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A framework for composable cellular automata devs modeling, simulation, and visualization

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A framework for composable cellular automata devs modeling, ...

2020 Spring Simulation Multiconference, SpringSim 2020

作者： Zhang, Chao Sarjoughian, Hessam S. Seok, Moon Gi Arizona Center for Integrative Modeling and Simulation Schl. of Computing Informatics and Decision Sys. Engr. Arizona State University TempeAZ85261-8809 United States School of Computer Science and Engineering Nanyang Technical University 639798 Singapore

ISBN: (纸本)9781713812883

The concept of cellular automata is one of the cornerstones of scientific and engineering pursuit with wide applicability in various domains. The rise of system complexity points to developing frameworks that can aid in developing composable models at multiple spatiotemporal levels. Toward this goal, an input/output modular discrete-event cellular automata framework based on the Discrete Event System Specification (DEVS) is proposed. It is adapted from a Composable Cellular Automata (CCA) formalism that is defined in terms of a multicomponent discrete time system specification. The resulting Composable Cellular Automata DEVS (CCA-DEVS) framework is built using the Cellular Automata DEVS that is a part of the DEVS-Suite simulator. The approach and realization of the proposed CCA-DEVS framework are detailed. The framework is used to develop an example model composed of CCA-DEVS agent and partial differential equation models. © 2020 Society for Modeling & Simulation International (SCS)

关键词： Cellular automata

Systematic comparison of graph embedding methods in practical tasks

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arXiv 2021年

作者： Zhang, Yi-Jiao Yang, Kai-Cheng Radicchi, Filippo Institute of Computational Physics and Complex Systems Lanzhou University Gansu Lanzhou730000 China Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Network embedding techniques aim at representing structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the non-trivial choice of selecting the proper approach for a given application. The present work attempts to close this gap of knowledge through a systematic comparison of eleven different methods for graph embedding. We consider methods for embedding networks in the hyperbolic and Euclidean metric spaces, as well as non-metric community-based embedding methods. We apply these methods to embed more than one hundred real-world and synthetic networks. Three common downstream tasks - mapping accuracy, greedy routing, and link prediction - are considered to evaluate the quality of the various embedding methods. Our results show that some Euclidean embedding methods excel in greedy routing. As for link prediction, community-based and hyperbolic embedding methods yield overall performance superior than that of Euclidean-space-based approaches. We compare the running time for different methods and further analyze the impact of different network characteristics such as degree distribution, modularity, and clustering coefficients on the quality of the different embedding methods. We release our evaluation framework to provide a standardized benchmark for arbitrary embedding methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Modulated Model Predictive Control of a Two-Level Inverter with Even-Order Harmonic Elimination

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Modulated Model Predictive Control of a Two-Level Inverter w...

2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2020

作者： Dahlmann, Alexander Yaramasu, Venkata Dekka, Apparao Yadlapati, Kishore Northern Arizona University School of Informatics Computing and Cyber Systems Flagstaff United States Lakehead University Department of Electrical Engineering Thunder Bay Canada Jawaharlal Nehru Technological University Department of Electrical and Electronics Engineering Narasaraopet India

ISBN: (纸本)9781728165752

The even-order harmonics have detrimental effect on the grid compared with the triplen harmonics. In this paper, a novel modulated model predictive current control strategy capable of eliminating even-order harmonic components from the two-level inverter output voltage/current waveforms is proposed. The proposed control strategy combines the features of fast dynamic response from the classical model predictive control and fixed switching frequency operation from the linear control. The proposed control strategy regulates output currents through the duty cycle based cost function optimization. The optimal voltage vectors are used to form a P-type and N-type switching sequences depending on the sector number and generate the gating signals for the inverter. The performance of the proposed control strategy is validated through MATLAB simulations. © 2020 IEEE.

关键词： Model predictive control

3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning

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arXiv 2022年

作者： Chen, Baian Nan, Liangliang Xie, Haoran Lu, Dening Wang, Fu Lee Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Urban Data Science Section Delft University of Technology Delft Netherlands The Department of Computing and Decision Sciences Lingnan University Hong Kong The Department of Systems Design Engineering University of Waterloo Waterloo Canada The School of Science and Technology Hong Kong Metropolitan University Hong Kong

Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, either abandon considerable local features during pooling operations or ignore many global features in the whole scene context. This paper explores new modules to simultaneously learn local-global features of scene point clouds that serve 3OD positively. To this end, we propose an effective 3OD network via simultaneous local-global feature learning (dubbed 3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a Dynamic Points Interaction (DPI) module that preserves effective local features during pooling. Besides, DPI is detachable and can be incorporated into existing 3OD networks to boost their performance. Second, it develops a Global Context Aggregation module to aggregate multi-scale features from different layers of the encoder to achieve scene context-awareness. Our method shows improvements over thirteen competitors in terms of detection accuracy and robustness on both the SUN RGB-D and ScanNet datasets. Source code will be available upon publication. © 2022, CC BY.

