic scalar potentials in 3-dimensional multiply-connected finite element meshes. The algorithm is based on the algebraic structures of (co)homology theory with differential forms and developed in the context of the finite element method and finite element data structures. The paper also studies the computational complexity of the algorithm and examines how the topology of the region can create an obstruction to finding cuts in O(m2 0) time and O(m0) storage, where m0 is the number of vertices in the finite element mesh. We argue that in a problem where there is no a priori data about the topology, the algorithm complexity is O(m2 0) in time and O(m4/3 0) in storage. We indicate how this complexity can be achieved in implementation and optimized in the context of adaptive mesh refinement.

In this paper we will investigate transformations that serve as tools in the design of new data structures. Specifically, we study general methods for converting static structures (in which all elements are known before any searches are performed) to dynamic structures (in which the insertion of a new element can be mixed with searches). We will see three classes of such transformations (each based on a different counting scheme for representing the integers) and then use a combinatorial model to show the optimality of many of the transformations. Issues such as online data structures and deletion of elements are also examined. To demonstrate the applicability of these tools, we will study six new data structures that have been developed by applying the transformations.