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Journal articles on the topic 'Multi-domain'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Skarżyński, Mirosław. "Multi-domain logistics." Systemy Logistyczne Wojsk 55, no. 2 (December 31, 2021): 213–24. http://dx.doi.org/10.37055/slw/145862.

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W artykule przedstawiono koncepcję nowego rodzaju starcia zbrojnego, jakim jest bitwa wielodomenowa. Rozważania skupiono na przyszłych operacjach wojskowych, w których bitwy będą prowadzone w wielu sferach. Obecnie bada się jednoczesne wykorzystanie siły kinetycznej i siły niekinetycznej w pięciu wymiarach przestrzeni: lądowej, morskiej, powietrznej, kosmicznej i cybernetycznej. Wielodomenowe siły zadaniowe będą dysponowały zaawansowanym uzbrojeniem i sprzętem oraz zdolnościami zapewniającymi osiągnięcie przewagi nad przeciwnikiem dążącym do konfrontacji ze Stanami Zjednoczonymi. Doświadczenia historyczne potwierdzają, że logistyka determinuje osiągnięcie zwycięstwa w działaniach wojennych, dlatego konieczne są analizy problematyki bitwy wielodomenowej z punktu widzenia możliwości zaspokojenia potrzeb wojsk zaangażowanych w nowy model starcia zbrojnego. W tym celu dokonano identyfikacji logistyki wielodomenowej i zaproponowano definicję pojęcia. Scharakteryzowano uwarunkowania funkcjonowania systemu logistycznego w bitwie wielodomenowej i zwrócono uwagę na rolę transportu w dostawach zaopatrzenia. Przedstawiono nowy wariant organizacji dowozu do wysoce mobilnych pododdziałów działających w dużym oddaleniu od sił głównych. Dzięki wykorzystaniu transportowych bezzałogowych statków powietrznych znajdujących się etatowo w kompanii wsparcia stanie się możliwe na obszarze operacji wielodomenowej samodzielne zaspokojenie potrzeb materiałowych pododdziałów wchodzących w skład brygady. Przeniesienie części dostaw w nowy wymiar przestrzeni wymagać będzie wdrożenia do wojsk lądowych nowych rodzajów bezzałogowych statków powietrznych i stworzenia procedur ich właściwego wykorzystania w celu wyzyskania w pełni ich możliwości działania w zróżnicowanym środowisku operacyjnym. Obecnie prowadzone testy prototypów są na tyle zaawansowane, że za kilka lat dostawy zaopatrzenia za pomocą bezzałogowych statków powietrznych do pododdziałów staną się rutynowym przedsięwzięciem realizowanym w ramach logistyki taktycznej, podobnie jak obecnie prowadzony dowóz środków bojowych i materiałowych kolumnami samochodów ciężarowo-terenowych.

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2

Jang, Won Hee, Young Joo Jeong, Sun Hee Choi, Won Hee Lee, Mooseong Kim, Sang-Jin Kim, Sang-Hwa Urm, Il Soo Moon, and Dae-Hyun Seog. "Parkin Interacts with the PDZ Domain of Multi-PDZ Domain Protein MUPP1." Journal of Life Science 24, no. 8 (August 30, 2014): 820–26. http://dx.doi.org/10.5352/jls.2014.24.8.820.

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3

Michaeli, T., Y. C. Eldar, and G. Sapiro. "Semi-supervised single- and multi-domain regression with multi-domain training." Information and Inference 1, no. 1 (November 12, 2012): 68–97. http://dx.doi.org/10.1093/imaiai/ias003.

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4

Aggarwal, Sonali. "Multi-Domain Sentiment Analysis." International Journal of Computer Trends and Technology 69, no. 3 (March 25, 2021): 85–89. http://dx.doi.org/10.14445/22312803/ijctt-v69i3p115.

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5

Poo, Chiang-Choon Danny, and Hongjun Lu. "Multi-domain expert systems." Expert Systems 8, no. 2 (May 1991): 67–73. http://dx.doi.org/10.1111/j.1468-0394.1991.tb00254.x.

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6

Liu, Wangyang, Nan Hua, Xiaoping Zheng, and Bingkun Zhou. "Intelligent Inter-domain Connection Provisioning for Multi-domain Multi-vendor Optical Networks." Journal of Optical Communications and Networking 7, no. 3 (February 27, 2015): 176. http://dx.doi.org/10.1364/jocn.7.000176.

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7

Lee, Seongmin, Hyunsik Jeon, and U. Kang. "Multi-EPL: Accurate multi-source domain adaptation." PLOS ONE 16, no. 8 (August 5, 2021): e0255754. http://dx.doi.org/10.1371/journal.pone.0255754.

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Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering labels of the features of each domain. They also do not fully utilize the target data without labels and rely on limited feature extraction with a single extractor. In this paper, we propose Multi-EPL, a novel method for MSDA. Multi-EPL exploits label-wise moment matching to align the conditional distributions of the features for the labels, uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that Multi-EPL provides the state-of-the-art performance for MSDA tasks in both image domains and text domains, improving the accuracy by up to 13.20%.

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8

van Elteren, Arjen, Inti Pelupessy, and Simon Portegies Zwart. "Multi-scale and multi-domain computational astrophysics." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2021 (August 6, 2014): 20130385. http://dx.doi.org/10.1098/rsta.2013.0385.

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Astronomical phenomena are governed by processes on all spatial and temporal scales, ranging from days to the age of the Universe (13.8 Gyr) as well as from kilometre size up to the size of the Universe. This enormous range in scales is contrived, but as long as there is a physical connection between the smallest and largest scales it is important to be able to resolve them all, and for the study of many astronomical phenomena this governance is present. Although covering all these scales is a challenge for numerical modellers, the most challenging aspect is the equally broad and complex range in physics, and the way in which these processes propagate through all scales. In our recent effort to cover all scales and all relevant physical processes on these scales, we have designed the Astrophysics Multipurpose Software Environment (AMUSE). AMUSE is a Python-based framework with production quality community codes and provides a specialized environment to connect this plethora of solvers to a homogeneous problem-solving environment.

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9

Iqbal, Farabi, Jeroen van der Ham, and Fernando Kuipers. "Technology-aware multi-domain multi-layer routing." Computer Communications 62 (May 2015): 85–96. http://dx.doi.org/10.1016/j.comcom.2015.01.010.

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10

Yuan, Zhigang, Sixing Wu, Fangzhao Wu, Junxin Liu, and Yongfeng Huang. "Domain attention model for multi-domain sentiment classification." Knowledge-Based Systems 155 (September 2018): 1–10. http://dx.doi.org/10.1016/j.knosys.2018.05.004.

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11

Grubinger, Thomas, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, and Tom Heskes. "Multi-Domain Transfer Component Analysis for Domain Generalization." Neural Processing Letters 46, no. 3 (April 6, 2017): 845–55. http://dx.doi.org/10.1007/s11063-017-9612-8.

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12

Jang, Won Hee, Young Joo Jeong, Sun Hee Choi, Won Hee Lee, Mooseong Kim, Sang-Jin Kim, Sang-Hwa Urm, Il Soo Moon, and Dae-Hyun Seog. "Muskelin Interacts with Multi-PDZ Domain Protein 1 (MUPP1) through the PDZ Domain." Journal of Life Science 25, no. 5 (May 30, 2015): 594–600. http://dx.doi.org/10.5352/jls.2015.25.5.594.

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13

Chen, Yen-Chiu, and Hui-Ching Hsieh. "A Robust Controllers’ Negotiation Model in Multi-Domain SDN Environments." SIJ Transactions on Computer Networks & Communication Engineering 02, no. 06 (October 8, 2014): 01–07. http://dx.doi.org/10.9756/sijcnce/v2i6/0207190201.

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Chen, Yen-Chiu, and Hui-Ching Hsieh. "A Robust Controllers’ Negotiation Model in Multi-Domain SDN Environments." SIJ Transactions on Computer Networks & Communication Engineering 06, no. 01 (February 13, 2018): 10–16. http://dx.doi.org/10.9756/sijcnce/v6i1/03010020201.

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15

Kim. "A Robust Frequency-Domain Multi-Reference Narrowband Adaptive Noise Canceller." Journal of the Acoustical Society of Korea 34, no. 2 (2015): 163. http://dx.doi.org/10.7776/ask.2015.34.2.163.

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16

Lyu, Fan, Shuai Wang, Wei Feng, Zihan Ye, Fuyuan Hu, and Song Wang. "Multi-Domain Multi-Task Rehearsal for Lifelong Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8819–27. http://dx.doi.org/10.1609/aaai.v35i10.17068.

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Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased forgetting of previous knowledge when moving to new tasks. However, the old tasks of the most previous rehearsal-based methods suffer from the unpredictable domain shift when training the new task. This is because these methods always ignore two significant factors. First, the Data Imbalance between the new task and old tasks that makes the domain of old tasks prone to shift. Second, the Task Isolation among all tasks will make the domain shift toward unpredictable directions; To address the unpredictable domain shift, in this paper, we propose Multi-Domain Multi-Task (MDMT) rehearsal to train the old tasks and new task parallelly and equally to break the isolation among tasks. Specifically, a two-level angular margin loss is proposed to encourage the intra-class/task compactness and inter-class/task discrepancy, which keeps the model from domain chaos. In addition, to further address domain shift of the old tasks, we propose an optional episodic distillation loss on the memory to anchor the knowledge for each old task. Experiments on benchmark datasets validate the proposed approach can effectively mitigate the unpredictable domain shift.

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17

Crepaldi, Marco, Alessandro Sanginario, Paolo Motto Ros, Michelangelo Grosso, Alessandro Sassone, Massimo Poncino, Enrico Macii, Salvatore Rinaudo, Giuliana Gangemi, and Danilo Demarchi. "Towards Multi-Domain and Multi-Physical Electronic Design." IEEE Circuits and Systems Magazine 15, no. 3 (2015): 18–43. http://dx.doi.org/10.1109/mcas.2015.2450635.

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18

Cornwell, Daniel J., and David K. Smith. "Photo-patterned multi-domain multi-component hybrid hydrogels." Chemical Communications 56, no. 51 (2020): 7029–32. http://dx.doi.org/10.1039/d0cc03004j.

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19

Pham, MinhQuang, Josep Maria Crego, and François Yvon. "Revisiting Multi-Domain Machine Translation." Transactions of the Association for Computational Linguistics 9 (February 2021): 17–35. http://dx.doi.org/10.1162/tacl_a_00351.

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When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.

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20

Zhao, Sicheng, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer. "Multi-Source Distilling Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12975–83. http://dx.doi.org/10.1609/aaai.v34i07.6997.

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Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

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21

Goucern, A. "Multi-domain modelling and simulation." IEE Review 45, no. 2 (March 1, 1999): 85–87. http://dx.doi.org/10.1049/ir:19990213.

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22

Kroft, Paul, and Christopher D. Wickens. "Displaying multi-domain graphical databases." Information Design Journal 11, no. 1 (September 26, 2003): 44–52. http://dx.doi.org/10.1075/idj.11.1.06kro.

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Display designers are often given the challenge of placing a large amount of information within a limited amount of display ‘real estate’. One possible solution to the problem is combining databases of information pertaining to the same spatial area into one, integrated display, reducing the amount of scanning required and allowing information to be presented in a larger display. On the other hand, integration will also increase the clutter of the displays. The effects of clutter may be mitigated through the use of decluttering techniques, but some of these solutions may require additional user interactivity. In the experiment, student pilots used six display configurations to answer multiple choice questions about the current airspace situation. Two databases, a navigation database and an air hazard database, were presented in each display. In addition, the type of question (focused or divided attention) was manipulated to assess the effects of the task on display performance. Responses were faster when the databases were integrated than separated, particularly when questions required integration across both databases, where accuracy also was greater. These results suggest that the combined benefits of reduced scanning and larger display size outweigh the costs of clutter. Interaction of any sort imposed a time cost, which was greatest when the questions involved both databases.

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23

Kuznetsov, Yu A. "Multi-level domain decomposition methods." Applied Numerical Mathematics 6, no. 4 (May 1990): 303–14. http://dx.doi.org/10.1016/0168-9274(90)90022-8.

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24

Liu, Huajun, Lei Chen, Haigang Sui, Qing Zhu, Dian Lei, and Shubo Liu. "Unsupervised multi-domain image translation with domain representation learning." Signal Processing: Image Communication 99 (November 2021): 116452. http://dx.doi.org/10.1016/j.image.2021.116452.

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25

Ren, Chuan-Xian, Yong-Hui Liu, Xi-Wen Zhang, and Ke-Kun Huang. "Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain." IEEE Transactions on Image Processing 31 (2022): 2122–35. http://dx.doi.org/10.1109/tip.2022.3152052.

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26

Griol, David, and José Manuel Molina. "Building multi-domain conversational systems from single domain resources." Neurocomputing 271 (January 2018): 59–69. http://dx.doi.org/10.1016/j.neucom.2017.01.112.

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27

Sunday Oladayo Oladejo, Stephen Obono Ekwe, and Lateef Adesola Akinyemi. "Multi-tier multi-tenant network slicing: A multi-domain games approach." ITU Journal on Future and Evolving Technologies 2, no. 6 (September 23, 2021): 57–82. http://dx.doi.org/10.52953/dxzq6155.

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The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.

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28

Jang, Won Hee, Young Joo Jeong, Sun Hee Choi, Sang-Jin Kim, Sang-Hwa Urm, Il Soo Moon, and Dae-Hyun Seog. "Cadms/SynCAMs/Necls/TSLCs Interact with Multi-PDZ Domain Protein MUPP1." Journal of Life Science 24, no. 12 (December 30, 2014): 1276–83. http://dx.doi.org/10.5352/jls.2014.24.12.1276.

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29

Yang, Fu-En, Jing-Cheng Chang, Chung-Chi Tsai, and Yu-Chiang Frank Wang. "A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification." IEEE Transactions on Image Processing 29 (2020): 2795–807. http://dx.doi.org/10.1109/tip.2019.2952707.

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30

Feng, Yang, Xinxiao Wu, and Yunde Jia. "Multi‐group–multi‐class domain adaptation for event recognition." IET Computer Vision 10, no. 1 (February 2016): 60–66. http://dx.doi.org/10.1049/iet-cvi.2014.0405.

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31

Innocenti, M. E., A. Beck, S. Markidis, and G. Lapenta. "Momentum conservation in Multi-Level Multi-Domain (MLMD) simulations." Journal of Computational Physics 312 (May 2016): 14–18. http://dx.doi.org/10.1016/j.jcp.2016.02.026.

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32

Yang, Zhenguo, Zehang Lin, Lingni Guo, Qing Li, and Wenyin Liu. "MMED: A multi-domain and Multi-modality event dataset." Information Processing & Management 57, no. 6 (November 2020): 102315. http://dx.doi.org/10.1016/j.ipm.2020.102315.

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33

Balaraman, Vevake, and Bernardo Magnini. "Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 866–73. http://dx.doi.org/10.1109/taslp.2021.3054309.

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34

Zhao, Sicheng, Bo Li, Pengfei Xu, Xiangyu Yue, Guiguang Ding, and Kurt Keutzer. "MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation." International Journal of Computer Vision 129, no. 8 (May 24, 2021): 2399–424. http://dx.doi.org/10.1007/s11263-021-01479-3.

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35

Wu, Hanrui, Yuguang Yan, Michael K. Ng, and Qingyao Wu. "Domain-attention Conditional Wasserstein Distance for Multi-source Domain Adaptation." ACM Transactions on Intelligent Systems and Technology 11, no. 4 (July 3, 2020): 1–19. http://dx.doi.org/10.1145/3391229.

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36

Zhang, Jianming, Chenjun Lu, Yuan Li, Lei Han, Pan Wang, and Guangyao Li. "A domain renumbering algorithm for multi-domain boundary face method." Engineering Analysis with Boundary Elements 44 (July 2014): 19–27. http://dx.doi.org/10.1016/j.enganabound.2014.04.009.

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37

UCHIUMI, Masaharu, and Tsuyoshi INOUE. "Multi-domain Dynamics for a Turbomachinery." Proceedings of Mechanical Engineering Congress, Japan 2020 (2020): J10101. http://dx.doi.org/10.1299/jsmemecj.2020.j10101.

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38

Zuo, Yukun, Hantao Yao, and Changsheng Xu. "Attention-Based Multi-Source Domain Adaptation." IEEE Transactions on Image Processing 30 (2021): 3793–803. http://dx.doi.org/10.1109/tip.2021.3065254.

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39

Wang Nan, Sun Shanwu, and Li Hui. "Formalizing Multi-Domain Workflow Modeling Process." International Journal of Digital Content Technology and its Applications 6, no. 15 (August 31, 2012): 28–34. http://dx.doi.org/10.4156/jdcta.vol6.issue15.4.

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40

Mező, András. "Report on JAPCC Multi-Domain Conference." Honvédségi Szemle 148, no. 1 (September 7, 2020): 69–78. http://dx.doi.org/10.35926/hdr.2020.1.6.

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41

Carugo, Oliviero. "Isoelectric points of multi-domain proteins." Bioinformation 2, no. 3 (November 1, 2007): 101–4. http://dx.doi.org/10.6026/97320630002101.

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Maldonado-Lopez, Ferney A., Eusebi Calle, and Yezid Donoso. "Checking Multi-domain Policies in SDN." International Journal of Computers Communications & Control 11, no. 3 (March 24, 2016): 428. http://dx.doi.org/10.15837/ijccc.2016.3.2560.

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Programmable Network like SDN allows administrators to program network nfrastructure according to service demand and custom-defined policies. Network olicies are interpreted by the centralized controller to define actions and rules to rocess the network traffic on devices that belong to a single domain. However, actual etworks are multi-domain where several domains are interconnected. Then, because DN controllers in a domain cannot define nor monitor policies in other domains, etwork administrators cannot ensure that their own policies, origin policies are being nforced by the domains not directly managed by them (i.e. foreign domains). e present AudiT, a multi-domain SDN policy verifier that identifies whether an rigin policy is enforced by foreign domains. AudiT comprises (1) model for network opology, policies, and flows, (2) an Audit protocol to gather information about the ctions performed by network devices to carry the flows of interest, and (3) a validation ngine that takes that information and detects security policy violations, and (4) an extension to the OpenFlow protocol to enable external auditing. This paper resents our approach and illustrates its application using an example considering ultiple SDN networks.

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43

Naveenkumar, Nagarajan, Gayatri Kumar, Narayanaswamy Srinivasan, Ramanathan Sowdhamini, and Sneha Vishwanath. "Fold combinations in multi-domain proteins." Bioinformation 15, no. 5 (May 31, 2019): 342–50. http://dx.doi.org/10.6026/97320630015342.

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44

Wesołowski, Jacek. "MULTI-DOMAIN NEYMAN-TCHUPROV OPTIMAL ALLOCATION." Statistics in Transition New Series 20, no. 4 (2019): 1–12. http://dx.doi.org/10.21307/stattrans-2019-031.

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45

Evans-Pughe, C. "Getting it together [multi-domain simulation]." Engineering & Technology 1, no. 9 (December 1, 2006): 38–41. http://dx.doi.org/10.1049/et:20060906.

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46

Goswami, Anirban, and Nabin Ghoshal. "Multi-image authentication in frequency domain." International Journal of Information and Communication Technology 11, no. 2 (2017): 166. http://dx.doi.org/10.1504/ijict.2017.086249.

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Goswami, Anirban, and Nabin Ghoshal. "Multi-image authentication in frequency domain." International Journal of Information and Communication Technology 11, no. 2 (2017): 166. http://dx.doi.org/10.1504/ijict.2017.10007024.

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48

Homilius, Max, John Wiedenhoeft, Sebastian Thieme, Christoph Standfuß, Ivan Kel, and Roland Krause. "Cocos: Constructing multi-domain protein phylogenies." PLoS Currents 3 (June 9, 2011): RRN1240. http://dx.doi.org/10.1371/currents.rrn1240.

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49

Hammond, Michelle, Rachel Clapp-Smith, and Michael Palanski. "An Exploration of Multi-Domain Leadership." Academy of Management Proceedings 2014, no. 1 (January 2014): 13961. http://dx.doi.org/10.5465/ambpp.2014.13961abstract.

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50

Liu, Jun, Guochen Wang, and Yanyan Luo. "Multi-Domain Modeling Based on Modelica." MATEC Web of Conferences 77 (2016): 07011. http://dx.doi.org/10.1051/matecconf/20167707011.

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