关键词： Object detection

Safe Open Science for Restricted Data

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Data and Information Management 2019年第1期3卷 50-60页

作者： Plale, Beth A. Dickson, Eleanor Kouper, Inna Harshani Liyanage, Samitha Ma, Yu McDonald, Robert H. Walsh, John A. Withana, Sachith Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University Bloomington United States HathiTrust University of Michigan Ann Arbor MI United States Department of Informatics School of Informatics Computing and Engineering Indiana University Bloomington United States HathiTrust Research Center Indiana University Bloomington United States University Libraries University of Colorado Boulder Boulder CO United States Department of Information and Library Science School of Informatics Computing and Engineering Indiana University Bloomington United States

Open science is prompting wide efforts to make data from research available for broader use. However, sharing data is complicated by important protections on the data (e.g., protections of privacy and intellectual property). The spectrum of options existing between data needing to be fully open access and data that simply cannot be shared at all is quite limited. This paper puts forth a generalized remote secure enclave as a socio-technical framework consisting of policies, human processes, and technologies that work hand in hand to enable controlled access and use of restricted data. Based on experience in implementing the enclave for computational, analytical access to a massive collection of in-copyright texts, we discuss the synergies and trade-offs that exist between software components and policy and process components in striking the right balance between safety for the data, ease of use, and efficiency. © 2019 © 2019 Beth A. Plale et al., published by Sciendo

关键词： Open Data

Symmetry breaking in optimal transport networks

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arXiv 2023年

作者： Patwardhan, Siddharth Barthelemy, Marc Erkol, Şirag Fortunato, Santo Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Université Paris-Saclay CNRS CEA Institut de Physique Théorique Gif-sur-Yvette91191 France 54 Avenue de Raspail Paris75006 France Center for Science of Science and Innovation Kellogg School of Management Northwestern University EvanstonIL60208 United States

Despite its importance for practical applications, not much is known about the optimal shape of a network that connects in an efficient way a set of points. This problem can be formulated in terms of a multiplex network with a fast layer embedded in a slow one. To connect a pair of points, one can then use either the fast or slow layer, or both, with a switching cost when going from one layer to the other. We consider here distributions of points in spaces of arbitrary dimension d and search for the fast-layer network of given size that minimizes the average time to reach a central node. We discuss the d = 1 case analytically and the d > 1 case numerically, and show the existence of transitions when we vary the network size, the switching cost and/or the relative speed of the two layers. Surprisingly, there is a transition characterized by a symmetry breaking indicating that it is sometimes better to avoid serving a whole area in order to save on switching costs, at the expense of using more the slow layer. Our findings underscore the importance of considering switching costs while studying optimal network structures, as small variations of the cost can lead to strikingly dissimilar results. Finally, we discuss real-world subways and their efficiency for the cities of Atlanta, Boston, and Toronto. We find that real subways are farther away from the optimal shapes as traffic congestion increases. Copyright © 2023, The Authors. All rights reserved.

关键词： Subways

Modular boundaries in recurrent neural networks

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arXiv 2023年

作者： Tanner, Jacob C. Mansour, Sina L. Coletta, Ludovico Gozzi, Alessandro Betzel, Richard F. Cognitive Science Program 2 School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States National University of Singapore Singapore Fondazione Bruno Kessler Trento Italy Functional Neuroimaging Lab Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems Rovereto Italy Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu

关键词： Recurrent neural networks

Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification 2nd

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Metaheuristic Based IDS Using Multi-objective Wrapper Featur...

2nd International Conference on Advances in Cyber Security, ACeS 2020

作者： Ghanem, Waheed Ali H. M. El-Ebiary, Yousef A. Baker Abdulnab, Mohamed Tubishat, Mohammad Alduais, Nayef A. M. Nasser, Abdullah B. Abdullah, Nibras Al-wesabi, Ola A. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Faculty of Engineering and Faculty of Education-Aden and Saber Aden University Aden Yemen Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia School of Computing and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pekan Malaysia National Advanced IPv6 Center Universiti Sains Malaysia Gelugor Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen

ISBN: (纸本)9789813368347

Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

Primitive Simultaneous Optimization of Similarity Metrics for Image Registration 9th

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Primitive Simultaneous Optimization of Similarity Metrics f...

9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image computing and Computer Assisted Intervention, MICCAI 2023

作者： Waldmannstetter, Diana Wiestler, Benedikt Schwarting, Julian Ezhov, Ivan Metz, Marie Bakas, Spyridon Baheti, Bhakti Chakrabarty, Satrajit Rueckert, Daniel Kirschke, Jan S. Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Informatics Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Munich Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden Helmholtz AI Helmholtz Zentrum München Munich Germany

ISBN: (纸本)9783031761591

Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration. To help closing this gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative Magnetic Resonance (MR) images of glioma. Employing the proposed optimization, we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) o on expert neuroradiologists’ landmark annotations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation